Resource processing method and device based on multi-end interaction, equipment and medium

Abstract

Embodiments of the present disclosure disclose a multi-end interaction-based resource processing method, device, equipment and medium. A specific implementation of the method includes: in response to currently receiving a resource processing order set, focusing screening out a matched resource pending order subset; performing abnormality detection on a corresponding resource hedging event to obtain a detection result; in response to the detection result representing no abnormality, generating an order withdrawal instruction set; in response to determining that at least one order withdrawal instruction is completed, storing resource virtual processing information to a blockchain, and sending a resource remaining processing order set to a resource retention end; in response to clicking a verification control, displaying a resource processing link, an abnormality detection link and an instruction execution link on a resource circulation page. The implementation can respond quickly and accurately to resource processing when receiving a resource processing request through the interaction between multiple resource terminals, so as to realize efficient processing of resources in a more intelligent and safer manner.

Description

Technical Field

[0001] The embodiments of this disclosure relate to the field of computer technology, and more specifically to resource processing methods, apparatuses, devices, and media based on multi-terminal interaction. Background Technology

[0002] Currently, the processing method for resource orders in various fields often determines the efficiency of resource processing. Effective processing methods can significantly improve resource processing efficiency. The common approach to processing resource orders is through manual resource scheduling by relevant experts on a resource processing platform.

[0003] However, when using the above method, the following technical problems often arise: When there are many resource orders, manually scheduling resources on the resource processing platform presents significant efficiency problems. Furthermore, the platform has limited processing capacity, leading to resource shortages and potential equipment downtime under high load. Additionally, the resource order processing logic suffers from poor interpretability and lacks effective anomaly detection methods.

[0004] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure propose resource processing methods, apparatuses, devices, and media based on multi-terminal interaction to solve one or more of the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a resource processing method based on multi-terminal interaction, including: in response to the resource processing terminal currently receiving a resource processing order set, focusing on filtering a matching subset of pending resource orders from a stored set of pending resource orders based on a resource knowledge graph; utilizing at least one intelligent agent deployed in an edge terminal of the resource processing terminal to perform anomaly detection on resource hedging events corresponding to the resource processing order set and the subset of pending resource orders, and obtaining a detection result; in response to the detection result indicating that no anomaly exists, generating a set of order withdrawal instructions; in response to determining that at least one order withdrawal instruction has been completed, processing the pending resource orders corresponding to the at least one order withdrawal instruction according to the resource order set... The single subset and the aforementioned resource processing order set generate resource virtual processing information and resource remaining processing order set; the aforementioned resource virtual processing information is stored in the blockchain, and the aforementioned resource remaining processing order set is sent to the downstream resource retention end for storage as resource pending orders; in response to clicking the verification control displayed in the corresponding page area of ​​the aforementioned resource virtual processing information on the resource flow verification end, the resource processing link, anomaly detection link, and instruction execution link corresponding to the aforementioned resource virtual processing information stored in the blockchain are displayed on the aforementioned resource flow page, and clicking on each link node in the link pops up a link pop-up window, the aforementioned resource flow verification end is the edge terminal of the aforementioned resource retention end.

[0008] Secondly, some embodiments of this disclosure provide a resource processing apparatus based on multi-terminal interaction, comprising: a filtering unit configured to, in response to a resource processing terminal currently receiving a resource processing order set, focus on filtering a matching subset of pending resource orders from a stored set of pending resource orders based on a resource knowledge graph; a detection unit configured to, using at least one intelligent agent deployed in an edge terminal of the resource processing terminal, perform anomaly detection on resource hedging events corresponding to the resource processing order set and the subset of pending resource orders, and obtain a detection result; a first generation unit configured to, in response to the detection result indicating that no anomaly exists, generate a set of order withdrawal instructions; and a second generation unit configured to, in response to determining that at least one order withdrawal instruction has been completed, generate a set of order withdrawal instructions based on the at least one order withdrawal instruction. The system generates resource virtual processing information and resource remaining processing order set by combining the resource pending order subset corresponding to the instruction with the aforementioned resource processing order set. A sending unit is configured to store the aforementioned resource virtual processing information in the blockchain and send the aforementioned resource remaining processing order set to the downstream resource retention end for storage as resource pending orders. A display unit is configured to respond to clicking the verification control displayed in the corresponding page area of ​​the resource virtual processing information on the resource flow verification end's resource flow page, displaying the resource processing link, anomaly detection link, and instruction execution link corresponding to the resource virtual processing information stored in the blockchain on the aforementioned resource flow page. Clicking on each link node in the link pops up a link pop-up window. The aforementioned resource flow verification end is the edge terminal of the aforementioned resource retention end.

[0009] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, such that when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any implementation of the first aspect.

[0010] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as described in any implementation of the first aspect.

[0011] The above embodiments of this disclosure have the following beneficial effects: Through the resource processing method based on multi-terminal interaction in some embodiments of this disclosure, by interacting between multiple resource terminals, a fast and accurate response to resource processing requests can be made when a resource processing request is received, achieving efficient resource processing in a more intelligent and secure manner. Specifically, the reasons for the inefficiency of related resource processing are: when there are many resource orders, the method of manually scheduling resources on the resource processing platform has significant efficiency problems, and the resource processing platform has limited processing resources. In high-load scenarios, there may be resource shortages and potential equipment downtime. Furthermore, the processing logic of resource orders has poor interpretability and lacks effective anomaly detection methods. Based on this, the resource processing method based on multi-terminal interaction in some embodiments of this disclosure firstly, in response to the resource processing terminal currently receiving a set of resource processing orders, focuses on filtering out a subset of matching pending resource orders from the stored set of pending resource orders based on a resource knowledge graph. Here, after receiving a processing request for a resource processing order at the resource processing end, a subset of pending resource orders for subsequent resource hedging processing can be matched from the pending resource order set stored at the resource retention end using a resource knowledge graph. Then, at least one intelligent agent deployed on the edge terminal of the resource processing end performs anomaly detection on the resource hedging events corresponding to the resource processing order set and the pending resource order subset, obtaining the detection results. Here, after obtaining the pending resource order subset, at least one intelligent agent deployed at the resource processing end can directly, automatically, and efficiently perform anomaly detection on resource hedging events to detect whether there are abnormal flow behaviors during resource transfer. Next, in response to the detection results indicating no anomalies, an order withdrawal instruction set is generated to withdraw the pending resource orders, facilitating subsequent resource hedging processing with the resource processing order. Furthermore, in response to determining that at least one order withdrawal instruction has been completed, resource virtual processing information and a set of remaining resource processing orders are generated based on the pending resource order subset corresponding to the at least one order withdrawal instruction and the resource processing order set. Here, virtual resource processing information is generated to facilitate the recording of the subsequent resource flow process. This allows for visualization of the execution content of each execution node on the resource flow page, improving the interpretability of the resource flow. A set of remaining resource processing orders is generated to facilitate the maintenance of subsequent resource processing orders. Furthermore, the aforementioned virtual resource processing information is stored on the blockchain, and the set of remaining resource processing orders is sent to the downstream resource retention end for storage as pending resource orders, enabling separate processing of the corresponding information.Finally, in response to clicking the verification control displayed on the corresponding page area of ​​the resource flow page on the resource flow verification terminal, the resource processing chain, anomaly detection chain, and instruction execution chain corresponding to the resource virtual processing information stored in the blockchain are displayed on the resource flow page. Clicking on each chain node pops up a chain pop-up window. The resource flow verification terminal is the edge terminal of the resource retention terminal. Here, through the resource flow verification terminal, the execution content of each node under each chain can be viewed on the resource flow page, so as to further verify the execution content of each node and provide the interpretability of the execution of each node, thereby improving the credibility of resource processing. In summary, through the interaction between various terminals, the automated execution of resource processing and the effective display of the resource processing process can be achieved. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of some embodiments of the resource processing method based on multi-terminal interaction according to the present disclosure; Figure 2 This is a schematic diagram of the structure of some embodiments of the resource processing apparatus based on multi-terminal interaction according to the present disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0016] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0017] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0018] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0019] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] refer to Figure 1 The diagram illustrates a flow 100 of some embodiments of a resource processing method based on multi-terminal interaction according to the present disclosure. This resource processing method based on multi-terminal interaction includes the following steps: Step 101: In response to the resource processing terminal receiving the current resource processing order set, based on the resource knowledge graph, focus on filtering out the matching resource pending order subset from the stored resource pending order set.

[0021] In some embodiments, in response to the resource processing terminal receiving a set of resource processing orders, based on a resource knowledge graph, the executing entity of the aforementioned multi-terminal interaction-based resource processing method (e.g., a resource processing system) can focus on filtering matching resource pending orders from a stored set of resource pending orders. The resource processing system can be a system that supports various processing of resources. The resource processing system may include: a resource processing terminal, a resource retention terminal, and a resource transfer verification terminal. The resource processing terminal can be a terminal that processes resources. Here, the resource processing terminal can be a terminal device deployed by an application carrying various resource processing functions. For example, the resource processing terminal can be a server. Resource processing functions may include: resource extraction operations, resource deposit operations, and resource transfer operations. In practice, for the futures market, resource processing functions can be operations that perform various transfer processing on futures contracts. For example, various transfer processing may include: account opening operations, fund deposit and withdrawal operations, hedging operations, and arbitrage operations. The resource retention terminal can be a terminal that retains resource orders. For example, in the trading field (based on the trading field corresponding to a shopping platform), the resource processing function can be the operation of various circulations (e.g., transfers) of goods, and the corresponding resource processing end can be the server side of the trading platform. In practice, the resource retention end can be a server that retains resource processing orders that have not yet been processed for later reprocessing. In practice, the resource retention end can store resource processing orders in the resource processing order set that have not yet undergone virtual processing. In the futures field, the resource retention end can be a downstream trading counter. Here, the trading counter can be a counter deployed on a server for order trading. In addition, in the goods trading field, the resource retention end can be a downstream trading server. Here, the trading server can be deployed based on region, with different trading servers deployed in different regions. The trading server is responsible for the retention and subsequent processing of goods in the corresponding region. In practice, the resource processing end can be an upstream server that performs order hedging processing. That is, this disclosure pre-implements order processing for corresponding orders based on the upstream resource processing end in the resource processing system, avoiding order returns due to subsequent order anomaly risk detection, which would affect order processing efficiency. A resource flow verification terminal can be a terminal that verifies the validity of processed flow information corresponding to resource processing orders. In practice, this validity verification can be anomaly risk verification or process risk verification. For example, in the futures market, the resource flow verification terminal can verify the processing results after virtual resource processing to ensure the validity of resource flow. It should be noted that the resource flow verification terminal can be deployed independently, or it can be deployed separately at the upstream resource processing terminal or the downstream resource retention terminal to perform resource flow verification. In practice, the resource flow verification terminal can be a server-based system used to detect various abnormal processing processes.In practice, the resource flow verification end can perform resource flow detection through relevant models deployed on the server or preset anomaly detection rules. Resource processing orders can be orders currently awaiting processing. In practice, for the futures market, resource processing orders can be futures processing orders received by the hedging end. The resource knowledge graph can be the relationships between resource objects and the resource value information of resource objects. For example, in a futures scenario, the corresponding resource object can be a futures processing object (e.g., a futures company or a futures order user). Resource value information can be the futures price. As another example, in a shopping transaction scenario, the corresponding resource object can be a transaction object, and the corresponding resource value information can be the item price. Nodes in the resource knowledge graph can be resource objects and their corresponding resource value information. Edges in the resource knowledge graph represent the relationships between resource objects or the mapping relationships between resource objects and resource value information. In practice, the resource knowledge graph can be generated using conventional graph generation techniques based on the resource processing end and the resource retention end's collection of information on various resource objects and their resource value. Specifically, the resource knowledge graph supports visualization and processing. A pending resource order set can be a collection of resource orders (e.g., orders in transit) obtained from the resource retention end that have not yet been processed and are in a pending state. For example, in the futures market, pending resource orders can be orders awaiting hedging. In the shopping transaction market, pending resource orders can be exchange orders. A subset of pending resource orders can be a set of orders that are processed together with some resource processing orders in the resource processing order set. In practice, the subset of pending resource orders corresponds to the same price and the same number of resources (e.g., the number of pending orders or the number of exchanges) as the corresponding subset of resource processing orders in the resource processing order set.

[0022] As an example, for each resource processing order, the aforementioned execution entity can query the abnormal resource object information and abnormal resource value information corresponding to the resource processing order from the resource knowledge graph. Then, it filters at least one pending resource order from the resource pending order set that has the same price and resource quantity (e.g., pending order number or exchange number) as the resource processing order. From this at least one pending resource order, it removes the pending resource orders corresponding to the abnormal resource object information and abnormal resource value information, obtaining a set of pending resource orders after removal. Based on the obtained set of pending resource orders after removal, and according to the matching requirement of the same price and the same resource quantity, it filters out a matching subset of pending resources from the set of pending resource orders after removal.

[0023] In some optional implementations of certain embodiments, the aforementioned execution entity may, based on a resource knowledge graph, focus on filtering a subset of matching resource pending orders from a stored set of resource pending orders, including the following steps: The first step is to retrieve the first set of pending resource orders that currently support resource processing from the target cache corresponding to the resource processing end. The target cache can be a temporary cache of all orders that need to be processed at present in the resource processing end. Resource processing can involve processing resource-related orders. For example, in a futures scenario, resource processing can involve hedging and eliminating resource-related orders. The first set of pending resource orders can be the stored orders awaiting resource processing.

[0024] The second step is to perform a filtering process for each resource processing order in the above resource processing order set: Sub-step 1: In response to determining that the current resource processing scenario is a high-concurrency scenario, the order filtering agent deployed on the resource processing end is scheduled to use the resource filtering script to filter the first set of pending resource orders, obtaining a subset of the first set of pending resource orders corresponding to the resource processing orders. A high-concurrency scenario can be a scenario where resource processing has high concurrency issues. In practice, for futures scenarios, the corresponding high-concurrency scenario can be measured by throughput (TPS / QPS), response time (RT) and long-tail latency, and the number of concurrent users. Throughput (TPS / QPS) can measure the system's ability to process transactions or queries per unit of time. For example, a well-performing hedging system can achieve a concurrent processing capacity of 100,000+ TPS. Response time (RT) and long-tail latency are core indicators for measuring the long-tail effect; core interfaces typically require ≤100ms. The number of concurrent users can be the number of active users performing operations simultaneously in the system at the same time point, directly reflecting the system's carrying capacity. The order filtering agent can be an agent that performs real-time filtering of orders. Here, for futures scenarios, the order filtering agent can be used to filter orders for resource hedging processing. In trading scenarios, an order filtering agent can be used to filter orders for resource trading. Specifically, in resource hedging scenarios, the order filtering agent is a core component designed for high-frequency hedging strategies in quantitative trading systems or shopping trading scenarios. Its main function is to identify order combinations that meet hedging conditions from massive order streams in real time. In practice, the order filtering agent can be an agent specifically configured for order filtering. The order filtering agent can support calling resource filtering scripts and content from resource knowledge graphs to perform multi-faceted order filtering, selecting at least one pending resource order that matches the order processing order for hedging. Specifically, the order filtering agent is deployed with a large model. This large model can understand the graph content in the resource knowledge graph and focus and match orders based on the graph content. In addition, after receiving scenario prompts related to high concurrency scenarios, the order filtering agent supports calling resource filtering scripts to perform resource filtering. This large model can be a hybrid expert model (MoE). This large model can be pre-trained based on the graph task dataset corresponding to the knowledge graph. The graph task data can include: knowledge graph, graph task, and image task execution results. For example, a mapping task could be an object correlation prediction task. A resource filtering script could be a pre-set script for automatic resource filtering and matching. This resource filtering script could be based on fixed indicators to filter out resources that can be offset against resource processing orders. In practice, resource filtering orders can be customized by relevant technical personnel based on indicator matching requirements. The first subset of pending resource orders can be any pending resource orders that meet the matching requirements of the resource filtering script and can be offset against resource processing orders.

[0025] As an example, in response to the order filtering agent obtaining information about high-concurrency scenarios, the order filtering agent can automatically call the resource filtering script to filter the first set of pending resource orders and obtain a subset of the first set of pending resource orders corresponding to the resource processing orders.

[0026] Sub-step 2 involves scheduling an order filtering agent to remove, based on the entity relationships in the resource knowledge graph, first-tier pending resource orders from the first subset of pending resource orders where the corresponding entity has a resource processing anomaly with the entity corresponding to the resource processing order, thus obtaining a second subset of pending resource orders. Here, a resource processing anomaly can be a situation where there is abnormal processing between entities. For example, a resource processing anomaly could be the same object being processed back and forth between different objects. Another example is resource processing anomalies related to entities suspected of engaging in illegal activities.

[0027] As an example, the aforementioned execution entity can schedule an order screening agent to determine a second subset of pending resource orders based on entity relationships in a resource knowledge graph.

[0028] Sub-step 3 involves scheduling the aforementioned order screening agent to filter out resource-pending orders from the second subset of pending resource orders, based on the entity value information in the resource knowledge graph. The selected resource-pending orders have entity value information focused within a target range, resulting in a candidate subset of pending resource orders. The entity value information can be the value information of the entity corresponding to the second resource-pending order. In practice, entity value information can be the market capitalization or capital information of the entity corresponding to the second resource-pending order, or it can be entity credit. In practice, the content represented by the numerical values ​​within the target area differs depending on the value category of the entity value information. For example, if the entity information is the entity's market capitalization or capital information, the corresponding target range can be a region used to measure whether the market capitalization is large or the capital is large. That is, the entity information within the target range is an entity with a large market capitalization or large capital. If the entity information is entity credit, the corresponding target range can be a range used to measure whether the entity has high credit.

[0029] Here, in the process of handling resource hedging events, only entities with good credit and high value are supported to handle resources, so as to avoid abnormal situations in resource handling caused by entities with poor credit or low value.

[0030] The third step is to determine the source pending order subsets as the subsets of candidate resource pending orders if there are no duplicate orders in the subsets of candidate resource pending orders obtained.

[0031] Optionally, the steps also include: The first step, in response to the existence of duplicate orders within the aforementioned subsets of candidate resource pending orders, involves obtaining at least one resource processing order with duplicates and its corresponding at least one subset of candidate resource pending orders. Duplicate orders can occur when at least two resource processing orders have duplicate candidate resource pending orders within their respective subsets. For example, the first resource processing order is order A, and the second resource processing order is order B. The subset of candidate resource pending orders corresponding to order A includes orders 1, 2, and 3. The subset of candidate resource pending orders corresponding to order B includes orders 2 and 3. During resource hedging, orders A and B conflict, meaning they are duplicates. At least one subset of candidate resource pending orders can be a summary set of all candidate resource pending orders corresponding to at least one resource processing order.

[0032] The second step involves generating a resource knowledge sub-graph based on the aforementioned resource knowledge graph, targeting at least one resource processing order and at least one subset of candidate resource pending orders. This sub-graph is then temporarily stored in the target cache. The resource knowledge sub-graph represents the resource association relationships between at least one resource processing order and at least one subset of candidate resource pending orders. In practice, nodes in the resource knowledge sub-graph can be orders and resource objects, and corresponding edges can represent resource association relationships, relationships between orders and resource objects, or mapping relationships between resource objects and resource value information.

[0033] As an example, firstly, based on at least one resource processing order and at least one subset of candidate pending orders, a basic knowledge graph is constructed according to the mapping relationship between the resource processing order and the candidate pending orders. Here, the nodes in the basic knowledge graph represent orders, and the edges represent the order mapping relationship between resource processing orders and candidate pending orders. Then, order information corresponding to the at least one resource processing order and the order information corresponding to the at least one subset of candidate pending orders are extracted from the resource knowledge graph. Finally, the order information is added to the basic knowledge graph to obtain the resource knowledge subgraph.

[0034] The third step involves determining the candidate resource pending order subsets corresponding to each resource processing order based on the priority and resource anomaly probability of at least one resource processing order, using the resource knowledge subgraph. This yields the candidate resource pending order subset group and the pending resource processing order group corresponding to the target resource processing order group. Priority represents the processing order of resource processing orders. Specifically, it can be sorted according to the order of the corresponding orders. Resource anomaly probability represents the probability that a resource processing order has an abnormal resource condition. In practice, the resource anomaly probability can be a value between 0 and 1; the higher the value, the higher the probability of resource anomaly. In practice, the resource anomaly probability can be output based on at least one intelligent agent. During the anomaly detection process for resource hedging events, at least one intelligent agent can output the resource anomaly probability corresponding to the detection result. That is, the detection result can include the resource anomaly probability. Pending resource processing orders can be resource processing orders that failed to match the corresponding candidate resource pending order subset.

[0035] As an example, firstly, for each resource processing order, a weighted score is obtained by weighting the order based on its priority and the probability of resource anomalies. Specifically, the score corresponding to the priority and the score corresponding to the probability of resource anomalies can be weighted and added together. Then, based on the weighted scores, the order processing order corresponding to at least one of the resource processing orders is determined. Finally, based on the order processing order, the candidate subset of pending orders corresponding to each resource processing order is determined sequentially from the resource knowledge subgraph. If a resource processing order cannot be determined to have a corresponding candidate subset of pending orders, it is designated as a pending resource processing order.

[0036] The fourth step involves temporarily caching the aforementioned pending resource processing orders on the target storage device. This is to await whether resource processing orders will be focused on within a predetermined future time period and the next available time, and to determine the candidate subset of pending resource orders as the final subset of pending resource orders. The target storage device can be a storage device that temporarily caches relevant order information. The pending resource processing will subsequently focus on and match pending resource orders within the predetermined future time period. The predetermined future time period can be the storage duration supported by the target storage device for temporary storage. For example, the predetermined future time period could be 1 hour.

[0037] In addressing the technical problems mentioned in the background section, and considering the application scenario—a scenario with high concurrency and limited filtering capabilities on the resource processing end—the following technical issues often arise: After high concurrency occurs, the large number of orders in the resource processing order set leads to significant pressure on the resource processing end, potentially causing device downtime. Furthermore, the limited order filtering capabilities of the resource processing end not only fail to perform effective filtering but also waste considerable resources. Therefore, this application scenario requires the following characteristics: how to effectively schedule device resources in high-concurrency scenarios. We have decided to adopt the following solution: Optionally, after responding to the determination that the current resource processing scenario is a high-concurrency resource scenario, scheduling the order filtering agent deployed on the resource processing terminal to use the resource filtering script to filter the first set of pending resource orders and obtain the first subset of pending resource orders corresponding to the resource processing orders, the process includes: The first step, in response to determining that the current resource processing scenario is a high-concurrency scenario and that the first set of pending resource orders is empty, is to retrieve the second set of pending resource orders that currently support resource processing from the disk file. The disk file can store all the pending resource orders that currently support resource processing.

[0038] The second step involves grouping the aforementioned set of pending second resource orders into multiple subgroups based on the determination that the number of such orders exceeds the target number. The target number can be a threshold used to measure the quantity of orders. In practice, the target number can be set based on historical experience. The pending orders within each subgroup exhibit similar order characteristics.

[0039] Here, data grouping can be based on feature similarity. Feature similarity here could mean that the corresponding orders for the second resource pending order have similar features. Order features can include: order subject, order time, and order value.

[0040] The third step involves compressing each of the multiple pending second resource order subgroups to generate compressed order information, resulting in multiple compressed order information sets. Here, considering the relatively large number of orders corresponding to each pending second resource order subgroup, and the large total number of bytes corresponding to each subgroup, it's feasible to compress each order as a whole for easier transmission later. The compressed order information here can be the result of compressing the pending second resource order subgroups. That is, the total number of bytes in the compressed order information is less than the total number of bytes corresponding to each pending second resource order subgroup.

[0041] As an example, each order in the second resource pending order subgroup can be encrypted to obtain an encrypted result. Then, the encrypted result is input into a pre-trained encoding model (e.g., a multi-layer concatenated convolutional layer) to obtain an encoded result, which serves as the order compression information.

[0042] The fourth step involves designating the resource processing terminal as the master node of the distributed server cluster. This master node is instructed to send the compressed order information to each child node within the cluster. Each child node's server deploys an intelligent order filtering simulator, whose filtering capability is no less than that of the intelligent order filtering agent. The distributed server cluster can be a distributed cluster built for resource order processing. The master node is used for order scheduling and task dispatching. Each child node can be a corresponding server. Each child node can process tasks dispatched by the master node. Here, child nodes can filter orders based on the intelligent order filtering simulator. The intelligent order filtering agent can be generated based on this simulator. That is, the model corresponding to the intelligent order filtering agent can undergo transfer learning and training based on the model corresponding to the intelligent order filtering simulator. Furthermore, the model corresponding to the intelligent order filtering simulator is trained periodically. The training period for the model corresponding to the intelligent order filtering simulator is shorter than that for the intelligent order filtering agent. Therefore, the filtering capability of the intelligent order filtering simulator is no less than that of the intelligent order filtering agent.

[0043] Fifth, in response to the master node receiving the filtering results sent by the various child nodes, the master node instructs the order filtering agent deployed in the master node to determine the first subset of pending resource orders corresponding to the resource processing orders based on the filtering results. The filtering results can be the filtering results of pending resource orders for the resource processing orders.

[0044] As an example, the order filtering agent can select the filtering result with the highest matching probability from various filtering results to obtain the first resource pending order subset corresponding to the above resource processing order.

[0045] The aforementioned "steps one through five" constitute another inventive point of this disclosure, solving another technical problem: "When high concurrency occurs, the number of orders corresponding to the resource processing order set is large, leading to significant pressure on the resource processing end and potentially causing equipment downtime. Furthermore, the order filtering capability of the resource processing end is limited, resulting in ineffective filtering and wasted resources." Based on this, when the current resource processing scenario is determined to be a high-concurrency scenario and the first pending resource order set is empty, this disclosure first obtains the second pending resource order set currently supporting resource processing for subsequent order-focused processing. Secondly, through data grouping, the second pending resource orders with similar characteristics can be grouped together for processing by different child nodes. Next, by using the resource processing end as the master node of the distributed server cluster, the server resources and order filtering capabilities of the distributed server cluster can assist the resource processing end in distributing the order filtering pressure and improving the order filtering accuracy, enabling effective order filtering and processing in high-concurrency scenarios.

[0046] In addressing the technical problems mentioned in the background section, and considering the application scenario—a scenario where numerous hot events lead to high concurrency—the following technical issues often arise: After numerous hot events, high concurrency often occurs. In high-concurrency scenarios, the number of orders corresponding to the resource processing order set is large, resulting in significant pressure on the resource processing end and potentially even device downtime. Given the following requirements for this application scenario: how to effectively schedule device resources in high-concurrency scenarios, we decided to adopt the following solution: In some optional implementations of certain embodiments, after step 101, the steps further include: The first step is to obtain the high concurrency probability and high concurrency severity information corresponding to the current time. The current time can be the processing time for the resource processing order set. The high concurrency probability information represents the probability of high concurrency occurring during order processing at the current time. In practice, the high concurrency probability information can be a value between 0 and 1; the higher the value, the higher the probability of high concurrency occurring at the current time. The high concurrency severity information represents the severity of high concurrency. In practice, the high concurrency severity information can be a value between 0 and 100; the higher the value, the higher the severity of high concurrency.

[0047] The second step is to determine, based on the high concurrency probability information mentioned above, whether to designate the resource processing end as the master node of the distributed server cluster, so that the child nodes can perform order screening.

[0048] As an example, in response to high concurrency probability information exceeding a target probability value (a threshold used to measure whether a probability value is high), it is determined whether to designate the resource processing end as the master node of the distributed server cluster so that order filtering can be performed by the child nodes.

[0049] The third step, in response to determining that the resource processing end will be the master node of the distributed server cluster, is to determine the number of child nodes to be scheduled based on the aforementioned high concurrency information. The number of child nodes to be scheduled can be used to schedule each child node for order filtering. Here, the higher the value of the high concurrency information, the higher the corresponding number of child nodes to be scheduled.

[0050] As an example, the number of child nodes corresponding to high concurrency information can be determined by querying a mapping table. The mapping table represents the mapping relationship between high concurrency information and the number of child nodes.

[0051] The fourth step is to use the resource knowledge graph, based on the number of child node scheduling mentioned above, to focus on and filter out a subset of matching resource pending orders from the stored resource pending orders set using each child node.

[0052] As an example, firstly, each child node is determined based on the number of child node scheduling tasks. Then, the subset of pending resource orders that each child node is responsible for processing is determined. The orders corresponding to each subset of pending resource orders are then aggregated into a set of pending resource orders. Next, the resource knowledge graph and the corresponding pending resource orders are sent to the respective child nodes to focus the resource processing orders on pending resource orders.

[0053] Optionally, the above-mentioned high concurrency probability information and high concurrency level information are generated through the following steps: The first step is to retrieve all trending events within a predetermined timeframe prior to the current time. Trending events can be any events related to trending content. The predetermined timeframe prior to the current time can be a period of time preceding the current time. Each trending event can be any trending content event that occurred within the predetermined timeframe prior to the current time.

[0054] The second step is to generate the processing level and probability of resource processing at the current time based on the aforementioned hot events. The processing level represents the urgency of resource processing, while the processing probability represents the accuracy corresponding to the processing level. The processing level can be a value between 0 and 100. A higher value indicates a more strained resource situation at the current time.

[0055] As an example, firstly, each hot event in the various hot events is input into the resource processing mapping model to output the numerical impact and probability information on subsequent resource processing. Then, the numerical impact is weighted to obtain the processing degree, and the probability information is weighted to obtain the processing probability.

[0056] The third step is to determine the above processing probability as the high concurrency probability, and to determine the high concurrency level information as the processing level.

[0057] The above content, as another inventive point of this disclosure, solves another technical problem: "After a number of hot events occur, high concurrency situations often arise. In high-concurrency scenarios, the number of orders corresponding to the resource processing order set is large, leading to significant pressure on the resource processing end and even potential equipment downtime." Based on this, this disclosure, firstly, uses high-concurrency probability and degree information determined before the current time to pre-schedule each node in the distributed server cluster. In the event of high concurrency at the current time, the server resources of each child node can be utilized for pre-scheduling of resources, thereby achieving rational order processing in high-concurrency scenarios and avoiding resource shortages. Secondly, by using the various hot events occurring within a predetermined time period before the current time, the processing degree and probability of resource processing at the current time can be accurately generated, enabling precise prediction of potential high-concurrency issues at the current time for effective resource allocation and scheduling.

[0058] Step 102: Using at least one intelligent agent deployed in the edge terminal of the above-mentioned resource processing terminal, perform anomaly detection on the resource hedging events corresponding to the above-mentioned resource processing order set and the above-mentioned resource pending order subset, and obtain the detection results.

[0059] In some embodiments, the aforementioned executing entity may utilize at least one agent deployed in an edge terminal of the aforementioned resource processing end to perform anomaly detection on resource hedging events corresponding to the aforementioned resource processing order set and the aforementioned resource pending order subset, and obtain detection results. The edge terminal may be a hardware terminal (e.g., a server deployment terminal) performing resource processing on the resource processing end. The agent may be an agent used for anomaly detection of resource hedging events. Anomaly detection may determine whether there are any abnormal problems in the resource hedging process corresponding to the resource hedging event. For example, an abnormal problem may be a conflict of interest between entities (e.g., the buying and selling entities in futures are actually the same entity and there is malicious behavior). In practice, the agent may perform anomaly detection based on a pre-trained large model. A resource hedging event may be a specific event involving order hedging processing between the resource processing order set and the resource pending order subset. The detection result may be one of the following: a detection result indicating that the resource hedging event is abnormal, or a detection result indicating that the resource hedging event is not abnormal. Here, a resource hedging event may be an event involving inflow and outflow hedging of resource inflow and outflow behaviors. Specifically, in the futures market, resource hedging can be achieved by offsetting buy and sell orders in futures contracts.

[0060] As an example, firstly, for each agent, using the large language model within the agent, anomaly detection is performed on the resource hedging events corresponding to the resource processing order set and the aforementioned subset of pending resource orders, yielding preliminary anomaly detection results. Finally, from each preliminary anomaly detection result, a first number representing the presence of anomalies and a second number representing the absence of anomalies are determined. In response to determining that the first number is greater than the second number, a first detection result representing the presence of anomalies is generated. In response to determining that the first number is less than the second number, a first detection result representing the absence of anomalies is generated.

[0061] Step 103: In response to the above detection results indicating no abnormalities, generate an order cancellation instruction set.

[0062] In some embodiments, in response to the detection results indicating the absence of anomalies, the executing entity can generate an order cancellation instruction set. The order cancellation instruction can be an instruction to cancel resource-pending orders stored in the resource retention terminal to achieve resource offsetting with the corresponding resource processing orders. In practice, there is a one-to-one correspondence between resource-pending orders in the resource-pending order subset and order cancellation instructions in the order cancellation instruction set. In practice, the order cancellation instruction can be cancellation content in a predetermined instruction format.

[0063] In some optional implementations of certain embodiments, after step 103, the steps further include: The first step, in response to the anomaly indicated by the above detection results, is to determine the first resource hedging sub-event set containing the anomaly and the second resource object sub-event set not containing the anomaly within the aforementioned resource hedging events. The first resource hedging sub-event can be an event where an anomaly occurs in the resource hedging detection. In practice, this anomaly can be of various types. For example, it could be an anomaly indicating a value conflict between entities.

[0064] The second step, for each first resource hedging sub-event, is to perform the following sending steps: Sub-step 1 involves sending the anomaly information corresponding to the first resource hedging sub-event to the resource anomaly processing terminal for re-verification. The resource anomaly processing terminal can be a terminal that verifies the anomaly status corresponding to a resource. In practice, the resource anomaly processing terminal can perform re-verification based on a verification agent. Here, at least one agent and the verification agent can be constructed synchronously. At least one agent is used for anomaly detection. The verification agent is used to verify the anomaly status detected by the at least one agent. In practice, the verification model corresponding to the verification agent and at least one detection model corresponding to the at least one agent are trained synchronously.

[0065] In practice, an anomaly detection dataset is acquired. This dataset serves as the training dataset for training at least one detection model and one verification model. The training data may include resource hedging event data and anomaly labels. The resource hedging event data may include event content related to resource hedging events. This data is input into at least one detection model, and the model is trained based on the difference between the output of the at least one detection model and the anomaly labels. Additionally, the verification model is trained based on the detection results and anomaly labels of the at least one detection model. In practice, the model structures of the verification model and the at least one detection model can be the same or different. The main difference lies in the input data. For example, both the verification model and the at least one detection model can use a Transformer architecture.

[0066] Sub-step 2, in response to confirming that the verification is correct, executes resource anomaly transfer alarm processing. This resource anomaly transfer alarm processing can involve issuing alarms for abnormal resource transfers. In practice, transfer alarms can be displayed via pop-up windows.

[0067] Sub-step 3: In response to the determination that the verification is incorrect, the above-mentioned abnormal information is sent to the resource abnormality manual processing terminal for final abnormality verification.

[0068] The third step is to generate a set of order cancellation instructions for the aforementioned second resource object sub-event set. Each order cancellation instruction can be an instruction to cancel the order corresponding to a second resource object sub-event. There is a one-to-one correspondence between the second resource object sub-events in the second resource object sub-event set and the order cancellation instructions in the order cancellation instruction set.

[0069] Step 104: In response to determining that at least one order withdrawal instruction has been completed, generate resource virtual processing information and resource remaining processing order set based on the resource pending order subset corresponding to the at least one order withdrawal instruction and the resource processing order set.

[0070] In some embodiments, in response to determining that at least one order withdrawal instruction has been completed, the executing entity can generate resource virtual processing information and a resource remaining processing order set based on the resource pending order subset corresponding to the at least one order withdrawal instruction and the resource processing order set. The at least one order withdrawal instruction can be a subset of instructions in the order withdrawal instruction set, i.e., a subset of order withdrawal instructions. The resource pending order subset corresponding to the at least one order withdrawal instruction can be resource pending orders with a one-to-one correspondence to the order withdrawal instruction. The resource virtual processing information can be processing information for performing resource virtual hedging processing on the resource pending order subset corresponding to the at least one order withdrawal instruction and the resource processing order set. In practice, the resource virtual processing information can include a specific virtual hedging process. The resource remaining processing order set can be the remaining resource processing orders that have not undergone hedging processing.

[0071] As an example, the aforementioned execution entity can use relevant intelligent agents to automatically perform resource offsetting processing on the resource pending order subset corresponding to at least one order withdrawal instruction and the aforementioned resource processing order set, thereby obtaining virtual resource processing information. Orders in the aforementioned resource processing order set that have not undergone resource offsetting processing are identified as resource remaining processing orders, thus obtaining the resource remaining processing order set.

[0072] In some optional implementations of certain embodiments, the execution entity may generate resource virtual processing information and resource remaining processing order set based on the resource pending order subset corresponding to at least one order withdrawal instruction and the resource processing order set, including the following steps: The first step is to perform a generation step for each resource processing order in the above resource processing order set: Sub-step 1 involves identifying at least one pending resource order from the aforementioned subset of pending resource orders that matches the aforementioned resource processing order. This matching relationship can be an order binding relationship. The specific implementation details are omitted here.

[0073] Sub-step 2 involves using the automatic resource processing process in the resource processing terminal to generate virtual resource processing sub-information corresponding to the aforementioned resource processing order and at least one pending resource order. The automatic resource processing process can be a process used to generate this virtual resource processing sub-information. In practice, the automatic resource processing process is triggered upon receiving a virtual resource processing request. The virtual resource processing sub-information can be resource processing information generated by the automatic processing process that has not been actually confirmed offline. In practice, the virtual resource processing sub-information can be virtual processing information used for online resource hedging of the resource processing order and at least one pending resource order. In practice, compared to offline resource processing sub-information, the virtual resource processing sub-information can include a virtual processing tag. The virtual processing tag indicates that the resource hedging is performed online.

[0074] The second step is to identify the obtained resource virtual processing sub-information set as resource virtual processing information.

[0075] The third step is to remove the aforementioned subset of pending resource orders from the subset of pending resource orders to obtain the set of remaining resource processing orders.

[0076] Step 105: Store the above-mentioned virtual processing information of resources in the blockchain, and send the above-mentioned remaining processing order set of resources to the downstream resource retention end for storage as pending resource orders.

[0077] In some embodiments, the executing entity may store the aforementioned virtual resource processing information on a blockchain and send the aforementioned remaining resource processing order set to a downstream resource retention end for storage as pending resource orders. The blockchain may store processing information between various virtual resources.

[0078] Step 106: In response to clicking the verification control displayed in the corresponding page area of ​​the above-mentioned virtual processing information of resources on the resource transfer verification terminal, the resource processing link, anomaly detection link and instruction execution link corresponding to the above-mentioned virtual processing information of resources stored in the blockchain are displayed on the above-mentioned resource transfer page.

[0079] In some embodiments, in response to clicking the verification control displayed in the corresponding page area of ​​the resource virtual processing information on the resource flow verification end, the execution entity can display the resource processing link, anomaly detection link, and instruction execution link corresponding to the resource virtual processing information stored in the blockchain on the resource flow page. Clicking on each link node in the link pops up a link pop-up window, and the resource flow verification end is the edge terminal of the resource retention end. The resource flow page can be a page that displays resource flow-related content. The verification control can be a control that supports information verification of the resource virtual processing information through page viewing. The resource processing link can be the processing procedure of resource virtual processing. The anomaly detection link can be the detection procedure for anomaly detection. The instruction execution link can be the instruction execution procedure corresponding to an order withdrawal instruction. The link pop-up window displays the node execution content of the corresponding link node.

[0080] In some optional implementations of certain embodiments, the resource transfer verification terminal obtains the associated information corresponding to the virtual processing information of the resource through the following steps: The first step is to obtain the resource processing records corresponding to the aforementioned resource hedging events. These resource processing records can be specific records of the processing of the resource hedging events. In practice, resource processing records may include: resource processing time, order-related information, processing number, and encryption information.

[0081] The second step involves generating the resource processing link, the anomaly detection link, and the instruction execution link based on the resource processing records and the link query process in the resource retention terminal. The link query process can be used to extract and verify processing information and query corresponding processing links. Specifically, the link conversion process can automatically match and query links based on order-related information after obtaining the processing number from the resource processing records.

[0082] As an example, firstly, in response to the processing number obtained from the resource processing record by the aforementioned link query process, the order-related information corresponding to the processing number is queried from the resource retention end. Then, based on the order-related information, a link database query statement is generated. Next, the link database query statement is executed to determine the aforementioned resource processing link, the aforementioned anomaly detection link, and the aforementioned instruction execution link.

[0083] The third step is to identify the above-mentioned virtual resource processing information, the above-mentioned resource processing link, the above-mentioned anomaly detection link, and the above-mentioned instruction execution link as related information and send them to the above-mentioned resource flow verification terminal.

[0084] In some optional implementations of certain embodiments, the aforementioned resource processing terminal, resource retention terminal, and resource transfer verification terminal interact and process information based on standardized protocols, low-latency technologies, distributed architecture, and target hardware acceleration. Standardized protocols can be unified and standardized communication formats and rules followed when systems exchange data. For example, in the futures market, this includes not only standardized contract terms established by the exchange (such as fixed contract size, delivery date, and margin requirements) but also interface specifications for data transmission between systems. Low-latency technologies can compress the time interval between the issuance and execution of trading instructions to an extremely short level (typically milliseconds, microseconds, or even nanoseconds) by optimizing network connections, hardware devices, and communication mechanisms. Low-latency technologies include using dedicated lines to directly connect to the exchange, using UDP instead of the TCP protocol, and drastically shortening physical paths (e.g., microwave communication). Distributed architecture can break down a complex system originally concentrated on a single node into multiple independently operating, collaborative microservice modules (e.g., separating services such as trading, risk control, settlement, and market data). These modules can collaboratively handle massive amounts of data through horizontal scaling and load balancing. Targeted hardware acceleration can involve using dedicated hardware devices (such as FPGAs, custom ASICs, or GPU superclusters) to replace traditional general-purpose CPUs for specific computational tasks. For example, market data decoding, risk verification, or order matching logic can be directly programmed into the hardware circuitry.

[0085] The above embodiments of this disclosure have the following beneficial effects: Through the resource processing method based on multi-terminal interaction in some embodiments of this disclosure, by interacting between multiple resource terminals, a fast and accurate response to resource processing requests can be made when a resource processing request is received, achieving efficient resource processing in a more intelligent and secure manner. Specifically, the reasons for the inefficiency of related resource processing are: when there are many resource orders, the method of manually scheduling resources on the resource processing platform has significant efficiency problems, and the resource processing platform has limited processing resources. In high-load scenarios, there may be resource shortages and potential equipment downtime. Furthermore, the processing logic of resource orders has poor interpretability and lacks effective anomaly detection methods. Based on this, the resource processing method based on multi-terminal interaction in some embodiments of this disclosure firstly, in response to the resource processing terminal currently receiving a set of resource processing orders, focuses on filtering out a subset of matching pending resource orders from the stored set of pending resource orders based on a resource knowledge graph. Here, after receiving a processing request for a resource processing order at the resource processing end, a subset of pending resource orders for subsequent resource hedging processing can be matched from the pending resource order set stored at the resource retention end using a resource knowledge graph. Then, at least one intelligent agent deployed on the edge terminal of the resource processing end performs anomaly detection on the resource hedging events corresponding to the resource processing order set and the pending resource order subset, obtaining the detection results. Here, after obtaining the pending resource order subset, at least one intelligent agent deployed at the resource processing end can directly, automatically, and efficiently perform anomaly detection on resource hedging events to detect whether there are abnormal flow behaviors during resource transfer. Next, in response to the detection results indicating no anomalies, an order withdrawal instruction set is generated to withdraw the pending resource orders, facilitating subsequent resource hedging processing with the resource processing order. Furthermore, in response to determining that at least one order withdrawal instruction has been completed, resource virtual processing information and a set of remaining resource processing orders are generated based on the pending resource order subset corresponding to the at least one order withdrawal instruction and the resource processing order set. Here, virtual resource processing information is generated to facilitate the recording of the subsequent resource flow process. This allows for visualization of the execution content of each execution node on the resource flow page, improving the interpretability of the resource flow. A set of remaining resource processing orders is generated to facilitate the maintenance of subsequent resource processing orders. Furthermore, the aforementioned virtual resource processing information is stored on the blockchain, and the set of remaining resource processing orders is sent to the downstream resource retention end for storage as pending resource orders, enabling separate processing of the corresponding information.Finally, in response to clicking the verification control displayed on the corresponding page area of ​​the resource flow page on the resource flow verification terminal, the resource processing chain, anomaly detection chain, and instruction execution chain corresponding to the resource virtual processing information stored in the blockchain are displayed on the resource flow page. Clicking on each chain node pops up a chain pop-up window. The resource flow verification terminal is the edge terminal of the resource retention terminal. Here, through the resource flow verification terminal, the execution content of each node under each chain can be viewed on the resource flow page, so as to further verify the execution content of each node and provide the interpretability of the execution of each node, thereby improving the credibility of resource processing. In summary, through the interaction between various terminals, the automated execution of resource processing and the effective display of the resource processing process can be achieved.

[0086] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a resource processing device based on multi-terminal interaction, and these device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this resource processing device based on multi-terminal interaction can be specifically applied to various electronic devices.

[0087] like Figure 2As shown, a resource processing device 200 based on multi-terminal interaction includes: a filtering unit 201, a detection unit 202, a first generation unit 203, a second generation unit 204, a sending unit 205, and a display unit 206. The filtering unit 201 is configured to, in response to a resource processing order set currently received by the resource processing terminal, focus on filtering a matching subset of pending resource orders from a stored set of pending resource orders based on a resource knowledge graph. The detection unit 202 is configured to, using at least one intelligent agent deployed in an edge terminal of the resource processing terminal, perform anomaly detection on resource hedging events corresponding to the resource processing order set and the subset of pending resource orders, and obtain a detection result. The first generation unit 203 is configured to, in response to the detection result indicating the absence of anomalies, generate a set of order withdrawal instructions. The second generation unit 204 is configured to, in response to determining that at least one order withdrawal instruction has been completed, generate a set of pending resource orders based on the subset of pending resource orders corresponding to the at least one order withdrawal instruction. The resource processing order set and the resource virtual processing information and the resource remaining processing order set are generated. The sending unit 205 is configured to store the resource virtual processing information in the blockchain and send the resource remaining processing order set to the downstream resource retention end for storage as pending resource orders. The display unit 206 is configured to respond to the verification control displayed in the corresponding page area of ​​the resource virtual processing information on the resource flow verification end, and display the resource processing link, anomaly detection link and instruction execution link corresponding to the resource virtual processing information stored in the blockchain on the resource flow page. Clicking on each link node in the link will pop up a link pop-up window. The resource flow verification end is the edge terminal of the resource retention end.

[0088] It is understandable that the units described in the multi-terminal interaction-based resource processing device 200 and the reference Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the resource processing device 200 based on multi-terminal interaction and the units contained therein, and will not be repeated here.

[0089] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device (e.g., an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0090] like Figure 3As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0091] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0092] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0093] It should be noted that, in some embodiments of this disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0094] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0095] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: respond to the resource processing terminal currently receiving a set of resource processing orders, based on a resource knowledge graph, focus on filtering a subset of matching pending resource orders from a stored set of pending resource orders; utilize at least one intelligent agent deployed in an edge terminal of the aforementioned resource processing terminal to perform anomaly detection on the resource hedging events corresponding to the set of resource processing orders and the subset of pending resource orders, and obtain a detection result; respond to the detection result indicating that no anomaly exists, generate a set of order withdrawal instructions; respond to determining that at least one order withdrawal instruction has been completed, and, according to the at least one order withdrawal instruction, [the device]... Given the corresponding subset of pending resource orders and the aforementioned resource processing order set, resource virtual processing information and resource remaining processing order set are generated. The aforementioned resource virtual processing information is stored in the blockchain, and the aforementioned resource remaining processing order set is sent to the downstream resource retention end for storage as pending resource orders. In response to clicking the verification control displayed in the corresponding page area of ​​the aforementioned resource virtual processing information on the resource flow verification end's resource flow page, the resource processing link, anomaly detection link, and instruction execution link corresponding to the aforementioned blockchain-stored resource virtual processing information are displayed on the aforementioned resource flow page. Clicking on each link node in the link pops up a link pop-up window. The aforementioned resource flow verification end is the edge terminal of the aforementioned resource retention end.

[0096] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0098] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a filtering unit, a detection unit, a first generation unit, a second generation unit, a sending unit, and a display unit. The names of these units do not necessarily limit the specific unit; for example, the first generation unit may also be described as "a unit that generates an order withdrawal instruction set in response to the detection result indicating the absence of anomalies."

[0099] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0100] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A resource processing method based on multi-terminal interaction, characterized in that, include: In response to the resource processing terminal receiving the current set of resource processing orders, based on the resource knowledge graph, a matching subset of pending resource orders is selected from the stored set of pending resource orders. Using at least one intelligent agent deployed in the edge terminal of the resource processing end, anomaly detection is performed on the resource hedging events corresponding to the resource processing order set and the resource pending order subset, and the detection results are obtained. In response to the detection result indicating that no abnormality exists, an order cancellation instruction set is generated; In response to determining that at least one order withdrawal instruction has been completed, resource virtual processing information and resource remaining processing order set are generated based on the resource pending order subset corresponding to the at least one order withdrawal instruction and the resource processing order set; The virtual processing information of the resources is stored in the blockchain, and the remaining processing order set of the resources is sent to the downstream resource retention end for storage as pending resource orders; In response to clicking the verification control displayed in the corresponding page area of ​​the resource virtual processing information on the resource flow verification terminal, the resource processing link, anomaly detection link and instruction execution link corresponding to the resource virtual processing information stored in the blockchain are displayed on the resource flow page. Clicking on each link node in the link pops up a link pop-up window. The resource flow verification terminal is the edge terminal of the resource retention terminal.

2. The method according to claim 1, characterized in that, After generating an order cancellation instruction set in response to the detection result indicating no abnormality, the method further includes: In response to the detection result indicating an anomaly, a first resource hedging sub-event set with an anomaly and a second resource object sub-event set without an anomaly are determined in the resource hedging event. For each first resource hedging sub-event, perform the following sending steps: The abnormal information corresponding to the first resource hedging sub-event is sent to the resource abnormality processing terminal so that the abnormality can be re-verified at the resource abnormality processing terminal. Upon confirming that the verification is correct, execute the resource anomaly transfer alarm processing; In response to the determination that the verification is incorrect, the abnormal information is sent to the resource abnormality manual processing terminal for final abnormality verification; Generate a set of order cancellation instructions for the second resource object sub-event set.

3. The method according to claim 1, characterized in that, The resource transfer verification terminal obtains the associated information corresponding to the virtual processing information of the resource through the following steps: Obtain the resource processing records corresponding to the resource hedging event; Based on the resource processing records, the resource processing link, the anomaly detection link, and the instruction execution link are generated using the link query process in the resource retention terminal. The resource virtual processing information, the resource processing link, the anomaly detection link, and the instruction execution link are identified as associated information and sent to the resource flow verification terminal.

4. The method according to claim 1, characterized in that, The step of generating resource virtual processing information and resource remaining processing order set based on the resource pending order subset corresponding to the at least one order withdrawal instruction and the resource processing order set includes: For each resource processing order in the resource processing order set, perform the generation step: Determine at least one resource pending order that matches the resource processing order from the subset of resource pending orders; The resource processing automatic process in the resource processing terminal is used to generate the resource processing order and the resource virtual processing sub-information corresponding to the at least one resource pending order; The obtained resource virtual processing sub-information set is determined as resource virtual processing information; The resource pending order subset is removed from the resource pending order set to obtain the resource remaining processing order set.

5. The method according to claim 1, characterized in that, The resource processing terminal, the resource retention terminal, and the resource transfer verification terminal are based on standardized protocols, low-latency technology, distributed architecture, and target hardware acceleration for information interaction and processing.

6. The method according to claim 1, characterized in that, The process of focusing on filtering a subset of matching pending resource orders from the stored set of pending resource orders based on the resource knowledge graph includes: Retrieve the first set of pending resource orders that currently support resource processing from the target cache corresponding to the resource processing terminal; For each resource processing order in the resource processing order set, perform a filtering step: In response to determining that the current resource processing scenario is a high-concurrency resource scenario, the order filtering agent deployed on the resource processing terminal is scheduled to use the resource filtering script to filter the first set of pending resource orders and obtain the first set of pending resource orders corresponding to the resource processing order. A scheduling order filtering agent is used to remove first resource pending orders from the first resource pending order subset based on the entity association relationship in the resource knowledge graph, where there is a resource processing anomaly between the corresponding entity and the entity corresponding to the resource processing order, to obtain a second resource pending order subset; The order screening agent is scheduled to select, based on the entity value information in the resource knowledge graph, the resource pending orders whose corresponding entity value information is focused within the target range from the second resource pending order subset, thus obtaining a candidate resource pending order subset; In response to the absence of duplicate orders in the obtained subsets of candidate resource pending orders, the subsets of candidate resource pending orders are determined as resource pending order subsets.

7. The method according to claim 6, characterized in that, The method further includes: In response to the existence of duplicate orders in the subset of candidate resource pending orders, at least one resource processing order with duplicates and at least one corresponding subset of candidate resource pending orders are obtained; Based on the resource knowledge graph, a resource knowledge subgraph is generated for the at least one resource processing order and the at least one subset of candidate resource pending orders, for temporary storage in the target cache; Based on the priority and resource anomaly probability of each resource processing order in the at least one resource processing order, and based on the resource knowledge subgraph, the candidate resource pending order subset corresponding to each resource processing order is determined in sequence, so as to obtain the candidate resource pending order subset group and the pending resource processing order group corresponding to the target resource processing order group. The pending resource processing order group is temporarily cached on the target storage side to wait for whether resource processing orders in the predetermined future time period and the next time period will be focused, and the candidate pending resource order subset group is determined as the pending resource order subset.

8. A resource processing device based on multi-terminal interaction, characterized in that, include: The filtering unit is configured to, in response to the resource processing end receiving the current set of resource processing orders, focus on filtering out a subset of matching resource pending orders from the stored set of resource pending orders based on the resource knowledge graph; The detection unit is configured to use at least one intelligent agent deployed in the edge terminal of the resource processing end to perform anomaly detection on the resource hedging events corresponding to the resource processing order set and the resource pending order subset, and obtain the detection result. The first generation unit is configured to generate an order withdrawal instruction set in response to the detection result indicating that there is no abnormality. The second generation unit is configured to, in response to determining that at least one order withdrawal instruction has been completed, generate resource virtual processing information and resource remaining processing order set based on the resource pending order subset corresponding to the at least one order withdrawal instruction and the resource processing order set; The sending unit is configured to store the virtual processing information of the resources in the blockchain and send the set of remaining processing orders of the resources to the downstream resource retention end for storage as pending resource orders; The display unit is configured to respond to the verification control displayed on the page area corresponding to the virtual processing information of the resource on the resource flow verification end when the verification control is clicked on the corresponding page area of ​​the resource flow verification end. The resource processing link, anomaly detection link and instruction execution link corresponding to the virtual processing information of the resource stored in the blockchain are displayed on the resource flow page. Clicking on each link node in the link pops up a link pop-up window. The resource flow verification end is the edge terminal of the resource retention end.

9. An electronic device, characterized in that, include: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

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