Government data processing methods, devices, equipment, media and program products

Abstract

The present disclosure provides a government data processing method, which can be applied to the field of big data technology. The government data processing method includes: based on historical government data, constructing a matter association graph, the matter association graph includes matter item sets, calculating the support for all the matter item sets respectively, obtaining matter frequent item sets, searching the matter association graph using the matter frequent item sets, obtaining matter association patterns, calculating the similarity between all the persons in the matter association graph, and recommending matter sets for persons whose similarity is greater than a preset threshold based on the matter association patterns. In this way, matter association patterns with logical associations and matter association patterns without logical associations can be obtained, and the larger the amount of government data, the more matter association patterns can be obtained. The present disclosure also provides a government data processing device, equipment, storage medium and program product.

Description

Technical Field

[0001] The present disclosure relates to the field of big data, and more specifically to a government data processing method, device, equipment, medium and program product. Background Art

[0002] "Internet + Government Services" includes multiple items, which are related to each other. For example, handling marriage registration, completing household registration transfer, handling birth registration, and applying for citizen cards are items with logical association patterns. Applying for marriage certificates, buying second-hand houses, applying for housing loans, and withdrawing housing provident funds are items with non-logical association patterns (people handle them in chronological order).

[0003] The existing method is to manually discover item association patterns through item combing, but this method can usually only discover certain item association patterns with logical associations and it is difficult to discover item association patterns with non-logical associations. At the same time, this method is also very dependent on business experts' understanding of item rules, and has low efficiency and accuracy. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a government data processing method, apparatus, device, medium and program product.

[0005] According to a first aspect of the present disclosure, there is provided a method for processing government data, comprising:

[0006] Based on historical government affairs data, construct an event association map, wherein the event association map includes an event item set;

[0007] The support of all the item sets is calculated respectively to obtain the frequent item sets;

[0008] Using the frequent item set of the item to search the item association graph, to obtain the item association pattern;

[0009] The similarities between all the agents in the matter association graph are calculated, and a set of matters to be handled is recommended to the agents whose similarities are greater than a preset threshold based on the matter association pattern.

[0010] In one embodiment of the present disclosure, the support of all the item sets is calculated respectively to obtain the item frequent item sets, which include:

[0011] The support of all the item sets is calculated respectively to obtain the support of each item set;

[0012] Determine whether the support of each item set is within a preset range;

[0013] If there is an item set whose support is within a preset range, the item set is determined as the item frequent item set.

[0014] In an embodiment of the present disclosure, the step of searching the matter association graph using the matter frequent item sets to obtain the matter association pattern includes:

[0015] Searching for the frequent item set of the matter in the matter association graph;

[0016] Frequent item sets having at least two identical items and the same processing order of the identical items are associated to obtain the item association pattern.

[0017] In one embodiment of the present disclosure, constructing a matter association graph based on historical government affairs data includes:

[0018] Obtain all items in the historical government affairs data, the order in which the persons handling the items handle the items, and the number of persons handling the items between two adjacent items;

[0019] Each of the matters is taken as a node, the order in which the persons handling the matters is taken as a directed edge, and the number of persons handling the two adjacent matters is taken as the feature of the edge to construct a relationship graph of the matters.

[0020] In one embodiment of the present disclosure, it also includes:

[0021] When the user is handling government affairs, obtain the items that the user has handled;

[0022] Acquire a target item association pattern, where the target item association pattern includes items that the user has handled;

[0023] Based on the target item association pattern, government affairs items are recommended to the user.

[0024] In one embodiment of the present disclosure, it also includes:

[0025] When a user handles government affairs, obtain the order of matters handled by the user and matters handled by the user;

[0026] Acquire a target item association pattern, wherein the target item association pattern includes the order of handling items handled by the user;

[0027] Based on the target item association pattern, government affairs items are recommended to the user.

[0028] A second aspect of the present disclosure provides a government data processing device, including:

[0029] A construction module, used to construct an event association map based on historical government affairs data, wherein the event association map includes an event item set;

[0030] A processing module, used for calculating the support of all the item sets to obtain frequent item sets;

[0031] A search module, used to search the item association graph using the item frequent item set to obtain an item association pattern;

[0032] The calculation module is used to calculate the similarity between all the agents in the matter association graph, and recommend a set of matters to be handled based on the matter association pattern for agents whose similarity is greater than a preset threshold.

[0033] In one embodiment of the present disclosure, the processing module includes:

[0034] A calculation submodule, used to calculate the support of all the item sets respectively, to obtain the support of each item set;

[0035] A judgment submodule is used to judge whether the support degree of each item set is within a preset range;

[0036] The determination submodule is used to determine the item set as the item frequent item set if the support of the item set exists within a preset range.

[0037] In one embodiment of the present disclosure, the search module includes:

[0038] A search submodule, used for searching the frequent item set of the matter in the matter association graph;

[0039] The association submodule is used to associate frequent item sets having at least two identical items and the same processing order of the identical items to obtain the item association pattern.

[0040] In one embodiment of the present disclosure, the building blocks include:

[0041] An acquisition submodule is used to acquire all items in the historical government affairs data, the order in which the staff handle the items, and the number of staff between two adjacent items;

[0042] A submodule is constructed to take each of the matters as a node, the order in which the persons handling the matters as a directed edge, and the number of persons handling the two adjacent matters as a feature of the edge, so as to construct a relationship graph of the matters.

[0043] In one embodiment of the present disclosure, the device further includes:

[0044] The first acquisition module is also used to obtain the items that the user has handled when the user is handling government affairs;

[0045] A pattern acquisition module, used to acquire a target item association pattern, wherein the target item association pattern includes items that the user has handled;

[0046] A recommendation module is used to recommend government affairs matters to the user based on the target matter association pattern.

[0047] In one embodiment of the present disclosure, the device further includes:

[0048] The second acquisition module is also used to obtain the order of the matters handled by the user and the matters handled by the user when the user is handling government affairs;

[0049] The mode acquisition module is further used to acquire a target item association mode, wherein the target item association mode includes the order of handling the items handled by the user and the order of handling the items handled by the user;

[0050] The recommendation module is also used to recommend government affairs matters to the user based on the target matter association pattern.

[0051] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned government data processing method.

[0052] The fourth aspect of the present disclosure also provides a computer-readable storage medium on which executable instructions are stored, and when the instructions are executed by a processor, the processor executes the above-mentioned government data processing method.

[0053] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above-mentioned government data processing method when executed by a processor.

[0054] According to the government data processing method, device, equipment, medium and program product provided by the embodiments of the present disclosure, an item association graph is constructed based on historical government data. The item association graph includes item sets. The support is calculated for all the item sets respectively to obtain item frequent item sets. The item association graph is searched using the item frequent item sets to obtain item association patterns. The similarity between all the persons handling the matter in the item association graph is calculated. Item sets are recommended for persons handling the matter whose similarity is greater than a preset threshold based on the item association patterns. In this way, item association patterns with logical associations and item association patterns with non-logical associations can be obtained, and the larger the amount of government data, the more item association patterns can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0056] Figure 1 The application scenario diagram of the government data processing method, device, equipment, medium and program product according to the embodiment of the present disclosure is schematically shown;

[0057] Figure 2 A flowchart of a method for processing government data according to an embodiment of the present disclosure is schematically shown;

[0058] Figure 3 A schematic diagram schematically shows a matter association map according to an embodiment of the present disclosure;

[0059] Figure 4 A flowchart of a method for processing government data according to an embodiment of the present disclosure is schematically shown;

[0060] Figure 5 A flowchart of a method for processing government data according to an embodiment of the present disclosure is schematically shown;

[0061] Figure 6 A schematic diagram schematically shows a matter association mode according to an embodiment of the present disclosure;

[0062] Figure 7 A schematic diagram of a structure of a government data processing device according to an embodiment of the present disclosure is shown; and

[0063] Figure 8 A block diagram of an electronic device suitable for implementing the government data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0064] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0065] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0066] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0067] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0068] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, disclosure and application of user personal information involved comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0069] In the technical solution of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0070] An embodiment of the present disclosure provides a method for processing government data, including constructing a matter association graph based on historical government data, the matter association graph including matter item sets, calculating support for all of the matter item sets respectively to obtain matter frequent item sets, searching the matter association graph using the matter frequent item sets to obtain matter association patterns, calculating similarities between all pairs of persons in the matter association graph, and recommending matter sets to persons whose similarities are greater than a preset threshold based on the matter association patterns. In this way, matter association patterns with logical associations and matter association patterns with non-logical associations can be obtained, and the larger the amount of government data, the more matter association patterns can be obtained.

[0071] Figure 1 The application scenario diagram of the government data processing method, device, equipment, medium and program product according to the embodiments of the present disclosure is schematically shown.

[0072] like Figure 1 As shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0073] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only examples).

[0074] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0075] The server 105 may be a server that provides various services, such as a background management server (only an example) that provides support for websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0076] It should be noted that the government data processing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the government data processing device provided in the embodiment of the present disclosure can generally be set in the server 105. The government data processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the government data processing device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0077] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0078] The following will be based on Figure 1 The scene described by Figure 2 to Figure 6 The government data processing method of the disclosed embodiment is described in detail.

[0079] Figure 2 A flowchart of a method for processing government data according to an embodiment of the present disclosure is schematically shown.

[0080] like Figure 2 As shown, the government data processing method of this embodiment includes operations S210 to S230.

[0081] In operation S210, a matter association graph is constructed based on historical government affairs data, and the matter association graph includes a matter item set.

[0082] In operation S220, the support is calculated for all the item sets to obtain frequent item sets.

[0083] In operation S230, the matter association graph is searched using the matter frequent item set to obtain a matter association pattern.

[0084] In operation S240, the similarities between the two agents in the matter association graph are calculated, and a set of processing items is recommended to the agents whose similarities are greater than a preset threshold based on the matter association pattern.

[0085] In this disclosure, government data refers to the data generated by people when handling government service matters, including user information, information on the matters handled, and the status of the case. Government services refer to administrative services such as licensing, confirmation, rulings, rewards, and penalties provided by governments at all levels, relevant departments, and institutions to social groups, enterprises, institutions, and individuals in accordance with laws and regulations. Government service matters include administrative power matters and public service matters. Administrative power matters handled upon application refer to specific administrative actions that directly affect the rights and obligations of natural persons, legal persons, and other organizations, implemented by statutory administrative organs, units, and organizations upon application by natural persons, legal persons, or other organizations, in accordance with laws, administrative regulations, departmental regulations, local regulations, government regulations, normative documents, and the provisions of departmental division of responsibilities, including administrative licensing, administrative payments, administrative confirmations, administrative rewards, administrative rulings, and other administrative powers. Public service matters refer to the granting of benefits handled by statutory administrative organs, units, organizations, and enterprises to natural persons, legal persons, and other organizations, in addition to administrative power matters.

[0086] In one embodiment of the present disclosure, Figure 3 As shown, based on the historical government data, constructing a matter association graph includes obtaining all matters in the historical government data, the order in which the persons handling the matters handle the matters, and the number of persons handling the matters between two adjacent matters, taking each matter as a node, the order in which the persons handling the matters handle the matters as a directed edge, and the number of persons handling the matters between two adjacent matters as the feature of the edge, to construct the matter association graph.

[0087] In the present disclosure, the edge feature may be the color of the edge, the thickness of the edge, Figure 3 In the example, the edge characteristics are illustrated by the thickness of the edge. Figure 3In this paper, a graph of item associations based on a graph data structure is constructed based on government data. Each item is taken as a node, and the number of people who complete item A and then do item B is taken as the association dimension and strength (the more people, the greater the association strength and the thicker the edge). The items that satisfy each association relationship are connected with an edge to form a graph. Each circle represents a different item, and the number in the circle represents the item name. The directed edge represents the starting node item of the edge that the person who handles the item first handles, and then the end node item of the edge that the person handles.

[0088] In this disclosure, the concept of an itemset is a set of items. A set containing k items is a k-itemset. The frequency of occurrence of an itemset is the number of items that contain the itemset, which is called the frequency of the itemset. If an itemset meets the minimum support, it is called a frequent itemset.

[0089] In this disclosure, an item set refers to a collection of items with association relationships, such as Figure 3 As shown, {A1, A2, A3, D8} represents an item set, {A1, A2, A3, D2} represents an item set, {B1, B2, B3, D5} represents an item set, {B1, B2, B3, D7} represents an item set, and {B1, B2, B3, D9} represents an item set. It can be understood that the above are only some examples.

[0090] In the present disclosure, the item set is an ordered item set, and different item sets contain different items and / or the order of items is different. For example, {A1, A2} and {A1, A2, A3} are different item sets, and {A1, A2, A3} and {A2, A3, A1} are different item sets.

[0091] In the present disclosure, an item association pattern is also called a frequent subgraph, which is a collection of multiple item frequent item sets, and the multiple item frequent item sets have at least a portion of the same items and the same processing order of the items.

[0092] In the present disclosure, the similarity between the two handlers in the matter association graph is calculated, and a set of items is recommended for the handlers whose similarity is greater than a preset threshold based on the matter association pattern. Specifically, if the user has handled the item set in the frequent item set, the corresponding value is 1, otherwise it is 0. The item set in the frequent item set includes, for example, [housing provident fund withdrawal, personal rights and interests information query for medical insurance participants, basic medical reimbursement registration for urban employees], [motor vehicle driver's license issuance and inspection, personal rights and interests information query for medical insurance participants, social security card replacement, replacement, and reissue, social security card loss and cancellation], [motor vehicle inspection certificate issuance, housing provident fund withdrawal], [motor vehicle inspection certificate issuance, social security card application], [urban and rural residents' pension insurance benefits application, temporary relief object identification, relief fund payment Then, by calculating the similarity between each pair of service providers, similar service providers can be found for each service provider. According to the services that a certain service provider has handled, other services that the service provider has handled are recommended to similar service providers. For example, if a certain service provider has handled the two service items of [applying for urban and rural residents' pension insurance benefits, identification of temporary assistance recipients, and payment of assistance funds] and [identification of minimum living security recipients, payment of security funds, identification of temporary assistance recipients, and payment of assistance funds], then these two service items are recommended to similar service providers.

[0093]

[0094] According to the disclosed embodiment, based on historical government data, a matter association graph is constructed, the matter association graph includes matter item sets, the support of all the matter item sets is calculated respectively, and the matter frequent item sets are obtained. The matter association graph is searched using the matter frequent item sets to obtain matter association patterns, and the similarity between the two persons in the matter association graph is calculated. For the persons whose similarity is greater than a preset threshold, a matter item set is recommended based on the matter association pattern. In this way, a matter association pattern with logical association and a matter association pattern with non-logical association can be obtained, and the larger the amount of government data, the more matter association patterns are obtained.

[0095] Figure 4 The flowchart of operation S220 according to an embodiment of the present disclosure is schematically shown.

[0096] like Figure 4 As shown, operation S220 of this embodiment includes operations S410 to S430.

[0097] In operation S410, the support of all the item sets is calculated respectively to obtain the support of each item set;

[0098] In operation S420, it is determined whether the support degree of each item set of the matter is within a preset range;

[0099] In operation S430, if there is an item set whose support is within a preset range, the item set is determined as the item frequent item set.

[0100] In this disclosure, support refers to the number of people who handle the item set. For example, if the number of people who handle the item set {A1, A2, A3} is 100, then the support of the item set {A1, A2, A3} is 100; if the number of people who handle the item set {A2, A3, A1} is 30, then the support of the item set {A2, A3, A1} is 30.

[0101] In the present disclosure, the preset range is not limited, and it may be greater than or equal to a certain threshold, for example, the preset range is greater than 100. When there is an item set whose support is greater than 100, the item set is determined as an item frequent item set.

[0102] According to the embodiment of the present disclosure, the support of all the item sets is calculated respectively, and the support of each item set is obtained. It is determined whether the support of each item set is within a preset range. If there is an item set whose support is within the preset range, the item set is determined as the frequent item set of the item. The larger the amount of data in this method, the more item association patterns are found.

[0103] Figure 5 The flowchart of operation S230 according to an embodiment of the present disclosure is schematically shown.

[0104] like Figure 5 As shown, operation S230 of this embodiment includes operations S510 to S520.

[0105] In operation S510, searching for the frequent itemsets of the matter in the matter association graph;

[0106] In operation S520, frequent item sets having at least two identical items and the same processing order of the identical items are associated to obtain the item association pattern.

[0107] In one example, {B1, B2, B3, D5}, {B1, B2, B3, D7}, {B1, B2, B3, D9} are frequent item sets obtained after operation S220. In this embodiment, Figure 3 In the event association graph shown in the figure, search for {B1, B2, B3, D5}, {B1, B2, B3, D7} and {B1, B2, B3, D9}, and finally get the following Figure 6 The frequent subgraph shown is the event association pattern.

[0108] According to an embodiment of the present disclosure, the frequent item set of the matter is searched in the matter association graph, and the frequent item sets of the matter having at least two identical matters and the same processing order of the identical matters are associated to obtain the matter association pattern. Both the matter association pattern with logical association and the matter association pattern with non-logical association can be found.

[0109] In one embodiment of the present disclosure, Figures 2 to 6 The method shown also includes: when the user is handling government affairs, obtaining the items that the user has handled, obtaining a target item association pattern, the target item association pattern including the items that the user has handled, and recommending government affairs items to the user based on the target item association pattern. According to the item association pattern, related to-do items are recommended to the user based on the items that the user has handled, thereby improving the quality of government services.

[0110] In one embodiment of the present disclosure, Figures 2 to 6 The method also includes: when the user is handling government affairs, obtaining the order of the matters handled by the user and the matters handled by the user, obtaining a target matter association pattern, the target matter association pattern including the order of the matters handled by the user and the matters handled by the user, and recommending government affairs matters to the user based on the target matter association pattern. According to the matter association pattern, more consistent related to-do matters are recommended to the user through the matters handled by the user and the order of the matters, thereby improving the quality of government services.

[0111] Based on the above-mentioned government data processing method, the present disclosure also provides a government data processing device. Figure 7 The device is described in detail.

[0112] Figure 7 The structural block diagram of the government data processing device according to an embodiment of the present disclosure is schematically shown.

[0113] like Figure 7 As shown, the government data processing device 700 of this embodiment includes a construction module 710, a processing module 720 and a search module 730.

[0114] The construction module 710 is used to construct an event association graph based on historical government affairs data, and the event association graph includes event item sets. In one embodiment, the construction module 710 can be used to perform the operation S210 described above, which will not be described in detail here.

[0115] The processing module 720 is used to calculate the support of all the item sets to obtain the frequent item sets. In one embodiment, the processing module 720 can be used to perform the operation S220 described above, which will not be described in detail here.

[0116] The search module 730 is used to search the matter association graph using the matter frequent item set to obtain the matter association pattern. In one embodiment, the search module 730 can be used to perform the operation S230 described above, which will not be described in detail here.

[0117] The calculation module 740 is used to calculate the similarity between the two agents in the matter association graph, and recommend a set of matter items based on the matter association pattern for agents whose similarity is greater than a preset threshold. In one embodiment, the search module 740 can be used to execute the operation S240 described above, which will not be repeated here.

[0118] According to an embodiment of the present disclosure, an event association graph is constructed based on historical government data. The event association graph includes event item sets. The support is calculated for all event item sets respectively to obtain event frequent item sets. The event frequent item sets are used to search the event association graph to obtain event association patterns. In this way, event association patterns with logical associations and event association patterns with non-logical associations can be obtained, and the larger the amount of government data, the more event association patterns can be obtained.

[0119] In one embodiment of the present disclosure, the processing module 720 includes:

[0120] The calculation submodule is used to calculate the support of all the item sets respectively to obtain the support of each item set;

[0121] The judgment submodule is used to judge whether the support of each item set of the matter is within a preset range;

[0122] The determination submodule is used to determine the item set as the frequent item set of the item if the support of the item set is within a preset range.

[0123] In one embodiment of the present disclosure, the search module 730 includes:

[0124] The search submodule is used to search for the frequent item sets of the matter in the matter association graph;

[0125] The association submodule is used to associate frequent item sets having at least two identical items and the same processing order of the identical items to obtain the item association pattern.

[0126] In one embodiment of the present disclosure, the building module 710 includes:

[0127] The acquisition submodule is used to obtain all the items in the historical government affairs data, the order in which the staff handle the items, and the number of staff between two adjacent items;

[0128] A submodule is constructed to take each item as a node, the order in which the staff handle the items as a directed edge, and the number of staff between two adjacent items as the feature of the edge, so as to construct a related graph of the items.

[0129] In one embodiment of the present disclosure, it also includes:

[0130] The first acquisition module is also used to obtain the items that the user has handled when the user is handling government affairs;

[0131] A pattern acquisition module is used to acquire a target item association pattern, where the target item association pattern includes items that the user has handled;

[0132] The recommendation module is used to recommend government affairs to the user based on the target affairs association pattern.

[0133] In one embodiment of the present disclosure, the device 700 further includes:

[0134] The second acquisition module is also used to obtain the order of the matters that the user has handled and the matters that the user has handled when the user is handling government affairs;

[0135] The mode acquisition module is further used to acquire the target item association mode, where the target item association mode includes the order of handling the items that the user has handled;

[0136] The recommendation module is also used to recommend government affairs to the user based on the target affairs association pattern.

[0137] According to an embodiment of the present disclosure, any multiple modules in the construction module 710, the processing module 720 and the search module 730 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the construction module 710, the processing module 720 and the search module 730 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the construction module 710, the processing module 720 and the search module 730 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0138] Figure 8 A block diagram of an electronic device suitable for implementing the government data processing method according to an embodiment of the present disclosure is schematically shown.

[0139] like Figure 8 As shown, the electronic device 800 according to an embodiment of the present disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 to a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include an onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0140] In RAM 803, various programs and data required for the operation of electronic device 800 are stored. Processor 801, ROM 802 and RAM 803 are connected to each other via bus 804. Processor 801 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 802 and / or RAM 803. It should be noted that the program can also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0141] According to an embodiment of the present disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to the bus 804. The electronic device 800 may further include one or more of the following components connected to the I / O interface 805: an input portion 806 including a keyboard, a mouse, etc.; an output portion 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 808 including a hard disk, etc.; and a communication portion 809 including a network interface card such as a LAN card, a modem, etc. The communication portion 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that a computer program read therefrom is installed into the storage portion 808 as needed.

[0142] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0143] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 802 and / or RAM 803 described above and / or one or more memories other than ROM 802 and RAM 803.

[0144] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the item recommendation method provided by the embodiment of the present disclosure.

[0145] The above functions defined in the system / device of the embodiment of the present disclosure are executed when the computer program is executed by the processor 801. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0146] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 809, and / or installed from a removable medium 811. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0147] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0148] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0149] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0150] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0151] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for processing government data, comprising: Based on historical government affairs data, construct an event association map, wherein the event association map includes an event item set; The construction of the matter association graph based on the historical government affairs data includes: obtaining all matters in the historical government affairs data, the order in which the persons handling the matters and the number of persons handling the matters between two adjacent matters; taking each matter as a node, the order in which the persons handling the matters as a directed edge, and the number of persons handling the matters between two adjacent matters as a feature of the edge, to construct the matter association graph; The support of all the item sets is calculated respectively to obtain the item frequent item set; specifically including: calculating the support of all the item sets respectively to obtain the support of each item set; judging whether the support of each item set is within a preset range; if there is an item set whose support is within the preset range, determining the item set as the item frequent item set; Using the frequent item set to search the item association graph to obtain the item association pattern; specifically including: searching the frequent item set in the item association graph; associating the frequent item sets of items having at least two identical items and the same processing order of the identical items to obtain the item association pattern; The similarities between all the agents in the matter association graph are calculated, and a set of matters to be handled is recommended to the agents whose similarities are greater than a preset threshold based on the matter association pattern.

2. The method according to claim 1, wherein: Also includes: When the user is handling government affairs, obtain the items that the user has handled; Acquire a target item association pattern, where the target item association pattern includes items that the user has handled; Based on the target item association pattern, government affairs items are recommended to the user.

3. The method according to claim 1, wherein: Also includes: When a user handles government affairs, obtain the order of matters handled by the user and matters handled by the user; Acquire a target item association pattern, wherein the target item association pattern includes items that the user has handled and the order in which the items that the user has handled are handled; Based on the target item association pattern, government affairs items are recommended to the user.

4. A government data processing device, comprising: A construction module, used to construct an event association map based on historical government affairs data, wherein the event association map includes an event item set; The construction of the matter association graph based on the historical government affairs data includes: obtaining all matters in the historical government affairs data, the order in which the persons handling the matters and the number of persons handling the matters between two adjacent matters; taking each matter as a node, the order in which the persons handling the matters as a directed edge, and the number of persons handling the matters between two adjacent matters as a feature of the edge, to construct the matter association graph; The processing module is used to calculate the support of all the item sets to obtain the frequent item sets; specifically, the processing module includes: calculating the support of all the item sets to obtain the support of each item set; judging whether the support of each item set is within a preset range; if there is an item set whose support is within the preset range, determining the item set as the frequent item set; A search module is used to search the item association graph using the item frequent item set to obtain the item association pattern; specifically comprising: searching the item frequent item set in the item association graph; associating the item frequent item sets having at least two identical items and the same processing order of the identical items to obtain the item association pattern; The calculation module is used to calculate the similarity between all the agents in the matter association graph, and recommend a set of matters to be handled based on the matter association pattern for agents whose similarity is greater than a preset threshold.

5. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors execute the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the method according to any one of claims 1 to 3.

7. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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