Hierarchical demand-based data viewing system

Abstract

The invention relates to the technical field of data display, in particular to a data viewing system based on hierarchical requirements. Comprising a data collection module which is used for associating all government affair systems, collecting government affair data from the government affair systems, identifying the government affair data, classifying the government affair data according to a preset classification standard, carrying out authority management on the government affair data according to a preset authority classification standard and then storing the government affair data in a database; the account information acquisition module is used for acquiring user information of a login user; the authority management module is used for determining access attribute parameters of the user for accessing various types of government affair data according to the administrative hierarchy and the duty responsibility of the user, determining the data range when the user accesses according to the administrative hierarchy and the local information, and sending the data range to the authority management module, the data granularity # imgabs0 # during user access is determined according to the duty responsibility, and the higher the data granularity # imgabs1 # is, the higher the data fineness degree is.

Description

Technical Field

[0001] The present invention relates to the technical field of data management and display, and in particular to a data viewing system based on hierarchical requirements. Background Art

[0002] Government data refers to the various forms of data generated, collected, stored, and used by government departments in their daily work. These include multimedia information such as text, images, audio, and video, as well as complex data types generated by emerging technologies like big data and cloud computing. This data encompasses not only information related to the internal operations of government departments but also information related to public interests, forming a crucial cornerstone of national informatization.

[0003] In the early days of government data, a lack of coordination between departments and inconsistent technical standards led to data silos. This meant that departments operated independently, without sharing data. This led to inefficient data management and even duplication of effort. While some government systems now aggregate and manage data from various departments, this also presents the problem of managing permissions for government data.

[0004] Permission management in government systems differs from that in other systems. In typical enterprise data management systems, data permissions are tied to rank, with higher rank granting higher permissions. However, data permissions for civil servants are strongly tied to their administrative hierarchy, job responsibilities, and data confidentiality, rather than simply determined by rank. Lower-level personnel may require access to large amounts of detailed data due to business needs, while senior decision-makers are more likely to have access to macro-aggregate data.

[0005] Therefore, how to allow different users to access only the data necessary to perform their duties when displaying data to them is a technical problem that needs to be solved urgently in the data permission management of the job system. Summary of the Invention

[0006] The technical problem solved by the present invention is to provide a data viewing system based on hierarchical requirements, which can help users display the necessary data to perform their duties.

[0007] The basic solution provided by the present invention is a data viewing system based on hierarchical requirements, including a server and a client. The server includes a data collection module, an account information acquisition module, a rights management module, an information viewing module, an importance judgment module, and a data maintenance module.

[0008] A data collection module connects to various government systems and collects government data from these systems, identifies the government data, classifies the government data according to preset classification standards, and manages the government data according to preset authority classification standards before storing it in a database. The classification standards include local classification and department classification.

[0009] An account information acquisition module is used to obtain the user information of the logged-in user. Acquisition of the logged-in user's user information requires the user's consent. The user information includes administrative level, job responsibilities, and local information. The administrative level and local information are used to determine data coverage, and the job responsibilities are used to determine business needs.

[0010] The authority management module is used to determine the access attribute parameters for users to access various types of government data based on the user's administrative level and job responsibilities. The access attribute parameters include data range and data granularity. The data range of the user's access is determined based on the administrative level and local information, and the data granularity G is determined based on the user's job responsibilities. The higher the data granularity G, the more refined the data.

[0011] The information viewing module is used to determine the access attribute parameters of the user to access the government data according to the classification of the accessed government data when obtaining the user's access to the government data, and to retrieve the corresponding government data and display it on the client according to the access attribute parameters;

[0012] The importance judgment module obtains the data access frequency F of different data granularities G for each type of government data within a period, and calculates the data importance score S based on the data access frequency F of each data granularity G. The larger S is, the higher the data importance;

[0013]

[0014] Where n is the number of levels of data granularity, W is the weight of data granularity, the higher the level of data granularity, the higher W;

[0015] The data maintenance module is used to sort the importance of various types of government data according to the data importance score, and perform data maintenance based on the data importance.

[0016] The principles and advantages of the present invention lie in: using a data collection module, data from different government systems is collected and integrated, then classified and stored according to the location and department to which the data belongs. For example, the first-level classification includes City A, the second-level classification includes District A, County A, and the Municipal Bureau under City A, and the third-level classification includes Town X and the County Bureau under County A. After the data is classified and stored, when a user logs into the government system, the user's administrative level, job responsibilities, and region are determined based on the login account. For example, based on the login account, the user's administrative level, job responsibilities, and region are determined. For example, a grassroots staff member in Street Y, District B, City A, or a district-level decision-maker in District C, City A, etc. are obtained. By obtaining the logged-in user's administrative level, job responsibilities, and region, the access attribute parameters of the logged-in user when accessing data are determined. The user's scope of data is determined based on the administrative level and local information. For example, a district-level decision-maker in District C, City A, has access to data within District C, while a grassroots staff member in Street Y has access to data within Street Y. The data granularity of data access is determined based on job responsibilities. The higher the data granularity, the more detailed the data access. For example, provincial-level decision-makers access data with a wider scope and lower granularity. In this case, they present aggregated data for the region, such as province-wide analysis reports on the economy, population, and environment. For users at the prefecture-level, data granularity is higher than at the provincial level. For example, they can access government data such as urban construction, municipal social security, and public security within their jurisdiction. At the district and county level, data granularity is even higher, such as business data such as community population and local fund disbursement records within the district and county. Township and sub-district levels access integrated data within their jurisdiction, such as village-level land registration and resident insurance enrollment. Different access attributes are also defined for different data categories. For example, when users access data for their own city, the scope attribute is larger and the data granularity is lower. However, when users access data for a neighboring city, the scope attribute is lower or zero, and the quantity granularity is also lower.

[0017] At the same time, in this application, data viewing is managed through two methods: data range and data granularity. Data granularity is not directly managed by the administrative level. Therefore, it is not the case that the higher the administrative level, the lower the data granularity, and vice versa. Instead, the administrative level and local information work together to lock the data range, and the data granularity is locked according to job responsibilities. There will also be users with high administrative levels who have higher data granularity when accessing certain types of data, and there will also be users with low administrative levels who have lower data granularity when accessing certain types of data. For example, senior decision makers at the National Audit Office need to retrieve original data such as transfer records and communication records when reviewing economic cases; district-level education bureau resources need to understand the proportion of each school's total funding when calculating the annual education funding allocation. Through this type of technical solution in this application, the flexibility of data viewing can be improved.

[0018] After assigning users access attribute parameters for various types of government data through the permission management module, when a user accesses a certain type of government data, the scope attributes and data granularity of this type of data when displayed to the user are determined based on the access attribute parameters for this type of government data. The data is processed (such as aggregating unaggregated data, sorting data, etc.) and then displayed on the page accessed by the user.

[0019] At the same time, obtain the data access frequency of each data granularity level of the same type within the cycle, such as determining the access frequency of township and street data, the access frequency of district and county data, the access frequency of prefecture-level data, and the access frequency of provincial data. Determine the importance of data based on the access frequency of data at each level. If the data access frequency at all levels is high, and the grassroots to decision-makers pay high attention to this type of data, then the data is more important. If only the grassroots data range frequency is high, it means that the data is only used for some daily trivial work, and the importance of this type of data is relatively low. After determining the importance of each type of data, you can give priority to the more important data in subsequent data maintenance to ensure the accuracy and timeliness of important data.

[0020] Compared with the existing technology, this solution has the following advantages:

[0021] 1. The data systems in the existing technology mostly manage permissions based on the level of job title. The higher the job title, the higher the permission to access data, and the lower the job title, the lower the permission to access data. This may result in a large amount of data that can be accessed by decision makers. If the data retrieval method is inappropriate, it often takes a lot of time to find the required data. The data access rights at the grassroots level are low, and some detailed data may not be visible. Approval is required before access, which reduces work efficiency. This solution does not divide the permission levels for data and personnel, but assigns access attribute parameters to each person to access various types of data. When users at different levels access the same type of data, the data levels they view are different according to the access data parameter data. Similarly, the same is true when the same person accesses different types of data.

[0022] 2. In this solution, the user's data access scope is locked by data scope, and the quantity and detail of the data within the user's access scope are determined by data granularity. This helps users quickly focus on and lock in the necessary data to perform their duties in their daily office work, thereby improving work efficiency.

[0023] 3. By assessing the importance of various types of government data based on the access frequency at different levels, important data can be screened to ensure its accuracy and timely updating.

[0024] Furthermore, the data collection module includes a department management module and a local management module;

[0025] The department management module is used to identify the data content of government data, determine the management department of government data based on the data content, and classify government data into departments based on the management department;

[0026] The local management module is used to identify the local content of each category of government data and to classify the government data into local categories based on the identification results of the local content of the government data.

[0027] By identifying the content of the collected government data, the management departments and places that are related to the government data can be identified.

[0028] Furthermore, the authority management module includes a scope management module and a data granularity management module;

[0029] The scope attribute management module is used to determine the user's administrative location and administrative department based on the user's local information, and to determine the user's data scope based on the administrative level of the user's location and administrative department;

[0030] The data granularity management module is used to identify the user's business scenario based on the user's job responsibilities, and determine the data granularity for each type of data based on the business scenario and data type.

[0031] Furthermore, the data granularity management module includes a job semantic analysis module, a business scenario classification module, a granularity dynamic mapping module, and a granularity matching module;

[0032] The job responsibility vectorization module is used to extract the keywords describing the user's job responsibilities for a certain type of data when the user accesses the data, forming a word set D = {d1, d2, ...d n}, generate semantic vector V through word embedding method D :

[0033]

[0034] Among them, Embedding() is the word embedding model; α f Representation word d i The functional weight of the functional dimension f, α f ∈[α1,α2,…,α n ];

[0035] The business scenario classification module is used to classify business scenarios according to the pre-defined business scenario set S = {s1, s2, ...s p}, combined with the semantic vector V DCalculate the business scenario distribution probability P of the user's job responsibilities through the classification model:

[0036] P=σ(W s ·V D +b s )

[0037] Where σ is the Softmax function, W is the classifier weight matrix, and b is the bias vector;

[0038] The granularity dynamic mapping module is used to map the scene distribution P to the data granularity level set G = {G1, G2, ... g q}, establish the scene-granularity mapping matrix:

[0039] M=[m sG ] p×q

[0040] where m sq The matching degree of data granularity G of business scenario s, m sq ∈[0,1];

[0041] The granularity matching module is used to obtain the matching score vector S at each granularity level of the business scenario based on the scenario-granularity mapping matrix G :

[0042] S G =M T ·P

[0043] Where T represents the matrix permutation operation;

[0044] Determine the data granularity of the user in the business scenario based on the granularity matching score:

[0045]

[0046] G * Indicates the data granularity level that the user ultimately selects regarding this type of data.

[0047] When a user accesses a certain type of data, the keywords describing the job responsibilities of the data are obtained. For example, when accessing economic data, the keywords describing the user's job responsibilities related to economic data are obtained. For example, "GDP analysis" and "financial budget" are converted into word vectors, such as Embedding(d i )=[0.8,0.6,0.2]. At the same time, the functional weights are decomposed according to the job responsibilities, for example, α=[α 决策 , α 监督 , α 执行]=[0.7,0.2,0.1]. The specific functional weight dimension can be set according to the data type or assigned by keyword frequency. For example, keywords such as "specify" and "analyze" belong to the decision dimension. The word vector is adjusted by the functional dimension weight. After that, the word vector is combined with a pre-defined set of business scenarios. For example, the business scenario includes development planning and budget allocation to obtain the word vector V D is [0.8, 0.6, 0.2], assuming b=[0.1,0.2].

[0048]

[0049] The probability distribution of the business scenario for development planning is 0.55, and the probability distribution of the business scenario for budget allocation is 0.45. Based on the preset scenario threshold, a scenario is activated when the threshold exceeds the scenario threshold for that scenario. For example, if the scenario threshold for s1 is 0.5 and the scenario threshold for s2 is 0.5, scenario s1 is activated, and the user is in a single-business scenario. If the scenario threshold for s2 is 0.4, scenarios s1 and s2 are planned, and the user is in a multi-business scenario.

[0050] In the business scenario classification model, W and b are obtained through supervised learning from labeled data.

[0051] When it is a single scene, the data granularity with the highest matching degree can be determined based on the mapping matrix between P and data granularity G. If it is multiple scenes, the final data granularity is analyzed through weighting.

[0052] Furthermore, the server further includes an access correlation analysis module, which includes an access frequency recording module and a correlation judgment module;

[0053] Access frequency recording module, used for each data category C k , record the access frequency of each level during the period:

[0054] F k1 ,F k2 ,……,F kL

[0055] Where k represents the kth data category, and L represents the Lth data granularity level;

[0056] The correlation judgment module is used to judge the correlation r between the access frequency of low-level data granularity and the access frequency of high-level data granularity:

[0057]

[0058] Where n represents the number of days in the cycle, Indicates the same data category Ck The access frequency of low-level data on day n in the period and, Indicates the same data category C k The sum of the access frequencies of high-level data on day n within the period;

[0059] The importance judgment module adjusts the data importance score based on the access frequency correlation r between the low-level and high-level levels:

[0060] S ′ k =S k ×(1+αr)

[0061] Where α is a preset adjustment coefficient used to control the impact of relevance on importance score.

[0062] When analyzing data importance, not only the access frequency of single-level data is considered, but also the association between different levels. When some detailed data is frequently accessed, the corresponding summary data is also frequently accessed. This type of data will play a disgusting role in daily office work, so it needs higher attention, such as priority caching, backup and other security measures. Indicates the same data category C k The sum of the access frequencies of low-level data on day n within a period, for example, the sum of the access frequencies of the first data granularity level, the second data granularity level, and other low-level data on a single day, Indicates the same data category C k The access frequency of high-level data on day n within a cycle, for example, the sum of the daily access frequencies of high-level data at the third and fourth data granularity levels. By analyzing the correlation between the access frequencies of low-level data and high-level data, we obtain the correlation coefficient r. When the correlation is strong, this data is more important, and its quality assurance requires priority. Therefore, the calculated correlation is used to adjust the data importance.

[0063] Furthermore, the server further includes a data level adjustment module;

[0064] Level adjustment module, when 0 <r≤r c When the first adjustment strategy is executed, the first adjustment strategy is to identify the correlation between the data granularity of each level and the data granularity of the previous level. m , in r m Add an intermediate level to the two data levels closest to the correlation r, and readjust the level allocation rules for the data type;

[0065] When r<0, merge r mThe two data levels closest to the correlation r are selected, and the level allocation rules of the data type are readjusted.

[0066] When 0 <r≤r c .

[0067] When 0 <r≤r c , the correlation between access frequencies at lower and higher levels is low. This could be due to an irrational data granularity hierarchy. For example, unclear hierarchical divisions prevent users from effectively finding the data they need. For example, users at lower data granularity levels can access detailed data sufficient for daily operations, while users at intermediate data granularity levels, seeking meso-level decision-making data, only see aggregated macro data that doesn't meet their daily needs. Over time, they stop accessing data directly and instead request data from users at lower data granularity levels, resulting in a decrease in correlation. In this case, adding an intermediate level is recommended. When r < 0, it indicates a negative correlation between access frequencies at different data granularity levels. This indicates redundant design at two or more data granularity levels, where one level presents data significantly better than the other. Therefore, merging these levels is necessary. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 Schematic diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following is further described in detail through specific implementation methods:

[0070] A data viewing system based on hierarchical requirements includes a server and a client, wherein the server includes a data collection module, an account information acquisition module, a permission management module, an information viewing module, an importance judgment module, and a data maintenance module;

[0071] A data collection module connects to various government systems and collects government data from these systems, identifies the government data, classifies the government data according to preset classification standards, and manages the government data according to preset authority classification standards before storing it in a database. The classification standards include local classification and department classification.

[0072] An account information acquisition module is used to obtain the user information of the logged-in user. Acquisition of the logged-in user's user information requires the user's consent. The user information includes administrative level, job responsibilities, and local information. The administrative level and local information are used to determine data coverage, and the job responsibilities are used to determine business needs.

[0073] The authority management module is used to determine the access attribute parameters for users to access various types of government data based on the user's administrative level and job responsibilities. The access attribute parameters include data range and data granularity. The data range of the user's access is determined based on the administrative level and local information, and the data granularity G is determined based on the user's job responsibilities. The higher the data granularity G, the more refined the data.

[0074] The information viewing module is used to determine the access attribute parameters of the user to access the government data according to the classification of the accessed government data when obtaining the user's access to the government data, and to retrieve the corresponding government data and display it on the client according to the access attribute parameters;

[0075] The importance judgment module obtains the data access frequency F of different data granularities G for each type of government data within a period, and calculates the data importance score S based on the data access frequency F of each data granularity G. The larger S is, the higher the data importance;

[0076]

[0077] Where n is the number of levels of data granularity, W is the weight of data granularity, the higher the level of data granularity, the higher W;

[0078] The data maintenance module is used to sort the importance of various types of government data according to the data importance score, and perform data maintenance based on the data importance.

[0079] The data collection module collects and integrates data from various government systems and then categorizes it. The collected government data is then stored according to the location and department to which it belongs. For example, the first-level categorization includes City A, the second-level categorization includes District A, County A, and the Municipal Bureau within City A, and the third-level categorization includes Town X and the County Bureau within County A. After data is categorized and stored, when a user logs into the government system, the user's administrative level, job responsibilities, and region are determined based on their login account. For example, a grassroots employee in Street Y, District B, City A, or a district-level decision-maker in District C, City A, can be identified based on their login account. This information is used to determine the user's access attribute parameters when accessing data. The user's administrative level and locality information determine the user's data scope. For example, a district-level decision-maker in District C, City A, can access data within District C, while a grassroots employee in Street Y can access data within Street Y. The granularity of data access is determined based on job responsibilities. The higher the granularity, the more detailed the data access. For example, provincial-level decision-makers access data at a wider scope and lower granularity. In this case, they present aggregated data for the region, such as province-wide analysis reports on the economy, population, and environment. For users at the prefecture-level, data granularity is higher than at the provincial level. For example, they can access government data such as urban construction, municipal social security, and public security within their jurisdiction. At the district and county level, data granularity is even higher, such as business data such as community populations and local fund disbursement records within their district and county. Township and sub-district levels access integrated data within their jurisdiction, such as village-level land registration and resident insurance enrollment. Different access attributes are also defined for different data categories. For example, when users access data for their own city, the scope attribute is larger and the data granularity is lower. However, when users access data for a neighboring city, the scope attribute is lower or zero, and the quantity granularity is also lower. Specific examples are shown in Table 1.

[0080] Table 1

[0081] Job Role Data granularity (G) Data field range Senior decision makers G1 (macro statistics) Regional GDP and annual budget Department Head G2 (Meso-level analysis) Budget allocation and monthly execution progress for each department Frontline clerk G3 (Detailed Data) Residents' social security payment records and subsidy payment details

[0082] However, it is worth noting that in this application, data is managed through two methods: data range and data granularity. Data granularity is not directly managed by administrative level. Therefore, it is not the case that the higher the administrative level, the lower the data granularity, and vice versa. Instead, the administrative level and local information work together to lock the data range, and the data granularity is locked according to job responsibilities. Similarly, users with high administrative levels may have higher data granularity when accessing certain types of data, while users with low administrative levels may have lower data granularity when accessing certain types of data.

[0083] Specifically, the rights management module includes a scope management module and a data granularity management module.

[0084] The scope attribute management module is used to determine the user's administrative location and administrative department based on the user's local information, and to determine the user's data scope based on the administrative level of the user's location and administrative department;

[0085] The data granularity management module is used to identify the user's business scenario based on the user's job responsibilities, and determine the data granularity for each type of data based on the business scenario and data type.

[0086] Specifically, in this embodiment, user positions are broken down into functional dimensions, such as "decision-making," "execution," and "supervision and review." Core business keywords (such as "budget approval," "project management," and "risk audit") are then extracted from the job descriptions. The user's responsibilities are then determined based on these keywords to define business scenarios. A specific example is shown in Table 2:

[0087] Table 2

[0088] Responsibility dimension Typical business scenarios Data demand characteristics Decision Making Development planning and budget allocation Macro Summary (G1), Trend Analysis Perform an action Task execution and process processing Meso-level operational data (G2) Supervision and Audit Compliance inspection and abnormal monitoring Detailed records (G3), original vouchers

[0089] The specific implementation is as follows:

[0090] The data granularity management module includes a job semantic analysis module, a business scenario classification module, a granularity dynamic mapping module and a granularity matching module;

[0091] The job responsibility vectorization module is used to extract the keywords describing the user's job responsibilities for a certain type of data when the user accesses the data, forming a word set D = {d1, d2, ...d n}, generate semantic vector V through word embedding method D :

[0092]

[0093] Among them, Embedding() is the word embedding model; α f Representation word d i The functional weight of the functional dimension f, α f ∈[α1,α2,…,α n ];

[0094] The business scenario classification module is used to classify business scenarios according to the pre-defined business scenario set S = {s1, s2, ...s p}, combined with the semantic vector V D Calculate the business scenario distribution probability P of the user's job responsibilities through the classification model:

[0095] P=σ(W s ·VD +b s )

[0096] Where σ is the Softmax function, W is the classifier weight matrix, and b is the bias vector;

[0097] The granularity dynamic mapping module is used to map the scene distribution P to the data granularity level set G = {G1, G2, ... G q}, establish the scene-granularity mapping matrix:

[0098] M=[m sG ] p×q

[0099] where m sq The matching degree of data granularity G of business scenario s, m sq ∈[0,1];

[0100] The granularity matching module is used to obtain the matching score vector S at each granularity level of the business scenario based on the scenario-granularity mapping matrix G :

[0101] S G =M T ·P

[0102] Where T represents the matrix permutation operation;

[0103] Determine the data granularity of the user in the business scenario based on the granularity matching score:

[0104]

[0105] G * Indicates the data granularity level that the user ultimately selects regarding this type of data.

[0106] When a user accesses a certain type of data, the keywords describing the job responsibilities of the data are obtained. For example, when accessing economic data, the keywords describing the user's job responsibilities related to economic data are obtained. For example, "GDP analysis" and "financial budget" are converted into word vectors, such as Embedding(d i )=[0.8,0.6,0.2]. At the same time, the functional weights are decomposed according to the job responsibilities, for example, α=[α 决策 , α 监督 , α 执行 ]=[0.7,0.2,0.1]. The specific functional weight dimension can be set according to the data type or assigned by keyword frequency. For example, keywords such as "specify" and "analyze" belong to the decision dimension. The word vector is adjusted by the functional dimension weight. After that, the word vector is combined with a pre-defined set of business scenarios. For example, the business scenario includes development planning and budget allocation to obtain the word vector VD is [0.8, 0.6, 0.2], assuming b=[0.1,0.2].

[0107]

[0108] The probability distribution of the business scenario for development planning is 0.55, and the probability distribution of the business scenario for budget allocation is 0.45. Based on the preset scenario threshold, a scenario is activated when the threshold exceeds the scenario threshold for that scenario. For example, if the scenario threshold for s1 is 0.5 and the scenario threshold for s2 is 0.5, scenario s1 is activated, and the user is in a single-business scenario. If the scenario threshold for s2 is 0.4, scenarios s1 and s2 are planned, and the user is in a multi-business scenario.

[0109] In the business scenario classification model, W and b are obtained through supervised learning from labeled data.

[0110] When it is a single scene, the data granularity with the highest matching degree can be determined based on the mapping matrix between P and data granularity G. If it is multiple scenes, the final data granularity is analyzed through weighting.

[0111] Therefore, the biggest difference between this application and the existing technology is that the data granularity is not determined according to the level of administrative hierarchy or position. For example, when reviewing economic cases, senior decision-makers at the National Audit Office need to retrieve original data such as transfer records and communication records; when the district-level education bureau's resources are calculating the annual education funding allocation, they need to understand the proportion of the total funding of each school. Through this type of technical solution in this application, the flexibility of data viewing can be improved.

[0112] After assigning users access attribute parameters for various types of government data through the permission management module, when a user accesses a certain type of government data, the scope attributes and data granularity of this type of data when displayed to the user are determined based on the access attribute parameters for this type of government data. The data is processed (such as aggregating unaggregated data, sorting data, etc.) and then displayed on the page accessed by the user.

[0113] At the same time, obtain the data access frequency of each data granularity level of the same type within the cycle, such as determining the access frequency of township and street data, the access frequency of district and county data, the access frequency of prefecture-level data, and the access frequency of provincial data. Determine the importance of data based on the access frequency of data at each level. If the data access frequency at all levels is high, and the grassroots to decision-makers pay high attention to this type of data, then the data is more important. If only the grassroots data range frequency is high, it means that the data is only used for some daily trivial work, and the importance of this type of data is relatively low. After determining the importance of each type of data, you can give priority to the more important data in subsequent data maintenance to ensure the accuracy and timeliness of important data.

[0114] Compared with the existing technology, this solution has the following advantages:

[0115] 1. The data systems in the existing technology mostly manage permissions based on job titles. The higher the job title, the higher the data access permissions, and the lower the job title, the lower the data access permissions. This may result in a large amount of data that can be accessed by decision-makers, and it often takes a lot of time to find the required data. The grass-roots level has low data access permissions and may not be able to see certain detailed data. It requires approval before access, which reduces work efficiency. This solution does not divide the permission levels for data and personnel, but assigns access attribute parameters to each person to access various types of data. When users at different levels access the same type of data, the data levels they view are different according to the access data parameter data. Similarly, the same is true when the same person accesses different types of data.

[0116] 2. In this solution, the user's data access scope is locked by data scope, and the quantity and detail of the data within the user's access scope are determined by data granularity. This helps users quickly focus on and lock in the necessary data to perform their duties in their daily office work, thereby improving work efficiency.

[0117] 3. By assessing the importance of various types of government data based on the access frequency at different levels, important data can be screened to ensure its accuracy and timely updating.

[0118] The data collection module includes a department management module and a local management module;

[0119] The department management module is used to identify the data content of government data, determine the management department of government data based on the data content, and classify government data into departments based on the management department;

[0120] The local management module is used to identify the local content of each category of government data and to classify the government data into local categories based on the identification results of the local content of the government data.

[0121] By identifying the content of the collected government data, the management departments and places that are related to the government data can be identified.

[0122] The server further includes an access correlation analysis module, which includes an access frequency recording module and a correlation judgment module;

[0123] Access frequency recording module, used for each data category C k , record the access frequency of each level during the period:

[0124] F k1 ,F k2 ,……,F kL

[0125] Where k represents the kth data category, and L represents the Lth data granularity level;

[0126] The correlation judgment module is used to judge the correlation r between the access frequency of low-level data granularity and the access frequency of high-level data granularity:

[0127]

[0128] Where n represents the number of days in the cycle, Indicates the same data category C k The access frequency of low-level data on day n in the period and, Indicates the same data category C k The sum of the access frequencies of high-level data on day n within the period;

[0129] The importance judgment module adjusts the data importance score based on the access frequency correlation r between the low-level and high-level levels:

[0130] S ′ k =S k ×(1+αr)

[0131] Where α is a preset adjustment coefficient used to control the impact of relevance on importance score.

[0132] When analyzing data importance, not only the access frequency of single-level data is considered, but also the association between different levels. When some detailed data is frequently accessed, the corresponding summary data is also frequently accessed. This type of data will play a disgusting role in daily office work, so it needs higher attention, such as priority caching, backup and other security measures. Indicates the same data category C k The sum of the access frequencies of low-level data on day n within a period, for example, the sum of the access frequencies of the first data granularity level, the second data granularity level, and other low-level data on a single day, Indicates the same data category C k The access frequency of high-level data on day n within a cycle, for example, the sum of the daily access frequencies of high-level data at the third and fourth data granularity levels. By analyzing the correlation between the access frequencies of low-level data and high-level data, we obtain the correlation coefficient r. When the correlation is strong, this data is more important, and its quality assurance requires priority. Therefore, the calculated correlation is used to adjust the data importance.

[0133] The server also includes a data level adjustment module;

[0134] Level adjustment module, when 0 <r≤r c When the first adjustment strategy is executed, the first adjustment strategy is to identify the correlation between the data granularity of each level and the data granularity of the previous level. m , in r m Add an intermediate level to the two data levels closest to the correlation r, and readjust the level allocation rules for the data type;

[0135] When r<0, merge r m The two data levels closest to the correlation r are selected, and the level allocation rules of the data type are readjusted.

[0136] When 0 <r≤r c .

[0137] When 0 <r≤r c , the correlation between access frequencies at lower and higher levels is low. This could be due to an irrational data granularity hierarchy. For example, unclear hierarchical divisions prevent users from effectively finding the data they need. For example, users at lower data granularity levels can access detailed data sufficient for daily operations, while users at intermediate data granularity levels, seeking meso-level decision-making data, only see aggregated macro data that doesn't meet their daily needs. Over time, they stop accessing data directly and instead request data from users at lower data granularity levels, resulting in a decrease in correlation. In this case, adding an intermediate level is recommended. When r < 0, it indicates a negative correlation between access frequencies at different data granularity levels. This indicates redundant design at two or more data granularity levels, where one level presents data significantly better than the other. Therefore, merging these levels is necessary.

[0138] The above are only embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme are not described in detail here. Ordinary technicians in the field are aware of all common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A data viewing system based on hierarchical requirements, characterized by: It includes a server and a client, wherein the server includes a data collection module, an account information acquisition module, a rights management module, an information viewing module, an importance judgment module and a data maintenance module; A data collection module connects to various government systems and collects government data from these systems, identifies the government data, classifies the government data according to preset classification standards, and manages the government data according to preset authority classification standards before storing it in a database. The classification standards include local classification and department classification. An account information acquisition module is used to obtain user information of the logged-in user, including administrative level, job responsibilities, and local information; the administrative level and local information are used to determine data coverage, and the job responsibilities are used to determine business needs; The authority management module is used to determine the access attribute parameters of users to various types of government data according to the user's administrative level and job responsibilities. The access attribute parameters include data range and data granularity. The data range of the user's access is determined according to the administrative level and local information, and the data granularity of the user's access to each data type is determined according to the job responsibilities. , data granularity The higher the value, the more refined the data. The information viewing module is used to determine the access attribute parameters of the user to access the government data according to the classification of the accessed government data when obtaining the user's access to the government data, and to retrieve the corresponding government data and display it on the client according to the access attribute parameters; Importance judgment module, obtains different data granularities of the same type of government data within a period Data access frequency , according to the granularity of each data Data access frequency Calculating data importance scores , The larger it is, the more important the data is; in is the number of levels of data granularity, is the weight of data granularity. The higher the data granularity level, The higher; The data maintenance module is used to sort the importance of various types of government data according to the data importance score and perform data maintenance based on the data importance; The rights management module includes a scope management module and a data granularity management module; The scope attribute management module is used to determine the user's administrative location and administrative department based on the user's local information, and to determine the user's data scope based on the administrative level of the user's location and administrative department; The data granularity management module is used to identify the user's business scenario based on the user's job responsibilities and determine the data granularity for each type of data based on the business scenario and data type; The data granularity management module includes a job semantic analysis module, a business scenario classification module, a granularity dynamic mapping module and a granularity matching module; The job responsibility vectorization module is used to extract the keywords describing the user's job responsibilities for a certain type of data when the user accesses the data, and form a word set. , generate semantic vectors through word embedding method : in is a word embedding model; Expressive words Functional dimension The functional weight of ; Business scenario classification module, used to classify business scenarios according to pre-defined , combined with semantic vector Calculate the business scenario distribution probability of the user's job responsibilities through the classification model : in is the Softmax function, W is the classifier weight matrix, b is the bias vector, and the scene activation condition is , is the scenario threshold corresponding to the business scenario; Granular dynamic mapping module for scene distribution and data granularity level collection , establish the scene-granularity mapping matrix: in The matching degree of the data granularity G of the business scenario s, ; The granularity matching module is used to obtain the matching score vector at each granularity level of the business scenario based on the scenario-granularity mapping matrix : Where T represents the matrix permutation operation; Determine the data granularity of the user in the business scenario based on the granularity matching score: Indicates the data granularity level finally selected by the user regarding this type of data.

2. The data viewing system based on hierarchical requirements according to claim 1, characterized in that: The data collection module includes a department management module and a local management module; The department management module is used to identify the data content of government data, determine the management department of government data based on the data content, and classify government data into departments based on the management department; The local management module is used to identify the local content of each category of government data and to classify the government data into local categories based on the identification results of the local content of the government data.

3. The data viewing system based on hierarchical requirements according to claim 1, characterized in that: The server further includes an access correlation analysis module, which includes an access frequency recording module and a correlation judgment module; Access frequency record module, used for each data category , record the access frequency of each level during the period: in Indicates the data categories, Indicates the Each data granularity level; The correlation judgment module is used to judge the correlation r between the access frequency of low-level data granularity and the access frequency of high-level data granularity: Where n represents the number of days in the cycle, Indicates the same data category The access frequency of low-level data on day n in the period and, Indicates the same data category The sum of the access frequencies of high-level data on day n within the period; The importance judgment module adjusts the data importance score based on the access frequency correlation r between the low-level and high-level levels: Where α is a preset adjustment coefficient used to control the impact of relevance on importance score.

4. The data viewing system based on hierarchical requirements according to claim 3, characterized in that: The server also includes a data level adjustment module; Level adjustment module, when When the first adjustment strategy is executed, the first adjustment strategy is to identify the correlation between the data granularity of each level and the data granularity of the previous level. ,exist The closest correlation Add an intermediate level between the two data levels and readjust the level allocation rules of the data type, where is the preset correlation threshold; when When merged The closest correlation The two data levels are divided and the level allocation rules of the data type are readjusted.

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