CN112543420B

CN112543420B - Task processing method, device and server

Info

Publication number: CN112543420B
Application number: CN202011212440.5A
Authority: CN
Inventors: 童海; 徐为恺; 杨杨; 江旻
Original assignee: WeBank Co Ltd
Current assignee: WeBank Co Ltd
Priority date: 2020-11-03
Filing date: 2020-11-03
Publication date: 2024-04-16
Anticipated expiration: 2040-11-03
Also published as: CN112543420A

Abstract

The invention discloses a task processing method, a task processing device and a server. The task processing method comprises the following steps: issuing a first task to each of the at least one second node device based on the first parameter of each of the at least one first node device; determining aggregate data corresponding to the first task based on the first data reported by each second node device in the at least one second node device; the first node equipment characterizes node equipment meeting first set constraint conditions of the first task in the mobile intelligent group sensing network; the first parameter characterizes the quality of data acquired by the node equipment; the second node equipment characterizes first node equipment of which the first parameter meets the set data quality condition; the aggregation data corresponding to the first task are obtained by aggregation of the first data meeting the second set constraint condition of the first task.

Description

Task processing method, device and server

Technical Field

The present invention relates to the technical field of financial science (Fintech), and in particular, to a task processing method, device and server.

Background

With the development of computer technology, more and more technologies are applied in the financial field, and the traditional financial industry is gradually changed into financial technology, however, the financial technology also puts higher demands on the technology due to the requirements of safety and real-time performance of the financial industry. In the field of financial science and technology, the mobile crowd sensing (Mobile Crowd Sensing) system takes mobile equipment carried by a common user as a sensing unit, and forms a crowd sensing network through network communication, so that sensing task distribution and sensing data collection are realized, and large-scale sensing tasks are completed.

In the related art, due to the influence of various factors, the quality of the perceived data collected by the mobile devices corresponding to the task participants is uneven, so that the perceived data obtained by aggregation based on the perceived data collected by all the mobile devices is inaccurate.

Disclosure of Invention

In view of this, the embodiments of the present invention are expected to provide a task processing method, device and server, so as to solve the technical problem that the perception data obtained by fusing the mobile crowd sensing systems in the related art is inaccurate.

In order to achieve the above purpose, the technical scheme of the invention is realized as follows:

the embodiment of the invention provides a task processing method, which comprises the following steps:

Issuing a first task to each of the at least one second node device based on the first parameter of each of the at least one first node device;

determining aggregate data corresponding to the first task based on the first data reported by each second node device in the at least one second node device; wherein,

the first node equipment represents node equipment meeting first set constraint conditions of the first task in the mobile intelligent group sensing network;

the first parameter characterizes the quality of data acquired by the node equipment;

the second node equipment characterizes first node equipment of which the first parameter meets the set data quality condition;

the aggregation data corresponding to the first task are obtained by aggregation of the first data meeting the second set constraint condition of the first task.

In the above scheme, the method further comprises:

a first parameter of each node device in the mobile intelligent group-aware network is determined.

In the above scheme, the method further comprises:

updating a first parameter of each second node device in the at least one second node device based on the score corresponding to the first task; wherein,

And the score corresponding to the first task is obtained after the aggregate data corresponding to the first task is determined.

In the above solution, the determining the first parameter of each node device in the mobile intelligent group-aware network includes:

determining an index value of each node device on each of the at least one set evaluation index;

and updating the first parameter of each node device in the mobile intelligent group sensing network based on the set corresponding relation between the first parameter and the index value.

In the above scheme, the set evaluation index includes at least one of the following:

position information of the node device;

the density of terminal equipment corresponding to the coverage area corresponding to the node equipment;

task completion rate of the node device.

In the above solution, when updating the first parameter of each second node device in the at least one second node device based on the score corresponding to the first task, the method includes:

determining a deviation value corresponding to second node equipment based on first data reported by the second node equipment, aggregate data corresponding to the first task and scores corresponding to the first task;

and updating the first parameter of the second node equipment based on the deviation value corresponding to the second node equipment.

In the above solution, the updating the first parameter of the second node device based on the deviation value corresponding to the second node device includes:

determining a first parameter calculation mode corresponding to the second node equipment based on the deviation value corresponding to the second node equipment;

and updating the first parameter of the second node equipment based on the first parameter calculation mode corresponding to the second node equipment.

In the above scheme, when determining the first parameter calculation mode corresponding to the second node device for updating the second node device based on the deviation value corresponding to the second node device, the method further includes one of the following steps:

determining a first parameter calculation mode corresponding to the second node equipment based on the median and the deviation value corresponding to the second node equipment;

determining a first parameter calculation mode corresponding to the second node equipment based on the set adjustment parameters, the median and the deviation value corresponding to the second node equipment; wherein,

and the median is determined based on the deviation values corresponding to all the second node devices.

In the above solution, the first parameter calculation mode includes one of the following:

calculating a new first parameter based on the current first parameter, the deviation value and the set first correction coefficient;

The first parameter remains unchanged;

and calculating a new first parameter based on the current first parameter, the deviation value, the median and the set second correction coefficient.

In the above scheme, the node device includes one of the following:

a server for mobile edge computation;

a terminal device for performing the first task.

The embodiment of the invention provides a task processing device, which comprises:

the task allocation unit is used for issuing a first task to each second node device in the at least one second node device based on the first parameter of each first node device in the at least one first node device;

an aggregation unit, configured to determine, based on first data reported by each second node device in the at least one second node device, aggregated data corresponding to the first task; wherein,

The embodiment of the invention also provides a server, which comprises: a processor and a memory for storing a computer program capable of running on the processor,

wherein the processor is configured to execute the steps of any one of the task processing methods described above when running the computer program.

The embodiment of the invention also provides a storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of any of the task processing methods described above.

In the embodiment of the invention, the server issues the first task to each second node device in at least one second node device based on the first parameter of each first node device in at least one first node device, and determines the aggregate data corresponding to the first task based on the first data reported by each second node device in at least one second node device. Wherein the first node device characterizes a node device in the mobile intelligent group-aware network that satisfies a first set constraint condition of the first task. Because the first parameter characterizes the quality of the data collected by the node equipment, the second node equipment is first node equipment of which the first parameter meets the set data quality condition, and the quality of the data collected by the second node equipment corresponding to the first parameter which meets the set data quality condition characterizes the set requirement, the server issues the first task to the second node equipment, the first data of which the data quality reported by the second node equipment meets the set requirement can be obtained, and the accuracy of the aggregated data corresponding to the first task obtained based on the obtained first data is further improved.

Drawings

FIG. 1 is a schematic diagram of a mobile crowd sensing system according to the related art;

fig. 2 is a schematic diagram of a mobile crowd sensing system according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of an implementation flow of a task processing method according to an embodiment of the present invention;

fig. 4 is a schematic implementation flow chart of determining, by a cloud server, a second node device in the task processing method provided by the embodiment of the present invention;

FIG. 5 is a schematic diagram of a task processing method according to another embodiment of the present invention;

FIG. 6 is a schematic diagram of an implementation flow of updating a first parameter in a task processing method according to an embodiment of the present invention;

fig. 7 is a schematic implementation flow chart of updating a first parameter in the task processing method according to the embodiment of the present invention;

fig. 8 is a graph of a first parameter when a second node device provided by an embodiment of the present invention continuously reports trusted data;

fig. 9 is a graph of a first parameter when a second node device provided by an embodiment of the present invention continuously reports untrusted data;

FIG. 10 is a schematic diagram of an implementation flow of updating a first parameter in a task processing method according to an embodiment of the present invention;

FIG. 11 is a schematic diagram of a task processing device according to an embodiment of the present invention;

Fig. 12 is a schematic diagram of a hardware composition structure of a server according to an embodiment of the present invention.

Detailed Description

Before describing the embodiment of the invention, the working principle of the mobile crowd sensing system in the related technology is described.

Referring to fig. 1, fig. 1 is a schematic diagram of a mobile crowd sensing system provided in the related art. As shown in fig. 1, the mobile crowd sensing system in the related art includes: the first terminal device 11 corresponding to the at least one task publisher, the cloud server 12 and the second terminal device 13 corresponding to the at least one task participant. The working principle of the mobile crowd sensing system is as follows:

the first terminal device 11 communicates with the cloud server and issues a perception task to the cloud server 12;

the cloud server 12 broadcasts a perception task to the second terminal device 13 when receiving the perception task issued by the first terminal device 11;

the second terminal device 13 executes the perception task selected by the task participant from the received perception tasks under the condition that the perception task broadcasted by the cloud server is received, and reports perception data corresponding to the perception task to the server 12;

the cloud server 12 determines, when receiving the sensing data corresponding to the sensing task reported by the second terminal device 13, from the sensing data corresponding to the sensing task reported by all the second terminal devices 13, the sensing data satisfying the constraint conditions corresponding to the sensing task based on the constraint conditions (constraint conditions such as task start time, completion time and position information) corresponding to the sensing task, and performs aggregation processing on the determined sensing data to obtain an aggregation result; the aggregation processing of the determined sensing data may be calculating a mean value of all the determined sensing data.

When determining the aggregation result corresponding to the sensing task, the cloud server 12 sends the aggregation result data corresponding to the sensing task to the first terminal device 11.

In the related art, due to the influence of various factors, for example, different equipment configuration parameters of the second terminal equipment, the quality of the perceived data reported to the cloud server by the second terminal equipment is different, so that the aggregation result obtained by the cloud server based on the perceived data reported by the second terminal equipment is inaccurate.

In order to solve the above technical problems, an embodiment of the present invention provides a mobile crowd sensing system, which adds a node device for mobile edge computing (MEC, mobile Edge Computing) on the basis of the mobile crowd sensing system corresponding to fig. 1. In the mobile crowd sensing system of the invention, the server can issue a first task to each second node device in at least one second node device based on the first parameter of each first node device in at least one first node device, and determine the aggregate data corresponding to the first task based on the first data reported by each second node device in at least one second node device. Wherein the first node device characterizes a node device in the mobile intelligent group-aware network that satisfies a first set constraint condition of the first task. When the server is a cloud server, the first node equipment and the second node equipment are both node equipment for mobile edge calculation; when the server is a node device for mobile edge calculation, the first node device and the second node device are both terminal devices used by task participants.

The first parameter characterizes the quality of the data acquired by the node equipment, the second node equipment is first node equipment with the first parameter meeting the set data quality condition, and the first parameter meets the set data quality condition, so that the server issues a sensing task to the second node equipment, the sensing data with the quality meeting the set requirement, which is reported by the second node equipment, can be acquired, and the accuracy of the aggregated data corresponding to the sensing task, which is obtained based on the acquired sensing data, is improved.

The technical scheme of the invention is further elaborated below by referring to the drawings in the specification and the specific embodiments.

Referring to fig. 2, fig. 2 is a schematic diagram of a mobile crowd sensing system architecture according to an embodiment of the invention. The mobile crowd sensing system as shown in fig. 2 comprises at least one terminal device 21 for issuing a first task, a cloud server 22, at least one node device 23 for mobile edge computing and at least one terminal device 24 for performing the first task. The node equipment for calculating the mobile edge is a server; the first task characterizes a perception task. The first task may be a task related to intelligent transportation, public safety, social recommendation, environmental monitoring, city public management, etc. The terminal device may be a mobile phone, a tablet computer, a wearable device, etc.

The working principle of the mobile crowd sensing system is as follows:

the terminal device 21 communicates with the cloud server 22, and issues a first task to the cloud server.

The cloud server 22 determines, on the basis of a first parameter of each first node device 23 of the at least one first node device 23, a MEC node device subset in case of receiving the first task issued by the terminal device 21; the first task is issued to each of the at least one second node device 23 based on the MEC node device subset. Wherein the MEC node equipment subset comprises information about at least one second node equipment 23; the first node device 23 is a node device 23 in the mobile intelligent group-aware network that satisfies a first set constraint condition of the first task; the second node device 23 is a first node device 23 whose first parameter fulfils the set data quality condition.

The second node device 23, upon receiving the first task issued by the cloud server 22, issues the first task to each of the at least one second terminal device 24 based on the first parameter of each of the at least one first terminal device 24 for performing the first task. Wherein the second node device 23 may issue the first task to the second terminal device 24 by sending a broadcast message; the first terminal device 24 is a terminal device 24 in the mobile intelligent group-aware network that satisfies a first set constraint condition of the first task; the second terminal device 24 is a first terminal device 24 whose first parameter fulfils the set data quality condition.

The second terminal device 24 executes the first task under the condition of receiving the first task issued by the second node device 23, and reports the first data corresponding to the first task acquired in real time to the second node device 23; wherein the first data is perceptual data.

The second node device 23 determines, based on all the received first data, first aggregate data corresponding to the first task when receiving all the first data corresponding to the first task reported by the second terminal device 24, and reports the first aggregate data corresponding to the first task to the cloud server 22. Based on all the received first data, the method for determining the first aggregate data corresponding to the first task may be: determining first data meeting second set constraint conditions of the first task from all received first data, performing aggregation processing on the first data meeting the second set constraint conditions of the first task according to a set data aggregation mode to obtain first aggregation data corresponding to the first task, and reporting the first aggregation data corresponding to the first task to the cloud server 22; the set data aggregation mode can represent the mean value of the calculated sensing data and can represent the weighted mean value of the calculated sensing data.

The cloud server 22 determines second aggregate data corresponding to the first task based on all the first aggregate data when receiving all the first aggregate data reported by the second node devices 23, and sends the second aggregate data corresponding to the first task to the terminal device 21. The cloud server 22 may determine, from all the received first aggregate data, first aggregate data that meets a second set constraint condition of the first task, and aggregate the first aggregate data that meets the second set constraint condition of the first task according to a set data aggregation manner, to obtain second aggregate data corresponding to the first task.

The terminal device 21, when receiving the second aggregate data corresponding to the first task sent by the cloud server 22, sends a score corresponding to the first task to the cloud server; the score of the first task may be input by the task issuer, or may be determined by the terminal device 21 based on the expected data of the first task and the second aggregate data, and the expected data of the first task may be input by the task issuer.

The cloud server 22 updates the first parameter of each second node device 23 based on the score corresponding to the first task when receiving the score corresponding to the first task sent by the terminal device 21, and issues the score corresponding to the first task to each second node device 23;

The second node device 23, upon receiving the score corresponding to the first task transmitted by the cloud server 22, updates the first parameter of each second terminal device 24 for executing the first task based on the score corresponding to the first task.

Having briefly introduced the working principle of the mobile crowd sensing system, the implementation process of the task processing method of the embodiment of the present invention is described in detail below with the cloud server 22 or the node device 23 for mobile edge computing as the execution subject.

Referring to fig. 3, fig. 3 is a schematic diagram illustrating an implementation flow of a task processing method according to an embodiment of the present invention. The execution body of the task processing method provided by the embodiment of the invention is a server, and the server is a cloud server 22 in fig. 2 or a node device 23 for mobile edge calculation in fig. 2. When the execution body is the cloud server 22 in fig. 2, the first node device and the second node device in the following embodiments are both node devices 23 for mobile edge calculation. When the execution body is the node device 23 for mobile edge calculation, the first node device and the second node device in the following embodiments are both the terminal device 24 for executing the first task.

As shown in fig. 3, the task processing method includes:

s301: and issuing a first task to each of the at least one second node device based on the first parameter of each of the at least one first node device. Wherein,

the second node device characterizes the first node device whose first parameter satisfies the set data quality condition.

Here, the server determines at least one node device satisfying the first set constraint condition of the first task from the node devices in the mobile intelligent group-aware network based on the first set constraint condition in the attribute information of the first task and based on the related information of all the node devices in the mobile intelligent group-aware network, and obtains at least one first node device. Wherein,

the server stores related information of all node devices in the access mobile intelligent group sensing network in advance, wherein the related information comprises device identification, geographical position information, first parameters and the like, and can also comprise a coverage area. In practical applications, the first parameter may be reputation. The larger the value of the first parameter is, the better the quality of the data collected by the corresponding node equipment is, namely, the higher the credibility of the data is.

The attribute information of the first task includes a task start time, a task end time, a place where the task is executed, a minimum number of people involved, and the like, and may further include a budget and an expected result corresponding to the first task. The budget characterizes resources expected to be allocated to all participants performing the first task. The resource may be a virtual resource in return for performing the first task. The expected result corresponding to the first task may be used to determine a score for the first task. The score for the first task characterizes satisfaction with the aggregate data of the first task.

The first set of constraints may characterize the location at which the task is performed. When the attribute information of the first task does not include a place for executing the task, all node devices in the mobile intelligent group sensing network are determined to be node devices meeting the first set constraint condition of the first task.

The server may determine, under the condition that the at least one first node device is determined, at least one first node device in which the first parameter meets a set data quality condition based on the determined first parameter of each first node device in the at least one first node device, to obtain at least one second node device; and issuing a first task to each second node device in the at least one second node device under the condition that the at least one second node device is determined. Wherein the set data quality condition may characterize that the value of the first parameter is greater than or equal to a set threshold value; the first node devices may also be characterized as selecting a first number in order of the value of the first parameter from greater to lesser, the first number being determined based on a minimum number of participants corresponding to the first task. Here, when the first node device is a node device for mobile edge calculation, the second number of all terminal devices for performing the first task corresponding to the first number of second node devices is greater than or equal to the minimum number of participants corresponding to the first task.

In practical applications, the cloud server may determine at least one second node device for mobile edge computing according to the flow shown in fig. 4. Wherein,

the cloud server judges whether the node equipment meets a first set constraint condition of the first task or not based on the attribute information of the first task. When the judging result represents that the node equipment meets a first set constraint condition of a first task, adding the corresponding node equipment to a candidate MEC node list, wherein the candidate MEC node list is used for storing related information of the first node equipment; and when the judging result indicates that the node equipment does not meet the first set constraint condition of the first task, adding the corresponding node equipment to the undetermined MEC node list. Here, the cloud server may determine whether the node device satisfies the first set constraint condition of the first task based on the first set constraint condition in the attribute information of the first task, and based on the location information and the coverage areas of all the node devices for the mobile edge calculation. The node device satisfying the first set constraint condition of the first task is a first node device.

The cloud server determines at least one second node device based on the first parameter of each first node device in the candidate MEC node list. Judging whether the total number of the terminal devices corresponding to the second node devices reaches the minimum number of participants corresponding to the first task; when the judging result represents that the total number of the terminal devices corresponding to the second node devices is smaller than the minimum number of the participants, continuing to determine at least one second node device; outputting an MEC node list when the judging result represents that the total number of the terminal devices corresponding to the second node device is greater than or equal to the minimum number of the participants; the MEC node list comprises the determined relevant information of all the second node devices.

Here, a third number of second node devices is determined from the candidate MEC node list in order of the first parameter from high to low based on the first parameter of each first node device in the candidate MEC node list and based on the number of terminal devices accessing each first node device. And when the total number of the terminal devices corresponding to all the second node devices in the candidate MEC node list is greater than or equal to the minimum number of participants corresponding to the first task, the second node devices are determined from the candidate MEC node list. When the total number of the terminal devices corresponding to all the first node devices in the candidate MEC node list is smaller than the minimum number of participants, selecting a fourth number of node devices from the pending MEC node list according to the sequence from high to low of the first parameter, so that the total number of the terminal devices for executing the first task is larger than or equal to the minimum number of participants. The terminal device corresponding to the second node device is a terminal device accessed to the second node device, and the number of the terminal devices corresponding to the second node device can be reported to the cloud server by the second node device.

In an embodiment, when the first node device is a terminal device for executing the first task, the server may broadcast a task notification message to at least one first node device when determining at least one first node device, so as to notify a user corresponding to the first node device that the first task to be executed exists currently, so that the user corresponding to the first node device determines whether to participate in the first task based on attribute information of the first task carried by the task notification message. When a user confirms participation in a first task through an interactive interface of first node equipment, triggering the first node equipment to send a confirmation message for representing participation in the first task to a server, and when the server receives the confirmation message sent by the first node equipment, determining at least one first node equipment of which the first parameter meets a set data quality condition based on a first parameter of each first node equipment in at least one first node equipment for sending the confirmation message, so as to obtain at least one second node equipment; and issuing a first task to each second node device in the at least one second node device under the condition that the at least one second node device is determined.

S302: and determining the aggregate data corresponding to the first task based on the first data reported by each second node device in the at least one second node device. The aggregation data corresponding to the first task are obtained by aggregation of the first data meeting the second set constraint condition of the first task.

The server acquires first data corresponding to the first task reported by each second node device, determines first data meeting second set constraint conditions of the first task from all received first data, and performs aggregation processing on the first data meeting the second set constraint conditions of the first task according to a set data aggregation mode to obtain aggregated data corresponding to the first task. Wherein,

the second set constraint is used for the server to screen out valid first data. The second set constraint condition characterizes a task execution period corresponding to the first task and a place for executing the task, wherein the task execution period is determined by a task starting time and a task ending time in the attribute information of the first task.

The set data aggregation mode can be used for representing and calculating the average value of the first data, and can also be used for representing and calculating the weighted average value of the first data.

Note that, when the server is the node device 23 for mobile edge calculation in fig. 2, the second node device is the terminal device 24 for executing the first task, and the second node device executes the first task, collects the first data corresponding to the first task, and reports the first data corresponding to the first task to the node device 23 for mobile edge calculation. The node device 23 for mobile edge calculation determines, when receiving the first data reported by all the second node devices, first data meeting the second set constraint condition of the first task, and performs aggregation processing on the first data meeting the second set constraint condition of the first task according to a set data aggregation mode to obtain first aggregation data corresponding to the first task.

When the server is the cloud server 22 in fig. 2, the second node device is the node device 23 for mobile edge calculation, and the first data reported to the server by the second node device is the first aggregate data corresponding to the first task. Because the first aggregate data is obtained by aggregating the first data in which the first aggregate data meets the second set constraint condition of the first task, the cloud server 22 aggregates all the first aggregate data according to the set data aggregation mode under the condition that all the first aggregate data reported by the second node devices are received, so as to obtain the second aggregate data corresponding to the first task.

And the cloud server sends the second aggregation data corresponding to the first task to the terminal equipment which issues the first task under the condition that the second aggregation data corresponding to the first task is determined.

In the scheme provided by the embodiment of the invention, the server issues the first task to each second node device in at least one second node device based on the first parameter of each first node device in at least one first node device, and determines the aggregate data corresponding to the first task based on the first data reported by each second node device in at least one second node device. Wherein the first node device characterizes a node device in the mobile intelligent group-aware network that satisfies a first set constraint condition of the first task. Because the first parameter characterizes the quality of the data collected by the node equipment, the second node equipment is first node equipment of which the first parameter meets the set data quality condition, and the quality of the data collected by the second node equipment corresponding to the first parameter which meets the set data quality condition characterizes the set requirement, the server issues the first task to the second node equipment, the first data of which the data quality reported by the second node equipment meets the set requirement can be obtained, and the accuracy of the aggregated data corresponding to the first task obtained based on the obtained first data is further improved.

As another embodiment of the present invention, the task processing method further includes:

Here, the server may set a first parameter of a node device that first accesses the mobile intelligent group-aware network to an initial value. The value of the first parameter is any value between 0 and 1. For example, the initial value may be 0.5.

The server may further update the first parameter of each node device based on the related information of the node device and the set first parameter configuration manner. The related information of the node device includes location information, a task completion rate, and the like. The task completion rate characterizes the ratio between the number of completed tasks and the total number of participating historical tasks. The set first parameter configuration mode represents that the set first parameter corresponding to the set urban center area is larger than the set first parameter corresponding to the suburban area; the higher the task completion rate is also characterized, the larger the value of the corresponding set first parameter is.

In order to improve the security of the information, when the node device applies for accessing the mobile intelligent group sensing network for the first time, the server performs identity verification on the node device, and when the identity verification passes, the node device is allowed to access the mobile intelligent group sensing network; when the authentication fails, the node device is not allowed to access the mobile intelligent group sensing network. The method for the server to perform identity verification on the node equipment can be as follows: the method comprises the steps of sending a first verification code to node equipment, comparing whether the first verification code is identical to a second verification code or not when the second verification code sent by the node equipment is received, and characterizing that identity verification passes when the first verification code is identical to the second verification code; and when the first verification code and the second verification code are different, the identity verification is represented to be failed.

In an embodiment, the determining the first parameter of each node device in the mobile intelligent group-aware network includes:

Here, the set first parameter may correspond to an index value of one set evaluation index, or may correspond to index values of at least two set evaluation indexes. That is, when the node device corresponds to at least two setting indexes, the index values of all the setting indexes corresponding to the node device determine a corresponding first parameter.

When determining the index values corresponding to all the evaluation indexes corresponding to the node equipment, the server determines a new first parameter corresponding to the node equipment based on the corresponding relation between the set first parameter and the index value and the index values corresponding to all the evaluation indexes, and updates the first parameter of the node equipment into the new first parameter.

In an embodiment, the set evaluation index includes at least one of the following:

Position information of the node device;

task completion rate of the node device.

When the density of the terminal equipment corresponding to the first coverage area is greater than that of the terminal equipment corresponding to the second coverage area, the set first parameter corresponding to the first coverage area is greater than that of the second coverage area.

In practical application, when the node device is a terminal device for executing the first task, the set evaluation index may be at least one of an active area corresponding to the node device and a task completion rate of the node device.

When the node device is a node device for mobile edge calculation, the set evaluation index includes at least one of:

position information of the node device;

task completion rate of the node device.

According to the scheme, the first parameter of the node equipment is determined based on the set evaluation index, the first parameter can be determined more accurately, and the first node equipment with the quality meeting the set requirement of the collected data can be determined accurately based on the first parameter.

Fig. 5 is a schematic flow chart of an implementation of a task processing method according to another embodiment of the present invention. The embodiment further includes, based on the embodiment corresponding to fig. 3:

s303: and updating the first parameter of each second node device in the at least one second node device based on the score corresponding to the first task. The score corresponding to the first task is obtained after the aggregate data corresponding to the first task is determined.

Here, when determining the second aggregate data corresponding to the first task, the cloud server sends the second aggregate data corresponding to the first task to the terminal device that issues the first task.

And the cloud server acquires the score corresponding to the first task and sends the score corresponding to the first task to the second node equipment for mobile edge calculation. The score corresponding to the first task corresponds to a value interval of 0 to 1.

And under the condition that the score corresponding to the first task is obtained, the cloud server updates the first parameter of the second node equipment for calculating the mobile edge to the score corresponding to the first task.

And the second node equipment for calculating the mobile edge updates the first parameter of the terminal equipment for executing the first task to the score corresponding to the first task under the condition that the score corresponding to the first task is acquired.

The score corresponding to the first task may be that the terminal device that issues the first task sends the score to the cloud server when the score input by the task issuer is acquired.

When the attribute information of the first task includes the expected result corresponding to the first task, because the attribute information of the first task is stored in both the terminal device and the cloud server that issue the first task, the score of the first task may also be determined by the terminal device or the cloud server that issue the first task based on the expected result corresponding to the first task and the second aggregate data corresponding to the first task. Here, the error value may be calculated based on the expected result corresponding to the first task and the second aggregate data corresponding to the first task, and the score corresponding to the calculated error value may be determined based on the correspondence between the set error value and the set score, so as to obtain the score corresponding to the first task.

It should be noted that, the server may allocate resources for each terminal device based on the first parameter corresponding to the terminal device for performing the first task and based on the budget corresponding to the first task. And the sum of the resources allocated to all the terminal devices is equal to the resources represented by the budget corresponding to the first task.

According to the scheme, the first parameter of each second node device in at least one second node device is updated based on the score corresponding to the first task, and the first parameter of the node device can be dynamically updated to improve accuracy of the first parameter.

In the embodiment corresponding to fig. 5, a method for updating the first parameter of the second node device based on the score corresponding to the first task is described, and another implementation method for updating the first parameter based on the score corresponding to the first task is described below. Referring to fig. 6, fig. 6 is a schematic flow chart illustrating an implementation of updating a first parameter in the task processing method according to the embodiment of the present invention. As shown in fig. 6, when updating the first parameter of each second node device in the at least one second node device based on the score corresponding to the first task, the method includes:

s601: and determining a deviation value corresponding to the second node equipment based on the first data reported by the second node equipment, the aggregate data corresponding to the first task and the score corresponding to the first task.

Here, the server calculates a product between the aggregate data corresponding to the first task and the score corresponding to the first task, calculates a difference value between the first data reported by the second node device and the product, and calculates a deviation value corresponding to the second node device based on the difference value.

In practical application, the deviation value corresponding to the second node device may be calculated based on the following formula:

wherein when the server is a node device for mobile edge calculation, the second node device is a second terminal device for performing the first task. At this time, the server determines a deviation value corresponding to the second terminal device based on the first data reported by the second terminal device for executing the first task, the first fusion data corresponding to the first task, and the score corresponding to the first task. d, d _i Representing a deviation value corresponding to the ith second terminal equipment; s is S _i Characterizing first data reported by the ith second terminal equipment; s is S _c Characterizing first fusion data corresponding to the first task determined by the server; g represents the score corresponding to the first task, 0<g<1, a step of; n represents the total number of second terminal devices accessing the server; r is (r) _i And characterizing a first parameter corresponding to the ith second terminal equipment.

When the server is a cloud service, the second node device is a second node device for performing mobile edge computation. At this time, the cloud server determines a deviation value corresponding to the second node device for executing the mobile edge calculation based on the first aggregate data reported by the second node device for executing the mobile edge calculation, the second fusion data corresponding to the first task, and the score corresponding to the first task. And determining second fusion data corresponding to the first task by the cloud server. d, d _i Characterizing an i-th deviation value corresponding to second node equipment for mobile edge calculation; s is S _i Characterizing first fusion data reported by the ith second node equipment for executing mobile edge calculation; s is S _c Representing second fusion data corresponding to the first task determined by the cloud server; g represents the score corresponding to the first task, and n represents the total number of second node devices accessed to the cloud server; r is (r) _i And characterizing the first parameter corresponding to the ith second node equipment.

D is the same as _i The smaller the value of (c), the higher the reliability of the data characterizing the corresponding second node device, and the better the data quality.

In one embodiment, when S _i For two-dimensional data or multidimensional data, e.g. multidimensional vectors, different S _i May not be of the same order of magnitude, and therefore, when S _i For S when the data is two-dimensional data or multidimensional data _i Normalization processing, d _i The expression of (2) is as follows:

wherein S is _ik Representing the first data of the kth dimension reported by the ith second node equipment; [ R ] _k ,L _k ]And representing a numerical value interval where the k-th dimensional data is located, and m represents that the first data is m-dimensional data.

It should be noted that, when the first data reported by the second node device is one-dimensional data, calculating a deviation value corresponding to the second node device based on the above formulas (1-1) and (1-2). And when the first data reported by the second node equipment is two-dimensional data or multidimensional data, calculating a deviation value corresponding to the second node equipment based on the formulas (1-3) and (1-2).

S602: and updating the first parameter of the second node equipment based on the deviation value corresponding to the second node equipment.

Here, when the deviation value of the second node device is zero, the first parameter of the second node device is kept unchanged. When the deviation value of the second node device is not equal to zero, the server can determine the adjustment amplitude corresponding to the second node device based on the set corresponding relation between the deviation value and the adjustment amplitude of the first parameter; and updating the first parameter of the second node equipment based on the current first parameter of the second node equipment and the determined adjustment amplitude.

According to the scheme, the deviation value corresponding to the second node equipment can be accurately determined based on the first data, the aggregate data corresponding to the first task and the score corresponding to the first task, which are reported by the second node equipment, so that the accuracy of the deviation value is improved, the first parameter of the second node equipment is updated based on the deviation value corresponding to the second node equipment, and the accuracy of the first parameter can be improved.

In an embodiment, fig. 7 is a schematic implementation flow chart of updating a first parameter in the task processing method according to the embodiment of the present invention. Referring to fig. 7, the updating the first parameter of the second node device based on the deviation value corresponding to the second node device includes:

S701: and determining a first parameter calculation mode corresponding to the second node equipment based on the deviation value corresponding to the second node equipment.

And the server compares the deviation value corresponding to the second node equipment with zero to obtain a first comparison result, and determines a first parameter calculation mode corresponding to the second node equipment based on the first comparison result. Wherein,

the first parameter calculation mode is preset.

And when the first comparison result indicates that the corresponding deviation value of the second node equipment is equal to zero, the determined first calculation mode indicates that the first parameter is kept unchanged.

And when the first comparison result represents that the corresponding deviation value of the second node equipment is larger than zero, the determined first calculation mode represents that the first parameter is increased.

And when the first comparison result represents that the corresponding deviation value of the second node equipment is smaller than zero, the determined first calculation mode represents that the first parameter is increased.

The first amplification is smaller than the second amplification. The first amplification is an increase in the magnitude of the first parameter when the corresponding bias value of the second node device is greater than zero. The second amplification is the magnitude of the increase in the first parameter when the corresponding deviation value of the second node device is less than zero.

S702: and updating the first parameter of the second node equipment based on the first parameter calculation mode corresponding to the second node equipment.

The server obtains the values of all the calculation parameters included in the first parameter calculation mode based on the first parameter calculation mode corresponding to the second node equipment, substitutes the obtained values of all the calculation parameters into the first parameter calculation mode, calculates new first parameters corresponding to the second node equipment, and updates the first parameters of the second node equipment into the new first parameters.

According to the scheme, the server calculates the new first parameter corresponding to the second node equipment based on the first parameter calculation mode, so that the accuracy of the first parameter can be further improved.

In an embodiment, in order to calculate the new first parameter corresponding to the second device more accurately, when determining the first parameter calculating manner corresponding to the second node device for updating the second node device based on the deviation value corresponding to the second node device, the method further includes one of the following steps:

Here, the median is determined in the following manner: and the server arranges the deviation values corresponding to all the second node devices in order from small to large to obtain a deviation value sequence, and determines the middle deviation value in the deviation value sequence as a median. Wherein when the total number of the offset values in the offset value sequence is an even number, any one of the 2 offset values in the middle may be determined as a median.

When the server is a node device for calculating a mobile edge, the median is determined based on the deviation value corresponding to each of the second terminal devices for performing the first task. When the server is a cloud server, the median is determined based on the deviation value corresponding to the second node device for mobile edge calculation.

In practical application, the server may compare the median with the offset value corresponding to the second node device to obtain a second comparison result, and determine, based on the second comparison result, a first parameter calculation mode corresponding to the second node device.

In practical application, in order to identify a malicious attacker, improve the reliability of the first data reported by the second node device, the server may also calculate a first sum between the set adjustment parameter and the median, compare the deviation value corresponding to the second node device with the first sum to obtain a third comparison result, and determine a first parameter calculation mode corresponding to the second node device based on the third comparison result.

In an embodiment, in order to improve the accuracy of the calculated new first parameter, the first parameter calculation mode includes one of the following:

the first parameter remains unchanged;

In practical application, when the second comparison result indicates that the deviation value corresponding to the second node device is smaller than the median, the determined first parameter calculation mode is characterized: and calculating a new first parameter based on the current first parameter, the deviation value and the set first correction coefficient.

And when the second comparison result represents that the corresponding deviation value of the second node equipment is equal to the median, the determined first parameter calculation mode represents: the first parameter remains unchanged.

When the second comparison result represents that the deviation value corresponding to the second node equipment is larger than the median, the determined first parameter calculation mode represents: and calculating a new first parameter based on the current first parameter, the deviation value, the median and the set second correction coefficient.

Illustratively, the expression of the first parameter calculation mode is:

wherein r is _i ^new Characterizing a new first parameter corresponding to the ith second node equipment; r is (r) _i Characterization of the ithCurrent first parameters corresponding to the second node devices; d, d _i Representing a deviation value corresponding to the ith second node equipment; the gamma represents a set first correction coefficient for correcting the first parameter; d, d _avg The calculated median was characterized.

In practical application, when the third comparison result indicates that the deviation value corresponding to the second node device is smaller than the first sum, the determined first parameter calculation mode is characterized: and calculating a new first parameter based on the current first parameter, the deviation value and the set first correction coefficient.

And when the third comparison result represents the second comparison result and represents the deviation value corresponding to the second node equipment to be equal to the first sum, determining a first parameter calculation mode representation: the first parameter remains unchanged.

When the third comparison result represents the second comparison result and represents the deviation value corresponding to the second node equipment to be larger than the first sum, the determined first parameter calculation mode represents: and calculating a new first parameter based on the current first parameter, the deviation value, the median and the set second correction coefficient.

Illustratively, the expression of the first parameter calculation mode is:

wherein η represents a second correction coefficient set for correcting the first parameter; λ represents a set adjustment parameter for adjusting the median. (d) _avg +λ) characterizes the first sum.

The first correction coefficient γ to be set and the second correction coefficient η to be set are different, and may be set according to actual situations.

Referring to fig. 8 and fig. 9 together, fig. 8 shows a graph of a first parameter when a second node device provided by the embodiment of the present invention continuously reports trusted data; fig. 9 is a graph showing a first parameter when a second node device continuously reports untrusted data. Wherein,

the vertical axis in fig. 8 and 9 characterizes the first parameter. When the deviation value corresponding to the first data is smaller than or equal to the calculated median, characterizing the first data reported by the corresponding second node equipment as trusted data; and when the deviation value corresponding to the first data is larger than the calculated median, characterizing that the first data reported by the corresponding second node equipment is unreliable data. Alternatively, when the deviation value corresponding to the first data is less than or equal to (d _avg +lambda), representing the first data reported by the corresponding second node equipment as trusted data; the first data corresponds to a deviation value greater than (d _avg And +lambda), characterizing the first data reported by the corresponding second node equipment as untrusted data.

Fig. 8 reflects the effect of different gamma on the first parameter as the number of times trusted data is submitted increases. When the second node equipment continuously submits the trusted data, the first parameter of the second node equipment gradually converges to 1; the larger the gamma is, the faster the first parameter converges, so the proper gamma value should be dynamically set according to the actual application scenario. When gamma is larger, the second node equipment can easily obtain a high first parameter, which may cause that malicious node equipment provides certain times of trusted data to rapidly accumulate and promote the first parameter, and then provides false data, so that the first parameter is always larger than or equal to a set threshold value corresponding to the first parameter; however, γ may not be set too small, otherwise, the increasing amplitude of the first parameter of the node device is not obvious, so that the first parameter cannot reach the set threshold corresponding to the first parameter.

Fig. 9 shows that the first parameter gradually converges to 0 as the second node device continuously submits spurious untrusted data, the larger η, the faster the first parameter converges. Similarly, an appropriate value of η should be dynamically set, and when η is larger, the second node device may provide first data with larger deviation due to occasional network failure or other objective reasons, so that the first parameter of the second node device is reduced too much; but η should not be too small, otherwise, the malicious second node device may intermittently submit false untrusted data so that the first parameter remains in a safe range, that is, the first parameter is greater than or equal to a set threshold corresponding to the first parameter.

As an application embodiment of the present invention, fig. 10 shows a schematic implementation flow chart of updating a first parameter in the task processing method provided by the application embodiment of the present invention. Referring to fig. 10, a method of updating a first parameter includes:

s801: and receiving first data corresponding to the first task reported by the second node equipment.

And the server receives first data corresponding to the first task reported by the second node equipment.

When the server is a second node device for mobile edge calculation, the second node device reporting the first data is corresponding to a terminal device for executing the first task, and the first data reported is data acquired by the terminal device when executing the first task.

When the server is a cloud server, the second node device corresponds to the node device for mobile edge calculation, and the reported first data is first aggregation data.

S802: and judging whether the identity of the second node equipment is legal or not.

The server judges whether the identity of the second node equipment is legal or not by carrying out identity verification on the second node equipment. When the identity verification result indicates that the identity verification of the second node equipment fails, the identity verification result indicates that the identity of the second node equipment is illegal, and S803 is executed; and when the identity verification result indicates that the identity verification of the second node equipment passes, the identity verification result indicates that the identity of the second node equipment is legal, and S804 is executed.

S803: and discarding the first data reported by the second node equipment.

S804: and storing the first data reported by the second node equipment.

S805: and judging whether the set cut-off condition is met.

The set cut-off condition may represent the task end time when the first task is reached, or may represent that all the first data reported by the second node devices have been saved.

When the set cutoff condition is satisfied, S806 is performed; when the set cutoff condition is not satisfied, the routine returns to S801.

S806: and determining the aggregation data corresponding to the first task based on all the stored first data.

The implementation process of S806 is described with reference to S302 in the corresponding embodiment of fig. 3, which is not repeated here.

S807: and judging whether the score corresponding to the first task is obtained.

Executing S808 when the judgment result represents that the score corresponding to the first task is not obtained;

and when the judgment result characterization obtains the score corresponding to the first task, executing S809.

S808: the score corresponding to the first task is set to 1.

Here, after S808 is performed, S809 is performed.

S809: and updating the first parameter of the second device corresponding to each stored first data based on the scores corresponding to the first tasks and the aggregate data corresponding to the first tasks.

Here, the server calculates a new first parameter based on the formula (1-4) or the formula (1-5) in the above-described embodiment, and updates the first parameter of the second device to the new first parameter.

According to the scheme, the server can accurately calculate the new first parameter based on the first parameter calculation mode, so that the first parameter of the second node equipment is updated based on the new first parameter, and the accuracy of the first parameter can be improved.

In order to implement the method of the embodiment of the present invention, the embodiment of the present invention further provides a task processing device, which is disposed on a server, as shown in fig. 11, and the task processing device includes:

a task allocation unit 11, configured to issue a first task to each of the at least one second node device based on a first parameter of each of the at least one first node device;

an aggregation unit 12, configured to determine, based on first data reported by each second node device in the at least one second node device, aggregate data corresponding to the first task; wherein,

In an embodiment, the task processing device further includes:

and the first determining unit is used for determining a first parameter of each node device in the mobile intelligent group sensing network.

In an embodiment, the first determining unit is configured to:

position information of the node device;

task completion rate of the node device.

In an embodiment, the task processing device further includes:

a first updating unit, configured to update a first parameter of each second node device in the at least one second node device based on the score corresponding to the first task; wherein,

In an embodiment, the task processing device further includes:

the second determining unit is used for determining a deviation value corresponding to the second node equipment based on the first data reported by the second node equipment, the aggregate data corresponding to the first task and the score corresponding to the first task;

and the second updating unit is used for updating the first parameter of the second node equipment based on the deviation value corresponding to the second node equipment.

In an embodiment, the second updating unit is configured to:

In an embodiment, the second updating unit is further configured to perform one of:

In an embodiment, the first parameter calculation mode includes one of the following:

the first parameter remains unchanged;

In an embodiment, the node device comprises one of:

a server for mobile edge computation;

a terminal device for performing the first task.

In practical application, each unit included in the task processing device may be implemented by a processor in the task processing device, or implemented by the processor and the communication interface together. Of course, the processor needs to execute the program stored in the memory to realize the functions of the program modules.

It should be noted that: in the task processing provided in the above embodiment, only the division of each program module is used for illustration, and in practical application, the processing allocation may be performed by different program modules according to needs, that is, the internal structure of the task processing device is divided into different program modules, so as to complete all or part of the processing described above. In addition, the task processing device and the task processing method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the task processing device and the task processing method are detailed in the method embodiments, which are not described herein again.

Based on the hardware implementation of the program modules, and in order to implement the method of the embodiment of the present invention, the embodiment of the present invention further provides a server. Fig. 12 is a schematic diagram of a hardware composition structure of a server according to an embodiment of the present invention, where, as shown in fig. 12, the server includes:

a communication interface 1 capable of information interaction with other devices such as a server or the like;

and the processor 2 is connected with the communication interface 1 to realize information interaction with other devices and is used for executing the task processing method provided by one or more technical schemes when running the computer program. And the computer program is stored on the memory 3.

Of course, in practice, the various components in the server are coupled together by a bus system 4. It will be appreciated that the bus system 4 is used to enable connected communications between these components. The bus system 4 comprises, in addition to a data bus, a power bus, a control bus and a status signal bus. But for clarity of illustration the various buses are labeled as bus system 4 in fig. 12.

The memory 3 in the embodiment of the present invention is used to store various types of data to support the operation of the server. Examples of such data include: any computer program for operating on a server.

It will be appreciated that the memory 3 may be either volatile memory or nonvolatile memory, and may include both volatile and nonvolatile memory. Wherein the nonvolatile Memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM, programmable Read-Only Memory), erasable programmable Read Only Memory (EPROM, erasable Programmable Read-Only Memory), electrically erasable programmable Read Only Memory (EEPROM, electrically Erasable Programmable Read-Only Memory), magnetic random access Memory (FRAM, ferromagnetic random access Memory), flash Memory (Flash Memory), magnetic surface Memory, optical disk, or compact disk Read Only Memory (CD-ROM, compact Disc Read-Only Memory); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be random access memory (RAM, random Access Memory), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (SRAM, static Random Access Memory), synchronous static random access memory (SSRAM, synchronous Static Random Access Memory), dynamic random access memory (DRAM, dynamic Random Access Memory), synchronous dynamic random access memory (SDRAM, synchronous Dynamic Random Access Memory), double data rate synchronous dynamic random access memory (ddr SDRAM, double Data Rate Synchronous Dynamic Random Access Memory), enhanced synchronous dynamic random access memory (ESDRAM, enhanced Synchronous Dynamic Random Access Memory), synchronous link dynamic random access memory (SLDRAM, sync Link Dynamic Random Access Memory), direct memory bus random access memory (DRRAM, direct Rambus Random Access Memory). The memory 3 described in the embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.

The method disclosed in the above embodiment of the present invention may be applied to the processor 2 or implemented by the processor 2. The processor 2 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 2 or by instructions in the form of software. The processor 2 described above may be a general purpose processor, DSP, or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor 2 may implement or perform the methods, steps and logic blocks disclosed in embodiments of the present invention. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiment of the invention can be directly embodied in the hardware of the decoding processor or can be implemented by combining hardware and software modules in the decoding processor. The software modules may be located in a storage medium in the memory 3 and the processor 2 reads the program in the memory 3 to perform the steps of the method described above in connection with its hardware.

The process corresponding to the multi-core processor in each method of the embodiment of the present invention is implemented when the processor 2 executes the program, and for brevity, will not be described herein.

In an exemplary embodiment, the present invention also provides a storage medium, i.e. a computer storage medium, in particular a computer readable storage medium, for example comprising a memory 3 storing a computer program executable by the processor 2 for performing the steps described in the embodiments corresponding to the foregoing fig. 3 to 7 and 10. The computer readable storage medium may be FRAM, ROM, PROM, EPROM, EEPROM, flash Memory, magnetic surface Memory, optical disk, or CD-ROM.

In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.

The units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated in one processing module, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware associated with program instructions, where the foregoing program may be stored in a computer readable storage medium, and when executed, the program performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.

The technical schemes described in the embodiments of the present invention may be arbitrarily combined without any collision.

The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method of task processing, comprising:

determining an index value of each node device in the mobile intelligent group sensing network on each evaluation index in at least one set evaluation index;

updating the first parameter of each node device in the mobile intelligent group sensing network based on the corresponding relation between the set first parameter and the index value;

issuing a first task to each of at least one second node device based on the first parameter of each of the at least one first node device;

determining aggregate data corresponding to the first task based on the first data reported by each second node device in the at least one second node device;

Updating a first parameter of each second node device in the at least one second node device based on the score corresponding to the first task;

the set evaluation index comprises at least one of position information of the node equipment, density of terminal equipment corresponding to a coverage area corresponding to the node equipment and task completion rate of the node equipment; the first node equipment characterizes node equipment meeting first set constraint conditions of the first task in the mobile intelligent group-aware network; the attribute information of the first task includes a budget characterizing a sum of resources allocated to the at least one second node device; the first parameter characterizes the quality of data acquired by the node equipment, the first parameter is used for indicating resources allocated to each second node equipment in the at least one second node equipment, and the sum of the resources allocated to each second node equipment in the at least one second node equipment is equal to the sum of the resources; the second node equipment characterizes first node equipment of which the first parameter meets the set data quality condition; the aggregation data corresponding to the first task are obtained by aggregation of the first data meeting the second set constraint condition of the first task; and the score corresponding to the first task is obtained after the aggregate data corresponding to the first task is determined.

2. The method of claim 1, wherein the updating the first parameter of each of the at least one second node device based on the score corresponding to the first task comprises:

3. The method of claim 2, wherein updating the first parameter of the second node device based on the corresponding offset value of the second node device comprises:

4. The method of claim 3, wherein when determining the first parameter calculation mode corresponding to the second node device for updating the second node device based on the deviation value corresponding to the second node device, the method further comprises one of:

5. The method of claim 3 or 4, wherein the first parameter calculation means comprises one of:

the first parameter remains unchanged;

6. The method of claim 1, wherein the node device comprises one of:

a server for mobile edge computation;

a terminal device for performing the first task.

7. A task processing device, comprising:

the task allocation unit is used for determining an index value of each node device in the mobile intelligent group sensing network on each evaluation index in at least one set evaluation index; updating the first parameter of each node device in the mobile intelligent group sensing network based on the corresponding relation between the set first parameter and the index value; issuing a first task to each of at least one second node device based on the first parameter of each of the at least one first node device;

An aggregation unit, configured to determine, based on first data reported by each second node device in the at least one second node device, aggregated data corresponding to the first task; updating a first parameter of each second node device in the at least one second node device based on the score corresponding to the first task; the set evaluation index comprises at least one of position information of the node equipment, density of terminal equipment corresponding to a coverage area corresponding to the node equipment and task completion rate of the node equipment; the first node equipment characterizes node equipment meeting first set constraint conditions of the first task in the mobile intelligent group-aware network; the attribute information of the first task includes a budget characterizing a sum of resources allocated to the at least one second node device; the first parameter characterizes the quality of data acquired by the node equipment, the first parameter is used for indicating resources allocated to each second node equipment in the at least one second node equipment, and the sum of the resources allocated to each second node equipment in the at least one second node equipment is equal to the sum of the resources; the second node equipment characterizes first node equipment of which the first parameter meets the set data quality condition; the aggregation data corresponding to the first task are obtained by aggregation of the first data meeting the second set constraint condition of the first task; and the score corresponding to the first task is obtained after the aggregate data corresponding to the first task is determined.

8. A server, comprising: a processor and a memory for storing a computer program capable of running on the processor,

wherein the processor is adapted to perform the steps of the method of any of claims 1 to 6 when the computer program is run.

9. A storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the method according to any of claims 1 to 6.