A node importance degree determination method and device based on data correlation and a medium

Abstract

The application provides a node importance degree determination method and device based on data correlation and a medium. The method comprises the following steps: determining a total influence value vector and a node influence value vector corresponding to each data node according to the influence value of each node feature when each data node performs a plurality of historical tasks in a first historical time period and a second historical time period, so as to determine the first importance degree and the second importance degree corresponding to each node feature of each data node; and processing the first importance degree and the second importance degree corresponding to each node feature according to the matching degree between the node influence value vector and the total influence value vector, so as to obtain the target importance degree corresponding to each node feature of each data node. The specificity of different node features of different data nodes is considered, so that the accuracy of the task execution efficiency of the data node obtained subsequently is improved by the determined target importance degree.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, and in particular to a node importance degree determination method based on data correlation, a device and a medium. BACKGROUND

[0002] Different data nodes will have different efficiencies when processing the same data due to the differences in the node characteristics (such as processor performance, number of processing threads, etc.) of each data node itself. Therefore, the data processing efficiency of a plurality of data nodes in the same processing system needs to be counted to reasonably allocate the data nodes.

[0003] The current data node processing efficiency analysis method is based on node characteristics, and the importance degree (such as weight) given to the node characteristics of the data nodes is the same. The influence of different node characteristics on different types of tasks is not considered when performing different types of tasks. Therefore, the accuracy of the data processing efficiency of the data nodes determined by the importance degree is low. SUMMARY

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

[0005] According to one aspect of the present application, a node importance degree determination method based on data correlation is provided, comprising:

[0006] Step S100: every time a preset determination time elapses, a total influence value vector is determined according to the influence value of each node characteristic of each data node on each type of historical task when each data node executes a plurality of types of historical tasks in a first historical time period; the end time of the first historical time period is the current time, and the length of the first historical time period is greater than the preset determination time;

[0007] Step S200: a node influence value vector corresponding to each data node is determined according to the influence value of each node characteristic of each data node on each type of historical task when each data node executes a plurality of types of historical tasks in a second historical time period; the end time of the second historical time period is the current time, and the length of the second historical time period is the preset determination time;

[0008] Step S300: a first importance degree and a second importance degree corresponding to each node characteristic of each data node are determined according to the influence value of each node characteristic in the total influence value vector and the influence value of each node characteristic in each node influence value vector;

[0009] Step S400, according to the matching degree of each node influence value vector and the total influence value vector, processing the first importance degree and the second importance degree corresponding to each node feature to obtain the target importance degree corresponding to each node feature of each data node.

[0010] In an exemplary embodiment of the present application, step S100 comprises:

[0011] Step S110, obtaining the influence value of each node feature on a type of historical task when each data node executes the type of historical task in a first historical time period, to obtain a first task influence value list set Y1, Y2,..., Y i ,...,Y j ; wherein i=1, 2,..., j; j is the number of data nodes; Y i is the first task influence value list set corresponding to the i th data node in the first historical time period;

[0012] Y i =(Y i1 ,Y i2 ,...,Y im ,...,Y in ); m=1, 2,..., n; n is the number of node features corresponding to the data node; Y im is the task influence value list corresponding to the m th node feature of the i th data node in the first historical time period;

[0013] Y im =(Y im1 ,Y im2 ,...,Y ime ,...,Y imf ); e=1, 2,..., f; f is the number of type groups of historical tasks; Y ime is the influence value of the m th node feature on the e th type of historical task when the i th data node executes the e th type of historical task in the first historical time period;

[0014] Step S120, according to the first task influence value list set corresponding to each data node, determining the first feature influence value list X1, X2,..., X i ,...,X j ; wherein X i is the feature influence value list corresponding to the i th data node;

[0015] X i =(X i1 ,X i2 ,...,X im ,...,X in);X im is the feature influence value corresponding to the mth node feature of the ith data node; X im = (∑ e＝1 f Y ime ) / f;

[0016] Step S130, determining a total influence value vector C = (C1, C2,..., C m ,...,C n ) according to the first feature influence value list corresponding to each data node; wherein C m = (∑ i＝1 j X im ) / j.

[0017] In an exemplary embodiment of the present application, step S200 comprises:

[0018] Step S210, obtaining the influence value of each node feature of each data node on a type of historical task when each data node executes the type of historical task in a second historical time period, to obtain a second task influence value list set D1, D2,..., D i ,...,D j ; wherein D i is the second task influence value list set corresponding to the ith data node in the second historical time period;

[0019] D i = (D i1 ,D i2 ,...,D im ,...,D in ); D im is the task influence value list corresponding to the mth node feature of the ith data node in the second historical time period;

[0020] D im = (D im1 ,D im2 ,...,D ime ,...,D imf ); D ime is the influence value of the mth node feature on the e-type historical task when the ith data node executes the e-type historical task in the second historical time period;

[0021] Step S220, determining a node influence value vector E1, E2,..., E i ,...,E j corresponding to each data node according to the second task influence value list set corresponding to each data node; wherein E iis the node influence value vector corresponding to the ith data node; E i i1 i2 im in im e＝1 f ime

[0022] In an example embodiment of the present application, step S300 comprises:

[0023] Step S310, processing the total influence value vector C according to a preset index importance determination method to obtain a first importance degree list F = (F1, F2,..., F m n m is the first importance degree corresponding to the mth node feature of each data node.

[0024] Step S320, processing the node influence value vector E i corresponding to the ith data node according to a preset index importance determination method to obtain a second importance degree list G i i1 i2 im in im is the second importance degree corresponding to the mth node feature of the ith data node.

[0025] In an example embodiment of the present application, step S400 comprises:

[0026] Step S410, determining the first adjustment coefficient of the first importance degree and the second adjustment coefficient of the second importance degree corresponding to each node feature of each data node according to the matching degree of each node influence value vector and the total influence value vector;

[0027] Step S420, determining the target importance degree corresponding to each node feature of each data node according to the first importance degree, the first adjustment coefficient, the second importance degree, and the second adjustment coefficient corresponding to each node feature of each data node.

[0028] In an example embodiment of the present application, step S410 comprises:

[0029] Step S411, performing matching degree processing on each node influence value vector and the total influence value vector to obtain a matching degree list L = (L1, L2,..., L​​​​​​​​​​​​​​​i L j ); wherein, L i is a matching degree of the node influence value vector E i and the total influence value vector C corresponding to the ith data node.

[0030] Step S412, if L i is less than or equal to a preset matching degree threshold, determining that the first adjustment coefficient p i and the second adjustment coefficient q i corresponding to each node feature of the ith data node are both 0.5.

[0031] In an exemplary embodiment of the present application, step S411 further comprises:

[0032] Step S413, if L i is greater than the preset matching degree threshold, determining that the second adjustment coefficient q i corresponding to each node feature of the ith data node is 0.5-(0.5×(L i -MIN(L)) / (MAX(L)-MIN(L))); wherein, MIN() is a preset minimum value determination function; MAX() is a preset maximum value determination function.

[0033] Step S414, determining that the first adjustment coefficient p i corresponding to each node feature of the ith data node is 1-q i .

[0034] In an exemplary embodiment of the present application, step S420 comprises:

[0035] Step S421, determining that the target importance Z im corresponding to the mth node feature of the ith data node is p i ×F m +q i ×G im .

[0036] 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 the at least one program being loaded and executed by a processor to implement the aforementioned node importance determination method based on data correlation.

[0037] According to still another aspect of the present application, there is provided an electronic device comprising a processor and the aforementioned non-transitory computer readable storage medium.

[0038] The present application has at least the following beneficial effects:

[0039] The node importance degree determination method based on data correlation of the present application determines a total influence value vector according to the influence value of each node feature of each data node on a type of historical task when each data node executes the type of historical task in a first historical time period, and determines a node influence value vector corresponding to each data node according to the influence value of each node feature of each data node on the type of historical task when each data node executes the type of historical task in a second historical time period, and then determines a first importance degree and a second importance degree corresponding to each node feature of each data node according to the influence value of each node feature in the total influence value vector and the influence value of each node feature in the node influence value vector, and processes the first importance degree and the second importance degree corresponding to each node feature according to the matching degree between the node influence value vector and the total influence value vector to obtain a target importance degree corresponding to each node feature of each data node, and analyzes the influence correlation of different node features on different types of historical tasks according to the influence values of different node features of different data nodes when the node features execute historical tasks in the first historical time period and the second historical time period, so as to determine the specific importance degree of each node feature of each data node, and the processing specificity of different node features of different data nodes on the same type of task is considered to ensure that the accuracy of the task execution efficiency of the subsequent data node is improved by the determined target importance degree. BRIEF DESCRIPTION OF DRAWINGS

[0040] 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.

[0041] Figure 1 The flowchart of the node importance degree determination method based on data correlation provided by the embodiments of the present application. DETAILED DESCRIPTION

[0042] 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.

[0043] The node importance degree determination method based on data correlation provided by the embodiments of the present application, as shown in Figure 1 includes the following steps:

[0044] Step S100, every preset determination length, according to the influence value of each node feature of each data node on each type of historical task when each data node executes a plurality of types of historical tasks in the first historical time period, determine the total influence value vector;

[0045] The data node is an entity node for data processing during task execution, which can be a computer device or other data processing machine.

[0046] The node feature corresponding to the data node is the feature information that influences data processing when the data node performs data processing, such as the number of processors, performance parameters of processors, the number of processing threads, and the capacity of available computing power. The number and type of node features of a plurality of 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, performance parameters of processors, the number of processing threads, and the capacity of available computing power, and the node features of the second data node are also the number of processors, performance parameters of processors, 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 features of the data node subsequently.

[0047] The end time of the first historical time period is the current time, and the length of the first historical time period is greater than the preset determination length.

[0048] The historical tasks are divided into a plurality of types according to a task type or a classification rule preset by a worker. After a data node completes a type of historical task, the worker scores the influence value of each node feature on the execution of the type of historical task according to the execution efficiency or execution state of each node feature of the data node when executing the type of historical task. The influence value is determined according to a preset rule of the worker. The greater the influence value, the greater the influence of the corresponding node feature on the execution efficiency of the type of historical task (the influence includes positive influence and negative influence, and the specific influence is determined by the worker).

[0049] Further, step S100 includes steps S110-S130:

[0050] Step S110, obtaining the influence value of each node feature on the type of historical task when each data node executes a plurality of types of historical tasks in the first historical time period, to obtain a first task influence value list set Y1, Y2,..., Y i ,...,Y j ; wherein i=1, 2,..., j; j is the number of data nodes; Y i is the first task influence value list set corresponding to the i-th data node in the first historical time period;

[0051] Y i = (Y i1 ,Y i2 ,...,Y im ,...,Y in ) ; m = 1, 2,...,n; n is the number of node features corresponding to the data node; Y im is the list of task influence values corresponding to the mth node feature of the ith data node in the first historical time period;

[0052] Y im = (Y im1 ,Y im2 ,...,Y ime ,...,Y imf ) ; e = 1, 2,...,f; f is the number of class groups of historical tasks; Y ime is the influence value of the mth node feature of the ith data node on the e-th historical task when performing the e-th historical task in the first historical time period;

[0053] Step S120, determining the first feature influence value list X1, X2,...,X i ,...,X j corresponding to each data node according to the first task influence value list set corresponding to each data node; i is the feature influence value list corresponding to the ith data node;

[0054] X i = (X i1 ,X i2 ,...,X im ,...,X in ) ; X im is the feature influence value corresponding to the mth node feature of the ith data node; X im = (∑ e＝1 f Y ime ) / f;

[0055] Step S130, determining the total influence value vector C = (C1, C2,...,C m ,...,C n ) according to the first feature influence value list corresponding to each data node; wherein C m = (∑ i＝1 j X im ) / j.

[0056] Step S200: Based on the influence value of each node feature of each data node on each type of historical task when each data node performs several types of historical tasks in the second historical time period, determine the node influence value vector corresponding to each data node.

[0057] The end time of the second historical time period is the current time, and the length of the second historical time period is a preset fixed duration.

[0058] Furthermore, step S200 includes steps S210-S220:

[0059] Step S210: Obtain the influence value of each node feature of each data node on the type of historical task when each data node performs several types of historical tasks within the second historical time period, so as to obtain a list set D1, D2, ..., D corresponding to several data nodes of the second task influence value. i ,...,D j ; where D i This is a list of the second task impact values ​​corresponding to the i-th data node within the second historical time period;

[0060] D i =(D i1 D i2 ,...,D im ,...,D in );D im This is a list of task impact values ​​corresponding to the m-th node feature of the i-th data node within the second historical time period.

[0061] D im =(D im1 D im2 ,...,D ime ,...,D imf );D ime When the i-th data node performs the e-th type of historical task in the second historical time period, the influence value of the m-th node feature on the e-th type of historical task is given.

[0062] Step S220: Based on the second task influence value list set corresponding to each data node, determine the node influence value vector E1, E2, ..., E1 for each data node. i ,...,E j Among them, E i E is the node influence value vector corresponding to the i-th data node; i =(E i1 E i2 ,...,E im ,...,E in ); E im =(∑ e＝1f D ime ) / f.

[0063] Step S300: Based on the influence value corresponding to each node feature in the total influence value vector and the influence value corresponding to each node feature in each node influence value vector, determine the first importance and second importance of each node feature of each data node.

[0064] The first and second importance levels represent the degree of influence of the corresponding node features in the first historical time period and the degree of influence in the second historical time period, respectively.

[0065] Furthermore, step S300 includes steps S310-S320:

[0066] Step S310: According to the preset indicator importance determination method, process the total influence value vector C to obtain the first importance list F = (F1, F2, ..., F...). m ,...,F n ); where F m The first importance level corresponding to the m-th node feature of each data node;

[0067] Step S320: Based on the preset indicator importance determination method, determine the node influence value vector E corresponding to the i-th data node. i Process the data to obtain the second importance list G corresponding to the i-th data node. i =(G i1 G i2 ,...,G im ,...,G in ); where G im This represents the second importance level corresponding to the feature of the m-th node of the i-th data node.

[0068] The preset method for determining the importance of indicators can be determined by staff according to preset rules (such as staff defining a mapping table, which includes the mapping relationship between several influence values ​​and importance), or it can be an existing method for determining the importance of indicators (such as the statistical average method or the coefficient of variation method).

[0069] Step S400: Based on the matching degree between the influence value vector of each node and the total influence value vector, process the first importance and second importance of each node feature to obtain the target importance of each node feature of each data node.

[0070] After determining the first importance degree and the second importance degree of the node feature, in order to further improve the accuracy of the target importance weight and reflect the specificity of the data node, it is necessary to further process the first importance degree and the second importance degree corresponding to each node feature according to the matching degree of each node influence value vector and the total influence value vector, so as to reasonably allocate the proportion of the first importance degree and the second importance degree in the target importance degree.

[0071] Further, step S400 comprises steps S410-S420:

[0072] Step S410, according to the matching degree of each node influence value vector and the total influence value vector, determining the first adjustment coefficient of the first importance degree and the second adjustment coefficient of the second importance degree corresponding to each node feature of each data node;

[0073] Step S420, according to the first importance degree, the first adjustment coefficient, the second importance degree and the second adjustment coefficient corresponding to each node feature of each data node, determining the target importance degree corresponding to each node feature of each data node.

[0074] Wherein, step S410 comprises steps S411-S414:

[0075] Step S411, performing matching degree processing on each node influence value vector and the total influence value vector to obtain a matching degree list L=(L1, L2,..., L i ,...,L j ); wherein, L i is the matching degree of the node influence value vector E i and the total influence value vector C corresponding to the i th data node;

[0076] Step S412, if L i is less than or equal to a preset matching degree threshold, then the first adjustment coefficient p i and the second adjustment coefficient q i corresponding to each node feature of the i th data node are both 0.5;

[0077] Step S413, if L i is greater than the preset matching degree threshold, then the second adjustment coefficient q i corresponding to each node feature of the i th data node is 0.5-(0.5×(L i -MIN(L)) / (MAX(L)-MIN(L)));

[0078] Wherein, MIN() is a preset minimum value determination function; MAX() is a preset maximum value determination function;

[0079] Step S414, determining the first adjustment coefficient p corresponding to each node feature of the i th data node i = 1 - q i .

[0080] Step S420 includes step S421:

[0081] Step S421, determining the target importance degree Z corresponding to the m th node feature of the i th data node im = p i x F m + q i x G im .

[0082] In addition, on the other hand, the present 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 present application can be countries (such as the member states of the Shanghai Cooperation Organization), the node features can be the agricultural science and technology level parameters of each country (such as innovation foundation capacity, economic level, agricultural land area, agricultural water resources, agricultural fund investment, etc.), and the historical task can be the production task of planting crops in the historical period (such as the production task of planting soybeans). Since the demand for planting the same crop is different in each country at different time points, and different planting standards also affect the maturity of different crops, when the same crop needs to be planted in each country (i.e. when several data nodes receive the same to-be-processed task), the planting demand of the same crop in different countries at the same time node is different, it is necessary to analyze the planting demand of each crop and the planting demand in each country in the historical period to determine the planting efficiency or maturity of the same crop in each country at the corresponding time point. In the process of determining the planting efficiency, the specificity of each data node needs to be reflected to analyze the influence of specific node features on the efficiency of task execution, so that the obtained planting efficiency is more accurate. The planting efficiency can be the product of the target importance degree of each node feature of each data node and the efficiency value of the node feature in executing the corresponding historical task.

[0083] The node importance degree determination method based on data correlation of the present application determines a total influence value vector according to the influence value of each node feature of each data node on a type of historical task when each data node executes the type of historical task in a first historical time period, and determines a node influence value vector corresponding to each data node according to the influence value of each node feature of each data node on the type of historical task when each data node executes the type of historical task in a second historical time period, and then determines a first importance degree and a second importance degree corresponding to each node feature of each data node according to the influence value of each node feature in the total influence value vector and the influence value of each node feature in each node influence value vector, and processes the first importance degree and the second importance degree corresponding to each node feature according to the matching degree between each node influence value vector and the total influence value vector to obtain a target importance degree corresponding to each node feature of each data node, analyzes the influence correlation of different node features on different types of historical tasks through the influence values of different node features of different data nodes when the node features execute historical tasks in the first historical time period and the second historical time period, and determines the specific importance degree of each node feature of each data node, so that the accuracy of the task execution efficiency of the subsequent data node is improved by the determined target importance degree.

[0084] 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.

[0085] Furthermore, 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, multiple steps can be combined into one step, one step can be divided into multiple steps, and the like.

[0086] From the above description of the embodiments, those skilled in the art will readily appreciate that the example embodiments described herein can be implemented by software and / or by hardware combined with software. Accordingly, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, or the like) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the methods according to the embodiments of the present disclosure.

[0087] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-described method is also provided.

[0088] 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 as a whole hardware embodiment, a whole software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system" herein.

[0089] 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.

[0090] 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 storage and processor.

[0091] The storage stores program codes which can be executed by the processor, so that the processor performs the steps described in the above "Exemplary Method" section of the present specification according to various exemplary embodiments of the present application.

[0092] 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).

[0093] The storage can also include programs / utilities with a set of (at least one) program modules, such as operating systems, 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.

[0094] 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.

[0095] 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.

[0096] 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 of the present disclosure according to various example embodiments of the present disclosure when the program product runs on the terminal equipment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 method for determining the importance of a node based on data correlation, characterized in that, Comprising: Step S100, every interval preset determination length, according to each data node in the first historical time period, when performing several kinds of historical tasks, the influence value of each node feature of each data node to each kind of historical task is determined to obtain the total influence value vector; the end time of the first historical time period is the current time, and the length of the first historical time period is greater than the preset determination length; the total influence value vector includes the influence value of each node feature on each kind of historical task in the first historical time period; Step S200, according to each data node in the second historical time period, when performing several kinds of historical tasks, the influence value of each node feature of each data node to each kind of historical task is determined to obtain the node influence value vector corresponding to each data node; the end time of the second historical time period is the current time, and the length of the second historical time period is the preset determination length; the node influence value vector includes the influence value of each node feature of the corresponding data node on each kind of historical task in the second historical time period; Step S300, according to the influence value corresponding to each node feature in the total influence value vector and the influence value corresponding to each node feature in each node influence value vector, the first importance degree and the second importance degree corresponding to each node feature of each data node are determined; Step S400, according to the matching degree of each node influence value vector and the total influence value vector, the first importance degree and the second importance degree corresponding to each node feature are processed to obtain the target importance degree corresponding to each node feature of each data node; Wherein, the step S400 includes step S410-step S420: Step S410, according to the matching degree of each node influence value vector and the total influence value vector, the first adjustment coefficient of the first importance degree and the second adjustment coefficient of the second importance degree corresponding to each node feature of each data node are determined; Step S420, according to the first importance degree, the first adjustment coefficient, the second importance degree and the second adjustment coefficient corresponding to each node feature of each data node, the target importance degree corresponding to each node feature of each data node is determined; Wherein, the step S410 includes step S411-step S412: Step S411: Perform matching degree processing on each node influence value vector and the total influence value vector to obtain a matching degree list L=(L1,L2,...,L...). i ,...,L j ); where L i The node influence value vector E corresponding to the i-th data node. i The degree of matching with the total influence value vector C; Step S412, if L i is less than or equal to a preset matching degree threshold, it is determined that the first adjustment coefficient p i corresponding to each of the node features of the i-th data node is 0.5, and the second adjustment coefficient q i is also 0.

5. Wherein, the step S420 includes step S421: Step S421, determine the target importance degree Z corresponding to the mth node feature of the ith data node im = p i × F m + q i × G im .

2. The method of claim 1, wherein, The step S100 includes: Step S110, obtaining an influence value of each of the node features on each of the historical tasks when each of the data nodes executes the historical tasks, to obtain a first task influence value list set Y1, Y2,..., Y i ,...,Y j ; wherein i=1, 2,..., j; j is the number of the data nodes; Y i is the first task influence value list set corresponding to the i-th data node in the first historical time period. Y i =(Y i1 ,Y i2 ,...,Y im ,...,Y in ); m=1,2,...,n; n is the number of node features corresponding to the data node; Y im This is a list of task impact values ​​corresponding to the m-th node feature of the i-th data node within the first historical time period. Y im =(Y im1 ,Y im2 ,...,Y ime ,...,Y imf ); e = 1, 2, ..., f; f is the number of class groups for the historical task; Y ime When the i-th data node performs the e-type historical task in the first historical time period, the influence value of the m-th node feature on the e-type historical task; Step S120, determining a first feature influence value list X1, X2,..., Xi corresponding to each data node according to the first task influence value list set corresponding to each data node. i ,...,X j ; wherein X i is the feature influence value list corresponding to the i-th data node. X i =(X i1 ,X i2 ,...,X im ,...,X in );X im for the mth node feature of the ith data node; X im =(∑ e=1 f Y ime ) / f; Step S130, determining a total influence value vector C=(C1, C2,..., Cn) according to the first feature influence value list corresponding to each data node. m ,...,C n ); wherein C m =(∑ i=1 j X im ) / j.

3. The method of claim 2, wherein, The step S200 includes: In step S210, an influence value of each node feature of each data node on a type of historical task is obtained when each data node executes the type of historical task in the second historical time period, to obtain a second task influence value list set D1, D2,..., D corresponding to each of the data nodes. i ,...,D j ; wherein D i is the second task influence value list set corresponding to the i-th data node in the second historical time period. D i =(D i1 ,D i2 ,...,D im ,...,D in );D im is the mth node feature of the ith data node in the second historical time period Corresponding to the task influence value list; D im =(D im1 ,D im2 ,...,D ime ,...,D imf );D ime is the influence value of the mth node feature on the e-type historical task when the ith data node executes the e-type historical task in the second historical time period. Step S220, determining a node influence value vector E1, E2,..., E corresponding to each data node according to the second task influence value list set corresponding to each data node. i ,...,E j ; wherein E i is the node influence value vector corresponding to the ith data node; E i =(E i1 ,E i2 ,...,E im ,...,E in ); E im =(∑ e=1 f D ime ) / f.

4. The method of claim 3, wherein, The step S300 includes: Step S310, processing the total influence value vector C according to a preset index importance determination method to obtain a first importance degree list F=(F1, F2,..., Fm) corresponding to the m node features of each data node. m ,...,F n ); wherein Fm is the first importance degree corresponding to the mth node feature of each data node. m ​ Step S320, determining the node influence value vector E corresponding to the ith data node according to a preset index importance determination method i processing to obtain a second importance degree list G corresponding to the ith data node i =(G i1 ,G i2 ,...,G im ,...,G in ); wherein G im is the second importance degree corresponding to the mth node feature of the ith data node.

5. The method of claim 1, wherein, The step S411 further includes: Step S413, if L i If the matching degree is greater than a preset threshold, then the second adjustment coefficient q corresponding to each node feature of the i-th data node is determined. i 0.5-(0.5×(L) i -MIN(L)) / (MAX(L)-MIN(L))); where MIN() is the preset minimum value determination function; MAX() is the preset maximum value determination function; Step S414, determining the first adjustment coefficient p corresponding to each of the node features of the i-th data node i is 1-q i .

6. A non-transitory computer-readable storage medium, comprising: The storage medium stores at least one instruction or at least one program, the at least one instruction or the at least one program is loaded and executed by the processor to realize the method of any one of claims 1-5.

7. An electronic device, comprising: Comprising a processor and a non-transitory computer readable storage medium as claimed in claim 6.

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