A node ranking method, device and medium based on multi-dimensional data

Abstract

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

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a node sorting method based on multi-dimensional data, device and medium. BACKGROUND

[0002] When data nodes perform data processing, different node characteristics of each data node will lead to different efficiency of different data nodes in processing the same data, so it is necessary to statistically sort the data processing efficiency of a plurality of data nodes in the same processing system and reasonably allocate the data nodes.

[0003] The current data node processing efficiency sorting method is determined according to node characteristics, and the weight given to the node characteristics of the data node is the same, which does not reflect the difference of different node characteristics in data processing, and ignores the importance of processing requirements of data processing to the processing efficiency of data nodes, so that the accuracy of the sorting of the data processing efficiency of the data nodes is low. SUMMARY

[0004] To solve the above technical problems, the technical solution adopted by the present application is:

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

[0006] Step S100, determining a correlation list set corresponding to each data node according to the characteristic values corresponding to the node characteristics of each data node when performing a plurality of historical tasks, and the requirement values corresponding to each task requirement of the plurality of historical tasks; the correlation list set includes the correlation between each node characteristic of the same data node and each task requirement;

[0007] Step S200, determining the basis weight corresponding to each node characteristic of any data node according to the plurality of correlations corresponding to each node characteristic of the data node in the correlation list set corresponding to the data node;

[0008] Step S300, determining a target task requirement from a plurality of task requirements according to the requirement value corresponding to each task requirement of the to-be-processed task; the target task requirement is a task requirement whose requirement value is greater than a preset requirement value threshold;

[0009] Step S400, determining the adjustment weight corresponding to each node characteristic of each data node according to the plurality of correlations of each target task requirement in the correlation list set corresponding to each data node;

[0010] Step S500, according to the basic weight and the adjustment weight corresponding to each node feature of each data node, and the feature value and the requirement value, the data nodes are sorted; wherein, in the sorting order, the data node with smaller rank has higher adaptability to the task to be processed than the data node with larger rank.

[0011] In an exemplary embodiment of the present application, step S100 comprises:

[0012] Step S110, obtaining the feature value corresponding to each node feature of each data node when performing the historical tasks, to obtain a plurality of feature value list sets A1, A2,..., A i ,...,A j ; wherein, i=1, 2,..., j; j is the number of data nodes; A i is the feature value list set 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 is the feature value list corresponding to the m-th node feature 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 is the feature value corresponding to the m-th node feature of the i-th data node when performing the e-th historical task;

[0015] Step S120, obtaining the requirement value corresponding to each task requirement of the historical tasks, to obtain a requirement value list set B=(B1, B2,..., B p ,...,B q ); wherein, p=1, 2,..., q; q is the number of task requirements of the historical tasks; B p is the requirement value list corresponding to the p-th task requirement of the historical tasks;

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

[0017] The to-be-processed task and each historical task correspond to q task requirements, and the task requirements corresponding to the to-be-processed task are the same as the task requirements corresponding to each historical task;

[0018] Step S130, determining a relevance list set corresponding to each data node according to the plurality of feature value list sets A1, A2,..., A i ,...,A j and the requirement value list set B; the relevance list set includes the relevance between each node feature of the same data node and each task requirement.

[0019] In an exemplary embodiment of the present application, step S130 includes:

[0020] Step S131, determining the covariance of the feature value list A im corresponding to the m th node feature of the i th data node and the requirement value list B p corresponding to the p th task requirement as the relevance C imp between the m th node feature of the i th data node and the p th task requirement to obtain the relevance list set C i =(C i1 ,C i2 ,...,C im ,...,C in ) corresponding to the i th data node; wherein C im is the relevance list corresponding to the m th node feature of the i th data node.

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

[0022] In an exemplary embodiment of the present application, step S200 includes:

[0023] Step S210, traversing the relevance list C im corresponding to the m th node feature of the i th data node, and if C imp is greater than a preset relevance threshold, determining C imp as a target relevance.

[0024] Step S220, determining a basic weight D im =(q-g im ) / q corresponding to the m th node feature of the i th data node; wherein 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 exceeds 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 an example embodiment of the present application, step S510 comprises:

[0037] Step S511, the sorting 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 the present application, there is provided a non-transitory computer readable storage medium, the storage medium storing at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by a processor to implement the aforementioned node sorting method based on multi-dimensional data.

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

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

[0041] The node sorting method based on multi-dimensional data provided by the embodiment of the present application comprises the following steps: when a to-be-processed task is received, first, a correlation list set corresponding to each data node is determined according to a plurality of characteristic values corresponding to a plurality of node characteristics of each data node when a plurality of historical tasks are executed and a plurality of requirement values corresponding to each task requirement of the plurality of historical tasks; then, a plurality of correlations corresponding to each node characteristic of the data node in the correlation list set corresponding to the data node are determined to determine a basic weight corresponding to each node characteristic of the data node; then, a target task requirement with a requirement value greater than a preset requirement value threshold is determined from the plurality of task requirements according to the requirement value corresponding to each task requirement of the to-be-processed task; then, an adjustment weight corresponding to each node characteristic of each data node is determined according to the plurality of correlations in the correlation list set corresponding to each data node; and finally, a sorting order of the adaptability of the plurality of data nodes to the to-be-processed task is determined according to the basic weight and the adjustment weight corresponding to each node characteristic of each data node and the plurality of characteristic values and the plurality of requirement values. The correlation analysis is performed on the different node characteristics of the plurality of data nodes and the task requirements of the plurality of historical tasks to determine the basic weight and the adjustment weight corresponding to each node characteristic of each data node, and then the sorting of the adaptability of the plurality of data nodes to the to-be-processed task is determined according to the basic weight and the adjustment weight, so that the correlation between the plurality of node characteristics of each data node and the plurality of task requirements is considered to realize the accurate analysis of the adaptability of different data nodes under different task requirements, thereby improving the accuracy of the sorting according to the processing efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0043] Figure 1 The flowchart of the node sorting method based on multi-dimensional data provided by the embodiment of the present application. DETAILED DESCRIPTION

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

[0045] The node sorting method based on multi-dimensional data provided by the present application, as shown in Figure 1 the method comprises the following steps:

[0046] In step S100, a relevance list set corresponding to each data node is determined according to characteristic values corresponding to node characteristics of each data node when performing a plurality of historical tasks, and requirement values corresponding to each task requirement of the plurality of historical tasks.

[0047] The data node is an entity node for data processing during task execution, which can be a computer device or other data processing machine.

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

[0049] Step S100 of the present application responds to receiving a to-be-processed task, which is a task that needs to be processed. The historical task is a task that each data node has processed in a historical period. After each historical task is executed, the staff scores each node characteristic of each data node according to the execution efficiency of each data node when executing the same historical task (or the processing system assigns scores to the node characteristics according to a pre-set assignment mechanism and in combination with the execution efficiency of the historical task), to obtain characteristic values corresponding to a plurality of node characteristics of each data node when performing a plurality of historical tasks.

[0050] In addition, each historical task also has a plurality of task requirements (or task demands, such as execution time, execution speed, and execution data volume), which are used to represent requirement information of the historical task when executing (such as completing the task within the execution time, controlling the execution speed within a pre-set speed range, etc.). Each task requirement also has a corresponding requirement value according to the demand degree. The requirement value is assigned when the historical task is generated. The requirement value is a numerical value between 0 and 1. The greater the demand degree, the greater the corresponding requirement value.

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

[0052] Step S110, obtaining a feature value corresponding to each node feature of each data node when performing a plurality of historical tasks to obtain a plurality of feature value list sets A1, A2,..., A i ,...,A j ; wherein i = 1, 2,..., j; j is the number of data nodes; A i is the feature value list set 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 is the feature value list of the m-th node feature 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 is the feature value of the m-th node feature of the i-th data node when performing the e-th historical task;

[0055] Step S120, obtaining a requirement value corresponding to each task requirement of a plurality of historical tasks to obtain a requirement value list set B = (B1, B2,..., B p ,...,B q ); wherein p = 1, 2,..., q; q is the number of task requirements of the historical task; B p is the requirement value list corresponding to the p-th task requirement of the historical task;

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

[0057] The to-be-processed task and each historical task correspond to q task requirements, and the task requirements corresponding to the to-be-processed task are the same as the task requirements corresponding to each historical task, such as 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 to-be-processed task are the same (such as execution time).

[0058] Step S130, determining the relevance list set corresponding to each data node according to the plurality of feature value list sets A1, A2,..., A i ,...,A j and the requirement value list set B;

[0059] The relevance in the relevance list set between each node feature of the same data node and each task requirement can represent the degree of association between the node feature and the task requirement. The greater the relevance, the closer the association between the corresponding node feature and the task requirement, and the greater the influence of the node feature on the task requirement.

[0060] The step S130 includes step S131:

[0061] Step S131, determining the relevance C imp between the mth node feature of the ith data node and the pth task requirement by taking the covariance of the feature value list A im corresponding to the mth node feature of the ith data node and the requirement value list B p corresponding to the pth task requirement, to obtain the relevance list set C i = (C i1 , C i2 ,..., C im ,..., C in ) of the ith data node; C im = (C im1 , C im2 ,..., C imp ,..., C imq ), wherein C im is the relevance list corresponding to the mth node feature of the ith data node.

[0062] The relevance between the node feature corresponding to any feature value list and the task requirement corresponding to any requirement value list is obtained by covariance processing of any feature value list and any requirement value list.

[0063] Step S200, determining the basic weight corresponding to each node feature of the data node according to the plurality of relevances corresponding to each node feature of the data node in the relevance list set corresponding to any data node;

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

[0065] Step S210, traversing the relevance list C im corresponding to the mth node feature of the ith data node, and if C imp is greater than a preset relevance threshold, Cimp determining the target relevance;

[0066] Step S220, determining the base weight D im =(q-g im ) / q; where g im is the number of target relevance in C im .

[0067] Step S300, determining the target task requirement from a plurality of task requirements according to the requirement value corresponding to each task requirement of the to-be-processed task;

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

[0069] Step S310, obtaining the requirement value corresponding to each task requirement of the to-be-processed task to obtain a to-be-processed task requirement value list E=(E p ,...,E q ); where E p is the requirement value corresponding to the pth task requirement of the to-be-processed task;

[0070] Step S320, traversing the to-be-processed task requirement value list E, if E p is greater than a preset requirement value threshold, the pth task requirement is determined as the target task requirement.

[0071] The target task requirement is a task requirement with a greater demand degree, indicating a task requirement with a greater impact on the execution efficiency of the to-be-processed task.

[0072] Step S400, determining the adjustment weight corresponding to each node feature of each data node according to a plurality of relevance in the relevance list set corresponding to each target task requirement in each data node;

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

[0074] Step S410, obtaining a plurality of relevance in the relevance list set C i corresponding to each target task requirement in the ith data node;

[0075] Step S420, if any relevance in C i is greater than a preset relevance threshold, the relevance is determined as the key relevance;

[0076] Step S430, obtaining the number r i of key relevance of the mth node feature of the ith data node in C im ;

[0077] Step S440, determining the adjustment weight F corresponding to the mth node feature of the ith data node im = (q-r im ) / q.

[0078] By analyzing the task requirements of the to-be-processed task, the adjustment weight corresponding to each node feature of each data node is determined, and through the adjustment weight, the analysis of the execution efficiency of the different data nodes in processing the same to-be-processed task is more accurate.

[0079] Step S500, sorting the data nodes according to the basic weight and the adjustment weight corresponding to each node feature of each data node, and the feature values and the requirement values;

[0080] Among the sorting order, the data node with smaller rank has higher adaptability to the to-be-processed task than the data node with larger rank.

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

[0082] Step S510, determining the sorting value Z im corresponding to the ith data node according to D im , F im , A p , B 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 embodiment, the sorting value can have a negative correlation with the execution efficiency, that is, the larger the sorting value is, the lower the execution efficiency of the corresponding data node to the to-be-processed task is.

[0084] Step S520, sorting the data nodes according to the numerical increasing order of the sorting values corresponding to the data nodes.

[0085] As a feasible embodiment, the order obtained after sorting the data nodes can be an execution efficiency order of the data nodes for the to-be-processed task, and the rank of each sorting value is the rank of the corresponding data node in the execution efficiency order after the sorting values corresponding to the data nodes are sorted in ascending order. The smaller the rank of the data node in the execution efficiency order, the higher the execution efficiency of the data node in executing the to-be-processed task.

[0086] In addition, on the other hand, the application can also be applied in the field of agricultural science and technology innovation capability analysis. Specifically, when applied in the field of agricultural science and technology, the data nodes of the application can be countries, the node features can be agricultural science and technology level parameters (such as innovation foundation capability, economic level, agricultural land area, agricultural water resource amount, agricultural fund investment, etc.) of each country, and the to-be-processed task can be a production task of a planted crop (such as a production task of planting soybeans). The task requirement is the planting demand (such as planting area, maturation time, planting temperature and humidity, etc.) of the planted crop in the production process. Since the demand of each country for the same crop is different at different time points, and different planting standards also have different maturation degrees for different crops, when the same crop needs to be planted in each country (that is, the same to-be-processed task is received by the data nodes), the planting demand of the same crop in different countries is different at the same time node. Therefore, it is necessary to analyze the planting demand and the planted crops in each country in the historical period to determine the planting efficiency or maturation degree order of the same crop in the corresponding time point (that is, the execution efficiency order of the data nodes for the to-be-processed task).

[0087] The node sorting method based on multi-dimensional data of the present application, when receiving a to-be-processed task, first determines a correlation list set corresponding to each data node according to a plurality of characteristic values corresponding to a plurality of node characteristics of each data node when executing a plurality of historical tasks and a plurality of requirement values corresponding to each task requirement of the plurality of historical tasks, then determines a plurality of correlations corresponding to each node characteristic of the data node in the correlation list set corresponding to the data node, determines a target task requirement from the plurality of task requirements according to the requirement value corresponding to each target task requirement, determines an adjustment weight corresponding to each node characteristic of each data node according to the plurality of correlations in the correlation list set corresponding to each data node for each target task requirement, and determines an execution efficiency order of the plurality of data nodes for the to-be-processed task according to the basis weight and the adjustment weight corresponding to each node characteristic of each data node and the plurality of characteristic values and the plurality of requirement values. Through correlation analysis on different node characteristics of the plurality of data nodes and task requirements of the plurality of historical tasks, the basis weight and the adjustment weight corresponding to each node characteristic of each data node are determined, and then the execution efficiency order of the plurality of data nodes is determined through the basis weight and the adjustment weight, so as to realize accurate analysis of the execution efficiency of different data nodes under different task requirements.

[0088] Embodiments of the present application also provide a computer program product comprising program code for causing an electronic device to perform the steps of the methods according to the various exemplary embodiments of the present application described above when the program product is run on the electronic device.

[0089] In addition, although the various steps of the methods of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all of the steps shown must be performed to achieve the desired result. Additionally or alternatively, certain steps can be omitted, a plurality of steps can be combined into one step, and / or one step can be divided into a plurality of steps, etc.

[0090] From the above description of the embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a plurality of instructions to make a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) execute the methods according to the embodiments of the present disclosure.

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

[0092] Those skilled in the art can understand that various aspects of the present application can be implemented as a system, a method or a program product. Therefore, various aspects of the present application can be embodied in the form of a complete hardware, a complete software (including firmware, microcode, etc.), or a combination of hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system".

[0093] The electronic device according to this embodiment of the present application. The electronic device is merely an example and should not impose any limitation on the function and use range of the embodiments of the present application.

[0094] The electronic device is in the form of a general computing device. The components of the electronic device can include, but are not limited to, the at least one processor described above, the at least one storage described above, and a bus connecting different system components, including the storage and the processor.

[0095] The storage stores program codes which can be executed by the processor, so that the processor performs the steps described in the "Exemplary Method" section of the present specification according to various exemplary embodiments of the present application.

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

[0097] The storage can also include programs / utilities with a set of (at least one) program modules, such as an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which can include the implementation of a network environment.

[0098] The bus can be one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures.

[0099] The electronic device can also be able to communicate with one or more external devices such as a keyboard or a pointing device, devices that enable a user to interact with the electronic device, and / or any devices (e.g., a router, a modem, a printer, etc.) that enable the electronic device to communicate with one or more other computing devices. Such communication can occur via an input / output (I / O) interface. Still yet, the electronic device can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or the Internet, through a network adapter.

[0100] In the example embodiments of the present disclosure, a computer readable storage medium is also provided, on which a program product capable of implementing the method described above is stored. In some possible implementation manners, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program codes for causing terminal equipment to perform the steps described in the "Example Method" section above according to various example embodiments of the present disclosure when the program product is run on the terminal equipment.

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

[0102] The computer readable signal medium can include a data signal propagated in baseband or propagated as a carrier wave, in which the readable program code is embodied. Such propagated data signal can take multiple forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit the program for use by or in connection with an instruction execution system, apparatus or device.

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

[0104] The program code may be executed by one or more programmable processing devices to perform a set of tasks on one or more computing devices. The program code may include one or more computer programs, which may be written in any of a plurality of programming languages, scripting languages, assembly languages, higher-level languages or lower-level languages, for example, Java, C++, C#, Fortran, Pascal, Rust, COBOL, PHP, Visual Basic, Ada, SQL, R, and the like. It will be apparent to those of ordinary skill in the art that other computer programs based on other system designs and configured to perform the tasks described herein can be used.

[0105] Furthermore, the above-described diagrams are merely schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended for limiting purposes. It is readily understood that the processes illustrated in the above-described diagrams do not indicate or limit the time sequence of the processes. In addition, it is readily understood that the processes can be executed synchronously or asynchronously, for example, in a plurality of modules.

[0106] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, such a division is not mandatory. Indeed, according to embodiments of the present disclosure, the features and functionalities of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functionalities of one module or unit described above can be further divided into embodied by a plurality of modules or units.

[0107] The above description is only specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any changes or replacements easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection 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. 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.

2. The method according to claim 1, 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 )。 3. The method according to claim 2, 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.

4. The method according to claim 3, 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.

5. The method according to claim 4, 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.

6. The method according to claim 5, 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.

7. The method according to claim 6, characterized in that, Step S510 includes: Step S511: The sorting 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 ).

8. 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 1-7.

9. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 8.

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