Node sorting method and device based on multi-dimensional data and medium

Abstract

The invention provides a node sorting method and device based on multi-dimensional data and a medium, and the method comprises the steps: determining a correlation list set corresponding to each data node according to a feature value corresponding to a node feature when each data node executes a historical task and a required value corresponding to each task requirement of a plurality of historical tasks; determining a basic weight of each node feature of the data node according to a plurality of correlations corresponding to each node feature of the data node in the correlation list set corresponding to any data node; according to a plurality of correlations in a correlation list set corresponding to each data node required by each target task, determining an adjustment weight corresponding to each node feature of each data node; and according to the basic weight and the adjustment weight corresponding to each node feature, determining the adaptation degrees of the plurality of data nodes to the to-be-processed task, so as to realize accurate analysis of the adaptation degrees of different data nodes under different task requirements.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a method, device and medium for node sorting based on multidimensional data. Background Technology

[0002] When data nodes process data, the efficiency of different data nodes in processing the same data varies due to the different characteristics of each data node. Therefore, it is necessary to statistically analyze and rank the data processing efficiency of several data nodes in the same processing system in order to allocate the data nodes reasonably.

[0003] Current methods for ranking data node processing efficiency are based on analysis of node characteristics, and the weights assigned to these characteristics are the same. This fails to reflect the differences in data processing due to varying node characteristics and ignores the importance of processing requirements to data node efficiency. Consequently, the accuracy of the ranking of data node processing efficiency is low. Summary of the Invention

[0004] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0005] According to one aspect of this application, a node sorting method based on multidimensional data is provided, comprising the following steps:

[0006] Step S100: Based on the feature values ​​corresponding to the node features of each data node when executing several historical tasks, and the requirement values ​​corresponding to each task requirement of several historical tasks, determine the correlation list set corresponding to each data node; the correlation list set includes the correlation between each node feature of the same data node and each task requirement.

[0007] Step S200: Based on the correlation list set corresponding to any data node, determine the basic weight corresponding to each node feature of the data node according to the several correlations corresponding to each node feature of the data node.

[0008] Step S300: Based on the requirement value corresponding to each task requirement of the task to be processed, determine the target task requirement from several task requirements; the target task requirement is the task requirement whose requirement value is greater than a preset requirement value threshold.

[0009] Step S400: Based on the correlations in the correlation list set corresponding to each data node for each target task requirement, determine the adjustment weight corresponding to each node feature of each data node.

[0010] Step S500: Sort several data nodes according to the basic weight and adjustment weight corresponding to each node feature of each data node, as well as several feature values ​​and several requirement values; wherein, in the sorting order, data nodes with smaller ranks have a higher adaptability to the task to be processed than data nodes with larger ranks.

[0011] In one exemplary embodiment of this application, step S100 includes:

[0012] Step S110: Obtain the feature values ​​corresponding to several node features when each data node performs several historical tasks, so as to obtain several feature value list sets A1, A2, ..., A i ,...,A j Where i = 1, 2, ..., j; j is the number of data nodes; A i This is the set of feature values ​​corresponding to the i-th data node;

[0013] A i =(A i1 A i2 ,...,A im ,...,A in ); m = 1, 2, ..., n; n is the number of node features corresponding to the data node; A im This is a list of feature values ​​corresponding to the feature of the m-th node of the i-th data node;

[0014] A im =(A im1 A im2 ,...,A ime ,...,A imf e = 1, 2, ..., f; f is the number of historical tasks; A ime Let be the feature value corresponding to the feature of the m-th node when the i-th data node executes the e-th historical task;

[0015] Step S120: Obtain the requirement value corresponding to each task requirement of several historical tasks to obtain a list set of requirement values ​​B = (B1, B2, ..., B...). p ,...,B q ); where p = 1, 2, ..., q; q is the number of tasks required by the historical task; B p This is a list of requirement values ​​corresponding to the p-th task in the history of tasks.

[0016] B p = (B p1 B p2 ,...,B pe ,...,B pf );B peThis represents the requirement value corresponding to the p-th task of the e-th historical task; each requirement value is greater than 0 and less than 1.

[0017] Each pending task and each historical task has q task requirements, and the task requirements for each pending task are the same as those for each historical task.

[0018] Step S130: Based on several feature value lists A1, A2, ..., A i ,...,A j Given a list of required values ​​(B), determine the relevance list for each data node; the relevance list includes the relevance between each node feature of the same data node and each task requirement.

[0019] In one exemplary embodiment of this application, step S130 includes:

[0020] Step S131: List A the feature values ​​corresponding to the m-th node feature of the i-th data node. im List B of the requirement values ​​corresponding to the p-th task requirement p The covariance is determined as the correlation C between the feature of the m-th node of the i-th data node and the p-th task requirement. imp To obtain the relevance list set C corresponding to the i-th data node. i =(C i1 C i2 ,...,C im ,...,C in ); where C im This is a list of correlations corresponding to the features of the m-th node of the i-th data node;

[0021] C im =(C im1 C im2 ,...,C imp ,...,C imq ).

[0022] In one exemplary embodiment of this application, step S200 includes:

[0023] Step S210: Traverse the correlation list C corresponding to the feature of the m-th node of the i-th data node. im If C imp If the correlation exceeds the preset threshold, then C will be... imp Determined to be of target relevance;

[0024] Step S220: Determine the basic weight D corresponding to the feature of the m-th node of the i-th data node. im =(qg im ) / q; where gim C im The number of targets related to the target.

[0025] In one exemplary embodiment of this application, step S300 includes:

[0026] Step S310: Obtain the requirement value corresponding to each task requirement of the task to be processed, so as to obtain the list of task requirement values ​​E = (E1, E2, ..., E...). p ,...,E q ); where E p This represents the requirement value corresponding to the p-th task in the pending tasks.

[0027] Step S320: Traverse the list E of required values ​​for tasks to be processed. If E p If the value exceeds the preset threshold, then the p-th task requirement will be determined as the target task requirement.

[0028] In one exemplary embodiment of this application, step S400 includes:

[0029] Step S410: Obtain the relevance list set C corresponding to the i-th data node for each target task requirement. i Several correlations in;

[0030] Step S420: If any target task requires C i If any correlation in the data is greater than a preset correlation threshold, then that correlation is identified as a key correlation.

[0031] Step S430: Obtain the feature of the m-th node of the i-th data node in C. i The number of key relevances r im ;

[0032] Step S440: Determine the adjustment weight F corresponding to the feature of the m-th node of the i-th data node. im =(qr im ) / q.

[0033] In one exemplary embodiment of this application, step S500 includes:

[0034] Step S510, according to D im 、F im A im B p Determine the sort value corresponding to the i-th data node;

[0035] Step S520: Sort the data nodes according to the ascending order of their corresponding sort values.

[0036] In one exemplary embodiment of this application, step S510 includes:

[0037] Step S511, the sort value Z corresponding to the i-th data node i =∑ n m＝1 ((D im ×F im )×(∑ q p＝1 (∑ f e＝1 (A ime -B pe ) 2 )) 1 / 2 ).

[0038] According to another aspect of this application, a non-transitory computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the aforementioned node sorting method based on multidimensional data.

[0039] According to another aspect of this application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0040] The present invention has at least the following beneficial effects:

[0041] The node sorting method based on multidimensional data of the present invention, upon receiving a task to be processed, first determines the relevance list set corresponding to each data node based on the feature values ​​corresponding to several node features when each data node executed several historical tasks, and the requirement values ​​corresponding to each task requirement of several historical tasks. Then, based on the several relevances corresponding to each node feature of the data node in the relevance list set corresponding to each data node, the basic weight corresponding to each node feature of the data node is determined. Based on the requirement values ​​corresponding to each task requirement of the task to be processed, target task requirements whose requirement values ​​are greater than a preset requirement value threshold are determined from several task requirements. Finally, based on the several relevances of each target task requirement in the relevance list set corresponding to each data node, the relevance list set for each data node is determined. The system adjusts the weights corresponding to each node feature of a data node. Based on the base weights and adjustment weights corresponding to each node feature of each data node, as well as several feature values ​​and several requirement values, the system determines the ranking order of the data nodes' suitability for the task to be processed. By performing correlation analysis on the different node features of several data nodes and the task requirements of several historical tasks, the system determines the base weights and adjustment weights corresponding to each node feature of each data node. Then, the system uses the base weights and adjustment weights to determine the ranking of the data nodes' suitability for the task to be processed. By considering the correlation between several node features of each data node and several task requirements, the system achieves accurate analysis of the suitability of different data nodes under different task requirements, thereby improving the accuracy of ranking based on processing efficiency. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A flowchart of a node sorting method based on multidimensional data provided in an embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] This application proposes a node sorting method based on multidimensional data, such as... Figure 1 As shown, it includes the following steps:

[0046] Step S100: Determine the relevance list set corresponding to each data node based on the feature values ​​corresponding to the node features when each data node performs several historical tasks, and the requirement values ​​corresponding to each task requirement of several historical tasks.

[0047] A data node is an entity node that processes data during task execution; it can be a computer device or other data processing machine.

[0048] The node features corresponding to a data node are the characteristic information that affects the data processing when the data node is processing data, such as the number of processors, processor performance parameters, number of processing threads, and available computing power capacity of the data node. The number and type of node features of several data nodes are the same, that is, the type of node features of each data node is consistent (for example, the node features of the first data node are the number of processors, processor performance parameters, number of processing threads, and available computing power capacity in sequence, the node features of the second data node are also the number of processors, processor performance parameters, number of processing threads, and available computing power capacity, and so on), so as to ensure data consistency when analyzing several node features of the data node in the future.

[0049] Step S100 of this application responds to receiving a task to be processed, which is a task that requires data processing; historical tasks are tasks in which each data node has performed data processing within a historical period. After each historical task is completed, the staff will score each node feature of each data node based on the execution efficiency of each data node when performing the same historical task (or the processing system will assign scores to node features based on a preset scoring mechanism and the execution efficiency of historical tasks) to obtain the feature values ​​corresponding to several node features of each data node when performing several historical tasks.

[0050] In addition, each historical task also has several task requirements (or task demands, such as execution time, execution speed, execution data volume, etc.) to characterize the required information of the historical task during execution (such as completing the task within the execution time, controlling the execution speed within the preset speed range, etc.). Each task requirement will also have a corresponding requirement value according to the degree of demand. The requirement value is assigned when the historical task is generated. The requirement value is a value between 0 and 1. The greater the degree of demand, the greater the corresponding requirement value.

[0051] Furthermore, step S100 includes steps S110-S130:

[0052] Step S110: Obtain the feature values ​​corresponding to several node features when each data node performs several historical tasks, so as to obtain several feature value list sets A1, A2, ..., A i ,...,A j Where i = 1, 2, ..., j; j is the number of data nodes; A i This is the set of feature values ​​corresponding to the i-th data node;

[0053] A i =(A i1 A i2 ,...,A im ,...,A in ); m = 1, 2, ..., n; n is the number of node features corresponding to the data node; A im This is a list of feature values ​​corresponding to the feature of the m-th node of the i-th data node;

[0054] A im =(A im1 A im2 ,...,A ime ,...,A imf e = 1, 2, ..., f; f is the number of historical tasks; A ime Let be the feature value corresponding to the feature of the m-th node when the i-th data node executes the e-th historical task;

[0055] Step S120: Obtain the requirement value corresponding to each task requirement of several historical tasks to obtain a list set of requirement values ​​B = (B1, B2, ..., B...). p ,...,B q ); where p = 1, 2, ..., q; q is the number of tasks required by the historical task; B p This is a list of requirement values ​​corresponding to the p-th task in the history of tasks.

[0056] B p = (B p1 B p2 ,...,B pe ,...,B pf );B pe This represents the requirement value corresponding to the p-th task of the e-th historical task; each requirement value is greater than 0 and less than 1.

[0057] Each pending task and each historical task has q task requirements. The task requirements for the pending task are the same as those for each historical task. For example, the third task requirement of the first historical task, the third task requirement of the second historical task, and the third task requirement of the pending task are the same (e.g., all are execution time).

[0058] Step S130: Based on several feature value lists A1, A2, ..., A i ,...,A j Given the required value list set B, determine the correlation list set corresponding to each data node;

[0059] The correlation list set includes the correlation between each node feature of the same data node and each task requirement. The correlation can characterize the degree of association between node features and task requirements. The greater the correlation, the closer the connection between the corresponding node feature and task requirements, and the greater the influence of the node feature on the task requirement.

[0060] Step S130 includes step S131:

[0061] Step S131: List A the feature values ​​corresponding to the m-th node feature of the i-th data node. im List B of the requirement values ​​corresponding to the p-th task requirement p The covariance is determined as the correlation C between the feature of the m-th node of the i-th data node and the p-th task requirement. imp To obtain the relevance list set C corresponding to the i-th data node. i =(C i1 C i2 ,...,C im ,...,C in ); C im =(C im1 C im2 ,...,C imp ,...,C imq ); where C im This is a list of correlations corresponding to the features of the m-th node of the i-th data node.

[0062] By performing covariance processing on any list of feature values ​​and any list of requirement values, the correlation between the node features corresponding to the list of feature values ​​and the task requirements corresponding to the list of requirement values ​​can be obtained.

[0063] Step S200: Based on the correlation list set corresponding to any data node, determine the basic weight corresponding to each node feature of the data node according to the several correlations corresponding to each node feature of the data node.

[0064] Furthermore, step S200 includes steps S210-S220:

[0065] Step S210: Traverse the correlation list C corresponding to the feature of the m-th node of the i-th data node. im If C imp If the correlation exceeds the preset threshold, then C will be...imp Determined to be of target relevance;

[0066] Step S220: Determine the basic weight D corresponding to the feature of the m-th node of the i-th data node. im =(qg im ) / q; where g im C im The number of targets related to the target.

[0067] Step S300: Determine the target task requirement from several task requirements based on the requirement value corresponding to each task requirement of the task to be processed.

[0068] Furthermore, step S300 includes steps S310-S320:

[0069] Step S310: Obtain the requirement value corresponding to each task requirement of the task to be processed, so as to obtain the list of task requirement values ​​E = (E1, E2, ..., E...). p ,...,E q ); where E p This represents the requirement value corresponding to the p-th task in the pending tasks.

[0070] Step S320: Traverse the list E of required values ​​for tasks to be processed. If E p If the value exceeds the preset threshold, then the p-th task requirement will be determined as the target task requirement.

[0071] The target task requirements are those with a high degree of demand, indicating that they have a significant impact on the execution efficiency of the task to be processed.

[0072] Step S400: Based on the correlations in the correlation list set corresponding to each data node for each target task requirement, determine the adjustment weight corresponding to each node feature of each data node.

[0073] Furthermore, step S400 includes steps S410-S440:

[0074] Step S410: Obtain the relevance list set C corresponding to the i-th data node for each target task requirement. i Several correlations in;

[0075] Step S420: If any target task requires C i If any correlation in the data is greater than a preset correlation threshold, then that correlation is identified as a key correlation.

[0076] Step S430: Obtain the feature of the m-th node of the i-th data node in C. i The number of key relevances r im ;

[0077] Step S440: Determine the adjustment weight F corresponding to the feature of the m-th node of the i-th data node. im =(qr im ) / q.

[0078] By analyzing the task requirements of the task to be processed, the adjustment weights corresponding to each node feature of each data node are determined. By adjusting the weights, the analysis of the execution efficiency of different data nodes when processing the same task can be made more accurate.

[0079] Step S500: Sort several data nodes according to the basic weight and adjustment weight corresponding to each node feature of each data node, as well as several feature values ​​and several requirement values.

[0080] Among them, in the sorting order, data nodes with smaller ranks are more suitable for the task to be processed than data nodes with larger ranks.

[0081] Furthermore, step S500 includes steps S510-S520:

[0082] Step S510, according to D im 、F im A im B p Determine the sort value Z corresponding to the i-th data node. i =∑ n m＝1 ((D im ×F im )×(∑ q p＝1 (∑ f e＝1 (A ime -B pe ) 2 )) 1 / 2 );

[0083] As a feasible implementation, the sorting value can be negatively correlated with the execution efficiency, that is, the larger the sorting value, the lower the execution efficiency of the corresponding data node for the task to be processed.

[0084] Step S520: Sort the data nodes according to the ascending order of their corresponding sort values.

[0085] As a feasible implementation, the order obtained after sorting the data nodes can be the execution efficiency order of several data nodes for the task to be processed. After sorting the sorting values ​​corresponding to several data nodes in ascending order, the rank of each sorting value is the rank of the corresponding data node in the execution efficiency order. The smaller the rank of the data node in the execution efficiency order, the higher the execution efficiency of the data node when executing the task to be processed.

[0086] Furthermore, this application can also be applied to the analysis of agricultural science and technology innovation capabilities. Specifically, when applied to the field of agricultural science and technology innovation, the data nodes of this application can be countries, and the node features can be parameters of each country's agricultural science and technology innovation level (such as basic innovation capabilities, economic level, agricultural land area, agricultural water resources, agricultural capital investment, etc.). The task to be processed is the production task of the planted crops (such as the production task of planting soybeans), and the task requirements are the planting needs of the planted crops during the production process (such as planting area, maturity time, planting temperature and humidity, etc.). Since each country has different requirements for planting at different points in time, the application can be applied to the analysis of agricultural science and technology innovation capabilities. The demand for the same crop varies, and different planting standards affect the maturity of different crops. Therefore, when the same crop needs to be planted in every country (i.e., several data nodes receive the same task to be processed), the planting demand for the same crop is different in different countries at the same time point. It is necessary to analyze the crops planted and the planting demand in each country during historical periods to determine the ranking of planting efficiency or maturity of the same crop in several countries at the corresponding time point (i.e., the execution efficiency order of several data nodes when executing the task to be processed).

[0087] The node ranking method based on multidimensional data of the present invention, upon receiving a task to be processed, first determines the relevance list set corresponding to each data node based on the feature values ​​corresponding to several node features when each data node executes several historical tasks, and the requirement values ​​corresponding to each task requirement of several historical tasks. Then, based on the relevance list set corresponding to each data node, the basic weight corresponding to each node feature of the data node is determined. Based on the requirement values ​​corresponding to each task requirement of the task to be processed, the target task requirement is determined from several task requirements. Based on the relevance of each target task requirement in the relevance list set corresponding to each data node, the adjustment weight corresponding to each node feature of each data node is determined. Based on the basic weight and adjustment weight corresponding to each node feature of each data node, as well as several feature values ​​and several requirement values, the execution efficiency order of several data nodes for the task to be processed is determined. By performing correlation analysis on the different node features of several data nodes and the task requirements of several historical tasks, the basic weight and adjustment weight corresponding to each node feature of each data node are determined. Then, the execution efficiency ranking of several data nodes is determined by the basic weight and adjustment weight, so as to achieve accurate analysis of the execution efficiency of different data nodes under different task requirements.

[0088] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.

[0089] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0090] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0091] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0092] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely hardware implementations, entirely software implementations (including firmware, microcode, etc.), or implementations combining hardware and software aspects, collectively referred to herein as “circuits,” “modules,” or “systems.”

[0093] An electronic device according to this embodiment of the invention. The electronic device is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the invention.

[0094] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).

[0095] The storage device stores program code that can be executed by the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of the present invention.

[0096] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0097] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0098] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0099] Electronic devices can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable users to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (such as routers, modems, etc.). This communication can be performed through input / output (I / O) interfaces. Furthermore, electronic devices can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapters.

[0100] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0101] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0102] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0103] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0104] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0105] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0106] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0107] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A node sorting method based on multidimensional data, characterized in that, include: Step S100: Based on the feature values ​​corresponding to the node features of each data node when executing several historical tasks, and the requirement values ​​corresponding to each task requirement of the several historical tasks, determine the correlation list set corresponding to each data node; the correlation list set includes the correlation between each node feature of the same data node and each task requirement. Step S200: Based on the correlation list set corresponding to any data node, determine the basic weight corresponding to each node feature of the data node according to several correlations corresponding to each node feature of the data node. Step S300: Based on the requirement value corresponding to each task requirement of the task to be processed, determine the target task requirement from several task requirements; the target task requirement is the task requirement whose requirement value is greater than a preset requirement value threshold. Step S400: Based on the several correlations in the correlation list set corresponding to each data node according to the requirements of each target task, determine the adjustment weight corresponding to each node feature of each data node. Step S500: Sort the data nodes according to the basic weight and adjustment weight corresponding to each node feature of each data node, as well as several feature values ​​and several requirement values; wherein, in the sorting order, the data nodes with smaller ranks have a higher adaptability to the task to be processed than the data nodes with larger ranks.

2. The method according to claim 1, characterized in that, Step S100 includes: Step S110: Obtain the feature values ​​corresponding to several node features of each data node when executing several historical tasks, so as to obtain several feature value list sets A1, A2, ..., A i ,...,A j Where i = 1, 2, ..., j; j is the number of data nodes; A i This is the feature value list set corresponding to the i-th data node; A i =(A i1 A i2 ,...,A im ,...,A in ); m = 1, 2, ..., n; n is the number of node features corresponding to the data node; A im This is a list of feature values ​​corresponding to the m-th node feature of the i-th data node; A im =(A im1 A im2 ,...,A ime ,...,A imf e = 1, 2, ..., f; f is the number of historical tasks; A ime The feature value corresponding to the m-th node feature when the i-th data node executes the e-th historical task; Step S120: Obtain the requirement value corresponding to each task requirement of the aforementioned historical tasks, to obtain a list set of requirement values ​​B = (B1, B2, ..., B...). p ,...,B q ); where p = 1, 2, ..., q; q is the number of tasks required by the historical task; B p This is a list of requirement values ​​corresponding to the p-th task in the history of tasks. B p = (B p1 B p2 ,...,B pe ,...,B pf ); B pe This represents the requirement value corresponding to the p-th task of the e-th historical task; each requirement value is greater than 0 and less than 1. The pending task and each of the historical tasks each have q task requirements, and the task requirements corresponding to the pending task are the same as the task requirements corresponding to each of the historical tasks. Step S130: Based on several feature value lists A1, A2, ..., A i ,...,A j Based on the required value list set B, determine the relevance list set corresponding to each data node.

3. The method according to claim 2, characterized in that, Step S130 includes: Step S131: List A the feature values ​​corresponding to the m-th node feature of the i-th data node. im List B of the requirement values ​​corresponding to the p-th task requirement p The covariance is determined as the correlation C between the m-th node feature of the i-th data node and the p-th task requirement. imp To obtain the relevance list set C corresponding to the i-th data node. i =(C i1 C i2 ,...,C im ,...,C in ); where C im This is a list of correlations corresponding to the m-th node feature of the i-th data node; C im =(C im1 ,C im2 ,...,C imp ,...,C imq )。 4. The method according to claim 3, characterized in that, Step S200 includes: Step S210: Traverse the correlation list C corresponding to the m-th node feature of the i-th data node. im If C imp If the correlation exceeds the preset threshold, then C will be... imp Determined to be of target relevance; Step S220: Determine the basic weight D corresponding to the m-th node feature of the i-th data node. im =(qg im ) / q; where g im C im The number of targets related to the target.

5. The method according to claim 4, characterized in that, Step S300 includes: Step S310: Obtain the requirement value corresponding to each task requirement of the task to be processed, so as to obtain a list of task requirement values ​​E = (E1, E2, ..., E...). p ,...,E q ); where E p The requirement value corresponding to the p-th task requirement of the task to be processed; Step S320: Traverse the list E of the required values ​​for the tasks to be processed. If E p If the value exceeds the preset threshold, then the p-th task requirement will be determined as the target task requirement.

6. The method according to claim 5, characterized in that, Step S400 includes: Step S410: Obtain the relevance list set C corresponding to the i-th data node for each of the target task requirements. i Several correlations in; Step S420: If any of the target tasks requires C i If any correlation in the data exceeds a preset correlation threshold, then that correlation is identified as a key correlation. Step S430: Obtain the m-th node feature of the i-th data node in C. i The number of key relevances r im ; Step S440: Determine the adjustment weight F corresponding to the m-th node feature of the i-th data node. im =(qr im ) / q.

7. The method according to claim 6, characterized in that, Step S500 includes: Step S510, according to D im F im A im 、B p Determine the sorting value corresponding to the i-th data node; Step S520: Sort the data nodes according to the ascending order of the sorting values ​​corresponding to the data nodes.

8. The method according to claim 7, characterized in that, Step S510 includes: Step S511: The sorting value corresponding to the i-th data node 9. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the method as described in any one of claims 1-8.

10. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 9.

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