A cloud data retrieval method, system, storage medium and intelligent terminal

Abstract

The present application relates to a cloud data retrieval method, system, storage medium and intelligent terminal, and relates to the field of data processing technology, which includes obtaining user retrieval request information and retrieval identity information; searching data group information; generating category label information and cloud suggestion information, and forming suggestion data strip information; obtaining retrieval label information; obtaining filtered data group information; filtering the suggestion data strip information based on the retrieval label information to obtain a suggestion data strip with composite retrieval label information, and defining the suggestion data strip as filtered suggestion data strip information; outputting the filtered data group information and the filtered suggestion data strip information on the client corresponding to the user retrieval request information. The present application enables users to find the most accurate data without having to filter by themselves, thereby improving the intelligence and pertinence of cloud data retrieval.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a cloud data retrieval method, system, storage medium and intelligent terminal. Background Art

[0002] As the focus of education in modern society shifts from exam-oriented education to quality-oriented education, improving students' physical fitness has become increasingly important. The proportion of physical education exams for primary and secondary school students in the evaluation of students' comprehensive quality has also gradually increased.

[0003] At present, with the application of digital information technology in the field of education, more and more educational network platforms and terminal educational software have emerged. Each educational institution has set up a corresponding educational system and cloud data platform to manage its own educational resources and student education data, basically realizing the information management of data.

[0004] The following problems exist in the existing technology. The data on the cloud data platform is basically just stored, and when the user retrieves it, all the data related to the user will be output. Some data is irrelevant to the current user's retrieval purpose, resulting in the user still needing to screen and identify the data themselves after retrieval and downloading, which greatly increases the user's processing time and reduces the user's retrieval efficiency. There is still room for improvement. Summary of the Invention

[0005] In order to improve the problem that users still need to screen and identify data themselves after downloading it from the cloud, which greatly increases the user's processing time and reduces the user's retrieval efficiency, the present application provides a cloud data retrieval method, system, storage medium and smart terminal.

[0006] In a first aspect, the present application provides a cloud data retrieval method, which adopts the following technical solutions:

[0007] A cloud data retrieval method, comprising:

[0008] Obtain user retrieval request information and retrieval identity information;

[0009] Searching for data strips that are allowed to be viewed from a preset cloud database based on the retrieved identity information, combining all the data strips, and defining the combination as data group information;

[0010] Generate category label information and cloud-based suggestion information based on the data group information, and form a data strip with a mapping relationship between the category label information and the cloud-based suggestion information, and define the data strip as suggestion data strip information;

[0011] Analyze the user's call request information to obtain call tag information;

[0012] Filtering the data group information based on the retrieved tag information to obtain data strips that match the retrieved tag information, combining the data strips that match the retrieved tag information, and defining the combination as filtered data group information;

[0013] Filtering the suggested data strip information based on the retrieved tag information to obtain a suggested data strip with the composite retrieved tag information, and defining the suggested data strip as filtered suggested data strip information;

[0014] The filtered data group information and the filtered suggested data item information are outputted on the client corresponding to the user's call request information.

[0015] By adopting the above technical solution, the data is first classified and suggestions are generated in the cloud, and then the corresponding data and suggestions that can be retrieved are found by verifying the corresponding identity. Finally, the data and suggestions with complex requirements are filtered from the data crowd according to the user's needs and packaged and sent to the user, so that the user can find the most accurate data without having to filter it by himself, reducing the processing time after downloading and improving the intelligence and pertinence of cloud data retrieval.

[0016] Optional methods for establishing a cloud database include:

[0017] Get the user input data information;

[0018] Analyze the identity account information input by the client, input the identity account definition into the identity account information, and form identity tag information;

[0019] Analyze the user input data strip information and identity tag information to determine similar data strip information corresponding to the identity tag information already stored in the cloud database;

[0020] Combining the user input data piece information and similar data piece information to form sample data group information;

[0021] Use a preset data detection model to perform model training on the sample data group information to obtain normal data group information and abnormal data group information;

[0022] The normal data group information is stored in the cloud database and the abnormal data group information is deleted.

[0023] By adopting the above technical solution, data is screened through model training to remove incorrect data and data irrelevant to identity information, avoiding data redundancy and improving the effectiveness and efficiency of cloud data storage.

[0024] Optionally, the method of storing normal data group information in a cloud database and deleting abnormal data group information includes:

[0025] The data strips falling into the abnormal data group information are defined as abnormal data strip information, and the identity tag information corresponding to the abnormal data strip information is defined as abnormal identity tag information;

[0026] When similar data pieces corresponding to the abnormal identity tag information are subsequently obtained and determined to fall into the abnormal data group information, the number of times information is accumulated, and the similar data pieces corresponding to the abnormal identity tag information and determined to fall into the abnormal data group information are defined as abnormal similar data pieces;

[0027] Determine whether the number of times is greater than or equal to a preset critical number of times;

[0028] If it is less than, the abnormal data piece information and abnormal similar data piece information are classified into the normal data group information and attached with the preset abnormal data label information;

[0029] When the number of times information no longer increases, the abnormal data piece information and abnormal similar data piece information are deleted;

[0030] If it is greater than or equal to, the preset human judgment information is output this time, and when the human judgment information is still abnormal data strip information or abnormal similar data strip information, the abnormal data strip information and abnormal similar data strip information are separately formed into a training sample set to train the data detection model.

[0031] By adopting the above technical solution, by marking and counting data with the same identity label, when it is judged as abnormal data multiple times, it means that it is very likely that the misjudgment is caused by model error. In this case, it is necessary to use seemingly abnormal data to train the model to optimize the model, thereby improving the dynamic learning ability and accuracy of model training.

[0032] Optionally, methods for obtaining identity information include:

[0033] Obtain identity identification request information entered by the user;

[0034] Analyze the current identity account information input into the identity identification request information;

[0035] Performing a matching analysis based on the identity account information stored in a preset account database and the identity recognition request information to determine the identity account corresponding to the identity recognition request information, and defining the identity account as historical identity account information;

[0036] Determine whether the current identity account information is consistent with the historical identity account information;

[0037] If they are consistent, the identity identification request information is input as the retrieved identity information;

[0038] If there is a mismatch, a matching analysis is performed based on the identity identification code information and the historical identity account information stored in the preset unique identification database to determine the identity identification code corresponding to the historical identity account information, and the identity identification code is defined as the historical identity identification code information;

[0039] Output identity code request information;

[0040] When receiving the identity identification code information corresponding to the identity identification code request information, matching it with the historical identity identification code information;

[0041] If the match is successful, the current identity account information and the historical identity account information are bound and the identity recognition request information is input as the retrieved identity information;

[0042] If the match is unsuccessful, an identity code input error message is output.

[0043] By adopting the above technical solution, when users use different accounts, they are identified by a unique identification code so that users can retrieve the content of data under the same identity on different accounts, avoiding the situation where historical data cannot be obtained due to account loss, and improving the traceability of cloud data.

[0044] Optionally, if the match is unsuccessful, the method of outputting an identity code input error message includes:

[0045] Determining permission to retrieve account information based on user retrieval request information;

[0046] Performing a matching analysis based on the identity identification code information stored in the unique identification database and the account information allowed to be retrieved to determine the identity identification code corresponding to the account information allowed to be retrieved, and defining the identity identification code as the allowed identity identification code information;

[0047] Matching the identity identification code information corresponding to the received identity identification code request information with the allowed identity identification code information;

[0048] If the match is successful, the identity recognition request information is input as the retrieved identity information;

[0049] If the match is unsuccessful, an identity code input error message is output.

[0050] By adopting the above technical solution and setting the allowed identity identification code information, different but related people who are allowed to view the data can also view the data through their own accounts. For example, students' data can also be viewed by teachers, which improves the diversity of data viewing.

[0051] Optionally, the method further includes searching for data group information if the identity identification code information corresponding to the identity identification code request information successfully matches the historical identity identification code information or the allowed identity identification code information. The method includes:

[0052] According to the current identity account information and the historical identity account information, the data group information is searched from the cloud database respectively, and the data group information corresponding to the current identity account information is defined as the current data group information, and the data group information corresponding to the historical identity account information is defined as the historical data group information;

[0053] The current data group information and the historical data group information are matched to obtain a union, and the union is output as the data group information.

[0054] By adopting the above technical solution, when two accounts have the same identity identification code or allowed identity identification code, it means that the data of the two accounts are interoperable. At this time, the data of this account can also be viewed through another account, thereby improving the interoperability of cloud data.

[0055] Optionally, a method of matching the current data group information with the historical data group information to obtain a union, and outputting the union as the data group information includes:

[0056] Determine whether the data items included in the current data group information are the same as the data items included in the historical data group information;

[0057] If so, the same data strip is defined as the same data strip in the data group information, and the same data strip in the historical data group information is output and the same data strip in the current data group information is deleted;

[0058] If not, decomposing the data strips contained in the current data group information and the data strips contained in the historical data group information to obtain the basic content and result content of the data strips contained in the current data group information and the data strips contained in the historical data group information, defining the basic content corresponding to the data strips contained in the current data group information as current basic content information, defining the result content corresponding to the data strips contained in the current data group information as current result content information, defining the basic content corresponding to the data strips contained in the historical data group information as historical basic content information, and defining the result content corresponding to the data strips contained in the historical data group information as historical result content information;

[0059] Determine whether the current basic content information is consistent with the historical basic content information;

[0060] If they are consistent, the data strips corresponding to the current result content information and the historical result content information are defined as the current similar data strip information and the historical similar data strip information respectively;

[0061] Use the data detection model to train the model on the current similar data strip information and the historical similar data strip information to obtain normal data strip information and abnormal data strip information;

[0062] Outputting the data strips belonging to normal data strip information among the current similar data strip information and the historical similar data strip information;

[0063] If they are inconsistent, both the data strips included in the current data group information and the data strips included in the historical data group information will be output.

[0064] By adopting the above technical solution, if the data strips of the same type are exactly the same, only one can be output. However, when the two are only partially the same, it is necessary to determine whether the anomaly is caused by the data anomaly or different collection nodes, so as to determine whether both data will be output. When merging the data, different merging methods are adopted according to different situations, thereby improving the rationality and intelligence of cloud data processing.

[0065] In a second aspect, the present application provides a cloud data retrieval system that adopts the following technical solutions:

[0066] A cloud data retrieval system, comprising:

[0067] The acquisition module is used to obtain user access request information, access identity information, user input data information, identity recognition request information and identity recognition code information;

[0068] A memory for storing a program for controlling any of the above-mentioned cloud data retrieval methods;

[0069] The program in the processor memory can be loaded and executed by the processor and implement the control method of any of the above-mentioned cloud data retrieval methods.

[0070] By adopting the above technical solution, the data is first classified and suggestions are generated in the cloud, and then the corresponding data and suggestions that can be retrieved are found by verifying the corresponding identity. Finally, the data and suggestions with complex requirements are filtered from the data crowd according to the user's needs and packaged and sent to the user, so that the user can find the most accurate data without having to filter it by himself, reducing the processing time after downloading and improving the intelligence and pertinence of cloud data retrieval.

[0071] In a third aspect, the present application provides a smart terminal, which adopts the following technical solutions:

[0072] The intelligent terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute any of the above-mentioned cloud data retrieval methods.

[0073] By adopting the above technical solution, the data is first classified and suggestions are generated in the cloud, and then the corresponding data and suggestions that can be retrieved are found by verifying the corresponding identity. Finally, the data and suggestions with complex requirements are filtered from the data crowd according to the user's needs and packaged and sent to the user, so that the user can find the most accurate data without having to filter it by himself, reducing the processing time after downloading and improving the intelligence and pertinence of cloud data retrieval.

[0074] Fourthly, the present application provides a computer storage medium that can store corresponding programs and has the characteristics of large memory and self-analysis processing.

[0075] Computer-readable storage medium, using the following technical solution:

[0076] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any of the above-mentioned cloud data retrieval methods.

[0077] By adopting the above technical solution, the data is first classified and suggestions are generated in the cloud, and then the corresponding data and suggestions that can be retrieved are found by verifying the corresponding identity. Finally, the data and suggestions with complex requirements are filtered from the data crowd according to the user's needs and packaged and sent to the user, so that the user can find the most accurate data without having to filter it by himself, reducing the processing time after downloading and improving the intelligence and pertinence of cloud data retrieval.

[0078] In summary, this application includes at least the following beneficial technical effects:

[0079] 1. Based on user needs, the system filters the data and suggestions required by the complex data from the public and packages them for users, allowing them to find the most accurate data without having to filter them themselves, thus improving the intelligence and pertinence of cloud data retrieval;

[0080] 2. Identification through a unique identification code avoids the situation where historical data cannot be obtained due to account loss, and improves the traceability of cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 This is a flow chart of a cloud data retrieval method in an embodiment of the present application.

[0082] Figure 2 It is a flowchart of the method for establishing a cloud database in an embodiment of the present application.

[0083] Figure 3 This is a flowchart of a method for storing normal data group information in a cloud database and deleting abnormal data group information in an embodiment of the present application.

[0084] Figure 4This is a flowchart of a method for obtaining and retrieving identity information in an embodiment of the present application.

[0085] Figure 5 This is a flowchart of a method for outputting an identity code input error message if the match is unsuccessful in an embodiment of the present application.

[0086] Figure 6 This is a flowchart of a method for searching for data group information if the identity identification code information corresponding to the identity identification code request information successfully matches the historical identity identification code information or the allowed identity identification code information in an embodiment of the present application.

[0087] Figure 7 This is a flowchart of a method for matching current data group information with historical data group information to obtain a union, and outputting the union as data group information in an embodiment of the present application.

[0088] Figure 8 This is a system module diagram of a cloud data retrieval method in an embodiment of the present application. DETAILED DESCRIPTION

[0089] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figures 1-8 It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0090] The present application embodiment discloses a method for retrieving cloud data. Figure 1 , a cloud data retrieval method includes:

[0091] Step 100: Obtain user retrieval request information and retrieval identity information.

[0092] User retrieval request information is a request sent by a user to the cloud from the software on their smart device to retrieve data. It includes the data object, data category, and data content to be retrieved. Retrieval identity information is the identity of the user issuing the user retrieval request information. For example, a teacher enters information in their teacher app to retrieve the height and weight data of a certain student.

[0093] Step 101: Search for data strips that are allowed to be viewed from a preset cloud database based on the retrieved identity information, combine all the data strips, and define the combination as data group information.

[0094] The cloud database is a cloud-based data platform that contains all data uploaded by the Education Bureau, teachers, and individuals. A data item represents all the data associated with a particular piece of data. For example, if the retrieved identity is a teacher, then the only data allowed to be viewed are their students. A data item represents all the data available for that student, including height, weight, and age. Data group information represents the combination of all the data items that are allowed to be viewed. For example, data related to all students taught by a teacher, including school classes and training.

[0095] Step 102: Generate category label information and cloud suggestion information based on the data group information, and form a data strip with a mapping relationship between the category label information and the cloud suggestion information, and define the data strip as suggestion data strip information.

[0096] Category label information is the label information of the category corresponding to the data. Here, the data group information is classified. Here, the problems caused by the data are classified. Therefore, the cloud will analyze and judge the data to classify the types of students, such as obesity rate, thinness rate, myopia rate, and poor athletic ability. This allows the cloud to more accurately search for these students. Cloud recommendation information is the recommended information output by the cloud for different category label information. For example: if the category label information is obesity, then the cloud's recommendation is to exercise to lose weight. The recommended data item information is the mapping relationship between category label information and cloud recommendation information.

[0097] In this embodiment of the present application, the cloud outputs different suggestions based on the retrieved identity information, that is, different suggestions are output based on different ports. For example, the education bureau receives suggestions such as: control each school and conduct four major phenomena assessments on the schools; the physical education teacher receives suggestions from the cloud and also receives suggestions from the education bureau and the school, so that he or she can adjust his or her teaching methods and use new teaching methods to reduce the four major phenomena; and the parent receives suggestions from the cloud and the physical education teacher, so that he or she can teach the student to exercise effectively, thereby reducing the four major phenomena.

[0098] Step 103: Analyze the user's retrieval request information to obtain retrieval tag information.

[0099] Retrieving tag information involves retrieving tags from the user's request. This includes any type of tag. For example, a PE teacher could enter a certain student or an obese student. The analysis method is similar to the viewing method, and each data item is labeled with a corresponding tag to facilitate user search.

[0100] Step 104: Filter the data group information based on the retrieved tag information to obtain data strips that match the retrieved tag information, combine the data strips that match the retrieved tag information, and define the combination as filtered data group information.

[0101] The screening method is essentially a text matching method.

[0102] Step 105: Filter the suggested data strip information based on the retrieved tag information to obtain a suggested data strip compounded with the retrieved tag information, and define the suggested data strip as filtered suggested data strip information.

[0103] Step 106: Output the filtered data group information and the filtered suggested data item information on the client corresponding to the user's request information.

[0104] Reference Figure 2 ,The method of establishing a cloud database includes :

[0105] Step 200: Obtain user input data strip information.

[0106] The data strip information input by the user is the data strip information input by the user on their respective smart devices. The acquisition method is manual input, for example, the cloud data platform receives data uploaded by various health management APPs, such as: the teacher-side app, the coach-side app and the student-side app. The teacher-side, coach-side and student-side apps can all be operated with smart tools as carriers, such as: apps on mobile phones. Teachers, coaches and students all have corresponding smart devices to operate, then there are corresponding options on the interface of the corresponding person’s smart device, such as: "Upload" and "View". After manual input, the system automatically queries the current time node and the previous correction and examination situation, and comprehensively analyzes the current node of correction or examination. The data transmission method can be Wifi, Internet, GSM, etc.

[0107] Real-time measurements are obtained using the corresponding smart devices tested and consumer-end products used for daily training. Examples of these smart devices include: a smart standing long jump machine, a smart standing long jump machine, a smart middle- and long-distance running machine, a smart sprint running machine, a smart rope skipping machine, a smart handgrip strength meter, a smart height and weight meter, a smart spirometer, a smart basketball dribbling machine, a smart soccer machine, a smart pull-up machine, a smart stair machine, a smart swimming machine, a smart sit-up machine, a smart push-up machine, a smart volleyball machine, a smart forward bend machine, a smart vision meter, a smart shot put machine, a 30-person middle- and long-distance running smart tester, and a 20- or 30-person rope skipping smart tester. Consumer-end products used for training include smart running shoes, a smart posture corrector, an eye protection rehabilitation trainer, a smart body fat scale, a smart rope skipping machine, a smart sit-up machine, an eight-electrode height and weight meter, a smart waist circumference meter, a smart ophthalmometer, a smart treadmill, a smart toilet, and a smart desk lamp, all capable of intelligently monitoring dynamic data. These devices monitor in real time and then upload the data to home and school sports and health management apps, such as student apps, teacher apps, and coach apps. The transmission methods can be Wi-Fi, Bluetooth, GSM, and the Internet.

[0108] Step 201: Analyze the identity account information inputted by the client, input the identity account definition into the identity account information, and form identity tag information.

[0109] The identity tag information is the tag information of the identity account information. Here, the tag is automatically generated for the purpose of binding the user's identity account and data.

[0110] Step 202: Analyze the user input data piece information and the identity tag information to determine similar data piece information corresponding to the identity tag information stored in the cloud database.

[0111] Similar data items are data items entered using the same identity account, including data items entered by other users using the same identity. These items are identified using identity tag information. Identity tag information can be either identified using the identity account during data transmission or entered by the user themselves.

[0112] Step 203: combining the user input data piece information and similar data piece information to form sample data group information.

[0113] The sample data group information is the information of the group formed by all the data strips bound to the identity tag information.

[0114] Step 204: Use a preset data detection model to perform model training on the sample data group information to obtain normal data group information and abnormal data group information.

[0115] A data detection model is used to detect data accuracy. This could be a support vector machine model, used to determine accuracy. A normal data group is defined as a group of data items that conform to common sense and a consistent trend. An abnormal data group is defined as a group of data items that are inconsistent with common sense or a consistent trend. For example, if height and weight have been increasing over time and then suddenly drop sharply, this is generally not common sense.

[0116] Step 205: Store the normal data group information in the cloud database and delete the abnormal data group information.

[0117] Reference Figure 3 The method of storing normal data group information in a cloud database and deleting abnormal data group information includes:

[0118] Step 300: defining a data strip that falls into the abnormal data group information as abnormal data strip information, and defining identity tag information corresponding to the abnormal data strip information as abnormal identity tag information.

[0119] Step 301: accumulating the number of times similar data pieces corresponding to abnormal identity tag information are subsequently obtained and determined to fall into abnormal data group information, defining similar data pieces corresponding to abnormal identity tag information and determined to fall into abnormal data group information as abnormal similar data pieces.

[0120] The number of times information is information about the number of times similar data pieces corresponding to abnormal identity tag information are obtained and determined to fall into abnormal data group information.

[0121] Step 302: Determine whether the number of times is greater than or equal to a preset critical number of times.

[0122] The critical number of times information is information of a number of times that is artificially defined.

[0123] Step 3021: If it is less than, the abnormal data piece information and abnormal similar data piece information are classified into normal data group information and attached with preset abnormal data label information.

[0124] Although abnormal data labels are stored in the cloud within the normal data group, they are for reference only and may not accurately reflect the data labels. If the data is less than , this indicates a possible data anomaly, but this could be an error in the data detection model. Therefore, the data is initially classified as normal data. However, to distinguish it from other data, the abnormal data labels are attached to alert users who retrieve it.

[0125] Step 3022: If it is greater than or equal to, then the preset human judgment information is output this time, and when the human judgment information is still abnormal data strip information or abnormally similar data strip information, the abnormal data strip information and the abnormally similar data strip information are separately formed into a training sample set to train the data detection model.

[0126] Human judgment information is information that is judged manually. When it is greater than , it means that an error is detected every time, and the user is required to make a judgment. When the user also judges it as an error, it can be used as a reference for error data to train the data detection model.

[0127] Step 303: When the number of times information no longer increases, the abnormal data piece information and the abnormal similar data piece information are deleted.

[0128] When there are no more abnormal data pieces or abnormally similar data pieces in the process of less than the critical number of times, it means that there is no problem with the model and the wrong data can be deleted.

[0129] Reference Figure 4 , the methods for obtaining identity information include:

[0130] Step 400: Obtain identity recognition request information input by the user.

[0131] The identity request information is the information that the user requests to retrieve data under a certain identity. The method of obtaining the information is the method input by the user.

[0132] Step 401: Analyze the current identity account information of the input identity recognition request information.

[0133] The current identity account information is the identity account information of the user who logged in to the app.

[0134] Step 402: Perform matching analysis based on the identity account information and the identity recognition request information stored in the preset account database to determine the identity account corresponding to the identity recognition request information, and define the identity account as historical identity account information.

[0135] Historical identity account information refers to the historical identity account information corresponding to the identification request information. A database stores a mapping between identity account information and identification request information. When a user enters an identification request information, the system automatically maps the input login identity account to the user's login identity and stores this mapping. For example, if the identification request information is "Requesting the identity of a certain physical education teacher," the historical identity account information would be the identity account of that certain physical education teacher. When the system receives the corresponding identification request information, it automatically locates the corresponding identity account and outputs it as the historical identity account information.

[0136] Step 403: Determine whether the current identity account information is consistent with the historical identity account information.

[0137] Step 4031: If they are consistent, the identity recognition request information is input as the retrieved identity information.

[0138] If they are consistent, it means that the identity identification request information can be met, and the corresponding retrieved identity information will be automatically input.

[0139] Step 4032: If there is no inconsistency, a matching analysis is performed based on the identity identification code information and the historical identity account information stored in the preset unique identification database to determine the identity identification code corresponding to the historical identity account information, and the identity identification code is defined as the historical identity identification code information.

[0140] Historical identity identification code information is the unique identifier for historical identity account information, i.e., information that can serve as the sole basis for identity identification. A database stores a mapping between identity identification code information and historical identity account information. This mapping is manually set by personnel in this field or automatically generated by the system when a user registers their identity account information. When the system receives the corresponding historical identity account information, it automatically searches the database for the corresponding identity identification code and outputs it as the historical identity identification code information.

[0141] Step 404: Output identity code request information.

[0142] The identity identification code request information is a request message displayed on the client where the identity identification request information is input, prompting the user to input a unique identification code.

[0143] Step 405: When the identity identification code information corresponding to the identity identification code request information is received, the identity identification code information is matched with the historical identity identification code information.

[0144] Step 4051: If the match is successful, the current identity account information and the historical identity account information are bound and the identity recognition request information is input as the retrieved identity information.

[0145] If the match is successful, it means that the user can use the content of the corresponding identity account and can normally view the data of the user identity corresponding to the identity recognition request information.

[0146] Step 4052: If the match is unsuccessful, an identity code input error message is output.

[0147] The information about incorrect identity identification code input is that the identity account number is incorrect and the identity identification code is incorrect.

[0148] Reference Figure 5 If the match is unsuccessful, the method of outputting the identity code input error message includes:

[0149] Step 500: Determine whether to allow access to account information based on the user's access request information.

[0150] The account information allowed to be retrieved is the information of the identity account corresponding to the user's request information.

[0151] Step 501: Perform matching analysis based on the identity identification code information stored in the unique identification database and the allowed account information to determine the identity identification code corresponding to the allowed account information, and define the identity identification code as the allowed identity identification code information.

[0152] The permitted identity identification code information is information about the identity identification code corresponding to the account information that is permitted to be retrieved. The database is established in the same manner as in step 4032 and will not be further described here. When the system receives the corresponding permitted account information, it automatically searches the database to obtain the identity identification code, thereby permitting the output of the identity identification code information.

[0153] Step 502: Match the identity identification code information corresponding to the received identity identification code request information with the allowed identity identification code information.

[0154] The purpose of matching is to ensure that even though the user is logged in as the client, they are also able to access data from other people. For example, if a physical education teacher enters a request for the identity of a certain student, the identity code of the student is allowed.

[0155] Step 5021: If the match is successful, the identity recognition request information is input as the retrieved identity information.

[0156] If successful, it means that it can be retrieved and it will be retrieved automatically.

[0157] Step 5022: If the match is unsuccessful, an identity code input error message is output.

[0158] Reference Figure 6 , further comprising a method for searching data group information if the identity identification code information corresponding to the identity identification code request information successfully matches the historical identity identification code information or the allowed identity identification code information, the method comprising:

[0159] Step 600: Search for data group information from the cloud database based on the current identity account information and the historical identity account information, define the data group information corresponding to the current identity account information as the current data group information, and define the data group information corresponding to the historical identity account information as the historical data group information.

[0160] When the match is successful, in order to combine the data of the two, it is necessary to search for the data group information input by the corresponding identity account respectively.

[0161] Step 601: Match the current data group information with the historical data group information to obtain a union, and output the union as the data group information.

[0162] The purpose of the union is to integrate the data entered by both ends. When registering an account, you may only enter the data but not retrieve it. When the two identity accounts are confirmed to be the same user, the data of the two can be shared.

[0163] Reference Figure 7 , matching the current data group information with the historical data group information to obtain a union, and outputting the union as the data group information includes:

[0164] Step 700: Determine whether the data strips included in the current data group information are the same as the data strips included in the historical data group information.

[0165] The purpose of judgment is to prevent duplication.

[0166] Step 7001: If yes, define the same data strip as the same data strip in the data group information, output the same data strip in the historical data group information and delete the same data strip in the current data group information.

[0167] If so, it means that the two data are the same. In order to avoid duplication, either one can be deleted.

[0168] Step 7002: If not, decompose the data strips contained in the current data group information and the data strips contained in the historical data group information to obtain the basic content and result content of the data strips contained in the current data group information and the data strips contained in the historical data group information, define the basic content corresponding to the data strips contained in the current data group information as current basic content information, define the result content corresponding to the data strips contained in the current data group information as current result content information, define the basic content corresponding to the data strips contained in the historical data group information as historical basic content information, and define the result content corresponding to the data strips contained in the historical data group information as historical result content information.

[0169] Basic content refers to the basic content in the data strip, such as name, gender, etc., and also includes the time when the data was recorded, etc. Result content refers to the data of different users at different times, such as height, weight, etc.

[0170] Step 701: Determine whether the current basic content information is consistent with the historical basic content information.

[0171] The purpose of the judgment is to determine whether it is the same situation.

[0172] Step 7011: If they are consistent, the data strips corresponding to the current result content information and the historical result content information are defined as current similar data strip information and historical similar data strip information respectively.

[0173] If they are consistent, it means that they are different results under the same circumstances, which means that at least one of the data is wrong, and a judgment is needed.

[0174] Step 7012: If there is inconsistency, both the data strips included in the current data group information and the data strips included in the historical data group information are output.

[0175] If there is inconsistency, it means that the two are not in the same situation, and there is no comparability, so they can be output directly.

[0176] Step 702: Use a data detection model to perform model training on current similar data strip information and historical similar data strip information to obtain normal data strip information and abnormal data strip information.

[0177] Step 703: Output the data strips that are normal data strips in the current similar data strip information and the historical similar data strip information.

[0178] Based on the same inventive concept, an embodiment of the present invention provides a cloud data retrieval system.

[0179] Reference Figure 8 , a cloud data retrieval system, comprising:

[0180] The acquisition module is used to obtain user access request information, access identity information, user input data information, identity recognition request information and identity recognition code information;

[0181] A memory for storing a program for a control method of a cloud data retrieval method;

[0182] The program in the processor memory can be loaded and executed by the processor and realize a control method for a cloud data retrieval method.

[0183] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the division of the above-mentioned functional modules is only used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-mentioned systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0184] An embodiment of the present invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed by a cloud data retrieval method.

[0185] Computer storage media include, for example, various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0186] Based on the same inventive concept, an embodiment of the present invention provides a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute a cloud data retrieval method.

[0187] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise stated, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise stated, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A cloud data retrieval method, characterized in that: include: Obtain user retrieval request information and retrieval identity information; Searching for data strips that are allowed to be viewed from a preset cloud database based on the retrieved identity information, combining all the data strips, and defining the combination as data group information; Generate category label information and cloud-based suggestion information based on the data group information, and form a data strip with a mapping relationship between the category label information and the cloud-based suggestion information, and define the data strip as suggestion data strip information; Analyze the user's call request information to obtain call tag information; Filtering the data group information based on the retrieved tag information to obtain data strips that match the retrieved tag information, combining the data strips that match the retrieved tag information, and defining the combination as filtered data group information; Filtering the suggested data strip information based on the retrieved tag information to obtain a suggested data strip with the composite retrieved tag information, and defining the suggested data strip as filtered suggested data strip information; Output the filtered data group information and the filtered data item information on the client corresponding to the user's request information; Methods for obtaining and retrieving identity information include: Obtain identity identification request information entered by the user; Analyze the current identity account information input into the identity identification request information; Performing a matching analysis based on the identity account information stored in a preset account database and the identity recognition request information to determine the identity account corresponding to the identity recognition request information, and defining the identity account as historical identity account information; Determine whether the current identity account information is consistent with the historical identity account information; If they are consistent, the identity identification request information is input as the retrieved identity information; If there is a mismatch, a matching analysis is performed based on the identity identification code information and the historical identity account information stored in the preset unique identification database to determine the identity identification code corresponding to the historical identity account information, and the identity identification code is defined as the historical identity identification code information; Output identity code request information; When receiving the identity identification code information corresponding to the identity identification code request information, matching it with the historical identity identification code information; If the match is successful, the current identity account information and the historical identity account information are bound and the identity recognition request information is input as the retrieved identity information; If the match is unsuccessful, an identity code input error message is output.

2. A cloud data retrieval method according to claim 1, characterized in that: Methods for establishing a cloud database include: Get the user input data information; Analyze the identity account information input by the client, input the identity account definition into the identity account information, and form identity tag information; Analyze the user input data strip information and identity tag information to determine similar data strip information corresponding to the identity tag information already stored in the cloud database; Combining the user input data piece information and similar data piece information to form sample data group information; Use a preset data detection model to perform model training on the sample data group information to obtain normal data group information and abnormal data group information; The normal data group information is stored in the cloud database and the abnormal data group information is deleted.

3. A cloud data retrieval method according to claim 2, characterized in that: The method of storing normal data group information in a cloud database and deleting abnormal data group information includes: The data strips falling into the abnormal data group information are defined as abnormal data strip information, and the identity tag information corresponding to the abnormal data strip information is defined as abnormal identity tag information; When similar data pieces corresponding to the abnormal identity tag information are subsequently obtained and determined to fall into the abnormal data group information, the number of times information is accumulated, and the similar data pieces corresponding to the abnormal identity tag information and determined to fall into the abnormal data group information are defined as abnormal similar data pieces; Determine whether the number of times is greater than or equal to a preset critical number of times; If it is less than, the abnormal data piece information and abnormal similar data piece information are classified into the normal data group information and attached with the preset abnormal data label information; When the number of times information no longer increases, the abnormal data piece information and abnormal similar data piece information are deleted; If it is greater than or equal to, the preset human judgment information is output this time, and when the human judgment information is still abnormal data strip information or abnormal similar data strip information, the abnormal data strip information and abnormal similar data strip information are separately formed into a training sample set to train the data detection model.

4. The cloud data retrieval method according to claim 1, characterized in that: If the match is unsuccessful, the method of outputting the identity code input error message includes: Determining permission to retrieve account information based on user retrieval request information; Performing a matching analysis based on the identity identification code information stored in the unique identification database and the account information allowed to be retrieved to determine the identity identification code corresponding to the account information allowed to be retrieved, and defining the identity identification code as the allowed identity identification code information; Matching the identity identification code information corresponding to the received identity identification code request information with the allowed identity identification code information; If the match is successful, the identity recognition request information is input as the retrieved identity information; If the match is unsuccessful, an identity code input error message is output.

5. A cloud data retrieval method according to claim 4, characterized in that: The method further includes searching for data group information if the identity identification code information corresponding to the identity identification code request information successfully matches the historical identity identification code information or the allowed identity identification code information, the method comprising: According to the current identity account information and the historical identity account information, the data group information is searched from the cloud database respectively, and the data group information corresponding to the current identity account information is defined as the current data group information, and the data group information corresponding to the historical identity account information is defined as the historical data group information; The current data group information and the historical data group information are matched to obtain a union, and the union is output as the data group information.

6. A cloud data retrieval method according to claim 5, characterized in that: The method of matching the current data group information with the historical data group information to obtain a union, and outputting the union as the data group information includes: Determine whether the data items included in the current data group information are the same as the data items included in the historical data group information; If so, the same data strip is defined as the same data strip in the data group information, and the same data strip in the historical data group information is output and the same data strip in the current data group information is deleted; If not, decomposing the data strips contained in the current data group information and the data strips contained in the historical data group information to obtain the basic content and result content of the data strips contained in the current data group information and the data strips contained in the historical data group information, defining the basic content corresponding to the data strips contained in the current data group information as current basic content information, defining the result content corresponding to the data strips contained in the current data group information as current result content information, defining the basic content corresponding to the data strips contained in the historical data group information as historical basic content information, and defining the result content corresponding to the data strips contained in the historical data group information as historical result content information; Determine whether the current basic content information is consistent with the historical basic content information; If they are consistent, the data strips corresponding to the current result content information and the historical result content information are defined as the current similar data strip information and the historical similar data strip information respectively; Use the data detection model to train the model on the current similar data strip information and the historical similar data strip information to obtain normal data strip information and abnormal data strip information; Outputting the data strips belonging to normal data strip information among the current similar data strip information and the historical similar data strip information; If they are inconsistent, both the data strips included in the current data group information and the data strips included in the historical data group information will be output.

7. A cloud data retrieval system, characterized in that: include: The acquisition module is used to obtain user access request information, access identity information, user input data information, identity recognition request information and identity recognition code information; A memory for storing a program of a control method for a cloud data retrieval method according to any one of claims 1 to 6; The program in the processor memory can be loaded and executed by the processor and implements a control method for a cloud data retrieval method as described in any one of claims 1 to 6.

8. Intelligent terminal, characterized in that, The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes a cloud data retrieval method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that A computer program is stored which can be loaded by a processor and executes a cloud data retrieval method according to any one of claims 1 to 6.

Images