CN115600979A - Data auditing method and device applied to market research and storage medium - Google Patents
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Abstract
The invention discloses a data auditing method and device applied to market research and relates to the technical field of market research. The invention comprises the following steps: forming a research standard library; establishing a mapping relation; generating a questionnaire; the investigated user fills in and submits the questionnaire, and the data manager generates the key according to the submission timestamp; modifying each node and storing information according to an MHT protocol; the cloud server generates a BLS signature on the file block according to the private key, the hash of the file block, the reporting time and a random element; and the third party auditor verifies the correctness of the audit of the market research data judged from the cloud server. The method stores the hash value of the data block of each node and the relative index of the node through the MHT protocol, and integrates the MHT and the relative index of the node, so that the calculation cost of searching the data block is reduced, the data auditing cost of market research is reduced, and the data auditing efficiency is improved.
Description
Technical Field
The invention belongs to the technical field of market research, and particularly relates to a data auditing method and device applied to market research and a storage medium.
Background
The scientific method for application in market research is a general term for market research and research, and is an essential component in the working process of systematically designing, collecting, recording, sorting, analyzing and researching various information data and report research results of the market and the market forecasting and operating decision process in the market research and the market research.
The traditional market research method adopts a point-to-point mode, one mode is to recruit a part of visitors, then various visitors meeting requirements are searched through the part of visitors to access and acquire data, and the other mode to acquire data for on-site detection is to automatically send personnel to addresses distributed all over the country through recruiting detection personnel to acquire data on site.
However, when research data of a plurality of projects are integrated, the quality of the research data may be uneven due to differences between project managers and data collectors, and when the research data is transmitted on the internet, a series of accidents such as messy codes, missed transmission, wrong transmission, repeated transmission and the like are easy to occur, which may cause the quality of the research data in the research field to be reduced. The enterprise and the unit use the investigation data with poor quality for analysis and application, and the cognition and decision making of the enterprise and the unit on the market can be negatively influenced.
Therefore, when performing on-line investigation, the investigation data needs to be checked to avoid the quality degradation of the investigation data in the research field.
Disclosure of Invention
The invention aims to provide a data auditing method, a device and a storage medium applied to market research, which are used for auditing questionnaires in different areas by generating a research standard library according to industry, research direction and a survey method and adopting a data auditing method of a relative index hash tree, thereby solving the problem of quality reduction of research data caused by abnormity in the existing transmission process of the research data.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a data auditing method applied to market research, which comprises the following steps:
step S1: dividing research fields of market research according to the industry, research directions and research methods, and formulating research standard indexes and index standard options of each research field to form a research standard library;
step S2: determining a corresponding research field according to the research data description, establishing a first mapping relation according to a research question and a research standard index corresponding to the research field, and establishing a second mapping relation according to a question option and a corresponding index standard option;
and step S3: a data manager acquires the research data and the research data description to generate a questionnaire and uploads the questionnaire to a cloud server;
and step S4: the investigated user selects and determines the corresponding investigation field by accessing the cloud server, and the investigation standard library generates an investigation questionnaire;
step S5: the investigated user fills in and submits the questionnaire, and the data manager generates the key according to the submission timestamp;
step S6: modifying each node and storing information according to an MHT protocol;
step S7: the cloud server generates a BLS signature on the file block according to the private key, the hash of the file block, the reporting time and a random element;
step S8: and the third party auditor verifies the evidence, the message and the public key received from the cloud server to judge the correctness of the audit of the market research data.
As a preferred technical solution, in the step S1, the classification of the research standard index is completed before the standard library is formed; the research standard index classification is specifically classified according to an execution index, an identity index, a value index and other indexes; the other indexes comprise the problem of investigation which cannot be standardized; the value indicator includes a research content portion and a research direction portion.
As a preferred technical solution, in step S2, a corresponding research field is determined according to the research data description, a first mapping relationship is established according to the research question and a corresponding research standard index in the research field, and a second mapping relationship is established according to the question option and a corresponding index standard option; meanwhile, the investigation question is converted into a standard index according to the first mapping relation, and the question option is converted into a standard option according to the second mapping relation.
As a preferred technical solution, in step S4, the investigated user needs to complete registration on the cloud server before accessing the cloud server, the cloud server obtains the ip attribution of the investigated user to determine whether the investigated user has the investigation qualification, and if the investigated user has the investigation qualification, the investigated user generates a questionnaire by using the investigation standard library and issues the questionnaire to the investigated user.
As a preferred technical solution, in the step S6, each node of the MHT is modified to store the Hash value of the data block and the relative index of the node; the relative index associated with each node specifies the number of leaf nodes that belong to the P sub-tree, and in the modified MHT, the relative index of a leaf node is set to 1.
As a preferred technical solution, in step S7, the third party verifier verification process includes:
step S71: the data manager outputs a key pair (public key, private key) ← (η, k) in accordance with the Keygen algorithm;
step S72: inputting the name, the private key, the partition number of the data blocks and the date and time of file preprocessing according to a FileTagGen algorithm to generate a label tau of a file F;
step S73: inputting a private key k, the hash of a file block, the date and time of file preprocessing and a random element u belonging to G according to a Block SigGen algorithm, and outputting a BLS signature on the file block;
step S74: after receiving the entrusted auditing right of the data manager, the third party auditor sends an inquiry message to the cloud server;
step S75: after receiving the inquiry message from the third-party reviewer, the cloud server immediately generates an evidence P through an algorithm f And is combined with P f Transmitting to a third party auditor for verification;
step S76: the third party auditor passes the inquiry information C and the P of the cloud server f And a public key g k And judging that the data passes the verification.
The invention relates to a data auditing device applied to market research, which comprises a data manager, a cloud server and a third party auditor, wherein the data manager is connected with the cloud server through a network;
the cloud server comprises a registration unit, a verification unit, a mapping unit, a research standard library, a key generation unit, a questionnaire generation unit, a HASH extraction unit, an MHT creation unit and an auditing unit;
the registration unit is used for completing the registration work of a data manager, a third party auditor and a researched user; the verification unit is used for verifying the identity of the registered user and judging whether the investigated user has the verification qualification or not; the mapping unit is used for establishing a mapping relation according to the investigation problem and the investigation standard index corresponding to the research field; the investigation standard library is used for dividing research fields of market investigation according to industries, investigation directions and investigation methods, and establishing the investigation standard library by formulating investigation standard indexes and index standard options of each research field; the key generation unit is used for generating a public key and a private key; the questionnaire generating unit is used for a data manager to obtain the research data and the research data description to generate questionnaires; the HASH extraction unit is used for acquiring a HASH value of a database of questionnaires; the MHT creating unit is used for creating a Hash value of each node to be modified so as to store the data block and a relative index of the node; the auditing unit is used for finishing auditing the questionnaire by a data manager and a third party auditor.
As a preferred technical solution, the auditing unit may be further configured to, when discovering that the auditing data is missing, analyze whether the missing investigation data is data that must be filled in, and if so, mark the investigation data as abnormal data; if not, the auditing unit judges whether the data is counting data or not, and if not, the numerical filling is carried out according to a preset rule.
As a preferred technical solution, the auditing unit examines that the investigation data which fails to pass is marked as abnormal data, and is further configured to perform secondary verification on the investigation data which passes the auditing, mark the investigation data which fails to pass the auditing as abnormal data, and send the investigation data which passes the auditing to a data manager; the auditing unit is also used for marking the investigation data which fails in auditing as abnormal data.
The present invention is a data auditing storage medium for market research, on which a computer program is stored, the computer program implementing the steps of the method according to any one of claims 1 to 6 when executed by a cloud server.
The invention has the following beneficial effects:
(1) The method stores the hash value of the data block of each node and the relative index of the node through an MHT protocol, and integrates the MHT and the relative index of the node so as to reduce the calculation cost of searching the data block; and then, by adding the final modification time of the data, the freshness of the data is ensured, the data auditing cost of market research is reduced, and the data auditing efficiency is improved.
(2) The invention not only uses the public key and the private key to carry out data protection in the process of data auditing, but also brings the timestamp into the process of auditing the questionnaire, thereby not only ensuring the freshness of the data, but also increasing the safety of the process of data auditing and improving the quality of data auditing.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a data auditing method applied to market research according to the present invention;
fig. 2 is a schematic structural diagram of a data auditing apparatus applied to market research according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, the present invention is a data auditing method applied to market research, including the following steps:
step S1: dividing research fields of market research according to the industry, research directions and research methods, and formulating research standard indexes and index standard options of each research field to form a research standard library; completing research standard index classification before forming a standard library; the research standard index classification is specifically classified according to an execution index, an identity index, a value index and other indexes; other indicators include research issues that cannot be standardized; the value indicator includes a research content portion and a research direction portion.
Step S2: determining a corresponding research field according to the research data description, establishing a first mapping relation according to a research question and a research standard index corresponding to the research field, and establishing a second mapping relation according to a question option and a corresponding index standard option;
step S2, determining a corresponding research field according to the investigation data description, establishing a first mapping relation according to the investigation question and a corresponding investigation standard index in the research field, and establishing a second mapping relation according to the question option and a corresponding index standard option; meanwhile, the investigation question is converted into a standard index according to the first mapping relation, and the question option is converted into a standard option according to the second mapping relation.
When the method is used specifically, research fields of market research are divided according to the industry, research direction and research method, research standard indexes and index standard options of each research field are formulated, and the research data of research projects acquired by subsequent processing have standardized reference objects.
After the research data and the research data description are obtained, confirming the corresponding research field according to the research data description; then, a first mapping relation is established according to the research question and the research standard index corresponding to the research field, and a second mapping relation is established according to the question option and the corresponding index standard option. Through the first mapping relation, the expression of the investigation problem can be standardized; through the second mapping relationship, the expression of the question option can be standardized. And then, converting the research question into a standard index according to the first mapping relation, and converting the question option into a standard option according to the second mapping relation. And converting different expressions of research question/question options into the same expression of standard indexes/standard options. And storing the research data converted into the standard indexes and the standard options.
And step S3: a data manager acquires the research data and the research data description to generate a questionnaire and uploads the questionnaire to a cloud server;
and step S4: the investigated user selects and determines the corresponding investigation field by accessing the cloud server, and the investigation standard library generates an investigation questionnaire; the method comprises the steps that a researched user needs to complete registration on a cloud server before accessing the cloud server, the cloud server acquires the ip attribution of the researched user to judge whether the researched user has the researched qualification, if the researched user has the researched qualification, a questionnaire is generated by using a research standard library and is issued to the researched user, due to the difference of consumption levels and service levels caused by different regions, when enterprises do the questionnaire work, the questionnaire in all the regions of the country does not need to be collected, only a checking set needs to be carried out in a specified service region, therefore, when the questionnaire is checked, the investigation range of the questionnaire can be preset in the cloud server, and when the user logs in the server and finds that the service range of the questionnaire is not met, the user does not issue the questionnaire.
Step S5: the investigated user fills in and submits the questionnaire, and the data manager generates the key according to the submission timestamp;
step S6: modifying each node and storing information according to an MHT protocol; in step S6, each node of the MHT is modified to store the Hash value of the data block and the relative index of the node; the relative index associated with each node specifies the number of leaf nodes that belong to the P sub-tree, and in the modified MHT, the relative index of a leaf node is set to 1.
In specific use, for example, a research question of a certain tobacco industry company is "how much you are satisfied with a cigarette delivery service? "the corresponding value index is: the content part of investigation "delivery service" + the direction part of investigation "satisfaction". The goods delivery service satisfaction is stored during storage, so that the goods delivery service satisfaction is clear and concise, no redundant information interference exists, and the use of purchasers is facilitated; taking the after-market response satisfaction as an example, the index criteria options are five numbers from 1 to 5, which mean very satisfactory, more satisfactory, less satisfactory, and unsatisfactory, respectively. This ensures consistency in the meaning of the stored research data.
Analyzing investigation parameters, namely judging whether the information updated by the industry standard is an issued draft or a solicited draft, if so, matching investigation parameter items corresponding to the theme from the investigation recommendation library, and adjusting and marking the weight values of the investigation parameter items according to the content of the solicited draft; and if the issued draft is issued, matching the investigation parameter item corresponding to the theme from the investigation recommendation library, judging whether a corresponding investigation draft modification mark exists, if so, performing weight modification and marking on the issued draft according to a first preset rule according to the content of the issued draft, and if not, performing weight modification and marking on the issued draft according to a second preset rule according to the content of the issued draft.
Step S7: the cloud server generates a BLS signature on the file block according to the private key, the hash of the file block, the reporting time and a random element; the third party reviewer verification process is as follows:
step S71: the data manager outputs a key pair (public key, private key) ← (η, k) according to the Keygen algorithm;
step S72: according to the FileTagGen algorithm, inputting the name of the file, a private key, the partition number of data blocks and the date and time of file preprocessing, and generating a label tau of the file F;
step S73: inputting a private key k, the hash of a file block, the date and time of file preprocessing and a random element u belonging to G according to a Block SigGen algorithm, and outputting a BLS signature on the file block;
step S74: after receiving the authorization and verification right of the data manager, the third party verifier sends a challenge message to the cloud server;
step S75: after receiving the inquiry message from the third party reviewer, the cloud server immediately generates an evidence P through an algorithm f And combining P f Transmitting to a third party auditor for verification;
step S76: the third party auditor passes the inquiry information C and the P of the cloud server f And a public key g k And judging whether the data passes the verification
Step S8: and the third party auditor verifies the evidence, the message and the public key received from the cloud server to judge the correctness of the audit of the market research data.
Example two
The invention relates to a data auditing device applied to market research, which comprises a data manager, a cloud server and a third party auditor, wherein the data manager is connected with the cloud server through a network;
the cloud server comprises a registration unit, a verification unit, a mapping unit, a research standard library, a key generation unit, a questionnaire generation unit, a HASH extraction unit, an MHT creation unit and an auditing unit;
the registration unit is used for completing the registration work of a data manager, a third party auditor and a researched user; the verification unit is used for verifying the identity of the registered user and judging whether the investigated user has the verification qualification; the mapping unit is used for establishing a mapping relation according to the investigation problem and the investigation standard index corresponding to the research field; the investigation standard library is used for dividing research fields of market investigation according to the industry, the investigation direction and the investigation method, and establishing the investigation standard library by formulating investigation standard indexes and index standard options of each research field; the key generating unit is used for generating a public key and a private key; the questionnaire generating unit is used for a data manager to obtain the research data and the research data description to generate questionnaires; the HASH extraction unit is used for acquiring a HASH value of a database of the questionnaire; the MHT creating unit is used for creating each node to modify so as to store the Hash value of the data block and the relative index of the node; the auditing unit is used for finishing auditing the questionnaire by a data manager and a third party auditor.
The data manager (DP) subsequently updates the data by modifying, inserting, appending, etc. DP is a data maintenance entity, usually a resource-constrained entity, that relies on cloud providers.
The Cloud Server (CSP) has sufficient computing resources and the storage server entity has sufficient computing resources and unlimited storage space. It is mainly responsible for outsourcing data preservation and maintenance, but the CP is generally considered to be an untrusted entity.
A Third Party Auditor (TPA) is a professional entity that audits on data of the DP. The TPA is an entity trusted by both the CSP and the DP, and can reduce the data auditing calculation pressure of the DP to the maximum extent. TPA trusted entity, CSP untrusted entity. Both TPA and CSP, however, may pose some threats to DP data, such as:
TPA poses a data security threat. DP relies on TPA to ensure data integrity and assumes that TPA is a truly reliable, independent entity. However, the TPA always has the possibility of snooping DP data, and thus, in the public auditing protocol, the privacy of the data may be violated by the TPA. The protocol presented herein does not take into account data privacy issues and assumes that TPA loyalty is reliable.
CSP, as a security threat. CSP can pose the following threats to DP data: the CSP may remove data it visits infrequently without notifying the DP to save server space: the CSP may cause some processing errors to the data that may cause unrecoverable corruption of the DP data.
Some external threat. Legitimate users may access the outsourced data through the applications provided by the CSP, while weaker applications may pose a risk to the DP data. Some legitimate users' login credentials may be used by strangers and may anonymously contaminate or delete data. In addition, a CSP administrator after leaving work may intrude into the cloud server, causing damage to the stored data. Therefore, it is crucial for the CSP to ensure accessibility of data and prevent outsourced data from external attacks while providing various cloud services.
The auditing unit can also be used for analyzing whether missing investigation data is the necessary data or not by combining with the attribute requirement of the data when the auditing data are missing, and marking the investigation data as abnormal data if the missing investigation data are the necessary data; if not, the auditing unit judges whether the data is counting data or not, and if not, the numerical filling is carried out according to a preset rule. The auditing unit is used for marking the investigation data which is not approved as abnormal data, performing secondary verification on the investigation data which is approved, marking the investigation data which is not approved as abnormal data, and sending the investigation data which is approved to the data manager; the auditing unit is also used for marking the investigation data which fails to be audited as abnormal data.
The invention is a data auditing storage medium for market research, on which a computer program is stored, which when executed by a cloud server performs the steps of the method of any one of claims 1 to 6.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it is understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing associated hardware, and the corresponding program may be stored in a computer-readable storage medium.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (10)
1. A data auditing method applied to market research is characterized by comprising the following steps:
step S1: dividing research fields of market research according to the industry, research directions and survey methods, and formulating research standard indexes and index standard options of each research field to form a research standard library;
step S2: determining a corresponding research field according to the research data description, establishing a first mapping relation according to a research question and a research standard index corresponding to the research field, and establishing a second mapping relation according to a question option and a corresponding index standard option;
and step S3: a data manager acquires the research data and the research data description to generate a questionnaire and uploads the questionnaire to a cloud server;
and step S4: the investigated user selects and determines the corresponding investigation field by accessing the cloud server, and the investigation standard library generates an investigation questionnaire;
step S5: the investigated user fills in and submits the questionnaire, and the data manager generates the key according to the submission timestamp;
step S6: modifying each node and storing information according to an MHT protocol;
step S7: the cloud server generates a BLS signature on the file block according to the private key, the hash of the file block, the reporting time and a random element;
step S8: and the third party auditor verifies the evidence, the message and the public key received from the cloud server to judge the correctness of the audit of the market research data.
2. The data auditing method applied to market research according to claim 1, characterized in that in step S1, research standard index classification is completed before forming a standard library; the investigation standard index classification is specifically classified according to an execution index, an identity index, a value index and other indexes; the other indexes comprise the problem of investigation which cannot be standardized; the value indicator includes a research content portion and a research direction portion.
3. The data auditing method applied to market research according to claim 1, characterized in that in step S2, the corresponding research field is determined according to the research data description, a first mapping relation is established according to the research question and the corresponding research standard index in the research field, and a second mapping relation is established according to the question option and the corresponding index standard option; meanwhile, the investigation question is converted into a standard index according to the first mapping relation, and the question option is converted into a standard option according to the second mapping relation.
4. The method as claimed in claim 1, wherein in step S4, the investigated user needs to complete registration on the cloud server before accessing the cloud server, the cloud server obtains the ip home location of the investigated user during login to determine whether the investigated user is qualified for investigation, and if the investigated user is qualified for investigation, the investigated user generates a questionnaire by using the investigation standard library and issues the questionnaire to the investigated user.
5. The data auditing method applied to market research according to claim 1, characterized in that in step S6, each node of MHT is modified to store the Hash value of the data block and the relative index of the node; the relative index associated with each node specifies the number of leaf nodes that belong to the P sub-tree, and in the modified MHT, the relative index of a leaf node is set to 1.
6. The method for auditing data applied to market research according to claim 1, wherein in step S7, the third party auditor verification process is as follows:
step S71: the data manager outputs a key pair (public key, private key) ← (eta, k) according to the Keygen algorithm;
step S72: according to the FileTagGen algorithm, inputting the name of the file, a private key, the partition number of data blocks and the date and time of file preprocessing, and generating a label tau of the file F;
step S73: inputting a private key k, the hash of a file block, the date and time of file preprocessing and a random element u belonging to G according to a Block SigGen algorithm, and outputting a BLS signature on the file block;
step S74: after receiving the authorization and verification right of the data manager, the third party verifier sends a challenge message to the cloud server;
step S75: after receiving the inquiry message from the third party reviewer, the cloud server immediately generates an evidence P through an algorithm f And combining P f Transmitting to a third party auditor for verification;
step S76: the third party auditor passes the inquiry information C and the P of the cloud server f And a public key g k And judging that the data passes the auditing.
7. A data auditing device applied to market research is characterized by comprising a data manager, a cloud server and a third party auditor;
the cloud server comprises a registration unit, a verification unit, a mapping unit, a research standard library, a key generation unit, a questionnaire generation unit, a HASH extraction unit, an MHT creation unit and an auditing unit;
the registration unit is used for completing the registration work of a data manager, a third party auditor and a researched user; the verification unit is used for verifying the identity of the registered user and judging whether the investigated user has the verification qualification; the mapping unit is used for establishing a mapping relation according to the investigation problem and the investigation standard index corresponding to the research field; the research standard library is used for dividing research fields of market research according to the industry, research directions and investigation methods, and establishing a research standard library by formulating research standard indexes and index standard options of each research field; the key generation unit is used for generating a public key and a private key; the questionnaire generating unit is used for a data manager to obtain the research data and the research data description to generate questionnaires; the HASH extraction unit is used for acquiring a HASH value of a database of questionnaires; the MHT creating unit is used for creating a Hash value of each node to be modified so as to store the data block and a relative index of the node; the auditing unit is used for finishing auditing the questionnaire by a data manager and a third party auditor.
8. The data auditing device applied to market research according to claim 7, characterized in that the auditing unit is further configured to, when discovering that the auditing data is missing, analyze whether the missing research data is the essential data in combination with the attribute requirements of the data, and if so, mark the research data as abnormal data; if not, the auditing unit judges whether the data is counting data or not, and if not, the numerical filling is carried out according to a preset rule.
9. The data auditing device applied to market research according to claim 7, wherein the auditing unit audits the failed research data as abnormal data, and is further configured to perform secondary verification on the approved research data, label the failed research data as abnormal data, and send the approved research data to the data manager; the auditing unit is also used for marking the investigation data which fails in auditing as abnormal data.
10. A data auditing storage medium for market research, having a computer program stored thereon, wherein the computer program, when executed by a cloud server, implements the steps of the method of any one of claims 1 to 6.
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