A Government Affairs Data Management Method, System, Device and Medium Based on a Cloud Platform

Abstract

The present invention discloses a government affair data management method, system, device and medium based on a cloud platform. The method includes: the cloud platform receives government affair data sent by a government affair data terminal, and classifies and labels the received government affair data; the cloud platform encrypts the classified and labeled government affair data and stores it in the cloud platform database; the cloud platform constructs a government affair data retrieval model, retrieves in the cloud platform database based on the government affair data retrieval model to obtain target government affair data; the cloud platform performs an update detection on the target government affair data, backs up the target government affair data with update situations, and stores the backed-up target government affair data in the cloud platform database; the cloud platform updates the target government affair data with update situations and stores the updated target government affair data in the cloud platform database. The present invention realizes the interconnection of government affair data and effectively improves the management efficiency of government affair data.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular, to a government affairs data management method, system, device and medium based on a cloud platform. Background Art

[0002] With the rapid development of society, for the government, the requirements for governing the city are getting higher and higher. The government affairs data collected from various channels need to be reasonably managed and applied to support the business needs of governing the city. For the city, there are numerous government affairs data terminals. If the data is only sorted locally on the government affairs data terminals, the data interconnection of multiple government affairs data terminals cannot be achieved. Moreover, due to the variety of government affairs data, different structures, and large quantity, it is inevitable to miss key information only by manual sorting, classification, and retrieval. At the same time, the security and stability of government affairs data during transmission and storage will also be tested to a certain extent, resulting in low management efficiency of government affairs data. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a government affairs data management method, system, device and medium based on a cloud platform, which realizes the interconnection of government affairs data and effectively improves the management efficiency of government affairs data.

[0004] The present invention provides a government affairs data management method based on a cloud platform, and the method includes:

[0005] The cloud platform receives government affairs data sent by a government affairs data terminal and classifies and labels the received government affairs data;

[0006] The cloud platform encrypts the classified and labeled government affairs data and stores it in the cloud platform database;

[0007] The cloud platform constructs a government affairs data retrieval model, retrieves in the cloud platform database based on the government affairs data retrieval model, and obtains target government affairs data;

[0008] The cloud platform performs update detection on the target government affairs data, backs up the target government affairs data with update situations, and stores the backed-up target government affairs data in the cloud platform database;

[0009] The cloud platform updates the target government affairs data with update situations and stores the updated target government affairs data in the cloud platform database.

[0010] Further, the classifying and labeling the received government affairs data includes:

[0011] Converting the government affairs data into a target recognition data set format;

[0012] Precisely analyze the classification categories of the government affairs data to obtain the precise classification data set of the government affairs data;

[0013] Extract the category response fields of each classification data in the precise classification data set of the government affairs data;

[0014] Classify and label the government affairs data based on the category response fields.

[0015] Furthermore, the precisely analyzing the classification categories of the government affairs data to obtain the precise classification data set of the government affairs data includes:

[0016] Extract the rough classification data set from the government affairs data, and collect the standard classification data set corresponding to the government affairs data;

[0017] Perform mapping correspondence analysis on the rough classification data set and the standard classification data set to obtain the differential classification data set;

[0018] Divide the rough classification data set into a data set to be repaired and a control classification data set, and construct a classification data set repair model based on the standard classification data set and the control classification data set;

[0019] Repair the duplicate classification data in the data set to be repaired and the differential classification data set based on the classification data set repair model to obtain the data set to be supplemented;

[0020] Add the data set to be supplemented to the rough classification data set to obtain the precise classification data set of the government affairs data.

[0021] Furthermore, the extracting the category response fields of each classification data in the precise classification data set of the government affairs data includes:

[0022] Extract the category response fields of each classification data in the precise classification data set of the government affairs data, and calculate the usage frequency of the category response fields;

[0023] Obtain the probability distribution coefficient of the category response fields based on the usage frequency of the category response fields;

[0024] Perform weighted processing on the category response fields based on the probability distribution coefficient.

[0025] Furthermore, the cloud platform encrypting the classified and labeled government affairs data includes:

[0026] Split and shuffle the classified and labeled government affairs data, and perform serialization processing to obtain several scattered government affairs data with different lengths;

[0027] Preset an encryption key, and respectively perform forward encryption and reverse encryption on the several scattered government affairs data based on the encryption key to obtain the forward encryption sequence and reverse encryption sequence of each piece of scattered government affairs data in the scattered government affairs data;

[0028] Randomly extract the forward encryption sequence or reverse encryption sequence of each piece of scattered government affairs data, and convert the extracted forward encryption sequence and reverse encryption sequence into a same-direction encryption sequence based on the forward and reverse mapping relationship;

[0029] Perform splicing processing on the same-direction encryption sequence to obtain the government affairs data after data encryption.

[0030] Further, the cloud platform constructs a government affairs data retrieval model, and performs retrieval in the cloud platform database based on the government affairs data retrieval model. The steps to obtain the target government affairs data include:

[0031] Obtain retrieval information, preprocess the retrieval information, and extract key retrieval information;

[0032] Extract the hash feature vector in the key retrieval information, and perform dimensionality reduction and encoding processing on the hash feature vector to obtain the hash coding value of the key retrieval information;

[0033] Calculate the loss deviation value of the hash coding value, and correct the hash coding value based on the loss deviation value to obtain the corrected hash coding value;

[0034] Perform similarity matching on the corrected hash coding value in the cloud platform database to obtain the target government affairs data.

[0035] Further, the cloud platform performs update detection on the target government affairs data, and backs up the target government affairs data with update situations. The steps to store the backed-up target government affairs data in the cloud platform database include:

[0036] The cloud platform performs update detection on the target government affairs data, copies and backs up the target government affairs data with update situations, inserts backup information, and stores the backed-up target government affairs data in the backup data storage area of the cloud platform database.

[0037] The present invention also provides a government affairs data management system based on a cloud platform. The government affairs data management system based on the cloud platform is used to implement the above-mentioned government affairs data management method based on the cloud platform. The system includes:

[0038] A classification and annotation module, which is used for the cloud platform to receive government affairs data sent by a government affairs data terminal and classify and annotate the received government affairs data;

[0039] An encryption module, which is used by the cloud platform to encrypt the classified and labeled government affairs data and store it in the cloud platform database;

[0040] A retrieval module, which is used by the cloud platform to build a government affairs data retrieval model, retrieve in the cloud platform database based on the government affairs data retrieval model, and obtain target government affairs data;

[0041] An update detection module, which is used by the cloud platform to detect the update of the target government affairs data, back up the target government affairs data with update situations, and store the backed-up target government affairs data in the cloud platform database;

[0042] An update storage module, which is used by the cloud platform to update the target government affairs data with update situations and store the updated target government affairs data in the cloud platform database.

[0043] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the above-mentioned government affairs data management method based on the cloud platform.

[0044] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned government affairs data management method based on the cloud platform.

[0045] The present invention provides a government affairs data management method, system, device, and medium based on the cloud platform. By collecting and sorting government affairs data through the cloud platform, data intercommunication between various government affairs data terminals is realized. By extracting category response fields in the category data of government affairs data classification categories, the government affairs data is classified and labeled, effectively improving the efficiency of classifying and sorting government affairs data; after the government affairs data is scattered, forward encryption and reverse encryption are respectively performed, and a forward encryption sequence or a reverse encryption sequence is randomly selected for splicing, effectively improving the security of government affairs data and avoiding the risk of government affairs data leakage; retrieval of government affairs data is realized based on the hash algorithm, effectively improving the accuracy of retrieving target government affairs data and effectively improving the management efficiency of government affairs data. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 It is the flowchart of the government affairs data management method based on the cloud platform in the first embodiment of the present invention;

[0048] Figure 2 It is the flowchart of classifying and labeling government affairs data in the first embodiment of the present invention;

[0049] Figure 3 It is the flowchart of obtaining the accurate classification data set of government affairs data in the first embodiment of the present invention;

[0050] Figure 4 It is the flowchart of extracting the category response field of government affairs data in the first embodiment of the present invention;

[0051] Figure 5 It is the flowchart of data encryption for the classified and labeled government affairs data in the first embodiment of the present invention;

[0052] Figure 6 It is the flowchart of obtaining the target government affairs data in the first embodiment of the present invention;

[0053] Figure 7 It is the architecture diagram of the government affairs data management system based on the cloud platform in the second embodiment of the present invention. Detailed implementation manners

[0054] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0055] In the present invention, it should be understood that terms such as "including" or "having" are intended to indicate the existence of features, numbers, steps, actions, components, parts, or combinations thereof disclosed in this specification, and do not intend to exclude the possibility of the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0056] In addition, it should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0057] Embodiment 1

[0058] Embodiment 1 of the present invention provides a government affairs data management method based on a cloud platform. The method includes: the cloud platform receives government affairs data sent by a government affairs data terminal, and classifies and labels the received government affairs data; the cloud platform encrypts the classified and labeled government affairs data and stores it in the cloud platform database; the cloud platform constructs a government affairs data retrieval model, retrieves in the cloud platform database based on the government affairs data retrieval model, and obtains target government affairs data; the cloud platform performs an update detection on the target government affairs data, backs up the target government affairs data with update situations, and stores the backed-up target government affairs data in the cloud platform database; the cloud platform updates the target government affairs data with update situations and stores the updated target government affairs data in the cloud platform database.

[0059] In an alternative implementation manner of this embodiment, as Figure 1 shown, Figure 1 shows the flowchart of the government affairs data management method based on the cloud platform in Embodiment 1 of the present invention, including the following steps:

[0060] S101. The cloud platform receives government affairs data sent by a government affairs data terminal, and classifies and labels the received government affairs data;

[0061] In an alternative implementation manner of this embodiment, since the amount of government affairs data collected by the government affairs data terminal is huge, and the types are numerous and the structures are different, if classification and labeling are not implemented, the sorting efficiency will be greatly reduced, which is not conducive to improving the management efficiency. Here, it is considered to use the cloud platform to receive the government affairs data sent by each government affairs data terminal and then uniformly manage it, effectively improving the government affairs data management efficiency.

[0062] In an alternative implementation manner of this embodiment, as Figure 2 shown, Figure 2 shows the flowchart of classifying and labeling government affairs data in Embodiment 1 of the present invention, including the following steps:

[0063] S201. Convert the government affairs data into the format of a target recognition data set;

[0064] In an alternative implementation manner of this embodiment, the format of the target recognition data set is a data set format that can be labeled. Commonly used target recognition data set formats include VOC data set format, COCO data set format, and YOLO data set format. Among them, the VOC data set format corresponds to xml format data, the COCO data set format corresponds to json format data, and the YOLO data set format corresponds to txt format data. In this embodiment, a preliminary analysis is performed on the source government affairs data terminal of the government affairs data, and the type of the target recognition data set format is selected according to the actual situation.

[0065] Since government affairs data usually includes not only document data but also other format data such as image data, video data, audio data, etc., converting the data into the format of the target recognition data set here can facilitate subsequent classification and annotation of the data, and improve the accuracy and efficiency of classification and annotation.

[0066] S202. Precisely analyze the classification categories of the government affairs data to obtain the precise classification data set of the government affairs data;

[0067] In an alternative implementation manner of this embodiment, as Figure 3 shown, Figure 3 shows the flowchart of obtaining the precise classification data set of government affairs data in the first embodiment of the present invention, including the following steps:

[0068] S301. Extract the rough classification data set from the government affairs data, and collect the standard classification data set corresponding to the government affairs data;

[0069] In an alternative implementation manner of this embodiment, extract the rough classification data from the government affairs data to form a rough classification data set, and at the same time collect a standard classification data set with better classification quality and accuracy than the rough classification data set.

[0070] S302. Perform mapping correspondence analysis on the rough classification data set and the standard classification data set to obtain a differential classification data set;

[0071] In an alternative implementation manner of this embodiment, perform mapping correspondence analysis on the classification data in the rough classification data set and the standard classification data set to obtain the confusion relationship between the two, and based on the confusion degree of this confusion relationship and a preset confusion threshold, select some classification data with a higher confusion degree to form a differential classification data set that is prone to confusion.

[0072] S303. Divide the rough classification data set into a classification data set to be repaired and a control classification data set, and construct a classification data set repair model based on the standard classification data set and the control classification data set;

[0073] In an alternative implementation manner of this embodiment, construct a classification data set repair model, sample some rough classification data in the rough classification data set to form a classification data set to be repaired, and the remaining rough classification data forms a control classification data set.

[0074] In an alternative implementation manner of this embodiment, use the standard classification data set as the validation set and the control classification data set as the training set to construct a classification data set repair model through training.

[0075] S304. Repair the duplicate classification data in the classification dataset to be repaired and the differential classification dataset based on the classification dataset repair model to obtain a classification dataset to be supplemented;

[0076] In an alternative implementation of this embodiment, extract the duplicate classification data in the classification dataset to be repaired and the differential classification dataset, list them as datasets with annotation conflicts and omissions, and repair them based on the classification dataset repair model, thereby obtaining a classification dataset to be supplemented.

[0077] S305. Add the classification dataset to be supplemented to the rough classification dataset to obtain an accurate classification dataset of the government affairs data.

[0078] In an alternative implementation of this embodiment, add the classification dataset to be supplemented obtained in step S304 to the rough classification dataset, replace the duplicate classification data, and supplement the classification data not existing in the rough classification dataset, thereby obtaining an accurate classification dataset of the government affairs data.

[0079] S203. Extract the category response fields of each classification data in the accurate classification dataset of the government affairs data;

[0080] In an alternative implementation of this embodiment, as Figure 4 described, Figure 4 shows the flowchart of extracting the category response fields of government affairs data in Embodiment 1 of the present invention, including the following steps:

[0081] S401. Extract the category response fields of each classification data in the accurate classification dataset of the government affairs data, and calculate the usage frequency of the category response fields;

[0082] In an alternative implementation of this embodiment, extract the category response fields of each classification data in the accurate classification dataset of the government affairs data. For different classification data, there are several category response fields, and the usage quantity of the category response fields is correspondingly counted, as well as the proportion of the total category response fields, to obtain the usage frequency of the category response fields corresponding to each classification data.

[0083] S402. Obtain the probability distribution coefficient of the category response fields based on the usage frequency of the category response fields;

[0084] In an alternative implementation of this embodiment, calculate the maximum possible annotation number and the expected annotation number corresponding to each category response field based on the usage frequency of each category response field, and set the difference between the maximum annotation number and the expected annotation number as the probability distribution coefficient of the corresponding category response field, which represents the change value of the probability distribution of the category response field.

[0085] S403. Perform weighted processing on the category response field based on the probability distribution coefficient.

[0086] In an alternative implementation of this embodiment, calculate the weighted value corresponding to the category response field based on the probability distribution coefficient, and perform weighted processing on the category response field based on the weighted value.

[0087] Specifically, the calculation formula for the weighted value includes:

[0088]

[0089] In the formula, w i is the weighted value of the i-th category response field, f i (a) is the maximum number of annotations of the i-th category response field, f i (b) is the expected number of annotations of the i-th category response field, f i is the average number of annotations of the i-th category response field.

[0090] In an alternative implementation of this embodiment, after calculating the weighted value corresponding to each category response field, perform weighted processing based on the weighted value to reduce the confusion probability between each category response field, facilitating subsequent retrieval.

[0091] S204. Classify and label the government affairs data based on the category response field.

[0092] In an alternative implementation of this embodiment, classify and label each piece of data in the government affairs data based on the category response field.

[0093] Here, by extracting the category response field in the government affairs data classification category data, the government affairs data is classified and labeled, effectively improving the efficiency of classifying and sorting the government affairs data.

[0094] S102. The cloud platform encrypts the classified and labeled government affairs data and stores it in the cloud platform database;

[0095] In an alternative implementation of this embodiment, as Figure 5 shown, Figure 5 shows the flowchart of encrypting the classified and labeled government affairs data in the first embodiment of the present invention, including the following steps:

[0096] S501. Split and shuffle the classified and labeled government affairs data, and perform serialization processing to obtain several scattered government affairs data with different lengths;

[0097] In an optional implementation of this embodiment, the classified and labeled government data is randomly split and shuffled based on different preset lengths, and then serialized and arranged in order from short to long.

[0098] S502, preset an encryption key, and perform forward encryption and reverse encryption on the plurality of scattered government data based on the encryption key, respectively, to obtain a forward encryption sequence and a reverse encryption sequence of each of the scattered government data;

[0099] In an optional implementation of the present embodiment, an encryption key is preset, and each of the plurality of scattered government data is forward encrypted and reverse encrypted simultaneously based on the encryption key to obtain a forward encryption sequence and a reverse encryption sequence of each of the scattered government data respectively.

[0100] Specifically, the forward encryption is forward feedback encryption, that is, starting from the first forward data of the scattered government data, feedback is performed backward in sequence until the last data. The forward encryption sequence of the several scattered government data is expressed as {F(s1), F(s2), F(s3), ..., F(si)}.

[0101] Furthermore, the reverse encryption is a post-feedback encryption, that is, starting from the first reverse data of the scattered government data, feedback is performed forward in sequence until the first data. The reverse encryption sequence of the several scattered government data is expressed as {R(s1), R(s2), R(s3), ..., R(si)}.

[0102] S503, randomly extracting a forward encryption sequence or a reverse encryption sequence of each scattered government data in the scattered government data, and converting the extracted forward encryption sequence and reverse encryption sequence into a same-direction encryption sequence based on a forward-reverse mapping relationship;

[0103] In an optional implementation of this embodiment, a forward encryption sequence or a reverse encryption sequence of each scattered government data in the scattered government data is randomly extracted.

[0104] Specifically, for each scattered government data, only the forward encryption sequence or the reverse encryption sequence is extracted, where the ratio of the extracted forward encryption sequence to the reverse encryption sequence is between 1:2 and 2:1, that is, the number of extracted forward encryption sequences is not more than twice the number of extracted reverse encryption sequences, and is not less than one half of the number of extracted reverse encryption sequences.

[0105] Specifically, when i=10, {F(s1), R(s2), F(s3), F(s4), F(s5), R(s6), F(s7), R(s8), F(s9), R(s10) are extracted respectively.

[0106] In an alternative implementation of this embodiment, the extracted forward encryption sequence and reverse encryption sequence are converted into an in-phase encryption sequence based on the forward and reverse mapping relationship.

[0107] Specifically, if the number of extracted forward encryption sequences is greater than the number of reverse encryption sequences, all reverse encryption sequences are converted into forward encryption sequences based on the forward and reverse mapping relationship. If the number of extracted reverse encryption sequences is greater than the number of forward encryption sequences, all forward encryption sequences are converted into reverse encryption sequences based on the forward and reverse mapping relationship.

[0108] Specifically, when extracting {F(s1), R(s2), F(s3), F(s4), F(s5), R(s6), F(s7), R(s8), F(s9), R(s10)}, the number of forward encryption sequences is 6, and the number of reverse encryption sequences is 4, then the reverse encryption sequences are converted into forward encryption sequences.

[0109] S504. Concatenate the in-phase encryption sequences to obtain the government affairs data after data encryption.

[0110] In an alternative implementation of this embodiment, the forward encryption sequence or reverse encryption sequence obtained in step S503 is concatenated at the beginning and end in the original order to generate a complete encryption sequence, that is, the government affairs data after data encryption is obtained.

[0111] Here, the government affairs data is scattered and then subjected to forward encryption and reverse encryption respectively, and the forward encryption sequence or reverse encryption sequence is randomly selected for concatenation, effectively improving the security of the government affairs data and avoiding the risk of government affairs data leakage.

[0112] In an alternative implementation of this embodiment, after the encryption of the government affairs data is completed, the government affairs data after data encryption is stored in the cloud platform database.

[0113] S103. The cloud platform constructs a government affairs data retrieval model, and retrieves in the cloud platform database based on the government affairs data retrieval model to obtain the target government affairs data;

[0114] In an alternative implementation of this embodiment, as Figure 6 shown, Figure 6 shows the flowchart of obtaining the target government affairs data in the first embodiment of the present invention, including the following steps:

[0115] S601. Obtain the retrieval information, preprocess the retrieval information, and extract the key retrieval information;

[0116] In an alternative implementation of this embodiment, after obtaining the retrieval information, preprocess the retrieval information, and the preprocessing includes cleaning, deduplication, word segmentation, and calibration. After completing the above four preprocessing processes, perform keyword analysis on the preprocessed retrieval information based on a word popularity detection model to extract key retrieval information.

[0117] S602. Extract the hash feature vector from the key retrieval information, and perform dimensionality reduction and encoding processing on the hash feature vector to obtain the hash code value of the key retrieval information;

[0118] In an alternative implementation of this embodiment, based on a 256-dimensional bag-of-words model, convert the key retrieval information from text data into a hash feature vector, and the conversion formula is as follows:

[0119]

[0120] In the formula, v i is the hash feature vector of the key retrieval information, d i is the Euclidean distance of the corresponding selected normal distribution function in the high-dimensional space and the low-dimensional space, and σ is the standard deviation of the corresponding selected normal distribution function.

[0121] In an alternative implementation of this embodiment, after obtaining the hash feature vector, perform dimensionality reduction on the hash feature vector respectively, and then perform feature encoding processing to convert the hash feature vector into a hash code value format that can be processed by operations.

[0122] S603. Calculate the loss deviation value of the hash code value, and correct the hash code value based on the loss deviation value to obtain the corrected hash code value;

[0123] In an alternative implementation of this embodiment, calculate the loss deviation value of the hash code value based on a preset loss function, and the calculation formula of the loss function includes:

[0124]

[0125] In the formula, L is the loss deviation value, μ is the trade-off parameter, a i is the Hamming distance metric, b i is the internal code of the hash code value, W T is the loss data, and λ is the calibration value.

[0126] In an alternative implementation of this embodiment, correct the hash code value based on the calculated loss deviation value to obtain the corrected hash code value.

[0127] S604. Perform similarity matching on the corrected hash code value in the cloud platform database to obtain the target government affairs data.

[0128] In an alternative implementation manner of this embodiment, extract the similarity between the input terminal and the output terminal of the corrected hash code value, and perform a search based on the similarity with the data in the cloud platform database. According to the maximum similarity matching principle, preset the similarity index to match the closest target government affairs data.

[0129] Here, the retrieval of government affairs data is realized based on the hash algorithm, which effectively improves the accuracy of target government affairs data retrieval and effectively improves the management efficiency of government affairs data.

[0130] S104. The cloud platform performs an update detection on the target government affairs data, backs up the target government affairs data with update situations, and stores the backed-up target government affairs data in the cloud platform database.

[0131] In an alternative implementation manner of this embodiment, the cloud platform performs an update detection on the target government affairs data, makes a copy backup of the target government affairs data with update situations, inserts backup information, and stores the backed-up target government affairs data in the backup data storage area of the cloud platform database.

[0132] Specifically, the backup information includes the data size, data format of the target government affairs data with update situations, and the time of making the copy backup.

[0133] It should be noted that storing the backed-up target government affairs data in the backup data storage area of the cloud platform database distinguishes it from other target government affairs data that have not been copy-backed.

[0134] S105. The cloud platform updates the target government affairs data with update situations and stores the updated target government affairs data in the cloud platform database.

[0135] In an alternative implementation manner of this embodiment, after completing the backup in step S104, update the target government affairs data with update situations based on the update situation, and store the updated target government affairs data in the cloud platform database.

[0136] It should be noted that after storing the updated target government affairs data in the cloud platform database, delete the target government affairs data before the update in the cloud platform database.

[0137] In summary, Embodiment 1 of the present invention provides a government affairs data management method based on a cloud platform. By collecting and organizing government affairs data through the cloud platform, data interconnection between various government affairs data terminals is realized. By extracting category response fields in the category data of government affairs data classification, the government affairs data is classified and labeled, effectively improving the efficiency of classifying and organizing government affairs data; after the government affairs data is scattered, forward encryption and reverse encryption are respectively performed, and a forward encryption sequence or a reverse encryption sequence is randomly selected for splicing, effectively improving the security of government affairs data and avoiding the risk of government affairs data leakage; based on the hash algorithm, the retrieval of government affairs data is realized, effectively improving the accuracy of retrieving target government affairs data and effectively improving the management efficiency of government affairs data.

[0138] Embodiment 2

[0139] Embodiment 2 of the present invention provides a government affairs data management system based on a cloud platform. The government affairs data management system based on the cloud platform is used to implement the above-mentioned government affairs data management method based on the cloud platform. The system includes a classification and labeling module, an encryption module, a retrieval module, an update detection module, and an update storage module.

[0140] In an optional implementation manner of this embodiment, as Figure 7 shown, Figure 7 shows the architecture diagram of the government affairs data management system based on the cloud platform in Embodiment 2 of the present invention, including the following modules:

[0141] Classification and labeling module 10, the classification and labeling module 10 is used to receive government affairs data sent by a government affairs data terminal on the cloud platform and classify and label the received government affairs data;

[0142] Encryption module 20, the encryption module 20 is used to encrypt the government affairs data after classification and labeling by the cloud platform and store it in the cloud platform database;

[0143] Retrieval module 30, the retrieval module 30 is used to build a government affairs data retrieval model by the cloud platform, retrieve in the cloud platform database based on the government affairs data retrieval model, and obtain target government affairs data;

[0144] Update detection module 40, the update detection module 40 is used to detect the update of the target government affairs data by the cloud platform, back up the target government affairs data with update situations, and store the backed-up target government affairs data in the cloud platform database;

[0145] Update storage module 50, the update storage module 50 is used to update the target government affairs data with update situations by the cloud platform and store the updated target government affairs data in the cloud platform database.

[0146] In summary, the second embodiment of the present invention provides a government affairs data management system based on a cloud platform, which is used to implement the government affairs data management method based on the cloud platform in the first embodiment. The government affairs data is collected and sorted through the cloud platform to achieve data interconnection between various government affairs data terminals. By extracting the category response fields in the category data of the government affairs data classification, the government affairs data is classified and labeled, effectively improving the efficiency of classifying and sorting the government affairs data. After the government affairs data is scattered, forward encryption and reverse encryption are performed respectively, and a forward encryption sequence or a reverse encryption sequence is randomly selected for splicing, effectively improving the security of the government affairs data and avoiding the risk of government affairs data leakage. Based on the hash algorithm, the retrieval of the government affairs data is realized, effectively improving the accuracy of retrieving the target government affairs data and effectively improving the management efficiency of the government affairs data.

[0147] Embodiment Three

[0148] The third embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the government affairs data management method based on the cloud platform described in the first embodiment.

[0149] Embodiment Four

[0150] The fourth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the government affairs data management method based on the cloud platform described in the first embodiment.

[0151] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0152] In addition, the above embodiments of the present invention have been introduced in detail. In this article, specific examples are used to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A government data management method based on a cloud platform, characterized in that: The method comprises: The cloud platform receives government data sent by government data terminals and classifies and annotates the received government data; The classification and labeling of the received government data includes: converting the government data into a target recognition data set format; accurately analyzing the classification categories of the government data to obtain an accurate classification data set of the government data; extracting the category response field of each classification data in the accurate classification data set of the government data; and classifying and labeling the government data based on the category response field; The step of accurately analyzing the classification categories of the government data to obtain an accurate classification data set of the government data includes: extracting a rough classification data set from the government data, and collecting a standard classification data set corresponding to the government data; mapping and correspondingly analyzing the rough classification data set with the standard classification data set to obtain a differential classification data set; dividing the rough classification data set into a classification data set to be repaired and a reference classification data set, and constructing a classification data set repair model based on the standard classification data set and the reference classification data set; repairing duplicate classification data in the classification data set to be repaired and the differential classification data set based on the classification data set repair model to obtain a classification data set to be supplemented; adding the classification data set to be supplemented to the rough classification data set to obtain an accurate classification data set of the government data; The mapping correspondence analysis between the rough classification data set and the standard classification data set to obtain the difference classification data set includes: obtaining a confusion relationship between the classification data in the rough classification data set and the standard classification data set, and selecting part of the classification data with a higher degree of confusion based on the degree of confusion in the confusion relationship and a preset confusion threshold to form the difference classification data set; The cloud platform encrypts the classified and labeled government data and stores it in the cloud platform database; The cloud platform constructs a government data retrieval model, and searches the cloud platform database based on the government data retrieval model to obtain target government data; The cloud platform performs update detection on the target government data, backs up the target government data that has been updated, and stores the backed-up target government data in the cloud platform database; The cloud platform updates the target government data that has been updated, and stores the updated target government data in the cloud platform database.

2. The cloud platform-based government data management method according to claim 1, characterized in that: The category response field of each classified data in the accurate classification data set of the government data extracted includes: Extracting the category response field of each classification data in the precise classification data set of the government data, and calculating the usage frequency of the category response field; acquiring a probability distribution coefficient of the category response field based on a usage frequency of the category response field; The category response fields are weighted based on the probability distribution coefficients.

3. The cloud platform-based government data management method according to claim 1, characterized in that: The cloud platform encrypts the classified and labeled government data including: The classified and labeled government data are split and disrupted, and serialized to obtain a number of scattered government data of different lengths; Preset an encryption key, and perform forward encryption and reverse encryption on the plurality of scattered government data based on the encryption key, respectively, to obtain a forward encryption sequence and a reverse encryption sequence of each of the scattered government data; Randomly extracting a forward encryption sequence or a reverse encryption sequence of each of the scattered government data, and converting the extracted forward encryption sequence and reverse encryption sequence into a same-direction encryption sequence based on a forward-reverse mapping relationship; The same-direction encryption sequences are concatenated to obtain encrypted government data.

4. The cloud platform-based government data management method according to claim 1, characterized in that: The cloud platform constructs a government data retrieval model, and searches the cloud platform database based on the government data retrieval model to obtain target government data, including: Acquire search information, pre-process the search information, and extract key search information; Extracting a hash feature vector from the key search information, and performing dimension reduction and encoding processing on the hash feature vector to obtain a hash code value of the key search information; Calculating a loss deviation value of the hash code value, correcting the hash code value based on the loss deviation value, and obtaining a corrected hash code value; The corrected hash code value is matched for similarity in the cloud platform database to obtain the target government data.

5. The cloud platform-based government data management method according to claim 1, characterized in that: The cloud platform performs update detection on the target government data, backs up the target government data that has been updated, and stores the backed-up target government data in the cloud platform database, including: The cloud platform performs update detection on the target government data, copies and backs up the target government data that has been updated, inserts backup information, and stores the backed-up target government data in the backup data storage area of ​​the cloud platform database.

6. A government data management system based on a cloud platform, characterized in that: The cloud platform-based government data management system is used to implement the cloud platform-based government data management method according to any one of claims 1 to 5, and the system comprises: A classification and labeling module, which is used for the cloud platform to receive government data sent by the government data terminal and to classify and label the received government data; The classification and labeling of the received government data includes: converting the government data into a target recognition data set format; accurately analyzing the classification categories of the government data to obtain an accurate classification data set of the government data; extracting the category response field of each classification data in the accurate classification data set of the government data; and classifying and labeling the government data based on the category response field; The step of accurately analyzing the classification categories of the government data to obtain an accurate classification data set of the government data includes: extracting a rough classification data set from the government data, and collecting a standard classification data set corresponding to the government data; mapping and correspondingly analyzing the rough classification data set with the standard classification data set to obtain a differential classification data set; dividing the rough classification data set into a classification data set to be repaired and a reference classification data set, and constructing a classification data set repair model based on the standard classification data set and the reference classification data set; repairing duplicate classification data in the classification data set to be repaired and the differential classification data set based on the classification data set repair model to obtain a classification data set to be supplemented; adding the classification data set to be supplemented to the rough classification data set to obtain an accurate classification data set of the government data; The mapping correspondence analysis between the rough classification data set and the standard classification data set to obtain the difference classification data set includes: obtaining a confusion relationship between the classification data in the rough classification data set and the standard classification data set, and selecting part of the classification data with a higher degree of confusion based on the degree of confusion in the confusion relationship and a preset confusion threshold to form the difference classification data set; An encryption module, which is used by the cloud platform to encrypt the classified and labeled government data and store it in the cloud platform database; A retrieval module, wherein the retrieval module is used for the cloud platform to construct a government data retrieval model, and to search the cloud platform database based on the government data retrieval model to obtain target government data; An update detection module, wherein the update detection module is used by the cloud platform to perform update detection on the target government data, back up the target government data that has been updated, and store the backed-up target government data in the cloud platform database; An update storage module is used by the cloud platform to update target government data that has been updated, and store the updated target government data in the cloud platform database.

7. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cloud platform-based government data management method described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the cloud platform-based government data management method described in any one of claims 1 to 5 is implemented.

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