Resource data processing method and device, storage medium and electronic equipment

Abstract

The invention discloses a resource data processing method and device, a storage medium and electronic equipment, and relates to the technical field of computers.The method comprises the steps that firstly, resource data plan flow direction data which is reported by all resource data summarization service platforms based on resource data element plan information and aims at a future data transfer day is determined; determining target resource data plan flow direction data corresponding to at least one resource standby node to determine an initial standby resource quantity time sequence prediction curve of the resource standby node, and obtaining resource data actual flow direction data collected by each resource data summarization service platform in a future data transfer day; and performing standby resource quantity curve updating processing to obtain a target standby resource quantity time sequence prediction curve of the resource standby node. Thus, through generating the initial standby resource quantity time sequence prediction curve of the resource standby node and updating to obtain the target standby resource quantity time sequence prediction curve of the resource standby node, fine management of the estimated standby resource quantity can be realized.

Description

Technical Field

[0001] This specification relates to the field of computer technology, and in particular to a resource data processing method, apparatus, storage medium, and electronic device. Background Technology

[0002] In computer resource management and financial resource management, the concept of liquidity management has been gradually introduced to address the increasing resource demands and allocation challenges. With the development of technologies such as cloud computing, big data analytics, and artificial intelligence, the resource needs of enterprises and organizations in related fields have become more dynamic and complex.

[0003] For example, in computer resource management, reserve resource management involves real-time monitoring and assessment of the usage status of various resources (such as storage and network bandwidth) in a computer environment to adjust resource allocation to adapt to rapidly changing computing demands. Similarly, in financial resource management, reserve resource management involves real-time monitoring and assessment of the usage status of various resources (such as currency) in a financial environment to adjust resource allocation to adapt to rapidly changing financial demands. Therefore, accurate reserve resource management has become a core function for improving operational efficiency, reducing costs, and avoiding resource waste. Thus, for those skilled in the art, how to achieve accurate management of estimated reserve resource quantities is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] This specification provides a resource data processing method, apparatus, storage medium, and electronic device, the technical solutions of which are as follows: Firstly, this specification provides a resource data processing method, the method comprising: Determine the planned flow of resource data for future data transfer dates, based on the resource data metadata plan information reported by each resource data aggregation service platform; Based on the resource data plan flow data, determine the target resource data plan flow data corresponding to at least one resource backup node, and based on the target resource data plan flow data, determine the initial backup resource quantity time series prediction curve of the resource backup node; Obtain the actual flow data of resource data collected by each of the resource data aggregation service platforms on the future data transfer date, and perform reserve resource quantity curve update processing based on the actual flow data of resource data and the initial reserve resource quantity time series prediction curve to obtain the target reserve resource quantity time series prediction curve of the resource reserve node.

[0005] Secondly, this specification provides a resource data processing apparatus, the apparatus comprising: The resource plan acquisition module is used to determine the resource data plan flow data for future data transfer dates reported by each resource data aggregation service platform based on the resource data metadata plan information. The resource curve determination module is used to determine the target resource data planned flow data corresponding to at least one resource backup node based on the resource data planned flow data, and to determine the initial backup resource quantity time series prediction curve of the resource backup node based on the target resource data planned flow data. The resource curve update module is used to obtain the actual flow data of resource data collected by each of the resource data aggregation service platforms on the future data transfer date, and to perform reserve resource quantity curve update processing based on the actual flow data of resource data and the initial reserve resource quantity time series prediction curve to obtain the target reserve resource quantity time series prediction curve of the resource reserve node.

[0006] Thirdly, this specification provides a computer storage medium having multiple instructions adapted to be loaded by a processor and executed by the method described above.

[0007] Fourthly, this specification provides a computer program product that stores at least one instruction, which is loaded by a processor and executes the method described above.

[0008] Fifthly, this specification provides an electronic device that may include: a memory and a processor; wherein the memory stores a computer program adapted to be loaded by the memory and to execute the methods described above.

[0009] The beneficial effects of the technical solutions provided in this specification include at least the following: The resource data processing method provided in this specification first determines the planned resource data flow data for future data transfer dates reported by each resource data aggregation service platform based on resource data metadata plan information. Then, it determines the target resource data flow data corresponding to at least one resource backup node. Based on the target resource data flow data, it determines the initial backup resource quantity time-series prediction curve for the resource backup node. It then obtains the actual resource data flow data collected by each resource data aggregation service platform on the future data transfer date. Finally, based on the actual resource data flow data and the initial backup resource quantity time-series prediction curve, it performs a backup resource quantity curve update process to obtain the target backup resource quantity time-series prediction curve for the resource backup node. In this way, the planned resource data flow data is reported according to the resource data metadata plan information, ensuring the data standardization of the planned resource data flow data and improving the parsing efficiency of the planned resource flow data. By generating the initial backup resource quantity time-series prediction curve for the resource backup node and updating the target backup resource quantity time-series prediction curve for the resource backup node, multiple versions of the backup resource quantity time-series prediction curve can be generated, thereby enabling refined management of estimated backup resource quantities and improving the management efficiency of estimated backup resource quantities. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of a data transfer service system provided in the embodiments of this specification; Figure 2 This is a flowchart illustrating a resource data processing method provided in an embodiment of this specification; Figure 3 This is a flowchart illustrating another resource data processing method provided in the embodiments of this specification; Figure 4 This is a schematic diagram of the structure of a resource data processing device provided in the embodiments of this specification; Figure 5 This is a schematic diagram of the structure of a resource curve determination module provided in an embodiment of this specification; Figure 6 This is a schematic diagram of the structure of an electronic device provided in the embodiments of this specification. Detailed Implementation

[0012] To make the inventive objectives, features, and advantages of the embodiments in this specification more apparent and understandable, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments in this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this specification.

[0013] In the description of this specification, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. In the description of this specification, it should be noted that, unless otherwise expressly specified and limited, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. Those skilled in the art can understand the specific meaning of the above terms in this specification based on the specific circumstances. Furthermore, in the description of this specification, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0014] The present specification will now be described in detail with reference to specific embodiments.

[0015] Please see Figure 1 This is a schematic diagram of a resource data processing system provided in an embodiment of this specification. Figure 1 As shown in the diagram, the scenario diagram may include at least a client cluster and a service platform 100.

[0016] In some embodiments, the client cluster may include at least one client, such as Figure 1 As shown, it specifically includes client 1 corresponding to user 1, client 2 corresponding to user 2, ..., client n corresponding to user n, where n is an integer greater than 0.

[0017] Each terminal in the client cluster can be a smart device with communication capabilities, including but not limited to: wearable devices, handheld devices, personal computers, tablets, smartphones, computing devices, or other processing devices connected to a wireless modem. In different networks, smart devices may be called by different names, such as: user equipment, access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent or user device, cellular phone, cordless phone, personal digital assistant (PDA), electronic devices in 5G networks or future evolved networks, etc.

[0018] In some embodiments, the service platform 100 is a hardware device with strong computing power. Specifically, the server can be a single server device, such as a rack-mount, blade, tower, or cabinet server device, or a workstation, mainframe computer, or other hardware device; it can also be a server cluster composed of multiple servers. The servers in the service cluster can be composed in a symmetrical manner, wherein each server is functionally and hierarchically equivalent in the transaction link, and each server can provide services to the outside world independently. Providing services independently can be understood as not requiring the assistance of other servers.

[0019] In some embodiments, the service platform 100 can establish a communication connection with clients in the client cluster, and complete data interaction during resource data processing based on this communication connection. For example, the service platform has the capability to execute resource data processing methods, and the client can be a resource data aggregation service platform. The client can report resource data plan flow data for a future data transfer date to the service platform based on resource data metadata plan information. Specifically, the service platform determines the resource data plan flow data for a future data transfer date reported by each resource data aggregation service platform based on resource data metadata plan information; determines the target resource data plan flow data corresponding to at least one resource backup node based on the resource data plan flow data; determines the initial backup resource quantity time-series prediction curve of the resource backup node based on the target resource data plan flow data; obtains the actual resource data flow data collected by each resource data aggregation service platform on the future data transfer date; and performs backup resource quantity curve update processing based on the actual resource data flow data and the initial backup resource quantity time-series prediction curve to obtain the target backup resource quantity time-series prediction curve of the resource backup node.

[0020] It should be noted that the server and client establish a communication connection through a network for interactive communication. This network can be a wireless network or a wired network. Wireless networks include, but are not limited to, cellular networks, wireless LANs, infrared networks, or Bluetooth networks. Wired networks include, but are not limited to, Ethernet, universal serial bus (USB), or controller area networks. In one or more embodiments of this specification, technologies and / or formats including HyperText Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network (such as target compressed packets). Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), and Internet Protocol Security (IPsec) can be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0021] The resource data processing system embodiments provided in this specification and the resource data processing methods described in one or more embodiments belong to the same concept. The execution entity corresponding to the resource data processing methods involved in one or more embodiments of this specification can be an electronic device, and the electronic device can be the aforementioned service platform. The specific implementation process of the resource data processing system embodiments can be found in the following method embodiments, and will not be repeated here.

[0022] In one embodiment, such as Figure 2 As shown, a resource data processing method is proposed. This method can be implemented using a computer program and can run on a resource data processing device based on the von Neumann architecture. This computer program can be integrated into applications or run as a standalone utility application.

[0023] Specifically, the resource data processing method includes: S202, determine the resource data plan flow data for future data transfer dates reported by each resource data aggregation service platform based on the resource data metadata plan information.

[0024] The resource data aggregation service platform is a service platform that manages the inflow and / or outflow of resource data. Different resource data aggregation service platforms manage different resource data flow services. The interaction objects for resource inflow and / or outflow processing on the resource data aggregation service platform can be external resource users. External resource users include at least users who use resource data and peer organizations that use resource data. Specifically, users of resource data can include individuals, enterprises, units, groups, organizations, etc. Peer organizations that use resource data are organizations that provide the same or similar resource data transfer services as the resource data aggregation service platform.

[0025] Resource data can include computer resource data, financial resource data, and other recyclable types of resource data.

[0026] Taking computer resource data as an example, resource data can specifically include device computing power, storage capacity, network bandwidth, etc. Resource data inflow processing can be positive update processing of resource data generated by the release of device computing power, storage capacity, or network bandwidth, while resource data outflow processing can be negative update processing of resource data generated by the occupancy of device computing power, storage capacity, or network bandwidth. Different resource data flow services can include video live streaming push service, content distribution network node control service, file backup service, edge monitoring backhaul service, interactive classroom live streaming gateway service, etc. Correspondingly, the resource data aggregation service platform can be a video live streaming push service platform, a content distribution network node control service platform, a file backup service platform, an edge monitoring backhaul service platform, an interactive classroom live streaming gateway service platform, etc.

[0027] Taking financial resource data as an example, resource data can specifically be currency. Resource data inflow processing can be positive update processing of resource data generated by currency transfers, while resource data outflow processing can be negative update processing of resource data generated by currency transfers. Different resource data flow services can include basic accounting management services, credit management services, wealth management services, foreign exchange management services, insurance management services, etc. Correspondingly, the resource data aggregation service platform can be a basic accounting management service platform, credit management service platform, wealth management service platform, foreign exchange management service platform, insurance management service platform, etc.

[0028] The resource data metadata plan information is used to define the data format and content for generating resource data plan flow data. In other words, the resource data metadata plan information specifies what content the generated resource data plan flow data should include, and in what format this content should be presented.

[0029] Resource data planned flow data can be understood as descriptive data about the flow of resource data that the resource data aggregation service platform anticipates may occur on future data transfer dates. Resource data planned flow data includes at least the estimated flow time, estimated flow direction, and estimated flow quantity of resource data on the future data transfer dates. Future data transfer dates include at least one data transfer date following the reporting date, which is the date the resource data aggregation service platform reports the resource data planned flow data.

[0030] For example, taking storage capacity in computer resource data as an example, the planned flow data of resource data can be: the resource data aggregation service platform estimates that it will occupy a first capacity of storage space at the first time on the future data transfer date, and release a second capacity of storage space at the second time on the future data transfer date. In the resource planned flow data of this example, the first time and the second time are the estimated flow time of storage capacity, and occupying and releasing are the estimated flow direction of storage capacity. Occupying can represent the inflow direction and releasing can represent the outflow direction. The first capacity and the second capacity are the estimated flow quantity of storage capacity.

[0031] Taking currency within financial resource data as an example, the planned flow data for resource data could be: the resource data aggregation service platform estimates that a first quantity of currency will be transferred in at the third time on the future data transfer date, and a second quantity of currency will be transferred out at the fourth time on the future data transfer date. In this example of planned flow data, the third and fourth times are the estimated flow times of the currency, "transfer in" and "transfer out" are the estimated flow directions of the currency, with "transfer in" indicating an inflow and "transfer out" indicating an outflow, and the first and second quantities being the estimated flow quantities of the currency.

[0032] Specifically, before determining the resource data flow plan data reported by each resource data aggregation service platform, the resource data service type corresponding to each resource data aggregation service platform is determined. Based on the resource data service type corresponding to each resource data aggregation service platform, resource data meta-plan information is determined for that platform. This meta-plan information is then sent to the resource data aggregation service platform, enabling it to generate resource data flow plan data for future data transfer dates. Here, the resource data service type refers to the type of resource data flow service managed by the resource data aggregation service platform. Using the example of currency in financial data, resource data flow services could include basic accounting management services, credit management services, wealth management services, foreign exchange management services, and insurance management services. Therefore, resource data service types could include basic accounting management type, credit management type, wealth management type, foreign exchange management type, and insurance management type.

[0033] For example, when the resource data is computer resource data, step S202 can specifically be: determining the computer resource data planned flow data for future data transfer dates reported by each computer resource data aggregation service platform based on the computer resource data metadata plan information.

[0034] For example, when the resource data is financial resource data, step S202 can specifically be: determining the financial resource data planned flow data for future data transfer dates reported by each financial resource data aggregation service platform based on the financial resource data meta-plan information.

[0035] S204, based on the resource data plan flow data, determine the target resource data plan flow data corresponding to at least one resource backup node, and based on the target resource data plan flow data, determine the initial backup resource quantity time series prediction curve of the resource backup node.

[0036] The resource standby node stores the amount of standby resources that can be immediately accessed during various time periods on the future data transfer date. For example, a specific resource standby node can be a resource standby management account, which stores the standby resources to support resource scheduling.

[0037] Since the future data transfer date has not yet arrived, the amount of reserve resources obtained by the target resource data plan to flow to the data is called the predicted reserve resource amount. The initial reserve resource amount time series prediction curve includes the predicted reserve resource amount corresponding to different reference reserve resource amount matching time points of the resource reserve node on the future data transfer date.

[0038] Specifically, firstly, the target resource data plan flow direction data corresponding to each resource backup node is determined. By performing resource data clearing processing on the target resource data plan flow direction data, the predicted backup resource quantity corresponding to different reference backup resource quantity matching time points on the future data transfer date is obtained. Then, the initial backup resource quantity time series prediction curve is determined based on the reference backup resource quantity matching time point and the predicted backup resource quantity.

[0039] For example, when the resource data is computer resource data, step S204 can specifically be: determining the target computer resource data plan flow data corresponding to at least one computer resource backup node based on the computer resource data plan flow data, and determining the initial backup computer resource quantity time series prediction curve of the computer resource backup node based on the target computer resource data plan flow data.

[0040] For example, when the resource data is financial resource data, step S204 can specifically be: determining the target financial resource data plan flow data corresponding to at least one financial resource backup node based on the financial resource data plan flow data, and determining the initial backup financial resource quantity time series prediction curve of the financial resource backup node based on the target financial resource data plan flow data.

[0041] S206: Obtain the actual flow data of resource data collected by each resource data aggregation service platform on the future data transfer date. Based on the actual flow data of resource data and the time series prediction curve of the initial reserve resource quantity, perform reserve resource quantity curve update processing to obtain the target reserve resource quantity time series prediction curve of the resource reserve node.

[0042] The actual flow data of resource data can be understood as the actual flow description data of resource data collected by the resource data aggregation service platform on the future data transfer date. The actual flow data of resource data should include at least the actual flow time, actual flow direction, and actual flow quantity of resource data on the future data transfer date.

[0043] Specifically, firstly, the actual reserve resource quantity corresponding to each reference reserve resource quantity matching time point can be determined by the actual flow data of the resource data. Then, the reserve resource quantity curve update processing is performed by using the actual reserve resource quantity and the predicted reserve resource quantity corresponding to each reference reserve resource quantity matching time point to obtain the time series prediction curve of the target reserve resource quantity of the resource reserve node.

[0044] For example, when the resource data is computer resource data, step S206 can specifically be as follows: Obtain the actual flow data of computer resource data collected by each computer resource data aggregation service platform on the future data transfer date; based on the actual flow data of computer resource data and the initial standby computer resource quantity time-series prediction curve, perform standby resource quantity curve update processing to obtain the target standby computer resource quantity time-series prediction curve for the computer resource standby node. Thus, the planned flow data of computer resource data is reported according to the computer resource data meta-plan information, ensuring the data standardization of the planned flow data of computer resource data and improving the parsing efficiency of the planned flow data of computer resource data. By generating the initial standby computer resource quantity time-series prediction curve for the standby computer resource node and updating the target standby computer resource quantity time-series prediction curve for the standby computer resource node, multiple versions of the standby computer resource quantity time-series prediction curve can be generated, thereby enabling refined management of the estimated standby computer resource quantity and improving the management efficiency of the estimated standby computer resource quantity.

[0045] For example, when the resource data is financial resource data, step S206 can specifically be as follows: Obtain the actual flow data of financial resource data collected by each financial resource data aggregation service platform on the future data transfer date; based on the actual flow data of financial resource data and the initial standby financial resource quantity time-series prediction curve, perform standby resource quantity curve update processing to obtain the target standby financial resource quantity time-series prediction curve for the financial resource standby node. Thus, the planned flow data of financial resource data is reported according to the financial resource data meta-plan information, ensuring the data standardization of the planned flow data of financial resource data and improving the parsing efficiency of the planned flow data of financial resource data. By generating the initial standby financial resource quantity time-series prediction curve for the financial resource standby node and updating the target standby financial resource quantity time-series prediction curve for the financial resource standby node, multiple versions of the standby financial resource quantity time-series prediction curve can be generated, thereby enabling refined management of the estimated standby financial resource quantity and improving the management efficiency of the estimated standby financial resource quantity.

[0046] The resource data processing method provided in this specification's embodiments determines the planned resource data flow data for future data transfer dates reported by each resource data aggregation service platform based on resource data metadata plan information. Based on this planned flow data, it determines the target planned resource data flow data corresponding to at least one resource backup node. Based on the target planned resource data flow data, it determines the initial backup resource quantity time-series prediction curve for the resource backup node. It then obtains the actual resource data flow data collected by each resource data aggregation service platform on the future data transfer date. Finally, based on the actual resource data flow data and the initial backup resource quantity time-series prediction curve, it performs backup resource quantity curve update processing to obtain the target backup resource quantity time-series prediction curve for the resource backup node. In this way, the planned resource data flow data is reported according to the resource data metadata plan information, ensuring the data standardization of the planned resource data flow data and improving the parsing efficiency of the planned resource data flow data. By generating the initial backup resource quantity time-series prediction curve for the resource backup node and updating the target backup resource quantity time-series prediction curve for the resource backup node, multiple versions of the backup resource quantity time-series prediction curve can be generated, thereby enabling refined management of estimated backup resource quantities and improving the management efficiency of estimated backup resource quantities.

[0047] Please see Figure 3 This is a flowchart illustrating another embodiment of a resource data processing method provided in this specification. Specifically, the method may include the following steps: S302, determine the resource data plan flow data for future data transfer dates reported by each resource data aggregation service platform based on the resource data metadata plan information.

[0048] For details on the implementation of step S302, please refer to [link / reference]. Figure 2The descriptions of the relevant steps in the illustrated embodiments will not be repeated here.

[0049] S304, determine the target resource data plan flow data corresponding to at least one resource standby node based on the resource data plan flow data.

[0050] Among them, the target resource data plan flow data is the resource data plan flow data belonging to the same resource standby node.

[0051] Specifically, the planned flow data of resource data includes the estimated flow time, estimated flow direction, and estimated flow quantity of resource data on the future data transfer date. This data may also include resource data clearing node identifiers, which identify the backup resource nodes used for clearing resource data. In the planned flow data of resource data reported by various resource data aggregation service platforms, the resource data clearing node identifiers may be the same for some platforms, or they may be different for each platform.

[0052] Identify the resource data clearing node identifiers in each resource data plan flow data, and determine the resource standby nodes indicated by each resource data clearing node identifier. For each resource standby node, determine that the resource data plan flow data containing its corresponding resource data clearing node identifier is its corresponding target resource data plan flow data.

[0053] S306, for each resource backup node, based on the target resource data plan flow data, determine the first predicted backup resource quantity corresponding to the reference backup resource quantity matching time point of each resource data aggregation service platform.

[0054] The reference backup resource quantity matching time point is the time point at which resource data is cleared to obtain the estimated backup resource quantity. It is also a specific time point within a future data transfer date. There are multiple reference backup resource quantity matching time points, and the time interval between these multiple time points is the same. The reference backup resource quantity matching time point can be preset and is related to the resource backup node. For different resource backup nodes, the reference backup resource quantity matching time point can be the same or different.

[0055] The first predicted reserve resource quantity is the amount of available reserve resources planned by the resource data aggregation service platform at the reference reserve resource quantity matching time point, based on the predicted resource reserve node according to the target resource data plan flow data.

[0056] Specifically, for each resource backup node, its corresponding target resource data plan flow data may include resource plan flow data reported by multiple resource data aggregation service platforms. Resource data clearing processing is performed based on the resource plan flow data reported by each resource data aggregation service platform to obtain the first predicted backup resource quantity corresponding to the reference backup resource quantity matching time point for each resource data aggregation service platform. Resource data clearing processing can be understood as determining the target estimated flow time of resource data located between adjacent reference backup resource quantity matching time points, and performing resource data clearing processing based on the estimated flow direction and estimated flow quantity corresponding to the target estimated flow time.

[0057] For example, a specific implementation of resource data clearing processing can be as follows: Let Y represent the first predicted reserve resource quantity corresponding to the current reference resource quantity matching time point, let X represent the first predicted reserve resource quantity corresponding to the previous reference resource quantity matching time point, let M represent the sum of all predicted flow quantities with the predicted flow direction being inflow for the target predicted flow time between the previous reference resource quantity matching time point and the current reference resource quantity matching time point, and let N represent the sum of all predicted flow quantities with the predicted flow direction being outflow for the target predicted flow time between the previous reference resource quantity matching time point and the current reference resource quantity matching time point. Then, by calculating Y=X+MN, the first predicted reserve resource quantity corresponding to each reference resource quantity matching time point can be obtained.

[0058] S308, determine the reserve resource quantity adjustment weight corresponding to each resource data aggregation service platform, and perform reserve resource quantity adjustment processing on the first predicted reserve resource quantity based on the reserve resource quantity adjustment weight to obtain the second predicted reserve resource quantity corresponding to each resource data aggregation service platform at the reference reserve resource quantity matching time point.

[0059] It is understood that the second predicted reserve resource quantity is the available reserve resource quantity planned by the resource reserve node for the resource data aggregation service platform at the reference reserve resource quantity matching time point, based on the first predicted reserve resource quantity. The second predicted reserve resource quantity can be equal to the first predicted reserve resource quantity, or the second predicted reserve resource quantity can be less than the first predicted reserve resource quantity.

[0060] In one embodiment, determining the adjustment weight of the reserve resource quantity corresponding to each resource data aggregation service platform may include the following steps: A2: Determine the historical resource data forecasting and evaluation results corresponding to each resource data aggregation service platform, and determine the resource data forecasting accuracy corresponding to each resource data aggregation service platform based on the historical resource data forecasting and evaluation results; A4: Determine the adjustment weight of the reserve resource quantity corresponding to each resource data aggregation service platform based on the accuracy of resource data forecast.

[0061] In step A2, the historical resource data forecast evaluation results include the forecast evaluation results of the planned flow data of historical resource data reported by the resource data aggregation service platform on at least one historical reporting date. The planned flow data of historical resource data can be reported once per historical reporting date. The historical resource data forecast evaluation results can be obtained by evaluating each historical predicted reserve resource quantity and the corresponding historical actual reserve resource quantity in the historical time-series prediction curve of the initial reserve resource quantity corresponding to the planned flow data of historical resource data reported on each reporting date. Specifically, the historical resource data forecast evaluation results can include matching results of multiple historical predicted reserve resource quantities and the corresponding historical actual reserve resource quantities. These matching results can include successful matching types and failed matching types. The accuracy of the resource data forecast corresponding to the resource data aggregation service platform can be determined by the number of successful matching results and the total number of matching results. The embodiments in this specification do not impose specific restrictions on the method of determining whether the matching result is a successful match or a failed match. For example, when the historical predicted reserve resource quantity is equal to the corresponding historical actual reserve resource quantity, their matching result can be determined to be a successful match. Alternatively, when the difference between the historical predicted reserve resource quantity and the corresponding historical actual reserve resource quantity is less than or equal to a preset difference, their matching result can be determined to be a successful match.

[0062] In step A4, a preset weight mapping relationship can be obtained. The preset weight mapping relationship can be used to store at least one reference resource data forecast accuracy and the reference reserve resource quantity adjustment weight corresponding to each reference resource data forecast accuracy. Based on the preset weight mapping relationship, the reserve resource quantity adjustment weight corresponding to the resource data forecast accuracy can be queried.

[0063] After determining the adjustment weight of the reserve resource quantity, the product of the adjustment weight of the reserve resource quantity and the first predicted reserve resource quantity is determined, and this product is determined as the second predicted reserve resource quantity corresponding to the reference reserve resource quantity matching time point of the resource data aggregation service platform.

[0064] In this way, the predicted reserve resource quantity corresponding to the resource data aggregation service platform can be dynamically adjusted, avoiding a large gap between the time series prediction curve of the reserve resource quantity obtained by completely following the resource data plan flow data and the actual reserve resource quantity, thereby avoiding waste of reserve resource quantity and reducing the cost of reserve resource quantity.

[0065] S310, determine the initial predicted reserve resource quantity of the resource reserve node at the reference reserve resource quantity matching time point based on the second predicted reserve resource quantity, and generate the initial reserve resource quantity time series prediction curve of the resource reserve node based on the initial predicted reserve resource quantity and the reference reserve resource quantity matching time point.

[0066] The initial predicted reserve resource quantity is the predicted reserve resource quantity that can be used in the resource reserve node at the reference reserve resource quantity matching time point.

[0067] Specifically, all second-predicted reserve resource quantities are summed to obtain the initial predicted reserve resource quantity of the resource reserve node at the reference reserve resource quantity matching time point. Then, an initial reserve resource quantity time-series prediction curve is generated, with time point as the horizontal axis and reserve resource quantity as the vertical axis, including the reference reserve resource quantity matching time point and the initial predicted reserve resource quantity corresponding to the reference reserve resource quantity matching time point.

[0068] S312, Obtain the actual flow data of resource data collected by each resource data aggregation service platform on the future data transfer date.

[0069] Specifically, actual resource data flow data can be understood as descriptive data about the actual flow of resource data collected by the resource data aggregation service platform on the future data transfer date. Actual resource data flow data should include at least the actual flow time, actual flow direction, and actual flow quantity of resource data on the future data transfer date.

[0070] S314, for each resource backup node, determine the backup resource quantity matching time point and the predicted backup resource quantity corresponding to the backup resource quantity matching time point based on the initial backup resource quantity time series prediction curve, determine the actual backup resource quantity of the resource backup node at the backup resource quantity matching time point based on the actual resource data flow data, and obtain the target backup resource quantity time series prediction curve by updating the backup resource quantity curve based on the actual backup resource quantity and the predicted backup resource quantity.

[0071] Specifically, for each resource standby node, the matching time point for the current time of the current time is determined from its initial standby resource quantity time-series prediction curve, and the predicted standby resource quantity corresponding to the matching time point is also determined. The matching time point is the reference standby resource quantity matching time point preceding the current time with the shortest time interval. The actual flow data of the target resource data corresponding to the resource standby node is determined from the actual flow data of the resource data. Based on the actual flow data of the target resource data, resource data clearing processing is performed to obtain the actual standby resource quantity of the resource standby node at the matching time point.

[0072] In one embodiment, performing a reserve resource quantity curve update process based on the actual reserve resource quantity and the predicted reserve resource quantity to obtain the target reserve resource quantity time series prediction curve may specifically include: B2: If the actual reserve resource quantity is inconsistent with the predicted reserve resource quantity, then determine whether the first target resource reserve node corresponding to the actual reserve resource quantity and the predicted reserve resource quantity has first resource data plan flow update data; B4: If so, then generate a target reserve resource quantity time series prediction curve with the reserve resource quantity matching time point as the origin based on the actual reserve resource quantity and the first resource data planned flow update data; B6: If not, then generate a time series prediction curve of the target reserve resource quantity with the time point of the reserve resource quantity matching as the time origin based on the actual reserve resource quantity and the target resource data plan flow data.

[0073] In step B2, the first target resource backup node is the resource backup node whose predicted backup resource quantity and actual backup resource quantity are inconsistent at the backup resource quantity matching time point. The first resource data plan flow update data is obtained after determining the target resource data plan flow data corresponding to the first target resource backup node, based on the latest uploaded updated resource data plan flow data for the future data transfer date. It is understandable that some resource data aggregation service platforms may initially upload relatively broad resource data plan flow data, and subsequently upload more detailed resource data plan flow data, thus resulting in updated resource data plan flow data and therefore updated target resource data plan flow data.

[0074] In step B4, when there is updated data for the first resource data plan flow, the actual reserve resource quantity is used as the base resource quantity. Resource data clearing processing is performed on the actual reserve resource quantity and the updated data for the first resource data plan flow to obtain the first predicted reserve resource quantity corresponding to each target reserve resource quantity matching time point after the reserve resource quantity matching time point. A target reserve resource quantity time series prediction curve is generated with the time point as the horizontal axis, the reserve resource quantity as the vertical axis, and the curve point formed by the reserve resource quantity matching time point and the actual reserve resource quantity as the origin, and the curve point formed by the target reserve resource quantity matching time point and the first predicted reserve resource quantity is included.

[0075] In step B6, when there is no first resource data plan flow update data, the actual reserve resource quantity is used as the base resource quantity. Resource data clearing processing is performed on the actual reserve resource quantity and the target resource data plan flow data to obtain the second predicted reserve resource quantity corresponding to each target reserve resource quantity matching time point after the reserve resource quantity matching time point. A target reserve resource quantity time series prediction curve is generated with the time point as the horizontal axis, the reserve resource quantity as the vertical axis, and the curve point formed by the reserve resource quantity matching time point and the actual reserve resource quantity as the origin, and the curve point formed by the target reserve resource quantity matching time point and the second predicted reserve resource quantity is included.

[0076] Thus, if the predicted reserve resource quantity and the actual reserve resource quantity at the time point of the reserve resource quantity matching are inconsistent, and if there is updated data on the first resource data planned flow direction, then a corrected time-series prediction curve for the reserve resource quantity is generated based on the updated data on the first resource data planned flow direction and the actual reserve resource quantity. This ensures that the time-series prediction curve for the reserve resource quantity is updated in a timely manner according to the latest resource data planned flow direction data, thereby ensuring the accuracy of the time-series prediction curve for the reserve resource quantity. If there is no updated data on the first resource data planned flow direction, then a corrected time-series prediction curve for the reserve resource quantity is generated based on the resource data planned flow direction data and the actual reserve resource quantity, thereby ensuring the accuracy of the time-series prediction curve for the reserve resource quantity.

[0077] In one embodiment, performing a reserve resource quantity curve update process based on the actual reserve resource quantity and the predicted reserve resource quantity to obtain the target reserve resource quantity time series prediction curve may also include: C2: If the actual reserve resource quantity is consistent with the predicted reserve resource quantity, then determine whether there is a second resource data plan flow update data for the second target resource reserve node corresponding to the actual reserve resource quantity and the predicted reserve resource quantity; C4: If yes, then generate a target reserve resource quantity time series prediction curve with the reserve resource quantity matching time point as the time origin based on the actual reserve resource quantity and the second resource data plan flow update data; C6: If not, determine the data transfer frequency corresponding to the second target resource backup node, and determine the target timing period corresponding to the second target resource backup node based on the data transfer frequency; C8: Obtain the initial time series period of the time series prediction curve of the initial reserve resource quantity corresponding to the target second resource reserve node; C10: Determine whether the target timing period is consistent with the initial timing period; C12: If not, then generate a target reserve resource quantity time series prediction curve with the reserve resource quantity matching time point as the time origin based on the target time series period, actual reserve resource quantity, and target resource data plan flow data.

[0078] In step C2, the second target resource backup node is the resource backup node whose predicted backup resource quantity matches the actual backup resource quantity at the backup resource quantity matching time point. The second resource data plan flow update data is obtained after determining the target resource data plan flow data corresponding to the second target resource backup node, based on the latest uploaded updated resource data plan flow data for the future data transfer date. It is understandable that some resource data aggregation service platforms may initially upload relatively broad resource data plan flow data, and subsequently upload more detailed resource data plan flow data, thus resulting in updated resource data plan flow data and therefore updated target resource data plan flow data.

[0079] In step C4, when there is updated data for the second resource data plan flow, the actual reserve resource quantity is used as the base resource quantity. Resource data clearing processing is performed on the actual reserve resource quantity and the updated data for the second resource data plan flow to obtain the third predicted reserve resource quantity corresponding to each target reserve resource quantity matching time point after the reserve resource quantity matching time point. A target reserve resource quantity time series prediction curve is generated with the time point as the horizontal axis, the reserve resource quantity as the vertical axis, and the curve point formed by the reserve resource quantity matching time point and the actual reserve resource quantity as the origin, and the curve point formed by the target reserve resource quantity matching time point and the third predicted reserve resource quantity.

[0080] In step C6, the data transfer frequency is the number of resource data transfers per unit time corresponding to the second target resource backup node. This number of resource data transfers per unit time includes the number of times resource data is transferred to the second target resource backup node and the number of times resource data is transferred out of the second target resource backup node per unit time. The unit time can be set according to the actual application environment, for example, it can be set to 1 minute, 5 minutes, 15 minutes, 30 minutes, etc. The data transfer frequency can be obtained based on the number of resource data transfers corresponding to the second target resource backup node within a specified historical time period. There can be multiple data transfer frequencies. The target time period corresponding to the second target resource backup node can be determined based on the average of multiple data transfer frequencies, or it can be determined based on the median of multiple data transfer frequencies. Specifically, a preset time period mapping relationship can be obtained, and the target time period corresponding to the average of multiple data transfer frequencies can be queried from the preset time period mapping relationship, or the target time period corresponding to the median of multiple data transfer frequencies can be queried from the preset time period mapping relationship.

[0081] In step C8, the initial timing period is the time interval between the reference reserve resource quantity matching time points in the initial reserve resource quantity timing prediction curve. The target timing period is the time interval between the adjusted matching time points.

[0082] In step C12, when the target time series period differs from the initial time series period, the actual reserve resource quantity is used as the basic resource quantity. Resource data clearing processing is performed on the actual reserve resource quantity and the target resource data plan flow data according to the target time series period to obtain the fourth predicted reserve resource quantity corresponding to each adjusted reserve resource quantity matching time point after the reserve resource quantity matching time point. A target reserve resource quantity time series prediction curve is generated with the time point as the horizontal axis, the reserve resource quantity as the vertical axis, and the curve point formed by the reserve resource quantity matching time point and the actual reserve resource quantity as the origin, and the curve point formed by the adjusted reserve resource quantity matching time point and the fourth predicted reserve resource quantity is included.

[0083] It is understandable that when the target time series period and the initial time series period are the same, the time interval granularity of the initial reserve resource quantity time series prediction curve can be not adjusted, and an updated reserve resource quantity time series prediction curve can be generated with the time point as the horizontal axis, the reserve resource quantity as the vertical axis, and the curve point formed by the reserve resource quantity matching time point and the actual reserve resource quantity as the origin.

[0084] Thus, if the predicted and actual reserve resource quantities at the time point of the reserve resource quantity matching are consistent, and if there is updated data on the second resource data plan flow direction, a corrected time-series prediction curve for the reserve resource quantity can be generated based on the updated data and the actual reserve resource quantity. This ensures the accuracy of the time-series prediction curve by updating the reserve resource quantity according to the latest resource data plan flow direction data. If there is no second resource data plan flow direction data, the time interval granularity of the time-series prediction curve for the reserve resource quantity can be adjusted to generate time-series prediction curves for the reserve resource quantity with different time interval granularities.

[0085] In one embodiment, performing reserve resource quantity curve update processing based on actual reserve resource quantity and predicted reserve resource quantity to obtain the target reserve resource quantity time series prediction curve may further include: D2: If it is determined that the actual amount of standby resources is less than the predicted amount of standby resources, check the data writing thread status of the resource standby node at the time point when the standby resource amount matches the time point. D4: Based on the statistics of the data writing thread status, the actual flow of the resource data to be cleared corresponding to the data writing thread in the mutex lock waiting state is counted. Based on the actual flow of the resource data to be cleared, the resource data writing delay accumulation of the resource standby node at the time point of matching the standby resource quantity is determined. D6: Determine whether the sum of the resource data write latency backlog and the actual reserve resource amount matches the predicted reserve resource amount; D8: If a match is found, the initial reserve resource quantity time series prediction curve is determined as the target reserve resource quantity time series prediction curve; D10: If there is no match, a target reserve resource quantity time series prediction curve is generated based on the actual reserve resource quantity, the resource data write delay backlog, and the target resource data planned flow direction data, with the reserve resource quantity matching time point as the time origin.

[0086] In step D2, the data writing thread status is the thread status of the data writing thread corresponding to the resource standby node at the time point of matching the standby resource quantity. The resource standby node can have at least one data writing thread performing resource data clearing processing on its corresponding resource data actual flow data; these data writing threads can perform resource data clearing processing in parallel. The data writing thread status can include the thread status of at least one data writing thread corresponding to the resource standby node.

[0087] In step D4, the mutex lock waiting state represents a thread state where the actual flow data of the resource data to be cleared has been acquired but is suspended and unable to be cleared. By statistically analyzing the actual flow data of the resource data to be cleared corresponding to the data writing threads in the mutex lock waiting state, resource data clearing processing is performed on the actual flow data of the resource data to be cleared, resulting in the resource data write latency backlog at the resource standby node corresponding to the standby resource quantity matching time point. Specifically, the actual flow time of the resource data is determined from the actual flow data of the resource data to be cleared, and resource data clearing processing is performed based on the flow direction and flow quantity to be cleared corresponding to the actual flow time, resulting in the resource data write latency backlog.

[0088] In step D6, determining whether the sum of the resource data write delay accumulation and the actual reserve resource amount matches the predicted reserve resource amount can be done by determining whether the sum of the resource data write delay accumulation and the actual reserve resource amount is equal to the predicted reserve resource amount.

[0089] In step D10, when the sum of the resource data write delay accumulation and the actual reserve resource quantity matches the predicted reserve resource quantity, it indicates that the data delay is caused by the thread's lock waiting state, resulting in the actual reserve resource quantity being less than the predicted reserve resource quantity. Therefore, the reserve resource quantity timing curve does not need to be adjusted, and the initial reserve resource quantity timing prediction curve is determined as the target reserve resource quantity timing curve.

[0090] In step D12, when the sum of the resource data write delay accumulation and the actual reserve resource quantity does not match the predicted reserve resource quantity, it indicates that the data delay is not caused by the thread's lock waiting state, thus causing the actual reserve resource quantity to be less than the predicted reserve resource quantity. Using the actual reserve resource quantity and the resource data write delay accumulation as the basic resource quantity, resource data clearing processing is performed on the basic resource quantity and the target resource data plan flow data to obtain the fifth predicted reserve resource quantity corresponding to each target reserve resource quantity matching time point after the reserve resource quantity matching time point. A target reserve resource quantity time-series prediction curve is generated with the time point as the horizontal axis, the reserve resource quantity as the vertical axis, and the curve point formed by the reserve resource quantity matching time point and the basic resource quantity as the origin, and the curve point formed by the target reserve resource quantity matching time point and the fifth predicted reserve resource quantity is included.

[0091] Thus, when the actual amount of standby resources at the time point of the standby resource quantity matching is less than the predicted amount of standby resources, the system determines whether to update the standby resource quantity time-series prediction curve by judging whether the delay is due to the data writing thread in the mutex lock waiting state not completing the resource data clearing process. This avoids incorrect updates and generation of incorrect standby resource quantity time-series prediction curves due to false prediction deviations caused by the underlying thread lock mechanism of the system.

[0092] In one embodiment, after performing steps S302-S314 as described above, the embodiments of this specification may further perform the following steps: determine a historical backup resource quantity matching time point that is less than the current time and has a minimum time interval with the current time, obtain the resource data plan flow execution details corresponding to the historical backup resource quantity matching time point; detect the execution result of the resource data plan flow execution details; if the execution result is incomplete, determine the unexecuted resource data plan flow data in the resource data plan flow execution details, and perform early warning processing based on the unexecuted resource data plan flow data.

[0093] It is understandable that the resource data plan execution details can include records of resource data flows to be executed between the historical reserve resource quantity matching time point and the previous reserve resource quantity matching time point. The system retrieves actual resource data flow records between the historical backup resource quantity matching time point and the previous backup resource quantity matching time point. By comparing the actual resource data flow records with the planned resource data flow records, if the actual resource data flow records include all planned resource data flow records, the execution result of the planned resource data flow execution details can be determined as complete. If the actual resource data flow records do not include all planned resource data flow records, the execution result of the planned resource data flow execution details can be determined as incomplete. The system identifies unexecuted planned resource data flow data in the planned resource data flow execution details, generates a resource data plan deviation warning message including the unexecuted planned resource data flow data, and sends the warning message to the target resource data aggregation service platform corresponding to the unexecuted planned resource data flow data. This allows the target resource data aggregation service platform to process the warning output based on the resource data plan deviation warning message.

[0094] In the resource data processing method provided in this specification, the resource data planning flow data for future data transfer dates reported by each resource data aggregation service platform based on resource data metadata planning information is determined. Based on the resource data planning flow data, target resource data planning flow data corresponding to at least one resource backup node is determined. For each resource backup node, the first predicted backup resource quantity corresponding to the reference backup resource quantity matching time point of each resource data aggregation service platform is determined based on the target resource data planning flow data. The backup resource quantity adjustment weight corresponding to each resource data aggregation service platform is determined. Based on the backup resource quantity adjustment weight, the first predicted backup resource quantity is adjusted to obtain the backup resource quantity. Each resource data aggregation service platform determines the initial predicted reserve resource quantity of a resource reserve node at the reference reserve resource quantity matching time point based on the second predicted reserve resource quantity corresponding to the reference reserve resource quantity matching time point. Based on the initial predicted reserve resource quantity and the reference reserve resource quantity matching time point, a time-series prediction curve for the initial reserve resource quantity of the resource reserve node is generated. This allows for dynamic adjustment of the predicted reserve resource quantity corresponding to the resource data aggregation service platform, avoiding excessive discrepancies between the time-series prediction curve for reserve resource quantity obtained by strictly following the resource data plan flow and the actual reserve resource quantity, thereby preventing waste of reserve resources and reducing their overall reserve capacity. Cost; subsequently, if the predicted and actual reserve resource quantities at the corresponding reserve resource quantity matching time points are inconsistent, and if there is updated data on the first resource data planned flow direction, then a corrected time-series prediction curve for the reserve resource quantity is generated based on the updated data and the actual reserve resource quantity. This ensures timely updates to the time-series prediction curve based on the latest resource data planned flow direction data, guaranteeing the accuracy of the time-series prediction curve. If there is no updated data on the first resource data planned flow direction, then a corrected time-series prediction curve for the reserve resource quantity is generated based on the resource data planned flow direction data and the actual reserve resource quantity, ensuring the accuracy of the time-series prediction curve. The accuracy of the forecast curve; when the predicted reserve resource quantity and the actual reserve resource quantity are consistent at the time point of the reserve resource quantity matching, if there is updated data on the second resource data plan flow direction, then a corrected time-series forecast curve for the reserve resource quantity is generated based on the updated data on the second resource data plan flow direction and the actual reserve resource quantity. This ensures that the time-series forecast curve for the reserve resource quantity is updated in a timely manner according to the latest resource data plan flow direction data, thereby guaranteeing the accuracy of the time-series forecast curve for the reserve resource quantity. If there is no second resource data plan flow direction data, the time interval granularity of the time-series forecast curve for the reserve resource quantity can also be adjusted to generate time-series forecast curves for the reserve resource quantity with different time interval granularities.When the actual amount of standby resources at the time point corresponding to the standby resource quantity matching is less than the predicted amount, the system determines whether to update the standby resource quantity time-series prediction curve by checking if the delay is due to data write threads in a mutex lock waiting state not completing resource data clearing. This avoids incorrect updates and generation of erroneous standby resource quantity time-series prediction curves caused by false prediction deviations due to the underlying thread lock mechanism of the system.

[0095] The following will combine Figure 4 This specification provides a detailed description of the resource data processing apparatus provided in the embodiments. It should be noted that... Figure 4 The resource data processing device shown is used to execute this specification. Figures 2-3 The methods shown in the embodiments are illustrated for ease of explanation, showing only the parts related to the embodiments of this specification. For specific technical details not disclosed, please refer to this specification. Figures 2-3 The example shown.

[0096] Please see Figure 4 This diagram illustrates the structure of a resource data processing apparatus according to an embodiment of this specification. The resource data processing apparatus 40 can be implemented as all or part of the apparatus through software, hardware, or a combination of both. According to some embodiments, the resource data processing apparatus 40 includes a resource plan acquisition module 410, a resource curve determination module 420, and a resource curve update module 430, specifically used for: The resource plan acquisition module 410 is used to determine the resource data plan flow data for future data transfer dates reported by each resource data aggregation service platform based on the resource data metadata plan information. The resource curve determination module 420 is used to determine the target resource data plan flow data corresponding to at least one resource backup node based on the resource data plan flow data, and to determine the initial backup resource quantity time series prediction curve of the resource backup node based on the target resource data plan flow data. The resource curve update module 430 is used to obtain the actual flow data of resource data collected by each of the resource data aggregation service platforms on the future data transfer date, and to perform reserve resource quantity curve update processing based on the actual flow data of resource data and the initial reserve resource quantity time series prediction curve to obtain the target reserve resource quantity time series prediction curve of the resource reserve node.

[0097] Optional, the resource curve update module 430 includes: The resource curve update unit is used to determine, for each of the resource backup nodes, a backup resource quantity matching time point and a predicted backup resource quantity corresponding to the backup resource quantity matching time point based on the initial backup resource quantity time-series prediction curve, determine the actual backup resource quantity of the resource backup node at the backup resource quantity matching time point based on the actual resource data flow data, and perform backup resource quantity curve update processing based on the actual backup resource quantity and the predicted backup resource quantity to obtain a target backup resource quantity time-series prediction curve.

[0098] Optional, resource curve update unit, specifically used for: If the actual reserve resource quantity is inconsistent with the predicted reserve resource quantity, then it is determined whether the first target resource reserve node corresponding to the actual reserve resource quantity and the predicted reserve resource quantity has first resource data plan flow direction update data; If so, then a target reserve resource quantity time series prediction curve is generated based on the actual reserve resource quantity and the first resource data planned flow direction update data, with the reserve resource quantity matching time point as the time origin; If not, then a time-series prediction curve for the target reserve resource quantity is generated based on the actual reserve resource quantity and the target resource data planned flow direction data, with the time point matching the reserve resource quantity as the time origin.

[0099] Optional, resource curve update unit, specifically used for: If the actual reserve resource quantity is consistent with the predicted reserve resource quantity, then determine whether there is second resource data plan flow direction update data for the second target resource reserve node corresponding to the actual reserve resource quantity and the predicted reserve resource quantity; If so, then a target reserve resource quantity time series prediction curve is generated based on the actual reserve resource quantity and the second resource data plan flow update data, with the reserve resource quantity matching time point as the time origin.

[0100] Optionally, the resource curve update unit is also used for: If not, then determine the data transfer frequency corresponding to the second target resource backup node, and determine the target timing period corresponding to the second target resource backup node based on the data transfer frequency; Obtain the initial time series period of the time series prediction curve of the initial reserve resource quantity corresponding to the second target resource reserve node; Determine whether the target timing period and the initial timing period are consistent; If not, then a target reserve resource quantity time series prediction curve is generated based on the target time series period, the actual reserve resource quantity, and the target resource data planned flow data, with the reserve resource quantity matching time point as the time origin.

[0101] Optional, resource curve update unit, specifically used for: If it is determined that the actual amount of reserve resources is less than the predicted amount of reserve resources, the data writing thread status of the resource reserve node at the time point corresponding to the reserve resource amount matching time is detected. Based on the statistical data of the data writing thread status, the actual flow of the resource data to be cleared corresponding to the data writing thread in the mutex lock waiting state is determined, and the resource data writing delay accumulation of the resource standby node at the time point when the standby resource quantity matches is determined. Determine whether the sum of the resource data write latency backlog and the actual amount of spare resources matches the predicted amount of spare resources. If a match is found, the initial reserve resource quantity time series prediction curve is determined as the target reserve resource quantity time series prediction curve; If there is a mismatch, a time-series prediction curve for the target reserve resource quantity is generated based on the actual reserve resource quantity, the resource data write delay accumulation, and the target resource data planned flow direction data, with the time point of the reserve resource quantity matching as the time origin.

[0102] Optionally, the resource data processing device 40 is also used for: Determine a historical backup resource quantity matching time point that is less than the current time and has the smallest time interval with the current time, and obtain the resource data plan flow execution details corresponding to the historical backup resource quantity matching time point; Detect the execution results of the resource data plan flow to the execution details; If the execution result is incomplete, then the unexecuted resource data plan flow data in the resource data plan flow execution details is determined, and an early warning is issued based on the unexecuted resource data plan flow data.

[0103] Optional, see below Figure 5 The diagram shows a structural schematic of a resource curve determination module. The resource curve determination module 420 includes a resource quantity calculation unit 421, a resource quantity adjustment unit 422, and a curve generation unit 423, specifically used for: Resource quantity calculation unit 421 is used to determine, for each of the resource backup nodes, the first predicted backup resource quantity corresponding to the reference backup resource quantity matching time point of each of the resource data aggregation service platforms based on the target resource data plan flow direction data; Resource quantity adjustment unit 422 is used to determine the reserve resource quantity adjustment weight corresponding to each of the resource data aggregation service platforms, and perform reserve resource quantity adjustment processing on the first predicted reserve resource quantity based on the reserve resource quantity adjustment weight to obtain the second predicted reserve resource quantity corresponding to each of the resource data aggregation service platforms at the reference reserve resource quantity matching time point. The curve generation unit 423 is used to determine the initial predicted reserve resource quantity of the resource reserve node at the reference reserve resource quantity matching time point based on the second predicted reserve resource quantity, and to generate the initial reserve resource quantity time series prediction curve of the resource reserve node based on the initial predicted reserve resource quantity and the reference reserve resource quantity matching time point.

[0104] Optionally, the resource quantity adjustment unit 422 is specifically used for: Determine the historical resource data forecast evaluation results corresponding to each of the resource data aggregation service platforms, and determine the resource data forecast accuracy corresponding to each of the resource data aggregation service platforms based on the historical resource data forecast evaluation results; The adjustment weight of the reserve resource quantity corresponding to each of the resource data aggregation service platforms is determined based on the accuracy of the resource data forecast.

[0105] Please refer to Figure 6 This diagram illustrates the structure of an electronic device provided in an exemplary embodiment of this specification. The electronic device in this specification may include one or more components such as a processor 110, a memory 120, an input device 130, an output device 140, and a bus 150. The processor 110, memory 120, input device 130, and output device 140 may be connected via the bus 150.

[0106] Processor 110 may include one or more processing cores. Processor 110 connects to various parts of the terminal using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 120, and by calling data stored in memory 120. Optionally, processor 110 may be implemented using at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). Processor 110 may integrate one or more of a central processing unit (CPU), graphics processing unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into processor 110 and may be implemented separately using a communication chip.

[0107] The memory 120 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 120 may include a non-transitory computer-readable storage medium. The memory 120 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 120 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (e.g., touch function, sound playback function, image playback function, etc.), instructions for implementing the various method embodiments described below, etc. The operating system may be the Android system, including systems deeply developed based on the Android system, the iOS system developed by Apple Inc., including systems deeply developed based on the iOS system, or other systems.

[0108] In order for the operating system to distinguish the specific application scenarios of third-party applications, it is necessary to establish data communication between the third-party applications and the operating system. This would allow the operating system to obtain the current scenario information of the third-party applications at any time, and then perform targeted system resource adaptation based on the current scenario.

[0109] The input device 130 is used to receive input instructions or data, and includes, but is not limited to, a keyboard, mouse, camera, microphone, or touch device. The output device 140 is used to output instructions or data, and includes, but is not limited to, a display device and a speaker. In one example, the input device 130 and the output device 140 can be combined, and the input device 130 and the output device 140 can be a touch display screen.

[0110] The touch display screen can be designed as a full-screen, curved screen, or irregularly shaped screen. It can also be designed as a combination of a full-screen and a curved screen, or a combination of an irregularly shaped screen and a curved screen; however, this specification does not limit the specific design of the embodiments described herein.

[0111] In addition, those skilled in the art will understand that the structure of the electronic device shown in the above figures does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the electronic device may also include radio frequency circuits, input units, sensors, audio circuits, Wireless Fidelity (WiFi) modules, power supplies, Bluetooth modules, etc., which will not be described in detail here.

[0112] In some embodiments, Figure 6 In the illustrated electronic device, the processor 110 can be used to call a program for resource data processing methods stored in the memory 120, and specifically perform the following operations: Determine the planned flow of resource data for future data transfer dates, based on the resource data metadata plan information reported by each resource data aggregation service platform; Based on the resource data plan flow data, determine the target resource data plan flow data corresponding to at least one resource backup node, and based on the target resource data plan flow data, determine the initial backup resource quantity time series prediction curve of the resource backup node; Obtain the actual flow data of resource data collected by each of the resource data aggregation service platforms on the future data transfer date, and perform reserve resource quantity curve update processing based on the actual flow data of resource data and the initial reserve resource quantity time series prediction curve to obtain the target reserve resource quantity time series prediction curve of the resource reserve node.

[0113] In one embodiment, when the processor 110 executes the process of updating the reserve resource quantity curve based on the actual flow data of the resource data and the initial reserve resource quantity time-series prediction curve to obtain the target reserve resource quantity time-series prediction curve of the resource reserve node, it specifically performs the following operations: For each of the resource backup nodes, the backup resource quantity matching time point and the predicted backup resource quantity corresponding to the backup resource quantity matching time point are determined based on the initial backup resource quantity time-series prediction curve. The actual backup resource quantity of the resource backup node at the backup resource quantity matching time point is determined based on the actual resource data flow data. The target backup resource quantity time-series prediction curve is obtained by updating the backup resource quantity curve based on the actual backup resource quantity and the predicted backup resource quantity.

[0114] In one embodiment, when the processor 110 performs the reserve resource quantity curve update process based on the actual reserve resource quantity and the predicted reserve resource quantity to obtain the target reserve resource quantity time series prediction curve, it specifically performs the following operations: If the actual reserve resource quantity is inconsistent with the predicted reserve resource quantity, then it is determined whether the first target resource reserve node corresponding to the actual reserve resource quantity and the predicted reserve resource quantity has first resource data plan flow direction update data; If so, then a target reserve resource quantity time series prediction curve is generated based on the actual reserve resource quantity and the first resource data planned flow direction update data, with the reserve resource quantity matching time point as the time origin; If not, then a time-series prediction curve for the target reserve resource quantity is generated based on the actual reserve resource quantity and the target resource data planned flow direction data, with the time point matching the reserve resource quantity as the time origin.

[0115] In one embodiment, when the processor 110 performs the reserve resource quantity curve update process based on the actual reserve resource quantity and the predicted reserve resource quantity to obtain the target reserve resource quantity time series prediction curve, it specifically performs the following operations: If the actual reserve resource quantity is consistent with the predicted reserve resource quantity, then determine whether there is second resource data plan flow direction update data for the second target resource reserve node corresponding to the actual reserve resource quantity and the predicted reserve resource quantity; If so, then a target reserve resource quantity time series prediction curve is generated based on the actual reserve resource quantity and the second resource data plan flow update data, with the reserve resource quantity matching time point as the time origin.

[0116] In one embodiment, the processor 110 also performs the following operations: If not, then determine the data transfer frequency corresponding to the second target resource backup node, and determine the target timing period corresponding to the second target resource backup node based on the data transfer frequency; Obtain the initial time series period of the time series prediction curve of the initial reserve resource quantity corresponding to the second target resource reserve node; Determine whether the target timing period and the initial timing period are consistent; If not, then a target reserve resource quantity time series prediction curve is generated based on the target time series period, the actual reserve resource quantity, and the target resource data planned flow data, with the reserve resource quantity matching time point as the time origin.

[0117] In one embodiment, when the processor 110 performs the reserve resource quantity curve update process based on the actual reserve resource quantity and the predicted reserve resource quantity to obtain the target reserve resource quantity time series prediction curve, it specifically performs the following operations: If it is determined that the actual amount of reserve resources is less than the predicted amount of reserve resources, the data writing thread status of the resource reserve node at the time point corresponding to the reserve resource amount matching time is detected. Based on the statistical data of the data writing thread status, the actual flow of the resource data to be cleared corresponding to the data writing thread in the mutex lock waiting state is determined, and the resource data writing delay accumulation of the resource standby node at the time point when the standby resource quantity matches is determined. Determine whether the sum of the resource data write latency backlog and the actual amount of spare resources matches the predicted amount of spare resources. If a match is found, the initial reserve resource quantity time series prediction curve is determined as the target reserve resource quantity time series prediction curve; If there is a mismatch, a time-series prediction curve for the target reserve resource quantity is generated based on the actual reserve resource quantity, the resource data write delay accumulation, and the target resource data planned flow direction data, with the time point of the reserve resource quantity matching as the time origin.

[0118] In one embodiment, the processor 110 also performs the following operations: Determine a historical backup resource quantity matching time point that is less than the current time and has the smallest time interval with the current time, and obtain the resource data plan flow execution details corresponding to the historical backup resource quantity matching time point; Detect the execution results of the resource data plan flow to the execution details; If the execution result is incomplete, then the unexecuted resource data plan flow data in the resource data plan flow execution details is determined, and an early warning is issued based on the unexecuted resource data plan flow data.

[0119] In one embodiment, when the processor 110 executes the time-series prediction curve for determining the initial reserve resource quantity of the resource reserve node based on the target resource data plan flow direction data, it specifically performs the following operations: For each of the resource backup nodes, the first predicted backup resource quantity corresponding to the reference backup resource quantity matching time point is determined based on the target resource data plan flow direction data; Determine the reserve resource quantity adjustment weight corresponding to each of the resource data aggregation service platforms, and perform reserve resource quantity adjustment processing on the first predicted reserve resource quantity based on the reserve resource quantity adjustment weight to obtain the second predicted reserve resource quantity corresponding to each of the resource data aggregation service platforms at the reference reserve resource quantity matching time point. Based on the second predicted reserve resource quantity, the initial predicted reserve resource quantity of the resource reserve node is determined at the reference reserve resource quantity matching time point, and the initial predicted reserve resource quantity time series prediction curve of the resource reserve node is generated based on the initial predicted reserve resource quantity and the reference reserve resource quantity matching time point.

[0120] In one embodiment, when the processor 110 performs the operation of determining the adjustment weight of the standby resource quantity corresponding to each of the resource data aggregation service platforms, it specifically performs the following operations: Determine the historical resource data forecast evaluation results corresponding to each of the resource data aggregation service platforms, and determine the resource data forecast accuracy corresponding to each of the resource data aggregation service platforms based on the historical resource data forecast evaluation results; The adjustment weight of the reserve resource quantity corresponding to each of the resource data aggregation service platforms is determined based on the accuracy of the resource data forecast.

[0121] This specification also provides a computer-readable storage medium storing at least one instruction that is executed by a processor to implement the resource data processing method as described in the above embodiments.

[0122] This specification also provides a computer program product that stores at least one instruction, which is loaded and executed by the processor to implement the resource data processing method described in the above embodiments.

[0123] Those skilled in the art will recognize that the functions described in the embodiments of this specification in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of a computer program from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0124] The above description is only an optional embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the protection scope of this specification.

[0125] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

Claims

1. A resource data processing method, characterized in that, The method includes: Determine the planned flow of resource data for future data transfer dates, based on the resource data metadata plan information reported by each resource data aggregation service platform; Based on the resource data plan flow data, determine the target resource data plan flow data corresponding to at least one resource backup node, and based on the target resource data plan flow data, determine the initial backup resource quantity time series prediction curve of the resource backup node; Obtain the actual flow data of resource data collected by each of the resource data aggregation service platforms on the future data transfer date, and perform reserve resource quantity curve update processing based on the actual flow data of resource data and the initial reserve resource quantity time series prediction curve to obtain the target reserve resource quantity time series prediction curve of the resource reserve node. The step of updating the reserve resource quantity curve based on the actual flow data of the resource data and the initial reserve resource quantity time-series prediction curve to obtain the target reserve resource quantity time-series prediction curve of the resource reserve node includes: for each resource reserve node, determining the reserve resource quantity matching time point and the predicted reserve resource quantity corresponding to the initial reserve resource quantity time-series prediction curve, determining the actual reserve resource quantity of the resource reserve node at the reserve resource quantity matching time point based on the actual flow data of the resource data, and obtaining the target reserve resource quantity time-series prediction curve based on the actual reserve resource quantity and the predicted reserve resource quantity.

2. The method according to claim 1, characterized in that, The step of updating the reserve resource quantity curve based on the actual reserve resource quantity and the predicted reserve resource quantity to obtain the time series prediction curve of the target reserve resource quantity includes: If the actual reserve resource quantity is inconsistent with the predicted reserve resource quantity, then it is determined whether the first target resource reserve node corresponding to the actual reserve resource quantity and the predicted reserve resource quantity has first resource data plan flow direction update data; If so, then a target reserve resource quantity time series prediction curve is generated based on the actual reserve resource quantity and the first resource data planned flow direction update data, with the reserve resource quantity matching time point as the time origin; If not, then a time-series prediction curve for the target reserve resource quantity is generated based on the actual reserve resource quantity and the target resource data planned flow direction data, with the time point matching the reserve resource quantity as the time origin.

3. The method according to claim 1, characterized in that, The step of updating the reserve resource quantity curve based on the actual reserve resource quantity and the predicted reserve resource quantity to obtain the time series prediction curve of the target reserve resource quantity includes: If the actual reserve resource quantity is consistent with the predicted reserve resource quantity, then determine whether there is second resource data plan flow direction update data for the second target resource reserve node corresponding to the actual reserve resource quantity and the predicted reserve resource quantity; If so, then a target reserve resource quantity time series prediction curve is generated based on the actual reserve resource quantity and the second resource data plan flow update data, with the reserve resource quantity matching time point as the time origin.

4. The method according to claim 3, characterized in that, The method further includes; If not, then determine the data transfer frequency corresponding to the second target resource backup node, and determine the target timing period corresponding to the second target resource backup node based on the data transfer frequency; Obtain the initial time series period of the time series prediction curve of the initial reserve resource quantity corresponding to the second target resource reserve node; Determine whether the target timing period and the initial timing period are consistent; If not, then a target reserve resource quantity time series prediction curve is generated based on the target time series period, the actual reserve resource quantity, and the target resource data planned flow data, with the reserve resource quantity matching time point as the time origin.

5. The method according to claim 1, characterized in that, The step of updating the reserve resource quantity curve based on the actual reserve resource quantity and the predicted reserve resource quantity to obtain the time series prediction curve of the target reserve resource quantity includes: If it is determined that the actual amount of reserve resources is less than the predicted amount of reserve resources, the data writing thread status of the resource reserve node at the time point corresponding to the reserve resource amount matching time is detected. Based on the statistical data of the data writing thread status, the actual flow of the resource data to be cleared corresponding to the data writing thread in the mutex lock waiting state is determined, and the resource data writing delay accumulation of the resource standby node at the time point when the standby resource quantity matches is determined. Determine whether the sum of the resource data write latency backlog and the actual amount of spare resources matches the predicted amount of spare resources. If a match is found, the initial reserve resource quantity time series prediction curve is determined as the target reserve resource quantity time series prediction curve; If there is a mismatch, a time-series prediction curve for the target reserve resource quantity is generated based on the actual reserve resource quantity, the resource data write delay accumulation, and the target resource data planned flow direction data, with the time point of the reserve resource quantity matching as the time origin.

6. The method according to claim 1, characterized in that, The method further includes: Determine a historical backup resource quantity matching time point that is less than the current time and has the smallest time interval with the current time, and obtain the resource data plan flow execution details corresponding to the historical backup resource quantity matching time point; Detect the execution results of the resource data plan flow to the execution details; If the execution result is incomplete, then the unexecuted resource data plan flow data in the resource data plan flow execution details is determined, and an early warning is issued based on the unexecuted resource data plan flow data.

7. The method according to claim 1, characterized in that, The step of determining the initial reserve resource quantity time-series prediction curve of the resource reserve node based on the target resource data planned flow data includes: For each of the resource backup nodes, the first predicted backup resource quantity corresponding to the reference backup resource quantity matching time point is determined based on the target resource data plan flow direction data; Determine the reserve resource quantity adjustment weight corresponding to each of the resource data aggregation service platforms, and perform reserve resource quantity adjustment processing on the first predicted reserve resource quantity based on the reserve resource quantity adjustment weight to obtain the second predicted reserve resource quantity corresponding to each of the resource data aggregation service platforms at the reference reserve resource quantity matching time point. Based on the second predicted reserve resource quantity, the initial predicted reserve resource quantity of the resource reserve node is determined at the reference reserve resource quantity matching time point, and the initial predicted reserve resource quantity time series prediction curve of the resource reserve node is generated based on the initial predicted reserve resource quantity and the reference reserve resource quantity matching time point.

8. The method according to claim 7, characterized in that, The determination of the reserve resource adjustment weights corresponding to each of the resource data aggregation service platforms includes: Determine the historical resource data forecast evaluation results corresponding to each of the resource data aggregation service platforms, and determine the resource data forecast accuracy corresponding to each of the resource data aggregation service platforms based on the historical resource data forecast evaluation results; The adjustment weight of the reserve resource quantity corresponding to each of the resource data aggregation service platforms is determined based on the accuracy of the resource data forecast.

9. A resource data processing device, characterized in that, The device includes: The resource plan acquisition module is used to determine the resource data plan flow data for future data transfer dates reported by each resource data aggregation service platform based on the resource data metadata plan information. The resource curve determination module is used to determine the target resource data planned flow data corresponding to at least one resource backup node based on the resource data planned flow data, and to determine the initial backup resource quantity time series prediction curve of the resource backup node based on the target resource data planned flow data. The resource curve update module is used to obtain the actual flow data of resource data collected by each of the resource data aggregation service platforms on the future data transfer date, and to perform reserve resource quantity curve update processing based on the actual flow data of resource data and the initial reserve resource quantity time series prediction curve to obtain the target reserve resource quantity time series prediction curve of the resource reserve node. The step of updating the reserve resource quantity curve based on the actual flow data of the resource data and the initial reserve resource quantity time-series prediction curve to obtain the target reserve resource quantity time-series prediction curve of the resource reserve node includes: for each resource reserve node, determining the reserve resource quantity matching time point and the predicted reserve resource quantity corresponding to the initial reserve resource quantity time-series prediction curve, determining the actual reserve resource quantity of the resource reserve node at the reserve resource quantity matching time point based on the actual flow data of the resource data, and obtaining the target reserve resource quantity time-series prediction curve based on the actual reserve resource quantity and the predicted reserve resource quantity.

10. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions adapted for loading by a processor and executing the method as described in any one of claims 1 to 8.

11. A computer program product, characterized in that, The computer program product stores at least one instruction, which is loaded by a processor and executed as described in any one of claims 1 to 8.

12. An electronic device, characterized in that, include: A processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and executed as described in any one of claims 1 to 8.

Images