A data query method and device based on cloud computing

Abstract

The present application discloses a data query method and device based on cloud computing, and relates to the field of data processing technology. First, the natural language query data input by the user is obtained, and information is extracted from the natural language query data to obtain a multidimensional query label, and a fuzzy query condition is constructed based on the multidimensional query label, and the corresponding data query result is obtained based on the fuzzy query condition. Then, according to the multidimensional query label, a data query result evaluation rule is constructed, and the data query result is screened according to the evaluation rule and the particle cluster algorithm to obtain the target data query result. Finally, the sensitive information of the target data query result is differentially desensitized to obtain the desensitized target data query result corresponding to different identity permissions, and the desensitized target data query result matching the identity permission is displayed to the user. In the present application, the accuracy and efficiency of data query are improved, and the security of the user's sensitive information is protected.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data query method and device based on cloud computing. Background Art

[0002] Data system refers to the system used to manage and process various related data in the medical field. The system mainly includes data collection, storage, management, analysis, sharing and application, providing users with convenient data query and management tools to better support the work.

[0003] At present, the data in existing data systems are not friendly enough to ordinary people. Most ordinary people have not received professional computer training, so they don’t know how to use computers or mobile devices to query data. In addition, the data displayed in data systems is usually complex, requiring a certain understanding of various professional terms and data structures to query data, so ordinary people usually cannot obtain the data they want. Summary of the invention

[0004] In view of the existing problems, this application proposes a data query method and device based on cloud computing. The specific technical solution is as follows:

[0005] In a first aspect of the present application, a cloud computing-based data query method is provided, which is applied to a query terminal, and the method includes:

[0006] Obtain natural language query data input by the user, and extract information from the natural language query data to obtain multi-dimensional query labels;

[0007] According to the multidimensional query label, at least one fuzzy query condition is constructed, and according to the fuzzy query condition, corresponding data query results are obtained;

[0008] According to the multi-dimensional query labels, the data query result evaluation rules are constructed, and the data query results are screened according to the evaluation rules and the particle swarm algorithm to obtain the target data query results;

[0009] Display the non-sensitive information of the target data query results to the user and send a confirmation reminder. After receiving the confirmation information fed back by the user, perform differential desensitization on the sensitive information of the target data query results to obtain desensitized target data query results corresponding to different identity permissions;

[0010] Obtain the user's identity permissions and display to the user the desensitized target data query results that match the identity permissions.

[0011] Optionally, the step of obtaining natural language query data input by a user, extracting information from the natural language query data, and obtaining a multi-dimensional query tag includes:

[0012] Performing natural language processing on natural language query data input by the user, wherein the natural language processing includes at least one of word segmentation, part-of-speech tagging, and named entity recognition processing;

[0013] Extracting information sentence by sentence from the natural language query data that has undergone natural language processing to obtain at least one candidate information extraction result;

[0014] According to the amount of information contained in the candidate information extraction results, the target information extraction results are screened out from the candidate information extraction results;

[0015] Combine the target information extraction results into multi-dimensional query labels.

[0016] Optionally, the step of combining the target information results into a multi-dimensional query tag includes:

[0017] determining attribute characteristics that define a multidimensional query label;

[0018] According to the attribute characteristics, the target information extraction results are mapped to multidimensional query labels.

[0019] Optionally, the step of constructing at least one fuzzy query condition according to the multidimensional query tag includes:

[0020] Determine the query keywords corresponding to each query dimension, and determine the corresponding query matching method based on the query keywords;

[0021] According to the matching method of the query corresponding to each query dimension and the preset format of the query statement, a fuzzy query condition is constructed.

[0022] Optionally, the step of screening the data query results according to the evaluation rules and the particle swarm algorithm to obtain the target data query results includes:

[0023] According to the evaluation rules, determine the fitness function;

[0024] Initialize the particle swarm and map each data query result into a particle;

[0025] Iteratively update the particle swarm, and according to the evaluation results of the fitness function, update the position and speed of the particle swarm, and calculate the new fitness value;

[0026] The optimal solution in the particle swarm is determined as the target data query result.

[0027] Optionally, the identity authority includes user authority and non-user authority, and the steps of performing differential desensitization processing on sensitive information of the target data query result to obtain desensitized target data query results corresponding to different identity authorities include:

[0028] When the user's identity authority is user authority, an encryption algorithm that does not carry a consistent salt value is called to desensitize sensitive information of the target data query result, and a desensitized target data query result consisting of fixed desensitized data is generated;

[0029] When the user's identity authority is non-user authority, an encryption algorithm with a consistent salt value is called to desensitize the sensitive information of the target data query result, and a desensitized target data query result consisting of random desensitized data is generated.

[0030] Optionally, when the user's identity authority is user authority, the method further includes:

[0031] Obtain the user's historical query records and preference attributes;

[0032] According to historical query records and preference attributes, the query results of desensitized target data consisting of fixed desensitized data are filtered.

[0033] In a second aspect, an embodiment of the present invention provides a data query device based on cloud computing, the device comprising:

[0034] An acquisition module is used to acquire natural language query data input by a user, and to extract information from the natural language query data to obtain a multi-dimensional query label;

[0035] A combination module, used to construct at least one fuzzy query condition according to the multi-dimensional query label, and obtain corresponding data query results according to the fuzzy query condition;

[0036] The query module is used to construct data query result evaluation rules based on multi-dimensional query tags, and filter the data query results according to the evaluation rules and particle swarm algorithm to obtain target data query results;

[0037] The desensitization module is used to display the non-sensitive information of the target data query results to the user and send a confirmation reminder, and after receiving the confirmation information fed back by the user, perform differential desensitization processing on the sensitive information of the target data query results to obtain desensitized target data query results corresponding to different identity permissions;

[0038] The display module is used to obtain the user's identity permissions and display the desensitized target data query results that match the identity permissions to the user.

[0039] Optionally, the acquisition module includes:

[0040] A language processing submodule, used to perform natural language processing on natural language query data input by the user, wherein the natural language processing includes at least one of word segmentation, part-of-speech tagging, and named entity recognition processing;

[0041] An information extraction submodule, configured to extract information sentence by sentence from the natural language query data that has undergone natural language processing, so as to obtain at least one candidate information extraction result;

[0042] The information screening submodule is used to screen out target information extraction results from the candidate information extraction results according to the amount of information contained in the candidate information extraction results;

[0043] The information combination submodule is used to combine the target information extraction results into multi-dimensional query labels.

[0044] Optionally, the information combination submodule includes:

[0045] An attribute determination unit, used to determine attribute features defining a multi-dimensional query tag;

[0046] The mapping unit is used to map the target information extraction result to the multi-dimensional query label according to the attribute characteristics.

[0047] Optionally, the combined module includes:

[0048] A query keyword determination submodule is used to determine the query keyword corresponding to each query dimension, and determine the corresponding query matching method according to the query keyword;

[0049] The construction submodule is used to construct fuzzy query conditions according to the matching method of the query corresponding to each query dimension and the preset format of the query statement.

[0050] Optionally, the query module includes:

[0051] A fitness function determination submodule is used to determine the fitness function according to the evaluation rules;

[0052] The initialization submodule is used to initialize the particle swarm and map each data query result into a particle;

[0053] The iterative submodule is used to iteratively update the particle swarm, and according to the evaluation results of the fitness function, update the position and speed of the particle swarm and calculate the new fitness value;

[0054] The output submodule is used to determine the optimal solution in the particle swarm as the target data query result.

[0055] Optionally, the desensitization module includes:

[0056] The first desensitizing submodule is used to call an encryption algorithm that does not carry a consistent salt value to desensitize sensitive information of the target data query result when the user's identity authority is a user authority, and generate a desensitized target data query result consisting of fixed desensitized data;

[0057] The second desensitizing sub-module is used to call an encryption algorithm with a consistent salt value when the user's identity authority is non-user authority, desensitize the sensitive information of the target data query result, and generate a desensitized target data query result consisting of random desensitized data.

[0058] Optionally, the first desensitization submodule further includes:

[0059] A record acquisition unit, used to acquire the user's historical query records and preference attributes;

[0060] The filtering unit is used to filter the desensitized target data query results consisting of fixed desensitized data according to historical query records and preference attributes.

[0061] A third aspect of an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0062] at least one processor; and a memory in communication with the at least one processor; wherein,

[0063] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can perform the method steps proposed in the first aspect of the embodiment of the present invention.

[0064] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the embodiment of the present invention.

[0065] This application has the following beneficial effects:

[0066] In the scheme of the present application, first, the natural language query data input by the user is obtained, and information is extracted from the natural language query data to obtain a multidimensional query label, and at least one fuzzy query condition is constructed based on the multidimensional query label, and the corresponding data query result is obtained based on the fuzzy query condition. Then, according to the multidimensional query label, a data query result evaluation rule is constructed, and the data query result is screened according to the evaluation rule and the particle cluster algorithm to obtain the target data query result. Finally, the sensitive information of the target data query result is differentially desensitized to obtain desensitized target data query results corresponding to different identity permissions, and the desensitized target data query results matching the identity permissions are displayed to the user. The present application has the following advantages:

[0067] 1. Improved the accuracy and efficiency of data query

[0068] By extracting information from natural language query data and constructing multi-dimensional query labels, we can more accurately capture the user's query intention and construct the corresponding fuzzy query conditions. This can effectively improve the accuracy and efficiency of data queries and reduce the waste of query time and resources.

[0069] 2. Optimized the quality of data query results

[0070] By constructing data query result evaluation rules based on multi-dimensional query tags and using particle clustering algorithm for screening, the quality of data query results can be optimized. In this way, some irrelevant or non-compliant data can be excluded, thereby obtaining more accurate and useful data query results.

[0071] 3. Protect the security of sensitive information

[0072] By performing differentiated desensitization on the target data query results, sensitive information in the query results can be protected to avoid information leakage and abuse. At the same time, according to the user's identity authority, the corresponding level of desensitized target data query results is provided to protect the security and privacy of sensitive information.

[0073] 4. Support various query methods

[0074] This solution supports the input of natural language query data, allowing users to express their query intentions through natural language. In this way, users' query methods are more flexible and diverse, do not require professional knowledge and skills, and are more convenient and easy to use.

[0075] 5. Improved user experience and satisfaction

[0076] By improving the accuracy and efficiency of data query, optimizing the quality of data query results, and protecting the security and privacy of sensitive information, the user experience and satisfaction can be improved. In this way, users are more willing to use the system for data query and management, so as to better maintain their own health. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0078] Figure 1 A schematic diagram of the electronic device structure of the hardware operating environment involved in an embodiment of the present application.

[0079] Figure 2 It is a step flow chart of a data query method based on cloud computing provided in an embodiment of the present application.

[0080] Figure 3It is a functional module diagram of a cloud computing-based data query device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0081] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0082] The solution of the present application is further described below in conjunction with the accompanying drawings.

[0083] Reference Figure 1 , Figure 1 A schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiment of the present application.

[0084] like Figure 1 As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0085] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0086] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating device, a data storage module, a network communication module, a user interface module and an electronic program.

[0087] exist Figure 1 In the electronic device shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be set in the electronic device, and the electronic device calls the cloud computing-based data query device stored in the memory 1005 through the processor 1001, and executes the cloud computing-based data query method provided in the embodiment of the present application.

[0088] Reference Figure 2 Based on the aforementioned hardware operating environment, an embodiment of the present application provides a data query method based on cloud computing, which is applied to a query terminal and may specifically include the following steps:

[0089] S201: Obtain natural language query data input by a user, and extract information from the natural language query data to obtain a multi-dimensional query tag.

[0090] In this embodiment, the query terminal refers to a device or software used by a user to query medical information, such as a computer, mobile phone, tablet computer and other intelligent terminal devices, and related medical information query software or applications. Through the query terminal, medical workers or users can query and obtain medical information, such as the patient's medical history, laboratory tests, imaging examinations, treatment plans, etc. In the medical care system, its main users are divided into two major categories: patients and users. For a very special group of patients, such as the elderly, due to their age and physical condition, they are usually unable to give very accurate query conditions to the medical care system through the query terminal, so they usually cannot obtain the query results they want. Therefore, voice recognition technology can be used to provide a voice query interface for the elderly patients, so that they can query in natural language without typing or inputting.

[0091] This method can greatly reduce the query threshold, improve query efficiency, and make patients and the elderly more comfortable and convenient. The specific implementation steps can be:

[0092] S201 - 1 : Perform natural language processing on natural language query data input by the user, wherein the natural language processing includes at least one of word segmentation, part-of-speech tagging, and named entity recognition processing.

[0093] In this embodiment, the purpose is to process the natural language query data input by the user and convert it into a computer-recognizable form. Through word segmentation, long sentences can be broken down into independent words, part-of-speech tagging can help understand the grammatical role of words, and named entity recognition can help identify entities in the text, such as names of people, places, and institutions. These processes can improve the accuracy and efficiency of information extraction.

[0094] S201 - 2 : Extracting information sentence by sentence from the natural language query data that has undergone natural language processing to obtain at least one candidate information extraction result.

[0095] In this embodiment, the purpose is to extract useful information from the natural language query data input by the user. Extracting information by sentence can improve the accuracy and readability of the extraction. Common information extraction methods include keyword extraction, entity extraction, relationship extraction, etc.

[0096] For example, when a user inputs "What medicines are available for treating insomnia?", after natural language processing, the query intent "What medicines are available for treating insomnia" can be extracted according to the sentence, and the keywords "treatment", "insomnia", and "medication" can be extracted.

[0097] S201-3: Filtering out target information extraction results from the candidate information extraction results according to the amount of information contained in the candidate information extraction results.

[0098] In this embodiment, the purpose is to select the most informative and relevant information from the candidate information extraction results as the target information extraction result. The evaluation and selection can be performed through some evaluation indicators, such as keyword importance, entity weight, relationship strength, etc.

[0099] As an example, after the keywords "treatment", "insomnia" and "drugs" are extracted from the user query, the target information extraction result of "insomnia drug treatment" can be filtered out according to the weights and relevance of these keywords.

[0100] S201-4: Combine the target information extraction results into a multi-dimensional query label.

[0101] In this embodiment, before combining the target information extraction results into a multidimensional query tag, it is necessary to determine which information is useful and which information can be ignored. Generally speaking, screening and filtering can be performed based on factors such as query subject, query field, query purpose, etc. Then, the useful information is combined according to certain rules to form a multidimensional query tag, so that users can perform more accurate queries in search engines or databases.

[0102] For example, if the user enters the query "I want to know what drugs are available for treating high blood pressure?", after natural language processing and information extraction, the following information extraction results can be obtained: Topic: Treatment of high blood pressure, Category: Drugs, Function: Blood pressure reduction. Then, this information is combined according to certain rules to form multi-dimensional query tags, such as "blood pressure reduction drugs", "drugs for treating high blood pressure", etc. Users can use these multi-dimensional query tags to perform more accurate queries in search engines or databases, thereby quickly obtaining the required information.

[0103] The steps of combining the target information results into multi-dimensional query labels include:

[0104] S201-4-1: Determine the attribute characteristics that define the multidimensional query label.

[0105] In this embodiment, when defining the attribute characteristics of the multidimensional query tag, multiple factors need to be considered, including the query subject, query field, query purpose, user needs, etc. Generally speaking, the multidimensional query tag should have the following attribute characteristics: Accuracy: The multidimensional query tag should accurately reflect the user's query subject and needs;

[0106] Completeness: Multidimensional query labels should contain as much relevant information as possible about the query subject and field; Readability: Multidimensional query labels should be easy for users to understand and use; Ease of use: Multidimensional query labels should be easy to input and query, and users do not need to remember complex keywords or professional terms.

[0107] S201-4-2: Map the target information extraction results to multi-dimensional query tags according to attribute characteristics.

[0108] In this embodiment, when mapping the target information extraction result to the multidimensional query tag, the attribute characteristics and user needs need to be considered. Generally speaking, the key information in the target information extraction result can be mapped to different attributes of the multidimensional query tag.

[0109] As an example, continue to use the above embodiment to explain, the information extraction results: Topic: Treatment of hypertension, Category: Drugs, Function: Blood pressure reduction. According to these target information extraction results, they can be mapped to different attributes of the multidimensional query tag, for example: Topic: Treatment of hypertension, Category: Drugs, Function: Blood pressure reduction, Prescription type: Prescription drugs. According to these attribute features, the target information extraction results can be mapped to the multidimensional query tag "blood pressure reduction prescription drugs" so that users can perform more accurate queries in search engines or databases.

[0110] S202: construct at least one fuzzy query condition according to the multidimensional query tag, and obtain corresponding data query results according to the fuzzy query condition.

[0111] In this embodiment, one or more fuzzy query conditions can be constructed according to the multidimensional query tag to obtain data query results. Fuzzy query conditions are a flexible query method that can be customized according to user needs and data characteristics. Generally speaking, fuzzy query conditions include fuzzy matching, range query, fuzzy time query, etc.

[0112] As an example, continuing with the above embodiment, based on the multi-dimensional query tag "antihypertensive prescription drugs" constructed previously, a fuzzy query condition "antihypertensive drugs" can be constructed to obtain corresponding data query results. The specific implementation process is as follows: In the database, the following results can be obtained by using fuzzy matching to query "antihypertensive drugs": over-the-counter drugs: vitamin C tablets, calcium magnesium zinc tablets, multidimensional element tablets, etc., prescription drugs: nifedipine sustained-release tablets, benazepril tablets, irbesartan tablets, etc. According to the query requirements and data characteristics, the price and inventory of "antihypertensive prescription drugs" can be queried using the range query method, for example: query prescription drugs with a price below 100 yuan: nifedipine sustained-release tablets, benazepril tablets, etc., query prescription drugs with an inventory of more than 1,000 boxes: irbesartan tablets, amlodipine tablets, etc. If you need to query the sales of "antihypertensive prescription drugs" within a certain period of time, you can use the fuzzy time query method to query, for example: query the prescription drugs with the highest sales volume in the past three months: irbesartan tablets, benazepril tablets, etc. By constructing fuzzy query conditions, you can obtain the corresponding data query results according to multidimensional query tags. This method is more accurate than single keyword matching, can meet the different needs of users, and improve the accuracy and efficiency of data query.

[0113] The step of constructing at least one fuzzy query condition according to the multi-dimensional query label includes:

[0114] S202-1: Determine a query keyword corresponding to each query dimension, and determine a corresponding query matching method according to the query keyword;

[0115] In this embodiment, one or more query keywords correspond to each query dimension. Query keywords are words or phrases used to describe query dimensions. For example, in the medical field, query dimensions can be disease names, drug names, patient ages, symptoms, etc., and corresponding query keywords can be "cancer", "headache", "over 50 years old", etc. For each query keyword, it is necessary to determine the matching method of the corresponding fuzzy query. The matching method of the fuzzy query determines how to find the corresponding data based on the query keywords input by the user. Common fuzzy matching methods include wildcards, fuzzy matching, regular expressions, etc.

[0116] As an example, for the query dimension "disease name", the corresponding query keyword may be "cancer", and the corresponding fuzzy matching method may be the wildcard "cancer" to match all disease names containing the word "cancer".

[0117] S202-2: Construct a fuzzy query condition according to the query matching method corresponding to each query dimension and in accordance with the preset format of the query statement.

[0118] For each query dimension, according to its corresponding fuzzy matching method, a corresponding fuzzy query condition is constructed according to a preset query statement format. Generally, the query statement format can use a query statement format supported by a database or search engine, such as an SQL query statement, a Lucene query statement, and the like.

[0119] As an example, for the query dimension "disease name", assuming that its corresponding fuzzy matching method is the wildcard "cancer", the following fuzzy query condition "SELECT*FROM medical_records WHERE disease_name LIKE'%cancer%" can be constructed according to the format of the SQL query statement.

[0120] S203: constructing data query result evaluation rules according to the multi-dimensional query tags, and screening the data query results according to the evaluation rules and the particle swarm algorithm to obtain target data query results.

[0121] In this embodiment, according to the multidimensional query label, a data query result evaluation rule is constructed to filter out the target data query results that meet the requirements. The formulation of the evaluation rule needs to consider multiple factors, such as the accuracy, completeness, relevance, etc. of the data, as well as the importance of different query dimensions. The evaluation rule usually adopts a weighted evaluation method to adjust the importance of different dimensions. In a multidimensional query, a large number of data query results can be obtained according to the defined multidimensional query labels and fuzzy query conditions, but there may be a large number of redundant or non-user-required results. Therefore, it is necessary to filter the query results to obtain the target data query results. The particle cluster algorithm is an optimization algorithm based on swarm intelligence, which is usually used to find the optimal solution. In the screening of data query results, each query result can be regarded as a particle, and each particle is continuously moved in the search space through continuous iteration to find the optimal solution. In the particle cluster algorithm, the position of each particle represents the value of a set of query conditions, and the speed of each particle represents the speed of change of the query conditions. By continuously updating the position and speed of the particle, the target data query result that meets the evaluation rules is finally found, and the step of filtering the data query results according to the evaluation rules and the particle cluster algorithm to obtain the target data query results includes:

[0122] S203-1: Determine the fitness function according to the evaluation rules.

[0123] In this embodiment, the fitness function can be defined according to the user's needs and query objectives to evaluate the quality of each query result. For example, in the medical field, the fitness function can be constructed based on factors such as the degree of match between the query result and the user's needs, the amount of information contained in the query result, and the credibility of the query result.

[0124] S203-2: Initialize the particle swarm and map each data query result into a particle.

[0125] In this embodiment, all data query results that meet the fuzzy query conditions are mapped to a particle, the position of each particle represents the specific value of the query result, and the speed represents the change rate of the query result in each dimension.

[0126] S203-3: Iteratively update the particle swarm, and according to the evaluation result of the fitness function, update the position and speed of the particle swarm, and calculate a new fitness value.

[0127] In this embodiment, in each iteration, the position and velocity of the particle are updated according to the evaluation result of the fitness function. The specific updating method can refer to the standard particle swarm algorithm. After the update, according to the new fitness value, it is determined whether the individual extreme value and the global extreme value of the particle need to be updated.

[0128] S203-4: Determine the optimal solution in the particle swarm as the target data query result.

[0129] In this embodiment, after the particle swarm iteration is completed, the fitness values ​​of all particles are compared to find the best particle, and the data query result represented by the particle is determined as the target data query result.

[0130] For example, if a user searches for "drugs for treating hypertension", the corresponding fuzzy query conditions can be constructed based on the multi-dimensional query tags, and a series of query results that meet the conditions can be obtained. Then, based on the evaluation rules and particle swarm algorithm, the data query results that best meet the user's needs are screened out, such as drugs such as "reserpine" and "valsartan".

[0131] S204: Display the non-sensitive information of the target data query result to the user and send a confirmation reminder, and after receiving the confirmation information fed back by the user, perform differential desensitization processing on the sensitive information of the target data query result to obtain desensitized target data query results corresponding to different identity permissions.

[0132] In this embodiment, the non-sensitive information of the target data query result is displayed and a confirmation reminder is sent to allow the user to have a preliminary understanding of the query result and confirm whether the query meets its expectations to avoid unnecessary misunderstandings or misoperations. Usually, before displaying non-sensitive information to the user, the system will first send a prompt message to the user to inform him of the information category and scope to be displayed, and ask whether he agrees to continue to display, and before displaying the target data query result to the user, the result must first be desensitized to protect the user's privacy information. Identity authority includes user authority and non-user authority. According to different identity authorities, different desensitization methods and levels can be adopted for users with different identity authorities to ensure the security and availability of data. For users with user authority, a partial desensitization method can be adopted, that is, only desensitizing sensitive information related to the user's privacy, such as name, ID number, mobile phone number and other personal identity information. For non-sensitive information, it can be directly displayed to the user for viewing so that he can perform operations such as medical diagnosis and treatment. For users who do not have personnel permissions, a complete desensitization method can be adopted, that is, all information related to the user's privacy is desensitized, such as replacing the name with X, displaying only the first few digits of the ID number or generating it randomly, etc., to protect the user's privacy.

[0133] The steps of performing differentiated desensitization processing on the sensitive information of the target data query result to obtain desensitized target data query results corresponding to different identity permissions may include:

[0134] S204-1: When the user's identity authority is user authority, call an encryption algorithm that does not carry a consistent salt value to desensitize sensitive information of the target data query result, and generate a desensitized target data query result consisting of fixed desensitized data.

[0135] S204-2: When the user's identity authority is non-user authority, call an encryption algorithm with a consistent salt value to desensitize sensitive information of the target data query result, and generate a desensitized target data query result consisting of random desensitized data.

[0136] In the implementation methods of S204-1 to S204-2, different desensitization methods are used to generate desensitized target data query results corresponding to different identity permissions. When the user identity permission is the user permission, an encryption algorithm that does not carry a consistent salt value is used for desensitization, and a desensitized target data query result consisting of fixed desensitized data is generated; when the user identity permission is a non-user permission, an encryption algorithm that carries a consistent salt value is used for desensitization, and a desensitized target data query result consisting of random desensitized data is generated. Among them, the encryption algorithm that does not carry a consistent salt value is a one-way hash function, which takes sensitive data as input and outputs an irreversible string of fixed length. The algorithm does not need to use a salt value, so the generated desensitized data is fixed. The encryption algorithm that carries a consistent salt value uses a random salt value to encrypt sensitive data, so that even if the same sensitive data is encrypted using different salt values, the generated desensitized data is different. At the same time, the algorithm uses a consistent salt value to ensure that the same sensitive data is encrypted with the same salt value at different times. The generated desensitized data is the same, thereby achieving the consistency of random desensitized data.

[0137] As an example: Assume that the target data query result contains two sensitive information fields: the user's name and ID number, which need to be desensitized. For users with personnel permissions, the SHA-256 algorithm can be used to encrypt the user's ID number to generate fixed desensitized data. For example, for a user with an ID number of "123456789012345678", the following desensitized data can be generated:

[0138] "d6e2c6a76b5335a5e5a5f5b682dd56a7be9fa738cfbd3b3c71f85f236b0ca945", for non-users, you can use the AES algorithm with a consistent salt value to encrypt the user's ID number to generate random desensitized data. Assuming that the salt value "salt123" is used for encryption, for a user with an ID number of "123456789012345678", the following desensitized data can be generated:

[0139] “Tk8yOVhXK1dLbFpTckl6ZGxtRWZ1Zz09”. The salt value “salt123” can be set in advance in the system and saved together with the encryption algorithm.

[0140] After generating the desensitized target data query result consisting of fixed desensitized data, the method further includes:

[0141] Obtain the user's historical query records and preference attributes;

[0142] According to historical query records and preference attributes, the query results of desensitized target data consisting of fixed desensitized data are filtered.

[0143] In this embodiment, this step is to improve the efficiency and accuracy of the user's use of data query results. By obtaining the user's historical query records and preference attributes, the data query results suitable for the user's usage habits and needs can be tailored for the user.

[0144] The specific process is as follows:

[0145] A: Obtain the user's historical query records and preference attributes, including query dimensions, query keywords, query result preferences, etc.

[0146] B: Based on historical query records and preference attributes, filter out query results that meet the needs of users, and sort and classify them.

[0147] C: Display and push query results that meet the needs of users, so that users can quickly obtain the required information.

[0148] D: For new query requests, we can predict the user's query needs based on historical query records and preference attributes, prepare query results that meet their needs in advance, and improve query efficiency and accuracy.

[0149] For example, doctors in a hospital often need to query the results of patients' routine blood tests, which include indicators such as hemoglobin, white blood cell count, and platelets. Therefore, after obtaining the doctor's historical query records and preference attributes, the results of routine blood tests can be used as a query dimension, and hemoglobin, white blood cell count, platelets and other indicators can be used as query keywords to filter out query results that meet the doctor's needs, and display and push them. If the doctor needs to query the results of a specific patient's routine blood tests, he can filter based on the patient information as another query dimension to provide more accurate query results.

[0150] S205: Obtain the user's identity authority and display the desensitized target data query results that match the identity authority to the user.

[0151] In this embodiment, the scope of sensitive data that users can access is different. For example, in a medical system, doctors and nurses may need to access the user's diagnosis and treatment records, while non-users can only access some basic user information, such as name, gender, age, etc., and must follow the rules of privacy protection. Therefore, when displaying the target data query results, it is necessary to filter and desensitize the data according to the user's identity authority. Specifically, the system needs to perform differentiated desensitization processing on the data query results according to different identity authorities, and display to the user the data that matches the level of his or her identity authority.

[0152] The embodiment of the present invention also provides a data query device based on cloud computing, referring to Figure 3 , shows a functional module diagram of a data query device based on cloud computing of the present invention, which may include the following modules:

[0153] The acquisition module 301 is used to acquire natural language query data input by the user, and extract information from the natural language query data to obtain a multi-dimensional query tag;

[0154] A combination module 302 is used to construct at least one fuzzy query condition according to the multi-dimensional query label, and obtain corresponding data query results according to the fuzzy query condition;

[0155] The query module 303 is used to construct a data query result evaluation rule according to the multi-dimensional query tag, and screen the data query result according to the evaluation rule and the particle swarm algorithm to obtain the target data query result;

[0156] The desensitization module 304 is used to display the non-sensitive information of the target data query result to the user and send a confirmation reminder, and after receiving the confirmation information fed back by the user, perform differential desensitization processing on the sensitive information of the target data query result to obtain desensitized target data query results corresponding to different identity permissions;

[0157] The display module 305 is used to obtain the user's identity authority and display the desensitized target data query results that match the identity authority to the user.

[0158] In a feasible implementation, the acquisition module includes:

[0159] A language processing submodule, used to perform natural language processing on natural language query data input by the user, wherein the natural language processing includes at least one of word segmentation, part-of-speech tagging, and named entity recognition processing;

[0160] An information extraction submodule, configured to extract information sentence by sentence from the natural language query data that has undergone natural language processing, so as to obtain at least one candidate information extraction result;

[0161] The information screening submodule is used to screen out target information extraction results from the candidate information extraction results according to the amount of information contained in the candidate information extraction results;

[0162] The information combination submodule is used to combine the target information extraction results into multi-dimensional query labels.

[0163] In a feasible implementation manner, the information combination submodule includes:

[0164] An attribute determination unit, used to determine attribute features defining a multi-dimensional query tag;

[0165] The mapping unit is used to map the target information extraction result to the multi-dimensional query label according to the attribute characteristics.

[0166] In a feasible implementation, the combined module includes:

[0167] A query keyword determination submodule is used to determine the query keyword corresponding to each query dimension, and determine the corresponding query matching method according to the query keyword;

[0168] The construction submodule is used to construct fuzzy query conditions according to the matching method of the query corresponding to each query dimension and the preset format of the query statement.

[0169] In a feasible implementation, the query module includes:

[0170] A fitness function determination submodule, used to determine the fitness function according to the evaluation rule;

[0171] An initialization submodule, used to initialize the particle swarm and map each of the data query results into a particle;

[0172] An iterative submodule, used for iteratively updating the particle swarm, and updating the position and speed of the particle swarm according to the evaluation result of the fitness function, and calculating a new fitness value;

[0173] The output submodule is used to determine the optimal solution in the particle swarm as the target data query result.

[0174] In a feasible implementation, the desensitization module includes:

[0175] A first desensitizing submodule is used to call an encryption algorithm that does not carry a consistent salt value to desensitize the sensitive information of the target data query result when the user's identity authority is the user authority, and generate a desensitized target data query result consisting of fixed desensitized data;

[0176] The second desensitizing sub-module is used to call an encryption algorithm carrying a consistent salt value when the user's identity authority is the non-user authority, desensitize the sensitive information of the target data query result, and generate a desensitized target data query result consisting of random desensitized data.

[0177] In a feasible implementation, the first desensitization submodule further includes:

[0178] A record acquisition unit, used to acquire the user's historical query records and preference attributes;

[0179] A filtering unit is used to filter the desensitized target data query results consisting of fixed desensitized data according to the historical query records and preference attributes.

[0180] It should be noted that the specific implementation of the cloud computing-based data query device 300 in the embodiment of the present application refers to the specific implementation of the cloud computing-based data query method proposed in the first aspect of the embodiment of the present application, and will not be repeated here.

[0181] Based on the same inventive concept, another embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus.

[0182] Memory, used to store computer programs;

[0183] The processor is used to implement the cloud computing-based data query method of the present invention when executing the program stored in the memory.

[0184] The communication bus mentioned in the above terminal can be a peripheral component interconnect standard (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface is used for communication between the above terminal and other devices. The memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0185] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0186] In addition, to achieve the above-mentioned purpose, an embodiment of the present application further proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the cloud computing-based data query method of the embodiment of the present application.

[0187] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, embodiments of the present invention may take the form of complete hardware embodiments, complete software embodiments, or embodiments combining software and hardware aspects. Moreover, embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable vehicles (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0188] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (apparatus), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0189] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0190] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0191] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. "" and / or "" indicate that either one of the two can be selected, or both can be selected. Moreover, the terms "" include "" "" contain "" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "" include one..."" does not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0192] The above is a detailed introduction to a cloud computing-based data query method and device. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the core idea of ​​the cloud computing-based data query method and device of the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A data query method based on cloud computing, characterized in that: Applied to a query terminal, the method comprises: Acquiring natural language query data input by a user, and extracting information from the natural language query data to obtain a multi-dimensional query tag includes: Performing natural language processing on natural language query data input by the user, wherein the natural language processing includes at least one of word segmentation, part-of-speech tagging, and named entity recognition processing; Extracting information sentence by sentence from the natural language query data that has undergone the natural language processing to obtain at least one candidate information extraction result; Filtering target information extraction results from the candidate information extraction results according to the amount of information contained in the candidate information extraction results; Combining the target information extraction results into the multi-dimensional query tag; Constructing at least one fuzzy query condition according to the multidimensional query tag, and obtaining corresponding data query results according to the fuzzy query condition; According to the multidimensional query tag, construct a data query result evaluation rule, and screen the data query result according to the evaluation rule and the particle swarm algorithm to obtain a target data query result; Display the non-sensitive information of the target data query result to the user and send a confirmation reminder, and after receiving the confirmation information fed back by the user, perform differential desensitization processing on the sensitive information of the target data query result to obtain desensitized target data query results corresponding to different identity permissions; Obtain the user's identity authority and display to the user the desensitized target data query results that match the identity authority; The step of combining the target extraction information results into the multi-dimensional query tag comprises: Determine attribute characteristics of multidimensional query labels; According to the attribute feature, the target information extraction result is mapped to the multi-dimensional query tag.

2. The cloud computing-based data query method according to claim 1, characterized in that: The step of constructing at least one fuzzy query condition according to the multidimensional query tag comprises: Determine the query keywords corresponding to each query dimension, and determine the corresponding query matching method according to the query keywords; According to the matching method of the query corresponding to each of the query dimensions and in accordance with the preset format of the query statement, the fuzzy query condition is constructed.

3. The data query method based on cloud computing according to claim 1, characterized in that: The step of screening the data query results according to the evaluation rules and the particle swarm algorithm to obtain target data query results includes: According to the evaluation rule, determining a fitness function; Initialize the particle swarm and map each of the data query results into a particle; Iteratively updating the particle swarm, and according to the evaluation result of the fitness function, updating the position and speed of the particle swarm, and calculating a new fitness value; The optimal solution in the particle swarm is determined as the target data query result.

4. The data query method based on cloud computing according to claim 1, characterized in that: The identity authority includes user authority and non-user authority, and the step of performing differential desensitization processing on the sensitive information of the target data query result to obtain desensitized target data query results corresponding to different identity authorities includes: In the case where the user's identity authority is the user authority, an encryption algorithm that does not carry a consistent salt value is called to desensitize the sensitive information of the target data query result, and a desensitized target data query result consisting of fixed desensitized data is generated; When the user's identity authority is the non-user authority, an encryption algorithm carrying a consistent salt value is called to desensitize the sensitive information of the target data query result, and a desensitized target data query result consisting of random desensitized data is generated.

5. The data query method based on cloud computing according to claim 4, characterized in that: In the case where the user's identity authority is the user authority, the method further includes: Obtaining the user's historical query records and preference attributes; According to the historical query records and preference attributes, the desensitized target data query results consisting of fixed desensitized data are filtered.

6. A data query device based on cloud computing, characterized in that: The device comprises: An acquisition module, used to acquire natural language query data input by a user, and extract information from the natural language query data to obtain a multi-dimensional query tag; A combination module, used to construct at least one fuzzy query condition according to the multi-dimensional query tag, and obtain corresponding data query results according to the fuzzy query condition; A query module, used to construct a data query result evaluation rule according to the multi-dimensional query tag, and screen the data query result according to the evaluation rule and the particle swarm algorithm to obtain a target data query result; A desensitization module is used to display the non-sensitive information of the target data query result to the user and send a confirmation reminder, and after receiving the confirmation information fed back by the user, perform differential desensitization processing on the sensitive information of the target data query result to obtain desensitized target data query results corresponding to different identity permissions; A display module is used to obtain the user's identity authority and display the desensitized target data query results that match the identity authority to the user; The acquisition module comprises: A language processing submodule, configured to perform natural language processing on natural language query data input by a user, wherein the natural language processing includes at least one of word segmentation, part-of-speech tagging, and named entity recognition processing; An information extraction submodule, configured to extract information sentence by sentence from the natural language query data that has undergone the natural language processing, so as to obtain at least one candidate information extraction result; An information screening submodule, used for screening target information extraction results from the candidate information extraction results according to the amount of information contained in the candidate information extraction results; An information combination submodule, used for combining the target information extraction results into the multi-dimensional query tag; The information combination submodule comprises: An attribute determination unit, used to determine the attribute characteristics of the multi-dimensional query label; A mapping unit is used to map the target information extraction result to the multi-dimensional query label according to the attribute feature.

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