CN110782150B - Natural resource information collection system and method based on crowd sensing - Google Patents
Natural resource information collection system and method based on crowd sensing Download PDFInfo
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- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
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Abstract
The invention provides a natural resource information collection system and a method based on crowd sensing, wherein the system comprises the following steps: the data collection task publishing and distributing module is used for publishing the data collection task and selecting a user to complete the published task; and the data screening module is used for receiving and calculating the data quality acquired by the user, returning the calculated data quality to the data collection task publishing and distributing module so as to update the reliability of the user, and calculating to obtain high-quality data or estimating to obtain a task true value. The invention utilizes a large number of intelligent mobile terminal users to complete large-scale data acquisition work in natural resource supervision, and designs different methods for screening out high-quality data according to different types of data, thereby realizing low-cost and high-quality large-scale data acquisition and enabling the whole-course supervision and efficient configuration of natural resources to be possible.
Description
Technical Field
The invention relates to the field of natural resource supervision, in particular to a natural resource information collection system and method based on crowd sensing.
Background
The natural resource supervision has the characteristics of complex supervision objects, heavy tasks, wide range and the like. In order to realize the whole-course supervision and efficient configuration of natural resources to meet the increasingly vigorous social service demands, a modern information means is required to be adopted to construct a natural resource core database, a large amount of natural resource data is tracked and collected in real time, and integrated, analyzed and mined, so that the formed service plays an important role in the fields of natural resource situation analysis, macroscopic configuration management and the like. However, the traditional natural resource data is mainly acquired by three modes of satellite images, professional personnel on-site data acquisition and check and professional equipment acquisition at fixed places. However, the granularity of the data that can be obtained from the satellite's speckle pattern is too coarse and the data that can be obtained is limited. The data acquisition by using professional personnel or installation professional equipment requires a great deal of manpower and material resource cost, and is difficult to completely cover all objects needing supervision, not to mention the whole-process real-time data acquisition and monitoring of the supervised objects. To solve the above problems, new methods and technical means must be devised.
The crowd sensing technology is an emerging network technology in recent years, and can help us to construct a large-scale and low-cost real-time data acquisition system.
Disclosure of Invention
The aim of the invention is achieved by the following technical scheme.
In order to solve the defects of the prior scheme, the invention introduces a crowd sensing technology into a natural resource information collection process, designs a natural resource information collection system based on crowd sensing, and is used for collecting and analyzing natural resources in a low-cost and large-scale manner, thereby realizing the whole-course real-time supervision and efficient configuration of the natural resources.
The invention designs a natural resource information collection system based on crowd sensing, which comprises a cloud platform, a task publisher and a plurality of task participants. In practical application, the cloud platform and the task publisher belong to the homeland resource management department, and the task participants are common mobile phones or other intelligent mobile terminal users (hereinafter referred to as users) interested in the data collection task. The designed system mainly comprises a data collection task publishing and distributing module and a data screening module.
The data collection task publishing and distributing module is mainly responsible for publishing data collection tasks and selecting proper task participants (hereinafter referred to as users) to complete the published tasks. When the data acquisition task needs to be completed, the specific implementation steps of the module are as follows:
s101: the task publisher publishes one or a group of data acquisition tasks on the cloud platform and adds the published tasks into a task buffer to be completed. Wherein the published tasks contain detailed descriptions and requirements of the tasks.
S102: and the user reads the task description and submits the interested task set, the consideration required for completing each task and the deadline capable of participating in the task to the cloud platform. With b i，j Representing the reward required by user i to complete task j, using t i，j Indicating that user i has reached time t from the current time i，j And may participate in completing task j during this time period in between.
S103: after receiving the interested task set sent by the user, the cloud platform adds the user into the current user buffer areas of all the interested tasks. The quality of the data ultimately submitted varies due to the expertise of the users involved in the task and the degree of care taken to complete the task. In order to ensure the reliability of the collected data, the cloud platform calculates and maintains the reliability of all users participating in the task according to the quality of the task completed by the previous users. The higher the reliability of the user, the greater the probability of submitting high quality data. With the symbol 0 < r i < 1 to indicate the reliability of user i to complete the task.
S104: and the cloud platform completes task allocation once at intervals of a given time interval T according to the information in the current task buffer to be completed and the current user buffer. The quality of the data ultimately submitted varies due to the expertise of the users involved in the task and the degree of care taken to complete the task. In order to ensure the reliability of the collected data, the cloud platform calculates and maintains the reliability of all users participating in the task according to the quality of the task completed by the previous users. The higher the reliability of the user, the greater the probability of submitting high quality data. And the cloud platform comprehensively considers rewards required by different users for completing tasks and the corresponding reliability to make task allocation decisions when the tasks are allocated.
S105: when the user distributed to the task completes the data acquisition task, the acquired data is submitted to the cloud platform, and the cloud platform sends the acquired data to the data screening module for data quality judgment.
S106: and the cloud platform receives the data quality calculated by the data screening module.
S107: if the data screening module determines that the data quality of the task j submitted by the user i is higher than the given threshold value Q, executing S108; otherwise, S109 is performed.
S108: the cloud platform receives payment amount b submitted by user i i，j And (5) completing payment.
S109: and updating the reliability of the user i according to the quality of the user i completing the task j at the time, and deleting the user i from the task user buffer for executing the task j. Wherein the updated user reliability r i ＝α ri +(1-α)q i，j Wherein alpha is more than or equal to 0 and less than or equal to 1 is a constant, and q is more than or equal to 0 i，j And the data quality of the task j submitted by the user i is less than or equal to 1.
Preferably, the task allocation specific implementation steps of each time interval described in step S104 are as follows:
s201: and sequencing the tasks in the task buffer to be distributed according to the release time from the early to the late.
S202: traversing the ordered task sequence in turn. Here we denote the ordering of the traversed tasks in the task sequence by j (initial value 1).
S203: assume that the task currently traversed is task j. The user in the current user buffer of the task j is subjected to task weight w i，j Ordering from big to small. Wherein the weight w of user i relative to task j i，j ＝r i /b i，j 。
S204: if the current user buffer of task j is not empty, executing S205; otherwise, S208 is performed.
S205: assigning task j to w i，j User i with the largest value;
s206: and adding the user i into the task executing user buffer of the task j.
S207: user i is deleted from the current user buffers for all tasks.
S208: determine whether all tasks have been traversed? If yes, executing S209; otherwise, j=j+1 is set, and the next task is continuously traversed, and the process returns to S202.
S209: the task allocation ends for this time interval.
The data screening module is used for judging the quality of data acquired by a user and screening out high-quality data for subsequent analysis and processing. The quality judgment and screening methods of the data to be collected are different according to the types of the collected data. The designed system mainly contains two different types of data. The first type of data is data submitted by a single user, and the quality of the data and whether the data meets the task requirement can be directly judged; the second type is that the platform needs to estimate the true value of a single task based on data submitted by multiple users. For the second class of data, the platform will assign k users to the corresponding tasks. When the data screening module receives the data of the task j submitted by the user i, the specific implementation steps of the module are as follows:
s301: determine if the submitted data belongs to the first class of data? If yes, executing S302; otherwise, S306 is performed.
S302: the platform calculates the quality of the data, and uses 0 < q i.j < 1.
S303: judgment q i.j Is greater than a given threshold Q? If yes, executing S304; otherwise, S305 is performed. Since the present invention is designed for a large-scale data collection system and does not involve subsequent data analysis and processing modules, the present invention does not provide specific steps for subsequent data processing. The data analysis and processing portion should be based on the particular service to be formed and there is no general approach.
S304: submitting the data to a data analysis and processing module and deleting task j from the task buffer to be allocated.
S305: will q i.j And S311 is executed by sending the data collection task issuing and distributing module.
S306: determine if task j has collected data submitted by k different users? If yes, executing S307; otherwise, S311 is performed.
S307: and removing outliers in the k data by using an outlier detection method, and setting the data quality of the outliers to be 0.
S308: and calculating the median value of the residual data as an estimated true value of the task j, and sending the estimated true value to a subsequent data analysis and processing module for forming corresponding service.
S309: marking the normalized deviation value between the non-outlier data i and the task j estimation truth value as E i，j . Set q i.j ＝1-E i，j 。
S310: will q i.j And the task j is deleted from the task buffer area to be distributed.
S311: the data processing ends.
Based on the technical scheme, the embodiment of the invention has the advantages that: on the basis of no need of full-time data acquisition personnel or installation of special data acquisition equipment, a large number of intelligent mobile terminal users are utilized to complete large-scale data acquisition work in natural resource supervision, different methods are designed for different types of data to screen out high-quality data, so that low-cost and high-quality large-scale data acquisition is realized, and whole-course supervision and efficient configuration of natural resources are possible.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 is a schematic diagram of the system architecture of the present invention.
FIG. 2 is a particular flow chart of data collection task publishing and distributing.
Fig. 3 is a specific flowchart of task allocation in each time interval.
Fig. 4 is a specific flow chart of data screening.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The structural schematic diagram of the natural resource information collection system based on crowd sensing designed by the invention is shown in figure 1, and is divided into a data collection task publishing and distributing module and a data screening module. The input of the data collection task release and distribution is a data collection task set to be completed and a randomly arrived user set, and the release of the data collection task and task distribution work are mainly completed. After the user completes the data collection task distributed by the data collection task distribution and distribution module, the collected data is sent to the data screening module, and the quality of the submitted data is calculated by the data screening module. And the data screening module returns the calculated data quality to the data collection task publishing and distributing module for updating the reliability of the user, and sends the obtained high-quality data or the estimated task true value to the subsequent data analysis and processing module for further forming corresponding natural resource supervision service. Since the present invention is designed for a large-scale data collection system and does not involve subsequent data analysis and processing modules, the present invention does not provide specific steps for subsequent data processing. The data analysis and processing portion should be based on the particular service to be formed and there is no general approach.
The flow chart of the designed data acquisition task release and task distribution module is shown in fig. 2, and the specific implementation steps are as follows:
s101: the task publisher publishes one or a group of data acquisition tasks on the cloud platform and adds the published tasks into a task buffer to be completed.
S102: and the user reads the task description and submits the interested task set, the consideration required for completing each task and the deadline capable of participating in the task to the cloud platform.
S103: after receiving the interested task set sent by the user, the cloud platform adds the user into the current user buffer areas of all the interested tasks.
S104: and the cloud platform completes task allocation once at intervals of a given time interval T according to the information in the current task buffer to be completed and the current user buffer.
S105: when the user distributed to the task completes the data acquisition task, the acquired data is submitted to the cloud platform, and the cloud platform sends the acquired data to the data screening module for data quality judgment.
S106: and the cloud platform receives the data quality calculated by the data screening module.
S107: if the data screening module determines that the data quality of the task j submitted by the user i is higher than the given threshold value Q, executing S108; otherwise, S109 is performed.
S108: the cloud platform receives payment amount b submitted by user i i，j And (5) completing payment.
S109: and updating the reliability of the user i according to the quality of the user i completing the task j at the time, and deleting the user i from the task user buffer for executing the task j.
Preferably, the flowchart of task allocation per time interval in step S104 is shown in fig. 3, and the specific implementation steps are as follows:
s201: and sequencing the tasks in the task buffer to be distributed according to the release time from the early to the late.
S202: traversing the ordered task sequence in turn. Here we denote the ordering of the traversed task in the task sequence by j (initial value set to 1).
S203: the user in the current user buffer of the task j is subjected to task weight w i，j Ordering from big to small.
S204: if the current user buffer of task j is not empty, executing S205; otherwise, S208 is performed.
S205: assigning task j to w i，j User i with the largest value;
s206: and adding the user i into the task executing user buffer of the task j.
S207: user i is deleted from the current user buffers for all tasks.
S208: determine whether all tasks have been traversed? If yes, executing S209; otherwise, j=j+1 is set, and the next task is continuously traversed, and the process returns to S202.
S209: the task allocation ends for this time interval.
The flow chart of the data screening module is shown in fig. 4, and the specific implementation steps are as follows:
s301: determine if the submitted data belongs to the first class of data? If yes, executing S302; otherwise, S306 is performed.
S302: the cloud platform calculates the quality of the data, and uses 0 < q i.j < 1.
S303: judgment q i.j Is greater than a given threshold Q? If yes, executing S304; otherwise, S305 is performed.
S304: submitting the data to an analysis and processing module and deleting task j from the task buffer to be allocated.
S305: will q i.j And S311 is executed by sending the data collection task issuing and distributing module.
S306: determine if task j has collected data submitted by k different users? If yes, executing S307; otherwise, S311 is performed.
S307: and removing outliers in the k data by using an outlier detection method, and setting the data quality of the outliers to be 0.
S308: and calculating the median value of the residual data as an estimated true value of the task j, and sending the estimated true value to a subsequent data analysis and processing module for forming corresponding service.
S309: marking the normalized deviation value between the non-outlier data i and the task j estimation truth value as E i，j . Set q i.j ＝1-E i，j 。
S310: will q i.j And the task j is deleted from the task buffer area to be distributed.
S311: the data processing ends.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in 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)
1. A natural resource information collection method based on crowd sensing is characterized by comprising the following steps:
issuing a data acquisition task and selecting a user to finish the issued task;
receiving and calculating the data quality acquired by a user, returning the calculated data quality to update the reliability of the user, and calculating to obtain high-quality data or estimating to obtain a task true value;
the issuing of the data acquisition task and the selection of the user to complete the issued task comprises the following steps:
s101: the task publisher publishes one or a group of data acquisition tasks on the cloud platform, and adds the published tasks into a task buffer area to be completed;
s102: the user reads the task description and submits the interested task set, the consideration required for completing each task and the deadline capable of participating in the task to the cloud platform;
s103: after receiving the interested task set sent by the user, the cloud platform adds the user into the current user buffer areas of all the interested tasks;
s104: the cloud platform completes task allocation once at intervals of a given time according to the information in the current task buffer area to be completed and the current user buffer area;
s105: when a user distributed to a task completes a data acquisition task, the acquired data is submitted to a cloud platform, and the cloud platform sends the acquired data to a data screening module to judge the data quality so as to obtain the data quality;
s106: the cloud platform receives the data quality obtained by calculation of the data screening module;
s107: if the data filtering module determines that the data quality of the task submitted by the user is higher than the given threshold value Q, executing S108; otherwise, S109 is executed;
s108: the cloud platform finishes payment according to the payment amount submitted by the user;
s109: updating the reliability of the user according to the quality of the task completed by the user at the time, and deleting the user from a task executing user buffer zone of the task;
the updated user reliability r i ＝αr i +(1-α)q i，j Wherein alpha is more than or equal to 0 and less than or equal to 1 is a constant, and q is more than or equal to 0 i，j Less than or equal to 1 is the data quality of the task j submitted by the user i at this time;
the specific implementation steps of the task allocation of each time interval are as follows:
s201: sequencing tasks in a task buffer area to be distributed according to release time from the morning to the evening;
s202: traversing the task sequence in sequence, and using j with an initial value of 1 to represent the sequence of the traversed tasks in the task sequence;
s203: the user in the current user buffer of the task j is subjected to task weight w i,j Ordering from big to small;
s204: if the current user buffer of task j is not empty, executing S205; otherwise, executing S208;
s205: assigning task j to w i,j User i with the largest value;
s206: adding a user i into an execution task user buffer of a task j;
s207: deleting the user i from the current user buffer areas of all tasks;
s208: determine whether all tasks have been traversed? If yes, executing S209; otherwise, setting j=j+1, continuing to traverse the next task, and returning to S202;
s209: the task allocation of the time interval is ended;
the data collected by the user comprises two different types of data, and the first type of data is data submitted by a single user, so that the quality of the data can be directly judged and whether the data meets the task requirement or not; the second category is that the true value of a single task can be estimated according to data submitted by a plurality of users;
the receiving and calculating the data quality collected by the user, returning the calculated data quality to update the reliability of the user, and calculating to obtain high-quality data or estimating to obtain a task true value, including:
s301: determine if the submitted data belongs to the first class of data? If yes, executing S302; otherwise, executing S306;
s302: the platform calculates the quality of the data, and uses 0 < q i.j < 1;
s303: judgment q i.j Is greater than a given threshold Q? If yes, executing S304; otherwise, S305 is performed;
s304: submitting the data to a data analysis and processing module and deleting the task j from the task buffer area to be allocated;
s305: will q i.j The data is sent to a data collection task publishing and distributing module, and S311 is executed;
s306: determine if task j has collected data submitted by k different users? If yes, executing S307; otherwise, S311 is performed;
s307: removing outliers in the k data by using an outlier detection method, and setting the data quality of the outliers to be 0;
s308: calculating the median value of the residual data as an estimated true value of the task j, and sending the estimated true value to a data analysis and processing module;
s309: marking the normalized deviation value between the non-outlier data i and the task j estimation truth value as E i,j ；
Set q i.j ＝1-E i,j ；
S310: will q i.j The task j is deleted from a task buffer area to be distributed;
s311: the processing of the data ends.
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