CN111105103B - Method for constructing space-time big data acquisition model based on crowdsourcing mode - Google Patents
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Abstract
The invention provides a method for constructing a spatio-temporal big data acquisition model based on a crowdsourcing mode, wherein an information issuing module sends initial information to a storage module, and a user side acquires the initial information from the storage module by accessing a crowdsourcing platform; the data classification module sends fragmentation information to a screening module, when the user side feeds back to the crowdsourcing platform, the screening module in the crowdsourcing platform carries out relevance identification on the fragmentation information stored in the crowdsourcing platform and feedback information of the user, and if identification results are consistent, the screening module stores the feedback information into the storage module; the screening module generates characteristic segment information, the screening module sends the characteristic segment information to a priority module of the server, and the priority module carries out priority judgment on the characteristic segment information according to a pre-stored priority evaluation method; the acquisition module calls feedback information from the storage module of the crowdsourcing platform according to a pre-stored calling rule.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method for constructing a space-time big data acquisition model based on a crowdsourcing mode.
Background
With the development of smart mobile devices and wireless networks, the urban life of the society is moving toward the smart life. From smart homes in life to intelligent planning of the whole city, it is very popular to use mobile equipment to collect information as a data source for future planning. However, after finishing the data arrangement, the cost of the client who needs to use the data is very high, which results in very slow and high cost of the whole data transmission chain.
The defects of the prior art are mainly reflected in the sustainability of the whole data acquisition process and the maintenance cost; in the prior art, a main body of data acquisition needs to bear the cost required by crowdsourcing first, then a corresponding amount of data can be acquired, a large amount of manpower and material resources still need to be consumed in the process of sorting and merging the data, and finally the whole reward can be retrieved after the whole data is acquired. The whole data acquisition process is tedious, and a material chain is broken possibly, so that the data acquisition quality and the data acquisition efficiency are difficult to ensure.
Disclosure of Invention
In this summary, concepts in a simplified form are introduced that are further described in the detailed description section. This summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
In order to overcome the problems in the prior art, the invention provides a method for constructing a spatiotemporal big data acquisition model based on a crowdsourcing mode, which comprises the following steps: the system comprises a server, a crowdsourcing platform and a user side;
the server and the crowdsourcing platform are in communication with each other, the server sending initial information to the crowdsourcing platform; the user side communicates with the crowdsourcing platform, and the user side acquires information links from the crowdsourcing platform; meanwhile, the user side sends feedback data to the crowdsourcing platform, and the server acquires the feedback data on the crowdsourcing platform according to the rule of the acquisition module;
the server comprises an information issuing module, a data classification module, the acquisition module and a priority module; the data classification module is used for performing fragmentation classification on initial information; the priority module is used for evaluating the priority level of each fragmentation information;
the crowdsourcing platform comprises a storage module and a screening module; the publishing information module sends initial information to the storage module, and the user side acquires the initial information from the storage module by accessing the crowdsourcing platform; the data classification module sends fragmentation information to the screening module, when the user side feeds back to the crowdsourcing platform, the screening module in the crowdsourcing platform carries out relevance identification on the fragmentation information stored in the crowdsourcing platform and feedback information of the user, and if the identification results are consistent, the screening module stores the feedback information into the storage module; meanwhile, the screening module generates characteristic segment information, the screening module sends the characteristic segment information to the priority module of the server, and the priority module carries out priority judgment on the characteristic segment information according to a pre-stored priority evaluation method; the acquisition module calls feedback information from the storage module of the crowdsourcing platform according to a pre-stored calling rule.
Further, the data classification module performs fragmentation classification on the initial information in a manner that: key word classification, key segment classification and key picture classification; the classification module is used for preparing fragmentation information for special components in initial information such as certain specific words, regional proper nouns, characteristic pictures and the like in the initial information; and the data classification module generates a classification matrix Q and then sends the matrix Q to the screening module.
Further, the data classification module stores a classification matrix Q (a1, a2, b1, c1, e); wherein, the code a1, the code a2, the code b1 and the code c1 represent fragmentation information; the code a1 represents a specific word in the initial information; the code a2 represents another kind of specific word in the initial information; code b1 represents a regional proper noun in the initial information; the code c1 represents the feature picture in the initial information; the code e is the lowest frequency value in the code a1, the code a2, the code b1 or the code c 1.
Furthermore, the user side comprises electronic equipment such as a computer, a tablet computer and a mobile phone; the method comprises the steps that a user obtains initial information from a crowdsourcing platform through a user side, and the user sends feedback information to the crowdsourcing platform; and the screening module of the crowdsourcing platform screens the feedback information uploaded by each user according to the classification matrix Q.
Further, the screening rule of the screening module is as follows: the screening module reads the feedback information, and records the occurrence frequency of each piece of fragmentation information in the feedback information related to the classification matrix Q while reading the feedback information; if at least two codes with different letters appear and the appearance frequency of each code is greater than the code e, the feedback information passes the screening, and the screening module sends the feedback information to the storage module for storage; meanwhile, the screening module generates a feedback matrix F based on the characteristic fragment information, and the screening module sends the feedback matrix to the priority module.
Further, the priority module determines the priority level of the feedback matrix F according to the address information in the feedback matrix F; the ideal address position is preset in the acquisition module, and the closer the address information in the feedback matrix F is to the ideal address position, the higher the priority level of the feedback matrix F is; and the acquisition module sequentially calls the feedback information corresponding to the feedback matrix F according to the sequence of the priority levels from high to low.
Further, the priority module determines the priority level of the feedback matrix F according to the time information in the feedback matrix F; ideal time nodes are preset in the acquisition module, and the closer the time information in the feedback matrix F is to the ideal time nodes, the higher the priority level of the feedback matrix F is; and the acquisition module sequentially calls the feedback information corresponding to the feedback matrix F according to the sequence of the priority levels from high to low.
Furthermore, the priority module determines the priority of each feedback matrix F according to an integral mode, the higher the total integral is, the higher the priority of the feedback matrix F is, and the acquisition module sequentially calls the feedback information corresponding to the feedback matrix F according to the sequence of the priority from high to low;
the integral is calculated as follows: an ideal time range and an ideal address range are preset in the acquisition module; if the address information of the feedback matrix falls into the ideal address range, the feedback matrix is integrated by one; if the time information of the feedback matrix falls into the ideal time range, the feedback matrix is integrated by one; if the address information of the feedback matrix falls into the ideal time range and the time information falls into the ideal time range, the feedback matrix integrates two points; the acquisition module is also stored with a plurality of preset specific words, preset regional proper nouns and preset characteristic pictures, and if a code number of each feedback matrix is identical with the preset specific words, the preset regional proper nouns or the preset characteristic pictures, the feedback matrix is integrated by one; the total integral of each feedback matrix is the sum of all the integrals of the matrix.
Further, the crowdsourcing platform is arranged on the internet in a webpage mode, the user side accesses the crowdsourcing platform through a web, and the crowdsourcing platform further comprises a transaction module; the transaction module charges the user according to the download amount and the download data type of the user; when the feedback information sent by the user side to the crowdsourcing platform is screened by the screening module, the transaction module sends a submission qualified notice to the user side and pays corresponding rewards; and when the feedback information sent to the crowdsourcing platform by the user side does not pass the screening of the screening module, the transaction module sends a submission unqualified notice to the user side and refunds partial cost.
Further, the web page of the crowdsourcing platform has the following functions:
the search function is that a user directly accesses the storage module to call information by inputting information such as key words and the like at a search port;
the crowdsourcing platform stores the past submitted qualified feedback information in the storage module, and a user can download the submitted qualified feedback information by clicking a successful case link for reference;
the user accesses the transaction module by clicking the expense inquiry link, and acquires related payment or reward information from the expense inquiry link;
the user accesses the storage module by clicking a demand release link, and acquires the initial information from the release information module;
and uploading a file, and transmitting feedback information to the screening module of the crowdsourcing platform by a user through a file uploading port.
Compared with the prior art, the invention has the following advantages:
the invention provides a method for constructing a space-time big data acquisition model based on a crowdsourcing mode, wherein a server comprises an information issuing module, a data classification module, an acquisition module and a priority module; the crowdsourcing platform comprises a storage module and a screening module; the publishing information module sends initial information to the storage module, and the user side acquires the initial information from the storage module by accessing the crowdsourcing platform; the data classification module sends fragmentation information to the screening module, when the user side feeds back to the crowdsourcing platform, the screening module in the crowdsourcing platform carries out relevance identification on the fragmentation information stored in the crowdsourcing platform and feedback information of the user, and if the identification results are consistent, the screening module stores the feedback information into the storage module; meanwhile, the screening module generates characteristic segment information, the screening module sends the characteristic segment information to the priority module of the server, and the priority module carries out priority judgment on the characteristic segment information according to a pre-stored priority evaluation method; the acquisition module calls feedback information from the storage module of the crowdsourcing platform according to a pre-stored calling rule. The invention improves the data examination efficiency, the data acquisition efficiency and the data acquisition cost in various ways, and greatly reduces the data acquisition cost.
Further, the invention carries out primary screening on the feedback data by constructing a classification matrix Q.
Further, after the primary screening is completed, the feedback matrix F is established on the result of the primary screening, and the time information t and the address information l of the feedback matrix are set for the feedback matrix F so as to meet the requirements on time and space during data acquisition.
Furthermore, the priority module of the invention comprises a plurality of priority evaluation methods for the feedback matrix F to complete secondary screening; the acquisition module calls the feedback information corresponding to the feedback matrix F in sequence from high to low according to the priority level, so that the acquisition efficiency is greatly improved, and the acquisition cost is reduced.
Furthermore, the invention is provided with a transaction module, and a user accesses the transaction module by clicking a fee inquiry link to acquire related payment or reward information; the transaction module charges the user according to the download amount and the download data type of the user; when the feedback information sent by the user side to the crowdsourcing platform is screened by the screening module, the transaction module sends a submission qualification notice to the user side and pays corresponding rewards; when the feedback information sent by the user side to the crowdsourcing platform does not pass the screening of the screening module, the transaction module sends a submission unqualified notice to the user side and refunds part of the cost.
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In order that the advantages of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings.
FIG. 1 is a schematic diagram of an embodiment of a method for constructing a spatiotemporal big data acquisition model based on a crowdsourcing mode according to the invention.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that embodiments of the invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in detail so as not to obscure the embodiments of the invention.
Referring to fig. 1, a method for constructing a spatiotemporal big data acquisition model based on a crowdsourcing mode includes: the system comprises a server, a crowdsourcing platform and a user side. The method comprises the following steps that a server and a crowdsourcing platform are communicated with each other, and the server sends initial information to the crowdsourcing platform; the user side communicates with the crowdsourcing platform, and acquires information links from the crowdsourcing platform; meanwhile, the user side can send feedback data to the crowdsourcing platform, and the server obtains the feedback data on the crowdsourcing platform according to the rule of the acquisition module.
Specifically, the server comprises an information issuing module, a data classification module, an acquisition module and a priority module; the data classification module is used for carrying out fragmentation classification on the initial information; the priority module is used for evaluating the priority level of each fragmentation information.
Specifically, the crowdsourcing platform comprises a storage module and a screening module; the information issuing module sends the initial information to the storage module, and the user side can obtain the initial information from the storage module by accessing the crowdsourcing platform. The data classification module sends the fragmentation information to the screening module, when a user side feeds back to the crowdsourcing platform after completing a task, the screening module in the crowdsourcing platform carries out relevance identification on the fragmentation information stored in the crowdsourcing platform and the feedback information of the user, if the identification result is consistent, the screening module stores the feedback information into the storage module, meanwhile, the screening module generates characteristic fragment information, the screening module sends the characteristic fragment information to the priority module of the server, and the priority module carries out priority judgment on the characteristic fragment information according to a pre-stored priority evaluation method; the acquisition module calls the feedback information from the storage module of the crowdsourcing platform according to the pre-stored calling rule.
Specifically, the way of the data classification module performing fragmentation classification on the initial information includes: keyword classification, key segment classification and key picture classification. In some embodiments of the present invention, the classification module generates fragmentation information for specific components in the initial information, such as specific words, regional proper nouns, feature pictures, etc. in the initial information. The data classification module stores a classification matrix Q (a)1、a2、b1、c1E); wherein, code a1Code a2Code b1And code c1All represent fragmentation information; code a1Representing a particular class of words in the initial information(ii) a Code a2Representing another type of specific word in the initial information; code b1Representing a regional proper noun in the initial information; code number c1Representing a characteristic picture in the initial information; code e is code a1Code a2Code b1Or code c1The lowest frequency of occurrence value. And the data classification module generates a classification matrix Q and then sends the matrix to the screening module.
Specifically, the user side comprises electronic equipment such as a computer, a tablet computer and a mobile phone; the method comprises the following steps that a user obtains initial information from a crowdsourcing platform through a user side, and meanwhile, the user sends feedback information to the crowdsourcing platform; and a screening module of the crowdsourcing platform screens the feedback information uploaded by each user according to the classification matrix Q.
Specifically, the screening rule of the screening module is as follows: the screening module reads the feedback information and records the occurrence frequency of each piece of fragmentation information in the classification matrix Q in the feedback information while reading the feedback information; if at least two codes with different letters appear and the appearance frequency of each code is greater than the code e, the feedback information passes through screening, and the screening module sends the feedback information to the storage module for storage; meanwhile, the screening module generates a feedback matrix F based on the characteristic fragment information, and the screening module sends the feedback matrix to the priority module.
In other embodiments of the present invention, the classification matrix Q includes Q (a)1、b1、c1E) in which the symbol a1Representing a specific word in the initial information; code b1Representing a regional proper noun in the initial information; code number c1Representing a characteristic picture in the initial information; code e is code a1Code b1Or code c1The lowest value of frequency of occurrence; the feedback matrix F is F (m, n, t, l), where m is the symbol a in the classification matrix Q1Code b1Or code c1The code with the most frequent occurrence; n is the code number a in the classification matrix Q1Code b1Or code c1Code of the second most frequent occurrence; t is a screening moduleAnd generating time information of the feedback matrix, wherein l is address information.
Specifically, the priority module obtains the feedback matrix F from the screening module1、F2、F3And so on. The priority module judges the priority of the feedback matrix F according to a pre-stored priority evaluation method; the judgment method is as follows:
in some embodiments of the present invention, the priority module determines the priority level of the feedback matrix F according to the address information in the feedback matrix F; the ideal address position is preset in the acquisition module, and the closer the address information in the feedback matrix F is to the ideal address position, the higher the priority level of the feedback matrix F is; and the acquisition module sequentially calls the feedback information corresponding to the feedback matrix F according to the sequence of the priority levels from high to low.
In other embodiments of the present invention, the priority module determines the priority of the feedback matrix F according to the time information in the feedback matrix F; an ideal time node is preset in the acquisition module, and the closer the time information in the feedback matrix F is to the ideal time node, the higher the priority level of the feedback matrix F is; and the acquisition module sequentially calls the feedback information corresponding to the feedback matrix F according to the sequence of the priority levels from high to low.
In still other embodiments of the present invention, the priority module determines the priority of each feedback matrix F according to an integral manner, the higher the total integral is, the higher the priority of the feedback matrix F is, and the acquisition module sequentially calls the feedback information corresponding to the feedback matrix F according to the order of the priority from high to low.
Specifically, the manner of integration is as follows: an ideal time range and an ideal address range are preset in the acquisition module; if the address information of the feedback matrix falls into the ideal address range, the feedback matrix is integrated by one; if the time information of the feedback matrix falls into the ideal time range, the feedback matrix is integrated by one; if the address information of the feedback matrix falls into the ideal time range and the time information falls into the ideal time range, the feedback matrix integrates two points; the acquisition module is also stored with a plurality of preset specific words, preset regional proper nouns and preset characteristic pictures, and if a code number of each feedback matrix is identical with the preset specific words, the preset regional proper nouns or the preset characteristic pictures, the feedback matrix is integrated by one; the total integral of each feedback matrix is the sum of all the integrals of the matrix.
In some embodiments of the present invention, the crowdsourcing platform is provided on the internet in the form of a web page, and the user terminal accesses the crowdsourcing platform through the web, and the crowdsourcing platform further includes a transaction module (not shown in the figure). And the user registers and logs in on the webpage and according to personal requirement data. The transaction module charges the user according to the download amount and the download data type of the user; when the feedback information sent by the user side to the crowdsourcing platform is screened by the screening module, the transaction module sends a submission qualification notice to the user side and pays corresponding rewards; when the feedback information sent by the user side to the crowdsourcing platform does not pass the screening of the screening module, the transaction module sends a submission unqualified notice to the user side and refunds part of the cost.
In some embodiments of the invention, a user accessing a web page of a crowdsourcing platform may enjoy the following functions:
the search function is that a user directly accesses the storage module at a search port by inputting information such as keywords and the like and calls the information from the storage module;
in case success, the crowdsourcing platform stores the past submitted qualified feedback information in the storage module, and the user can download the submitted qualified feedback information by clicking the successful case link for reference;
the method comprises the steps of inquiring expenses, wherein a user accesses a transaction module by clicking an expense inquiry link, and obtains related payment or reward information from the user;
the user accesses the storage module by clicking the demand release link, and acquires the initial information from the release information module;
and uploading the file, and transmitting the feedback information to a screening module of the crowdsourcing platform by the user through a file uploading port.
Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Terms such as "component" and the like, when used herein, can refer to either a single part or a combination of parts. Terms such as "mounted," "disposed," and the like, as used herein, may refer to one component as being directly attached to another component or one component as being attached to another component through intervening components. Features described herein in one embodiment may be applied to another embodiment, either alone or in combination with other features, unless the feature is otherwise inapplicable or otherwise stated in the other embodiment.
Claims (8)
1. A method for constructing a spatio-temporal big data acquisition model based on a crowdsourcing mode is characterized by comprising the following steps: the system comprises a server, a crowdsourcing platform and a user side;
the server and the crowdsourcing platform are in communication with each other, the server sending initial information to the crowdsourcing platform; the user side communicates with the crowdsourcing platform, and the user side acquires information links from the crowdsourcing platform; meanwhile, the user side sends feedback data to the crowdsourcing platform, and the server acquires the feedback data on the crowdsourcing platform according to the rule of the acquisition module;
the server comprises an information issuing module, a data classification module, the acquisition module and a priority module; the data classification module is used for performing fragmentation classification on initial information; the priority module is used for evaluating the priority level of each fragmentation information;
the crowdsourcing platform comprises a storage module and a screening module; the publishing information module sends initial information to the storage module, and the user side acquires the initial information from the storage module by accessing the crowdsourcing platform; the data classification module sends fragmentation information to the screening module, when the user side feeds back to the crowdsourcing platform, the screening module in the crowdsourcing platform carries out relevance identification on the fragmentation information stored in the crowdsourcing platform and feedback information of the user, and if the identification results are consistent, the screening module stores the feedback information into the storage module; meanwhile, the screening module generates characteristic segment information, the screening module sends the characteristic segment information to the priority module of the server, and the priority module carries out priority judgment on the characteristic segment information according to a pre-stored priority evaluation method; the acquisition module calls feedback information from the storage module of the crowdsourcing platform according to a prestored calling rule;
the data classification module is stored with a classification matrix Q (a)1、a2、b1、c1E); wherein, code a1Code a2Code b1And code c1All represent fragmentation information; code a1Representing a specific word in the initial information; code a2Representing another type of specific word in the initial information; code b1Representing a regional proper noun in the initial information; code number c1Representing a characteristic picture in the initial information; code e is code a1Code a2Code b1Or code c1The lowest value of frequency of occurrence; the data classification module generates a classification matrix Q and then sends the matrix to the screening module; the method comprises the following steps that a user obtains initial information from a crowdsourcing platform through a user side, and meanwhile, the user sends feedback information to the crowdsourcing platform; a screening module of the crowdsourcing platform screens the feedback information uploaded by each user according to the classification matrix Q; the screening rules of the screening module are as follows: the screening module reads the feedback information and records the occurrence frequency of each piece of fragmentation information in the classification matrix Q in the feedback information while reading the feedback information; if at least two codes with different letters appear and the appearance frequency of each code is greater than the code e, the feedback information passes through screening, and the screening module sends the feedback information to the storage module for storage; meanwhile, a feedback matrix F based on the characteristic fragment information is generated by the screening module, the screening module sends the feedback matrix to the priority module, and priority judgment is carried out according to address information, time information, an ideal time range and total integral information of the ideal address range in the feedback matrix F; the acquisition module is from high to low according to the priority levelSequentially calling feedback information corresponding to the feedback matrix F; the classification matrix Q includes Q (a)1、b1、c1E) in which the symbol a1Representing a specific word in the initial information; code b1Representing a regional proper noun in the initial information; code number c1Representing a characteristic picture in the initial information; code e is code a1Code b1Or code c1The lowest value of frequency of occurrence; the feedback matrix F is F (m, n, t, 1), where m is the symbol a in the classification matrix Q1Code b1Or code c1The code with the most frequent occurrence; n is the code number a in the classification matrix Q1Code b1Or code c1Code of the second most frequent occurrence; t is time information of the screening module for generating the feedback matrix, and 1 is address information.
2. The method for constructing a spatio-temporal big data acquisition model according to claim 1, wherein the data classification module performs fragmentation classification on the initial information in a manner of: key word classification, key segment classification and key picture classification; the classification module is used for preparing fragmentation information for special components in initial information such as certain specific words, regional proper nouns, characteristic pictures and the like in the initial information; and the data classification module generates a classification matrix Q and then sends the matrix Q to the screening module.
3. The method for constructing a spatio-temporal big data acquisition model according to claim 2, wherein the user side comprises electronic devices such as a computer, a tablet computer, a mobile phone and the like.
4. The method for constructing a spatio-temporal big data acquisition model according to claim 3, wherein the priority module determines the priority level of the feedback matrix F according to the address information in the feedback matrix F; the ideal address position is preset in the acquisition module, and the closer the address information in the feedback matrix F is to the ideal address position, the higher the priority level of the feedback matrix F is; and the acquisition module sequentially calls the feedback information corresponding to the feedback matrix F according to the sequence of the priority levels from high to low.
5. The method for constructing a spatio-temporal big data acquisition model according to claim 3, wherein the priority module determines the priority of the feedback matrix F according to the time information in the feedback matrix F; ideal time nodes are preset in the acquisition module, and the closer the time information in the feedback matrix F is to the ideal time nodes, the higher the priority level of the feedback matrix F is; and the acquisition module sequentially calls the feedback information corresponding to the feedback matrix F according to the sequence of the priority levels from high to low.
6. The method for constructing the spatio-temporal big data acquisition model according to claim 3, wherein the priority module determines the priority of each feedback matrix F according to an integral mode, the higher the total integral is, the higher the priority of the feedback matrix F is, and the acquisition module sequentially calls the feedback information corresponding to the feedback matrix F according to the priority from high to low;
the integral is calculated as follows: an ideal time range and an ideal address range are preset in the acquisition module; if the address information of the feedback matrix falls into the ideal address range, the feedback matrix is integrated by one; if the time information of the feedback matrix falls into the ideal time range, the feedback matrix is integrated by one; if the address information of the feedback matrix falls into the ideal time range and the time information falls into the ideal time range, the feedback matrix integrates two points; the acquisition module is also stored with a plurality of preset specific words, preset regional proper nouns and preset characteristic pictures, and if a code number of each feedback matrix is identical with the preset specific words, the preset regional proper nouns or the preset characteristic pictures, the feedback matrix is integrated by one; the total integral of each feedback matrix is the sum of all the integrals of the matrix.
7. The spatiotemporal big data acquisition model construction method according to claim 3, wherein the crowdsourcing platform is arranged on the internet in the form of a web page, the user side accesses the crowdsourcing platform through the web, and the crowdsourcing platform further comprises a transaction module; the transaction module charges the user according to the download amount and the download data type of the user; when the feedback information sent by the user side to the crowdsourcing platform is screened by the screening module, the transaction module sends a submission qualified notice to the user side and pays corresponding rewards; and when the feedback information sent to the crowdsourcing platform by the user side does not pass the screening of the screening module, the transaction module sends a submission unqualified notice to the user side and refunds partial cost.
8. The spatiotemporal big data acquisition model construction method according to claim 7, wherein the web pages of the crowdsourcing platform have the following functions:
the search function is that a user directly accesses the storage module to call information by inputting information such as key words and the like at a search port;
the crowdsourcing platform stores the past submitted qualified feedback information in the storage module, and a user can download the submitted qualified feedback information by clicking a successful case link for reference;
the user accesses the transaction module by clicking the expense inquiry link, and acquires related payment or reward information from the expense inquiry link;
the user accesses the storage module by clicking a demand release link, and acquires the initial information from the release information module;
and uploading a file, and transmitting feedback information to the screening module of the crowdsourcing platform by a user through a file uploading port.
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