CN111833022B - Cross-data, information, knowledge modality and dimension task processing method and component - Google Patents

Cross-data, information, knowledge modality and dimension task processing method and component Download PDF

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CN111833022B
CN111833022B CN202010693137.5A CN202010693137A CN111833022B CN 111833022 B CN111833022 B CN 111833022B CN 202010693137 A CN202010693137 A CN 202010693137A CN 111833022 B CN111833022 B CN 111833022B
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段玉聪
曹凯
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CERNET Corp
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Abstract

The invention discloses a method, a device, equipment and a readable storage medium for processing tasks across data, information, knowledge modes and dimensions, wherein the method comprises the following steps: acquiring a target task to be executed and task realization resources of the target task; the task implementation resources comprise at least one of task implementation data, task implementation information and task implementation knowledge; performing multi-dimensional, cross-modal and cross-dimensional comprehensive planning processing on the task implementation resources by using a comprehensive evaluation model to obtain task planning resources; and planning resources according to the tasks and executing the target tasks. According to the method, when the target task to be executed is obtained, the task planning resources can be obtained only by inputting the corresponding task implementation resources into the comprehensive task evaluation model, and then the target task is executed according to the task planning resources, so that the task execution can meet the requirements better.

Description

Cross-data, information, knowledge modality and dimension task processing method and component
Technical Field
The invention relates to the technical field of computer application, in particular to a method, a device and equipment for processing a task across data, information, knowledge modalities and dimensions and a readable storage medium.
Background
For the same task, different devices or execution flows are adopted, and the execution effects of the different devices or execution flows can be different.
At present, for task execution, devices for executing tasks are often predetermined, and task execution processes are often predefined. Tasks that are not predefined to execute tasks or execute devices often cannot be executed effectively.
With the increasing abundance of service functions of computer systems, more and more devices are available, and the problem of how to effectively execute tasks is a technical problem that needs to be solved by those skilled in the art at present.
Disclosure of Invention
The invention aims to provide a data, information, knowledge modality and dimension-crossing task processing method, device and equipment and a readable storage medium, which can realize resources based on tasks, complete task planning and further execute tasks according to task planning resources without predefining the task execution flow and equipment.
In order to solve the technical problems, the invention provides the following technical scheme:
a task processing method across data, information, knowledge modalities and dimensions comprises the following steps:
acquiring a target task to be executed and task implementation resources of the target task; the task implementation resources comprise at least one of task implementation data, task implementation information and task implementation knowledge;
performing multi-dimensional, cross-modal and cross-dimensional comprehensive planning processing on the task realization resources by using a comprehensive evaluation model to obtain task planning resources;
and executing the target task according to the task planning resources.
Preferably, the acquiring a target task to be executed and task implementation resources of the target task includes:
receiving the target task sent by a task issuing terminal;
and reading and/or receiving the task implementation resources sent by the task issuing terminal from a local system.
Preferably, executing the target task according to the task planning resource includes:
selecting task execution equipment corresponding to the task planning resources from a distributed system or a cluster;
and executing the target task by using the task execution equipment.
Preferably, executing the target task according to the task planning resource includes:
and executing the target task according to the task processing flow corresponding to the task planning resource.
Preferably, the comprehensive evaluation model comprises a compensation module, a resource frequency statistics module, different source resource processing modules, different modal resource processing modules, different dimensional resource processing modules, a value calculation module, a resource value correction module, a resource cleaning module and a fusion planning module;
correspondingly, the step of performing multi-dimensional, cross-modal and cross-dimensional ground comprehensive planning processing on the task realization resources by using the comprehensive evaluation model to obtain task planning resources comprises:
cleaning the task realization resources by using the resource cleaning module;
determining a frequency value corresponding to the task realization resource by using the resource frequency statistical module;
determining each resource source value corresponding to the task realization resource by using the different source resource processing module;
determining each modal resource value corresponding to the task realization resource by using the different modal resource processing module;
determining each dimension value corresponding to the task realization resources by using the different dimension resource processing module;
compensating the task implementation resources by using the compensation module;
determining a reference value of the task realization resource by using the value calculation module;
correcting the reference value of the task realization resource by using the resource value correction module;
and performing task planning on the task realization resources by using the fusion planning module and combining the processing results of all modules to obtain the task planning resources.
Preferably, the compensation module comprises a data-oriented information compensation sub-module and/or an information-oriented data compensation sub-module;
accordingly, compensating the task implementation resources includes:
performing information compensation processing on the task implementation resource by using the data direction information compensation submodule to obtain the task implementation resource after information compensation;
and/or performing data compensation processing on the task implementation resource by using the information to a data compensation submodule to obtain the task implementation resource after data compensation.
Preferably, the value calculation module comprises an information length value calculation submodule and/or an information breadth value calculation submodule;
accordingly, the determining the reference value of the task implementation resource comprises:
determining the information length value corresponding to the task realization resource by using the information length value calculation submodule;
and/or determining the information breadth value corresponding to the task realization resource by using the information breadth value calculation submodule.
A task processing device across data, information, knowledge modalities and dimensions, comprising:
the task implementation resource acquisition unit is used for acquiring a target task to be executed and task implementation resources of the target task; the task implementation resources comprise at least one of task implementation data, task implementation information and task implementation knowledge;
the task planning unit is used for carrying out multi-dimensional, cross-modal and cross-dimensional ground comprehensive planning processing on the task realization resources by utilizing the comprehensive evaluation model to obtain task planning resources;
and the task execution unit is used for executing the target task according to the task planning resources.
A task processing device across data, information, knowledge modalities and dimensions, comprising:
a memory for storing a computer program;
and the processor is used for realizing the steps of the cross-data, information, knowledge modality and dimension task processing method when the computer program is executed.
A readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described cross-data, information, knowledge modality and dimension task processing method.
By applying the method provided by the embodiment of the invention, the target task to be executed and the task realization resource of the target task are obtained; the task implementation resources comprise task implementation data, task implementation information and task implementation knowledge; performing multi-dimensional, cross-modal and cross-dimensional comprehensive planning processing on the task implementation resources by using a comprehensive evaluation model to obtain task planning resources; planning resources according to tasks, and executing target tasks
When the target task needs to be executed, the task implementation resource of the target task can be acquired, namely at least one of task implementation data, task implementation information and task implementation knowledge is acquired. And then, inputting the task realization resources into a comprehensive evaluation model oriented to data, information and knowledge fusion to perform multi-dimensional, cross-modal and cross-dimensional value task planning processing, so as to obtain task planning resources. In this manner, the target task may be executed according to the task-planning resources. The comprehensive evaluation model is oriented to data, information and knowledge fusion, and is a multidimensional, transmembrane and trans-dimensional value model, so that the comprehensive evaluation model can perform fusion processing on task implementation resources, realize multidimensional, transmembrane and trans-dimensional comprehensive evaluation, realize task planning processing, obtain reasonable task rule resources, and finally execute a target task according to task planning resources. Therefore, in the method, the execution equipment of the target task is not required to be defined in advance, the execution flow of the target task is not required to be defined in advance, and the task realization resources of the target task are obtained only when the target task to be executed is clear.
Accordingly, embodiments of the present invention further provide a cross-data, information, knowledge modality, and dimension task processing apparatus, a device, and a readable storage medium corresponding to the cross-data, information, knowledge modality, and dimension task processing method, which have the above technical effects, and are not described herein again.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart illustrating an implementation of a cross-data, information, knowledge modality and dimension task processing method according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a cross-data, information, knowledge modality and dimension task processing device according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a task processing device across data, information, knowledge modalities and dimensions according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a task processing device across data, information, knowledge modalities and dimensions according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
referring to fig. 1, fig. 1 is a flowchart of a cross-data, information, knowledge modality and dimension task processing method according to an embodiment of the present invention, the method including the following steps:
s101, obtaining a target task to be executed and task implementation resources of the target task.
The task implementation resources comprise at least one of task implementation data, task implementation information and task implementation knowledge.
The target task may be a file downloading task, a data processing task (such as data storage, migration, and backup), a monitoring task (such as online monitoring of a monitoring system, and message security monitoring of a firewall), an identification task (such as face identification), and other common computer tasks. In particular, the target task may be a task executed by a plurality of devices, or may be a task having a different processing flow. For example, the target task may be specifically to store target data in the distributed storage system, that is, the target data may be stored in any one or more storage devices of the distributed storage system; or, when the target data is stored, the processing flows corresponding to different storage modes such as encryption storage, compression storage, distributed storage, backup storage and the like can be selected for storage.
The task implementation data may be obtained by recording and supervising the target task, and for example, the task implementation data may specifically include an initiator of the target task, data content required when the target task is executed (for example, corresponding data storage task, the data content is data to be stored), and required data for the target task to be executed (for example, whether to require feedback of a task execution result, response time). The task implementation information is conveyed in the context of data and data combinations, suitable for analysis and interpretation, e.g. corresponding data storage tasks, and may have a relationship including a relationship between data to be stored and disk data as an existing relationship. The task realization knowledge is general understanding and experience obtained from accumulated information, and a new background can be inferred according to the knowledge, for example, when the task is a picture recognition task, the task realization knowledge may include binarizing a picture to be recognized and recognizing the picture based on the binarized picture.
Specifically, the process of acquiring the target task and the task implementation resource may include:
step 1, receiving a target task sent by a task issuing terminal;
and 2, reading and/or receiving the task implementation resources sent by the task issuing terminal from the local system.
That is, the target task may specifically come from the task issuing terminal. And the task implementation resource can come from a local system or a task issuing terminal. Certainly, part of the task implementation resources can come from the task issuing terminal or the local system.
The task implementation resources may be used to determine alternative devices to perform the task and/or to determine the flow of execution of the target task. When the task execution device needs to be determined, the task implementation resources may specifically include task implementation data such as a serial number of the alternative device, task execution efficiency reference data of the alternative device, a historical task processing record of the alternative device, status monitoring data of the alternative device, and the like; task implementation information such as an association between processing efficiency and energy consumption of the alternative device, an association between a historical task processing record and execution efficiency reference data, and the like; for example, the parallel processing task consumes fast time but has low processing efficiency, data synchronization is needed when a plurality of devices process one task together, and the device with low system resource occupancy rate processes the task with high efficiency and other task realization knowledge.
S102, comprehensive planning processing of multi-dimensional, cross-mode and cross-dimension ground is conducted on the task realization resources by means of the comprehensive evaluation model, and the task planning resources are obtained.
After the task realization resources are obtained, the task realization resources can be input into the comprehensive evaluation model for task planning, and the task planning resources are obtained.
For the task planning, the task can be planned according to different task processing effects, such as planning a task planning resource corresponding to the processing effect and at least one of the requirements of reliability, safety, energy consumption and efficiency.
The task planning resources represent data, information and knowledge related content corresponding to the execution of the target task. In particular, data, information or knowledge relating to the execution object and/or the execution flow of the specific target task may be specified.
The comprehensive evaluation model is a model for fusing data, information and knowledge of task realization resources, performing cross-modal and cross-dimensional value comprehensive evaluation, and further completing task planning to obtain task planning resources.
In practical application, the comprehensive evaluation model can preset a plurality of different processing modules, different task realization resources are compared, different processing modules are triggered, the task realization resources are processed, and then task planning is carried out. Specifically, the comprehensive evaluation model comprises a compensation module, a resource frequency statistics module, different source resource processing modules, different modal resource processing modules, different dimensional resource processing modules, a value calculation module, a resource value correction module, a resource cleaning module and a fusion planning module;
correspondingly, step S102 may specifically include, but is not limited to, performing some or all of the following processing manners:
resource cleaning: cleaning the task realization resources by using a resource cleaning module;
and (3) determining the frequency value: determining a frequency value corresponding to a task realization resource by using a resource frequency statistical module;
processing different resource sources: determining each resource source value corresponding to the task realization resource by using different source resource processing modules;
different modality treatment: determining each modal resource value corresponding to the task realization resource by using different modal resource processing modules;
treating in different dimensions: determining each dimension value corresponding to the task realization resources by using different dimension resource processing modules;
resource compensation: compensating the task realization resources by using a compensation module;
and (3) value calculation: determining the reference value of the task realization resources by using a value calculation module;
value correction: modifying the reference value of the task realization resource by using a resource value modification module;
fusion planning: and performing task planning on the task realization resources by using the fusion planning module and combining the processing results of all modules to obtain task planning resources.
The compensation module comprises a data direction information compensation submodule and/or an information direction data compensation submodule;
accordingly, compensating for task implementation resources includes:
performing information compensation processing on the task implementation resources by using the data direction information compensation submodule to obtain the task implementation resources after information compensation;
and/or performing data compensation processing on the task implementation resource by using the information to the data compensation submodule to obtain the task implementation resource after data compensation.
The value calculation module comprises an information length value calculation submodule and/or an information breadth value calculation submodule;
accordingly, determining a reference value for a task implementation resource includes:
determining the information length value corresponding to the task realization resource by using the information length value calculation submodule;
and/or determining the information breadth value corresponding to the task realization resource by using the information breadth value calculation submodule.
In order to facilitate understanding of the functions and implementation processes of the respective modules, specific functions and processes of the respective modules are illustrated below.
First, it should be noted that: resource Elements (Elements)DIK) The method comprises three forms of data, information and knowledge:
ElementsDIK∷＝＜Data,Information,Knowledge>；
the nature of the value is that it exists quantitatively, and the value is a quantitative value.
After the data are grouped, the number of the data distributed in each group is called frequency number or frequency number, and the ratio of the frequency number of each group to the sum of all the frequency numbers is called frequency or proportion.
Before processing the task realization resources, judging whether the sources, dimensions and modes of the task realization resources are the same or not, and processing different source resources by using different original resource processing modules; processing the resources with different dimensions by using resource processing modules with different dimensions; and for the resources in different modes, processing by using the resource processing modules in different modes.
(1) And for the resource frequency statistical module:
and calculating the resource value based on the resource frequency accumulation statistics for directly obtained homologous and same-dimension homomorphic resources.
For univariate numerical class resources, ValueDCumulativeThe sum of the frequencies within a certain numerical range is referred to as the value of the corresponding resource within the numerical range.
Figure BDA0002590071810000081
Wherein, Σ FreqERepresenting the sum of frequency frequencies, Freq, of resources within the range of values corresponding to resource EAllFrequency, Value, representing all resourcesECumulativeThe value of (a) is between 0 and 1.
For multivariable numerical Value resources, the clustering technology in the technical field of large resources is utilized to analyze the distribution of the resources, and the probability of resource classification is used as the resource Value (Value)E). Acquiring task implementation resource X ═ { a ═ a1,a2,……,amH, the classified class set C ═ y1,y2,……,ymRespectively calculating the probability P (y) of the task realizing resource division in different categories1|x),P(y2|x),……,P(ym| x), taking the maximum value of probability P (y)k|x)＝max{P(y1|x),P(y2|x),……,P(ym| x) } corresponding classification ykAs a result of the determination, the corresponding probability is the value of the resource to support the classification determination.
ValueE＝P(yk|x)＝max{P(y1|x),P(y2|x),……,P(ym|x)}
And selecting the occurrence frequency of the task realization resources as the resource value for the non-numerical task realization resources such as task realization information and task realization knowledge.
Figure BDA0002590071810000082
Wherein, FreqEIndicating the frequency of occurrence, Freq, of non-numerical resources DAllThe frequency count representing all of the resources is,ValueECumulativethe value of (a) is between 0 and 1.
The resource has validity based on the frequency of time, the value of the resource is reduced along with the time, and whether the time value change is considered is determined based on the purpose of the cumulative statistics of the frequency of the target resource.
ValueETimeliness＝ValueE×δTime
Wherein, deltaTimeThe time attenuation coefficient of the resource value needs to be calculated according to the attenuation rule.
And (3) the resource has effectiveness based on the spatial frequency, the value of the resource is reduced along with the spatial change, and whether the spatial value change is considered or not is determined based on the purpose of cumulative statistics of the target resource frequency.
ValueEbounded＝ValueE×δPosition
Wherein, deltaPositionThe spatial attenuation coefficient which is the resource value needs to be calculated according to the attenuation rule.
The task implementation resource acquisition may originate from different acquisition devices, the acquired task implementation resources include one or more dimensions, and the modality of the acquired resources may also be different, including but not limited to images, audio, numbers, and text.
(2) And for different source resource processing modules:
the method is limited by the reliability of the task realization resource sources, the resource sources are different, and the values of the acquired task realization resources are different.
ValueE＝αs×ValueEs
Wherein, ValueEsRepresenting the value of a resource irrespective of its source reliability, alphasThe reliability of the resource source is represented and needs to be obtained through reliability calculation of each part of the resource acquisition equipment.
(3) And for different modal resource processing modules:
tasks from different sources implement resources, which may differ in modality, and further, even resources from the same source may exist in different modalities. Different modalities include, but are not limited to, different dimensions and different value ranges of the acquired resources. Before processing, resource normalization processing is required, such as replacing resources with obvious linear relation.
The value of the task realization resources is normalized, namely the dimension of a unified mode is defined, and the value range is limited between (0, 1):
Figure BDA0002590071810000091
wherein, ValueEmValue representing the Value of a resource before the modality of the resource is convertedEmavgIs the arithmetic mean Value of resource Value under the same modeEmmaxIs the maximum Value of resource Value in the same mode, ValueEmminIs the minimum value of resource value, Mod, in the same modalityelementIs a dimension of the current modality.
(4) The resource processing module with different dimensions:
in the process of analyzing the task implementation resources, the single dimension has a limited effect on analyzing the task implementation resources, and the integration of multiple dimensions needs to consider the relevance of each dimension resource to the classification.
Using Lift in correlation analysis (Lift)Dim) Concepts define the relevance of the current dimension:
Figure BDA0002590071810000101
dimension (Dimension), also called Dimension number, is the number of independent parameters in mathematics. Because there is no correlation between the dimensions, the value resources of multiple dimensions are subjected to fusion calculation by adopting a weighted average mode:
Figure BDA0002590071810000102
wherein, Sigma LiftDimLift is the sum of the correlations of independent dimensions in a resourceDimjValue being the degree of promotion of dimension jEdRepresenting resource value without regard to the relevance of the dimension to the classification.
Therefore, the task is processed by calculating different sources, different modes and different dimensionality modules of the resources, and the resource fusion is completed.
(5) And the data direction information compensation sub-module:
information is passed through the context of the data and the data after it has been combined. In the case of a combination of a plurality of data, new information can be composed and processed according to the information and the new data. The information is basically combined by 2 data, and the value of the information is as follows:
ValueIDD＝1-σij×(1-ValueDi)×(1-ValueDj)
wherein, ValueIDDValue representing information combined from two dataDi、ValueDjRespectively the value of the data, σijThe relevance between the two data is larger, and the promotion of the information value after the data are combined is smaller.
And compensating the information through the data to generate new information value. And when the new information value exceeds the original data value, replacing the original data value with the new information value.
(6) And an information-to-data compensation sub-module:
from the above, the source of the task realization data can be directly obtained, and can also be compensated through information, the data is reversely deduced through the information, the information is sent to the data compensation calculation module, the compensated data value is calculated by using the information value, and the processing mode is weighted summation:
ValueDI＝η×ValueI
wherein, ValueDIValue representing the Value of the data after compensating the data with informationIThe value of the information, eta, is a correlation coefficient of the data and the information, represents the degree of reduction of the data by the information, depends on the probability of occurrence of the data event in the information event, and conforms to the principles of probability theory and statistics.
Through information to data compensation, the difficulty of data acquisition is solved, incomplete data can be supplemented, and inconsistent data can be corrected.
(7) And for the information length value calculation submodule:
for information with a certain length, the information can be regarded as a plurality of pieces of information which are related in turn, the Value obtained after the information fusion is calculated based on the information lengthIIDComprises the following steps: valueIIDepth＝ValueI1+ValueI1(ValueI2+ValueI2(ValueI3+ValueI3(……)))
Specifically, the value calculation that can be split into 2 pieces of information satisfies:
ValueIIDepth＝ValueI1+ValueI1(ValueI2)
specifically, the value calculation which can be split into 3 pieces of information satisfies:
ValueIIDepth＝ValueI1+ValueI1(ValueI2+ValueI2(ValueI3))。
(8) and for the information breadth value calculation module:
the information breadth refers to the acquisition of similar information, the Value calculation depends on the relevance of the information, the processing is carried out by adopting weighted summation, and the Value after information fusionIIWidthComprises the following steps:
Figure BDA0002590071810000111
wherein, ValueIiRepresenting the value, alpha, of the ith message of the n messagesiIs the weight of the value of the ith message. Calculating the variation coefficient, standard deviation and average of the influencing factors reflected by the sample data, and determining alpha by the variation coefficient methodiAnd satisfy
Figure BDA0002590071810000112
(9) And for the resource value correction module:
incomplete task implementation data and task implementation information can be complemented through task recognition knowledge, and the value of task implementation resources meeting conditions can be replaced.
Knowledge is a rule that is assumed under complete abstract conditions. The value is constant when the assumed conditions are satisfied. The processing mode of knowledge fusion is a direct substitution of value, and is directly used as a supplementary resource or used for correcting the existing resource. Using triplets (A, B, Value)K) Store knowledge rules where A, B represents two classes of resources, respectively, and obtaining the resources and the classes as inputs X and Y, belonging to class A and class B, respectively, then
Figure BDA0002590071810000121
Wherein, ValueEThe value of the resource before being corrected by knowledge; valueKEIs the value of the resource after being modified by knowledge. The knowledge is transitive, the output Y can be repeatedly calculated as the input X,
Figure BDA0002590071810000122
representing the knowledge transfer m times, corresponding to Value in the knowledge ruleKThe maximum value of (a) of (b),
Figure BDA0002590071810000123
representing the knowledge transfer m times, corresponding to Value in the knowledge ruleKThe minimum value of (c).
(10) And for the resource cleaning module:
based on certain knowledge, the classification and the rule of the resources can be abstracted, and the abstracted resources have the same expression to form redundancy of the resources. Meanwhile, the value of the task realization resources is improved after abstraction.
Figure BDA0002590071810000124
The value of the mutual compensation between the data and the information cannot be repeatedly calculated, and a resource form with relatively high value is selected for calculation through knowledge correction and abstraction.
After the task planning resources are processed, the target task can be planned based on the processing results of the modules participating in the current processing, and the task planning resources are obtained. Task planning resources are data, information, and knowledge related to performing a target task.
S103, planning resources according to the tasks and executing the target tasks.
For a target task that may be performed by any one or more of the plurality of devices, the task planning resource may specify a corresponding task performance device. Specifically, task execution devices corresponding to task planning resources may be selected from a distributed system or a cluster; and executing the target task by using the task execution equipment.
The processing flow for the target task is various and can be executed according to the task processing flow indicated in the task planning resource. Specifically, the target task is executed according to a task processing flow corresponding to the task planning resource.
Of course, if the execution device and the processing flow are both optional, the target task may be executed according to the execution device indicated in the task-planning resource and according to the task execution flow indicated in the task-planning resource.
By applying the method provided by the embodiment of the invention, the target task to be executed and the task realization resource of the target task are obtained; the task implementation resources comprise task implementation data, task implementation information and task implementation knowledge; performing multi-dimensional, cross-modal and cross-dimensional comprehensive planning processing on the task implementation resources by using a comprehensive evaluation model to obtain task planning resources; planning resources according to tasks, and executing target tasks
When the target task needs to be executed, the task implementation resource of the target task can be acquired, namely at least one of task implementation data, task implementation information and task implementation knowledge is acquired. And then, inputting the task realization resources into a comprehensive evaluation model oriented to data, information and knowledge fusion to perform multi-dimensional, cross-modal and cross-dimensional value task planning processing, so as to obtain task planning resources. In this manner, the target task may be executed according to the task-planning resources. The comprehensive evaluation model is oriented to data, information and knowledge fusion, and is a multidimensional, transmembrane and trans-dimensional value model, so that the comprehensive evaluation model can perform fusion processing on task implementation resources, realize multidimensional, transmembrane and trans-dimensional comprehensive evaluation, realize task planning processing, obtain reasonable task rule resources, and finally execute a target task according to task planning resources. Therefore, in the method, the execution equipment of the target task is not required to be defined in advance, the execution flow of the target task is not required to be defined in advance, and the task realization resources of the target task are obtained only when the target task to be executed is clear.
In order to facilitate understanding of the specific planning process of the comprehensive evaluation model provided by the embodiment of the present invention, the following describes the planning process in detail by taking a specific application scenario as an example.
(1) Performing resource frequency accumulation-based statistical calculation on the acquired task implementation resources for example:
if the target task is a data reading task, a certain distributed storage system acquires data reading time corresponding to each disk, and detects data reading time corresponding to 100 disks altogether, wherein 90 data reading times are concentrated in more than 3s, and the data value of the data reading time of more than 3s is as follows:
Figure BDA0002590071810000131
and for non-numerical value resources, selecting the occurrence frequency of the resources as the resource value.
Wherein, FreqEIndicating the frequency of occurrence, Freq, of non-numerical resources DAllFrequency, Value, representing all resourcesECumulativeThe value of (a) is between 0 and 1.
Specifically, the method comprises the following steps: the response time of the data reading request of the distributed storage system can be monitored to obtain the data reading time of each disk, and the data reading time is influenced by the data reading concurrency condition, so that the response time of the data reading request can be acquired in different time periods, so that the disk data reading time corresponding to different time periods can be obtained, the data acquisition is integrated for 100 times, and the data response condition is obtained for 51 times, so that the disk data reading time is normal (namely, the non-delayed non-response condition) and has the following value:
Figure BDA0002590071810000141
the resource has validity based on the frequency of time, the value of the resource is reduced along with the time, and whether the time value change is considered is determined based on the purpose of the cumulative statistics of the frequency of the target resource. For example, disk usage over time can cause device aging, resulting in a decrease in the value of response time to previously collected data read requests over time.
Considering the time decay as a linear decay from the start time to the end time of the resource lifetime, then
Figure BDA0002590071810000142
Wherein, ValueETimelinessRepresenting the value of the current Time resource, Time being the current Time value, TimeendIs the end Time of the resource value, TimestartIs the starting time of the resource value.
And (3) the resource has effectiveness based on the spatial frequency, the value of the resource is reduced along with the spatial change, and whether the spatial value change is considered or not is determined based on the purpose of cumulative statistics of the target resource frequency. For example, the data reading time of a certain type of disk in the same distributed system has a higher reference value for supporting other disks of the same type in the same distributed system, and the reference value of other disks not belonging to the same signal in the same distributed system is smaller.
Considering the spatial attenuation as a linear attenuation from the central position to the boundary, then
Figure BDA0002590071810000143
Wherein, ValueEboundedRepresenting the value of the resource at the current spatial Position, Position being the current spatial PositioncenterIs the central Position, of the space corresponding to the resource valueedgeIs the boundary position of the resource value correspondence space.
(2) And processing different source resources:
the method is limited by the reliability of resource sources, the resource sources are different, and the values of the acquired resources are different.
ValueE＝αs×ValueEs
Wherein, ValueEsRepresenting the value of a resource irrespective of its source reliability, alphasThe reliability of the resource source is represented by the reliability calculation of each part of the resource acquisition equipment, for example, the operational reliability of the data acquisition module is 90%, the operational reliability of the analysis module is 90%, the operational reliability of the storage module is 90%, and the reliability of the equipment consisting of the three parts is 90% × 90% × 90% × 72.9%.
For another example, in a case that the image capturing device is operating normally, the reliability of the captured image resource is limited by the performance of the capturing system device, and the higher the resolution of the image capturing device is, the higher the pixels of the image data are, and the higher the value of the image resource is.
(3) And processing different modal resources.
When the task realization resources are comprehensively considered, the overall result is greatly influenced by obvious fluctuation.
The value of the resource is normalized, namely the dimension of a uniform mode is defined, and the value range is limited between (0, 1):
Figure BDA0002590071810000151
wherein, ValueEmValue representing the Value of a resource before the modality of the resource is convertedEmavgIs the arithmetic mean Value of resource Value under the same modeEmmaxIs a same dieMaximum Value of resource Value in state, ValueEmminIs the minimum value of resource value, Mod, in the same modalityelementIs a dimension of the current modality.
(4) Processing resources with different dimensions:
the temperature and the humidity have influence on the processing speed of the CPU processing task, and the condition of the CPU processing efficiency can be obtained from the corresponding dimensionality after the data are collected. And performing correlation calculation before comprehensive analysis. Through sample analysis, the external environment and the processing speed condition within 100 days are counted, the local high-temperature days are 50 days, the high-humidity days are 20 days, the CPU processing speed is influenced by 40 days, wherein the humidity is influenced for 30 times, and the high-temperature is influenced for 15 times. Using Lift in correlation analysis (Lift)Dim) Concepts define the relevance of the current dimension:
Figure BDA0002590071810000152
Figure BDA0002590071810000153
the temperature dimension has a large influence on the correlation of the CPU processing speed, and the value resources of the two dimensions are subjected to fusion calculation in a weighted average mode. Assuming the value of the resources in two dimensions is 70% and 75%, respectively
Figure BDA0002590071810000161
(5) And compensating the information through the data, and increasing information acquisition sources:
under special conditions, the processing performance of the equipment is not convenient to obtain, information can be compensated by collecting the temperature of the CPU of the equipment and the starting time of the equipment, and the information value compensated by data is 0.4 when the value of the temperature of the CPU of the equipment to the processing performance of the judgment equipment is assumed and the value of the starting time of the equipment to the processing performance of the judgment equipment is 0.7
ValueIDD＝1-σij×(1-ValueDi)×(1-ValueDj)＝1-1×(0.3×0.7)
＝0.79
σijThe relevance between two data is larger, the more the data value is overlapped, and the smaller the value of the compensated information is.
And compensating the information through the data to generate new information value. And when the new information value exceeds the original data value, replacing the original data value with the new information value.
(6) And compensating data through information, and increasing data acquisition sources:
the source of the data can be directly obtained, the data can also be compensated through the information, the data is reversely deduced through the information, the information is used for calculating the compensated data value by the information value to the data compensation calculating module, and the processing mode is weighted summation:
ValueDI＝η×ValueI
wherein, ValueDIValue representing the Value of the data after compensating the data with informationIThe value of the information, eta, is a correlation coefficient of the data and the information, represents the reduction degree of the data by the information, and depends on the probability of the data event occurring in the information event.
(7) Calculating the value of the information based on the information length:
the information with a certain length can be regarded as a plurality of information which are mutually related pairwise, and the Value calculated based on the information length and fused by the method is providedIIDComprises the following steps: valueIIDepth＝ValueI1+ValueI1(ValueI2+ValueI2(ValueI3+ValueI3(……)))；
The value calculation which can be specifically divided into 2 pieces of information satisfies the following conditions:
ValueIIDepth＝ValueI1+ValueI1(ValueI2)
the value calculation which can be specifically divided into 3 pieces of information satisfies the following conditions:
ValueIIDepth＝ValueI1+ValueI1(ValueI2+ValueI2(ValueI3))；
for example: the A disk can support high writing efficiency of the A disk by recording the data of 1M in the A disk for 0.0001s, and if the data of 1M stored in the A disk for multiple times continuously is less than 0.0001s, the supporting degree of continuous information on the data storage effect of the A disk is continuously increased.
(8) Calculating the associated value based on the information breadth:
the information breadth refers to the acquisition of similar information, the Value calculation depends on the relevance of the information, the processing is carried out by adopting weighted summation, and the Value after information fusionIIWidthComprises the following steps:
Figure BDA0002590071810000171
wherein, ValueIiRepresenting the value, alpha, of the ith message of the n messagesiIs the weight of the value of the ith message. Calculating the variation coefficient, standard deviation and average of the influencing factors reflected by the sample data, and determining alpha by the variation coefficient methodiAnd satisfy
Figure BDA0002590071810000172
(9) And correcting the resource value by using knowledge:
incomplete data and information resources can be complemented through knowledge, and the value of the resources meeting the conditions can be replaced.
Knowledge is a rule that is assumed under complete abstract conditions. The value is constant when the assumed conditions are satisfied. The processing mode of knowledge fusion is a direct substitution of value, and is directly used as a supplementary resource or used for correcting the existing resource. The knowledge rules are stored by adopting the triples (A, B, R), and each knowledge rule corresponds to a ValueKWhere A, B denotes two classes of resources, respectively, and the acquisition of resources and classification as inputs X and Y, respectively belong to class A and class B, then
Figure BDA0002590071810000173
Wherein, ValueEThe value of the resource before being corrected by knowledge; valueKEIs the value of the resource after being modified by knowledge. The knowledge is transitive, the output Y can be repeatedly calculated as the input X,
Figure BDA0002590071810000174
representing the maximum value of input of the knowledge rule corresponding to the knowledge transfer m times,
Figure BDA0002590071810000175
the minimum value of input is shown by the m times of knowledge transfer.
For example, there are motor vehicles, pedestrians, and non-motor vehicles on the road, the speed measurement device collects that when the speed per hour of a certain object is 180 km/h, the value is 0.9, the object moving on the road is limited by the fact that the speed measurement device cannot clearly determine what the moving physics is, and cannot accurately determine what the object moving at 180 km/h is, if there is a knowledge rule that the speed per hour of the non-motor vehicle does not exceed 40 km/h, the highest speed of the person is 20.2 km/h, and it is determined that 180 km/h is greater than 40 km/h and 20.2 km/h, the value of the speed per hour of the motor vehicle on the road is 180 km/h is 0.9.
(10) And cleaning resources:
based on certain knowledge, the classification and the rule of the resources can be abstracted, and the abstracted resources have the same expression to form redundancy of the resources. At the same time, the value of the resource is promoted after abstraction.
Figure BDA0002590071810000181
The value of the mutual compensation between the data and the information cannot be repeatedly calculated, and a resource form with relatively high value is selected for calculation through knowledge correction and abstraction.
Example two:
corresponding to the above method embodiments, the embodiments of the present invention further provide a cross-data, information, knowledge modality and dimension task processing device, and the cross-data, information, knowledge modality and dimension task processing device described below and the cross-data, information, knowledge modality and dimension task processing method described above may be referred to correspondingly.
Referring to fig. 2, the apparatus includes the following units:
a task implementation resource obtaining unit 101, configured to obtain a target task to be executed and a task implementation resource of the target task; the task implementation resources comprise at least one of task implementation data, task implementation information and task implementation knowledge;
the task planning unit 102 is configured to perform comprehensive planning processing on the task implementation resources in a multidimensional, cross-modal and cross-dimensional manner by using the comprehensive evaluation model to obtain task planning resources;
and the task execution unit 103 is used for executing the target task according to the task planning resources.
By applying the device provided by the embodiment of the invention, the target task to be executed and the task realization resource of the target task are obtained; the task implementation resources comprise task implementation data, task implementation information and task implementation knowledge; performing multi-dimensional, cross-modal and cross-dimensional comprehensive planning processing on the task implementation resources by using a comprehensive evaluation model to obtain task planning resources; planning resources according to tasks, and executing target tasks
When the target task needs to be executed, the task implementation resource of the target task can be acquired, namely at least one of task implementation data, task implementation information and task implementation knowledge is acquired. And then, inputting the task realization resources into a comprehensive evaluation model oriented to data, information and knowledge fusion to perform multi-dimensional, cross-modal and cross-dimensional value task planning processing, so as to obtain task planning resources. In this manner, the target task may be executed according to the task-planning resources. The comprehensive evaluation model is oriented to data, information and knowledge fusion, and is a multidimensional, transmembrane and trans-dimensional value model, so that the comprehensive evaluation model can perform fusion processing on task implementation resources, realize multidimensional, transmembrane and trans-dimensional comprehensive evaluation, realize task planning processing, obtain reasonable task rule resources, and finally execute a target task according to task planning resources. Therefore, in the device, the execution equipment of the target task does not need to be defined in advance, the execution flow of the target task does not need to be defined in advance, and the task realization resources of the target task only need to be acquired when the target task to be executed is clear.
In a specific embodiment of the present invention, the task implementation resource obtaining unit 101 is specifically configured to receive a target task sent by a task issuing terminal; and reading and/or receiving the task implementation resources sent by the task issuing terminal from the local system.
In a specific embodiment of the present invention, the task execution unit 103 is specifically configured to select a task execution device corresponding to a task planning resource from a distributed system or a cluster; and executing the target task by using the task execution equipment.
In a specific embodiment of the present invention, the task execution unit 103 is specifically configured to execute the target task according to a task processing flow corresponding to the task planning resource.
In a specific embodiment of the invention, the comprehensive evaluation model comprises a compensation module, a resource frequency statistical module, different source resource processing modules, different modal resource processing modules, different dimensional resource processing modules, a value calculation module, a resource value correction module, a resource cleaning module and a fusion planning module;
correspondingly, the task planning unit 102 is specifically configured to:
cleaning the task realization resources by using a resource cleaning module;
determining a frequency value corresponding to a task realization resource by using a resource frequency statistical module;
determining each resource source value corresponding to the task realization resource by using different source resource processing modules;
determining each modal resource value corresponding to the task realization resource by using different modal resource processing modules;
determining each dimension value corresponding to the task realization resources by using different dimension resource processing modules;
compensating the task realization resources by using a compensation module;
determining the reference value of the task realization resources by using a value calculation module;
modifying the reference value of the task realization resource by using a resource value modification module;
and performing task planning on the task realization resources by using the fusion planning module and combining the processing results of all modules to obtain task planning resources.
In a specific embodiment of the present invention, the compensation module includes a data-oriented information compensation sub-module and/or an information-oriented data compensation sub-module;
accordingly, the task planning unit 102 is specifically configured to:
performing information compensation processing on the task implementation resources by using the data direction information compensation submodule to obtain the task implementation resources after information compensation;
and/or performing data compensation processing on the task implementation resource by using the information to the data compensation submodule to obtain the task implementation resource after data compensation.
In a specific embodiment of the invention, the value calculation module comprises an information length value calculation submodule and/or an information breadth value calculation submodule;
accordingly, the task planning unit 102 is specifically configured to:
determining the information length value corresponding to the task realization resource by using the information length value calculation submodule;
and/or determining the information breadth value corresponding to the task realization resource by using the information breadth value calculation submodule.
Example three:
corresponding to the above method embodiment, the embodiment of the present invention further provides a cross-data, information, knowledge modality and dimension task processing device, and a cross-data, information, knowledge modality and dimension task processing device described below and a cross-data, information, knowledge modality and dimension task processing method described above may be referred to correspondingly.
Referring to fig. 3, the task processing device across data, information, knowledge modalities and dimensions includes:
a memory 332 for storing a computer program;
the processor 322 is configured to implement the steps of the cross-data, information, knowledge modality and dimension task processing method of the above method embodiments when executing the computer program.
Specifically, referring to fig. 4, fig. 4 is a schematic diagram illustrating a specific structure of a cross-data, information, knowledge modality and dimension task processing device provided in this embodiment, the cross-data, information, knowledge modality and dimension task processing device may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 322 (e.g., one or more processors) and a memory 332, where the memory 332 stores one or more computer applications 342 or data 344. Memory 332 may be, among other things, transient or persistent storage. The program stored in memory 332 may include one or more modules (not shown), each of which may include a sequence of instructions operating on a data processing device. Still further, the central processor 322 may be configured to communicate with the memory 332 to execute a series of instruction operations in the memory 332 on the task processing device 301 across data, information, knowledge modalities and dimensions.
The task processing device 301 across data, information, knowledge modalities and dimensions may also include one or more power sources 326, one or more wired or wireless network interfaces 350, one or more input-output interfaces 358, and/or one or more operating systems 341.
The steps in the cross-data, information, knowledge modality and dimension task processing method described above may be implemented by the structure of a cross-data, information, knowledge modality and dimension task processing device.
Example four:
corresponding to the above method embodiment, the embodiment of the present invention further provides a readable storage medium, and a readable storage medium described below and a cross-data, information, knowledge modality and dimension task processing method described above may be referred to in correspondence.
A readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of task processing across data, information, knowledge modalities and dimensions of the above-described method embodiments.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

Claims (7)

1. A task processing method across data, information, knowledge modalities and dimensions is characterized by comprising the following steps:
acquiring a target task to be executed and task implementation resources of the target task; the task implementation resources comprise at least one of task implementation data, task implementation information and task implementation knowledge;
performing multi-dimensional, cross-modal and cross-dimensional comprehensive planning processing on the task realization resources by using a comprehensive evaluation model to obtain task planning resources;
executing the target task according to the task planning resources;
the comprehensive evaluation model comprises a compensation module, a resource frequency statistics module, different source resource processing modules, different modal resource processing modules, different dimensional resource processing modules, a value calculation module, a resource value correction module, a resource cleaning module and a fusion planning module;
correspondingly, the step of performing multi-dimensional, cross-modal and cross-dimensional ground comprehensive planning processing on the task realization resources by using the comprehensive evaluation model to obtain task planning resources comprises:
cleaning the task realization resources by using the resource cleaning module;
determining a frequency value corresponding to the task realization resource by using the resource frequency statistical module;
determining each resource value corresponding to the task realization resource by using the different source resource processing module;
determining each modal resource value corresponding to the task realization resource by using the different modal resource processing module;
determining each dimension value corresponding to the task realization resources by using the different dimension resource processing module;
compensating the task implementation resources by using the compensation module;
determining a reference value of the task realization resource by using the value calculation module;
correcting the reference value of the task realization resource by using the resource value correction module;
performing task planning on the task realization resources by using the fusion planning module and combining the processing results of the modules to obtain the task planning resources;
the compensation module comprises a data direction information compensation submodule and/or an information direction data compensation submodule;
accordingly, compensating the task implementation resources includes:
performing information compensation processing on the task implementation resource by using the data direction information compensation submodule to obtain the task implementation resource after information compensation;
and/or, performing data compensation processing on the task implementation resource by using the information to a data compensation submodule to obtain the task implementation resource after data compensation;
the value calculation module comprises an information length value calculation submodule and/or an information breadth value calculation submodule;
accordingly, the determining the reference value of the task implementation resource comprises:
determining the information length value corresponding to the task realization resource by using the information length value calculation submodule;
and/or determining the information breadth value corresponding to the task realization resource by using the information breadth value calculation submodule.
2. The cross-data, information, knowledge modality and dimension task processing method according to claim 1, wherein the acquiring target tasks to be executed and task implementation resources of the target tasks comprises:
receiving the target task sent by a task issuing terminal;
and reading and/or receiving the task implementation resources sent by the task issuing terminal from a local system.
3. The cross-data, information, knowledge modality and dimension task processing method of claim 1, wherein executing the target task according to the task planning resources comprises:
selecting task execution equipment corresponding to the task planning resources from a distributed system or a cluster;
and executing the target task by using the task execution equipment.
4. The cross-data, information, knowledge modality and dimension task processing method of claim 1, wherein executing the target task according to the task planning resources comprises:
and executing the target task according to the task processing flow corresponding to the task planning resource.
5. A task processing device across data, information, knowledge modalities and dimensions, comprising:
the task implementation resource acquisition unit is used for acquiring a target task to be executed and task implementation resources of the target task; the task implementation resources comprise at least one of task implementation data, task implementation information and task implementation knowledge;
the task planning unit is used for carrying out multi-dimensional, cross-modal and cross-dimensional ground comprehensive planning processing on the task realization resources by utilizing the comprehensive evaluation model to obtain task planning resources;
the task execution unit is used for planning resources according to the tasks and executing the target tasks;
the comprehensive evaluation model comprises a compensation module, a resource frequency statistics module, different source resource processing modules, different modal resource processing modules, different dimensional resource processing modules, a value calculation module, a resource value correction module, a resource cleaning module and a fusion planning module;
correspondingly, the mission planning unit is specifically configured to:
cleaning the task realization resources by using the resource cleaning module;
determining a frequency value corresponding to the task realization resource by using the resource frequency statistical module;
determining each resource value corresponding to the task realization resource by using the different source resource processing module;
determining each modal resource value corresponding to the task realization resource by using the different modal resource processing module;
determining each dimension value corresponding to the task realization resources by using the different dimension resource processing module;
compensating the task implementation resources by using the compensation module;
determining a reference value of the task realization resource by using the value calculation module;
correcting the reference value of the task realization resource by using the resource value correction module;
performing task planning on the task realization resources by using the fusion planning module and combining the processing results of the modules to obtain the task planning resources;
the compensation module comprises a data direction information compensation submodule and/or an information direction data compensation submodule;
correspondingly, the mission planning unit is specifically configured to:
performing information compensation processing on the task implementation resource by using the data direction information compensation submodule to obtain the task implementation resource after information compensation;
and/or, performing data compensation processing on the task implementation resource by using the information to a data compensation submodule to obtain the task implementation resource after data compensation;
the value calculation module comprises an information length value calculation submodule and/or an information breadth value calculation submodule;
correspondingly, the mission planning unit is specifically configured to:
determining the information length value corresponding to the task realization resource by using the information length value calculation submodule;
and/or determining the information breadth value corresponding to the task realization resource by using the information breadth value calculation submodule.
6. A task processing device across data, information, knowledge modalities and dimensions, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method of task processing across data, information, knowledge modalities and dimensions as claimed in any one of claims 1 to 4 when executing the computer program.
7. A readable storage medium, having stored thereon a computer program which, when executed by a processor, carries out the steps of the method of task processing across data, information, knowledge modalities and dimensions as claimed in any one of claims 1 to 4.
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