CN118035251A - Urban data model management system and method based on multi-feature fusion - Google Patents
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Abstract
The invention discloses a city data model management system and method based on multi-feature fusion, which relate to the technical field of database management and are used for carrying out data collection on data in different databases, creating an information template, marking data sequence numbers of the data in the data sequence in the template, creating information indexes according to the data included in the same data item in the template, obtaining data acquisition sequences of different data types in the information template according to the number of the information indexes, establishing a data cache area, extracting a complete data sequence corresponding to one type of data in the information target, hiding the data corresponding to the data sequence numbers not included in the information template in the data cache area, extracting the data corresponding to the data sequence numbers included in the information template, carrying out data filling on the information template, and extracting the data stored in the different databases according to the data acquisition sequences until filling of all the data filling positions in the information template is completed.
Description
Technical Field
The invention relates to the technical field of database management, in particular to a city data model management system and method based on multi-feature fusion.
Background
Along with the continuous improvement of the level of digital construction of cities, an informatization system is gradually applied to various aspects of urban management and citizen life. However, due to the huge amount of information of the urban bottom data system, different databases are required to manage different kinds of data.
When different business departments build databases used by the departments, business scenes and common technologies of the departments are mainly considered, so that the problem that overall planning is lacked among different databases, such as serious data fragmentation and poor data interaction capability, so that data islands which are not communicated with each other are formed is caused. The method seriously hinders information intercommunication and data fusion, and is difficult to realize information sharing and cross-department business coordination, so that the utilization rate of data is not improved, and the waste of information resources is caused.
Disclosure of Invention
The invention aims to provide a city data model management system and method based on multi-feature fusion, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides a city data model management system and method based on multi-feature fusion, which comprises the following technical scheme:
Step S100: in a city information management system, which comprises a plurality of databases for storing city information data, the data stored in each database by the same user are respectively ordered to obtain data sequences of which the data in each database are arranged, and the positions of each piece of data are marked in the data sequences;
Step S200: the user performs data collection on city information data stored in different databases, acquires a history record of a user calling the databases, creates an information template, marks a data sequence number of the data in a data sequence in the template, and creates an information index according to the data included in the same data item in the template;
step S300: classifying the data to be filled in the information template, and obtaining the data acquisition sequence of different data types in the information template according to the information index quantity among different types of data from at least more;
Step S400: establishing a data cache area, extracting a complete data sequence corresponding to certain type of data in an information target, storing the complete data sequence in the data cache area, acquiring a data sequence number in an information template, hiding data corresponding to the data sequence number which is not included in the information template in the data cache area, extracting data corresponding to a position of the data sequence number which is included in the information template, and filling the data in the information template;
Step S500: classifying and managing user information verification rules of different databases to obtain identity verification items which need to be supplemented by users, releasing a data sequence in a data cache area after the current type of data is filled into an information template according to a data acquisition sequence, acquiring a data sequence corresponding to the latter type of data, storing the data sequence in the data cache area, and repeating the data extraction process until filling of all data filling positions in the information template is completed
Further, step S100 includes:
Step S101: in a database A in a plurality of databases, acquiring all data stored in the database A by the user ID1, and arranging the data according to the time sequence of each piece of data creation to obtain a data sequence L A 1 of the user ID1 in the database A;
the city information data represent data generated and used in municipal construction or civilian life in a digital city scene;
For technical reasons or management reasons, these several databases storing urban information data are independent of each other;
Step S102: marking the serial numbers of all pieces of data in the data sequence L A 1 in L A 1, wherein each marking position corresponds to one piece of data;
Step S103: and traversing the data stored in each database by the user ID1, and respectively arranging the user ID1 into a data sequence corresponding to the corresponding database in each database.
Further, step S200 includes:
Step S201: a plurality of data entries are arranged in the information template, each data entry comprises a plurality of data filling positions, the serial numbers of the data to be filled in the corresponding data sequences in the plurality of data filling positions are obtained, and the serial numbers are marked on the corresponding data filling positions in the information template;
Step S202: setting data stored in the same database as a data type, wherein the classification mode of the data filling position is consistent with that of the data, and screening out data entries at least comprising two data filling positions with different types from all data entries, and marking the data entries as index entries;
In the data fusion process, in the same use area, data respectively stored in different databases are required to be combined for use, and each index entry represents a use area for fusion use of certain data;
Step S203: and setting information indexes between two different types of data filling positions in the index entry, wherein the information indexes are connected with serial numbers corresponding to the two data filling positions.
Further, step S300 includes:
Step S301: classifying the data filling positions in the information template K according to the mode in the step S202 to obtain information indexes between two different types of data filling positions;
Step S302: acquiring a class corresponding to a data filling position with the largest quantity of connection information indexes, setting the class as a first class data position, selecting the class corresponding to the data filling position with the largest quantity of information indexes from the classes corresponding to the data filling positions without information indexes connected with the first class data position, setting the class corresponding to the data filling position with the largest quantity of information indexes as a second class data position, and the like until no data position with information indexes connected with the first class data position, the second class data position, the third class data position, … … or the N class data position exists, wherein N represents the maximum quantity of the classes corresponding to the selected data position;
Step S303: recording the first type data position, the second type data position, the third type data position, … … and the N type data position into a first type information retrieval target, and recording the category corresponding to the residual data filling position in the information template K into a second type information retrieval target;
Step S304: judging the position relation of any two types of data positions connected by the existing information indexes as an adjacent relation, and acquiring the total number M k of the adjacent relation in the information template K and the number of the information indexes connected among the various data positions in the information template K;
Step S305: calculating a retrieval parameter Q Ni of the Ni-th class data position in the first class information retrieval target, wherein Q Ni=（MNi/Mk）×RNi and Ni is less than or equal to N, M Ni represents the number of data positions with adjacent relation with the Ni-th class data position, and R Ni represents the number of information indexes connected with the Ni-th class data position;
Step S306: calculating search parameters corresponding to all N types of data positions in the first type of information search targets, obtaining a data acquisition sequence of the first type of information search targets according to the arrangement sequence of the search parameters from large to small, and obtaining the data acquisition sequence of the second type of information search targets according to the number of information indexes connected with each data filling position in the second type of information search targets from large to small;
The importance of which type of data filling position is measured by connecting the information index quantity of various types of data filling positions, M k represents the total number of adjacent relations among all types of data positions, M Ni represents the quantity of adjacent relations with the Ni-th type of data position, M Ni/Mk represents an importance coefficient, and when M Ni/Mk is larger, the higher the relevance of the Ni-th type of data position in the information template K and other types of data filling positions is, the higher the importance of the Ni-th type of data position is;
further, step S400 includes:
step S401: acquiring a p-th type data position K p in the information template K, wherein data in K p are stored in a database B p;
Step S402: acquiring a data sequence L Bp 1 of the user ID1 in a database B p, and forming a data sequence number corresponding to each data filling position in K p into an acquisition sequence number set E kp;
Step S403: acquiring all data sequence numbers of the L Bp 1 to form a set E 1, and calculating a hidden sequence number set E hide, wherein E hide= E1-Ekp;
Step S404: in L Bp 1, hiding the data corresponding to the sequence number included in E hide, extracting the data of which L Bp 1 is not hidden, and filling the data into the information template K according to the corresponding relationship of the data sequence numbers, wherein the corresponding relationship of the data sequence numbers indicates that the corresponding relationship of the data sequence numbers exists when the data sequence numbers in L Bp 1 are consistent with the data sequence numbers in the information template K;
in the process of extracting data, the data can be extracted only according to the requirement of the information template, identity verification and information template verification are combined, the data which are not in the information template range are hidden, and related users can only extract the data in the information template range;
further, step S500 includes:
step S501: acquiring user authentication rules corresponding to different databases, and classifying according to authentication items used in the user authentication rules;
Step S502: according to the data acquisition sequence, all verification items corresponding to the jth database are composed into a verification item set W j, all verification items corresponding to the (j+1) th database are composed into a verification item set W j+1, when When the user submits the authentication information, extracting the authentication information corresponding to the authentication item included in the W j+1 from the authentication information submitted by the user, and forwarding the authentication information to the (j+1) th database for authentication;
step S503: calculating a supplementary verification item set W *,W*=Wj+1-Wj, when W * is not equal to ∅, submitting a verification item included in W * by a user, supplementing the identity verification information to the user identity verification rule meeting the j+1th database, and extracting a data sequence in the j+1th database;
step S504: after acquiring all data corresponding to the first type information retrieval targets, acquiring the data corresponding to the second type information retrieval targets, and filling data in all data filling positions in the information template K.
In order to better implement the method, a city data model management system is also provided, and the system comprises:
The system comprises a storage management module, a template management module, a sorting management module, a data cache area management module and a verification information management module, wherein the storage management module is used for managing data sequences of databases, the template management module is used for generating and managing information templates, the sorting management module is used for managing the retrieval sequence of data, the data cache area management module is used for managing the data cache area, and the verification information management module is used for classifying and managing user information verification rules of different databases;
Further, the template management module includes: the system comprises a classification unit, an index entry management unit and an information index management unit, wherein the classification unit is used for classifying data filling positions, the index entry management unit is used for screening index entries, and the information index management unit is used for managing information indexes;
Further, the ordering management module includes: the system comprises a first search target management unit, a second search target management unit, a search parameter calculation unit and a sequencing unit, wherein the first search target management unit is used for acquiring a data filling position corresponding to a first type of information search target, the second search target management unit is used for acquiring a data filling position corresponding to a second type of information search target, the search parameter calculation unit is used for calculating search parameters of the first type of information search target, and the sequencing unit is used for sequencing data acquisition sequences;
Further, the data cache area management module includes: the device comprises a data extraction unit, a sequence number management unit and a data sending unit, wherein the data extraction unit is used for extracting a data sequence, the sequence number management unit is used for managing a data position needing to be hidden, and the data sending unit is used for returning the extracted data.
Compared with the prior art, the invention has the following beneficial effects: 1. according to the method, the data in the database is structured, the information template is generalized according to the use condition of the user, and the automatic corresponding operation is realized by calling the data through the corresponding relation of the data use and the data searching sequence, so that the repeated labor of manually traversing the database is reduced; 2. in the process of extracting data, the invention is provided with the data cache area, the data cache area can reduce data format exchange interfaces among different databases, and all databases only need to be in data butt joint with the data cache area, thereby reducing the complexity of data relay and format conversion and reducing the use of a data format conversion protocol; and the data buffer area increases the data isolation degree, so that the data stored in the database is not excessively read.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a system for managing a model of urban data based on multi-feature fusion according to the present invention;
FIG. 2 is a flow chart of the city data model management method based on multi-feature fusion of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 and 2, the present invention provides the following technical solutions:
Step S100: in a city information management system, which comprises a plurality of databases for storing city information data, the data stored in each database by the same user are respectively ordered to obtain data sequences of which the data in each database are arranged, and the positions of each piece of data are marked in the data sequences;
In an embodiment, there are A, B and C three databases, respectively creating three data sequences L A 1、LB 1 and L C 1 for user ID 1;
Wherein, L A 1 is a data sequence corresponding to database a, and L A 1 includes (a 1、a2、a3、……、ai1), where a 1、a2、a3、……、ai1 represents data of item a1, item 2, item 3, item … …, and item i1, respectively;
L B 1 is a data sequence corresponding to the database B, and L B 1 comprises (B 1、b2、b3、……、bi2), wherein B 1、b2、b3、……、bi2 represents data of 1 st, 2 nd, 3 rd, … … nd and i2 nd of A respectively;
L C 1 is a data sequence corresponding to the database C, and L C 1 comprises (C 1、c2、c3、……、ci3), wherein C 1、c2、c3、……、ci2 represents data of 1 st, 2 nd, 3 rd, … … th and i3 rd of A respectively;
Step S200: the user performs data collection on city information data stored in different databases, acquires a history record of a user calling the databases, creates an information template, marks a data sequence number of the data in a data sequence in the template, and creates an information index according to the data included in the same data item in the template;
Step S201: in the information template K, a plurality of data filling positions are included, for example, the corresponding data filling position of the data a 1 in the information template K is denoted by a 1 ,;
Step S202: screening index entries, wherein the information template K comprises four data entries, namely title1, title2, title3 and title4;
the data pad locations included in title1 are: a 3'、a6'、b2 'and c 4';
the data pad locations included in title2 are: a 2'、b3'、b4 'and c 1';
the data pad locations included in title3 are: a 5'、b1'、c2'、c3 'and c 6';
the data pad locations included in title4 are: b 5 'and c 5';
Classifying the data filling positions according to the storage positions of the corresponding data of the data filling positions;
The first type of data location includes: a 2'、a3'、a5 'and a 6';
The second type of data location includes: b 2'、b3'、b4 'and b 5';
The third type of data location includes: c 1'、c2'、c3'、c5 'and c 6';
Step S203: creating an information index;
the information index between the first class data location and the second class data location includes:
r1：a3'→b2',r2：a6' →b2',r3：a2' →b3',r4：a2' →b4',r5：a5' →b1';
the information index between the first type of data location and the third type of data location includes:
r6：a3' →c4',r7：a6' →c4',r8：a2' →c1',r9：a5' →c2',r10：a5' →c3',r11：a5' →c6';
The information index between the second type of data location and the third type of data location includes:
r12：b2' →c4',r13：b3' →c1',r14：b4' →c1',r15：b1' →c2',r16：b1' →c3',r17：b1' →c6',r18：b5' →c5';
The information template is composed of the data filling position and the data index;
step S300: classifying the data to be filled in the information template, and obtaining the data acquisition sequence of different data types in the information template according to the information index quantity among different types of data from at least more;
step S301: 5 information indexes are arranged between the first type data position and the second type data position, 6 information indexes are arranged between the first type data position and the third type data position, and 7 information indexes are arranged between the second type data position and the third type data position;
step S302: acquiring a class corresponding to a data filling position with the largest quantity of connection information indexes, setting the class as a first class data position, selecting the class corresponding to the data filling position with the largest quantity of information indexes from the classes corresponding to the data filling positions without information indexes connected with the first class data position, setting the class as a second class data position, and the like until the class corresponding to the data filling position without information indexes connected with the first class data position, the second class data position, the third class data position, … … or the N class data position does not exist, wherein N represents the maximum quantity of the classes corresponding to the selected data position;
Step S303: recording the first type data position, the second type data position, the third type data position, … … and the N type data position into a first type information retrieval target, and recording the category corresponding to the residual data filling position in the information template K into a second type information retrieval target;
In this embodiment, the first type data location, the second type data location, and the third type data location all belong to the first type information retrieval target;
Step S304: judging the position relation of any two types of data positions connected by the existing information indexes as an adjacent relation, and acquiring the total number M k of the adjacent relation in the information template K and the number of the information indexes connected among the various data positions in the information template K;
In this embodiment, the first type data location, the second type data location and the third type data are all adjacent to each other, so M k =3;
Step S305: calculating a retrieval parameter Q Ni of the Ni-th class data position in the first class information retrieval target, wherein Q Ni=（MNi/Mk）×RNi and Ni is less than or equal to N, M Ni represents the number of data positions with adjacent relation with the Ni-th class data position, and R Ni represents the number of information indexes connected with the Ni-th class data position;
Calculating a search parameter Q 1,Q1 = (2/3) × (5+6) =7.33 for the first type of data location, calculating a search parameter Q 2,Q2 = (2/3) × (5+7) =8 for the second type of data location, and calculating a search parameter Q 3,Q2 = (2/3) × (6+7) =8.67 for the third type of data location;
step S306: calculating retrieval parameters corresponding to all N types of data positions in the first type of information retrieval targets, arranging the retrieval parameters from large to small according to the retrieval parameters to obtain a data acquisition sequence of the first type of information retrieval targets, and obtaining the data acquisition sequence of the second type of information retrieval targets according to the fact that the number of information indexes connected to each data filling position in the second type of information retrieval targets is from large to small;
in this embodiment, the data acquisition order is a third type data position, a second type data position, and a first type data position;
Step S400: establishing a data cache area, extracting a complete data sequence corresponding to certain type of data in an information target, storing the complete data sequence in the data cache area, acquiring a data sequence number in an information template, hiding data corresponding to the data sequence number which is not included in the information template in the data cache area, extracting data corresponding to a position of the data sequence number which is included in the information template, and filling the data in the information template;
Step S401: the third type data position K 3 in the acquired information template K comprises 1,2,3, 5 and 6;
Step S402: extracting a data sequence corresponding to the third type of data position and storing the data sequence into a data cache area, namely a data sequence L C 1 stored in a database C;
Step S403: hiding all data items except the data corresponding to numbers 1,2,3, 5 and 6 in a data sequence L C 1;
Step S404: transmitting the data corresponding to numbers 1, 2, 3, 5 and 6 in the L C 1 in the data cache area to the corresponding position of the data template K, and completing data filling;
Step S500: classifying and managing user information verification rules of different databases to obtain identity verification projects which need to be supplemented by users, releasing a data sequence in a data cache area after the current type of data is filled into an information template according to a data acquisition sequence, acquiring a data sequence corresponding to the latter type of data, storing the data sequence in the data cache area, and repeating the data extraction process until filling of all data filling positions in the information template is completed;
step S501: acquiring user authentication rules corresponding to different databases, and classifying according to authentication items used in the user authentication rules;
Step S502: according to the data acquisition sequence, all verification items corresponding to the jth database are composed into a verification item set W j, all verification items corresponding to the (j+1) th database are composed into a verification item set W j+1, when When the user submits the authentication information, extracting the authentication information corresponding to the authentication item included in the W j+1 from the authentication information submitted by the user, and forwarding the authentication information to the (j+1) th database for authentication;
Step S503: calculating a supplementary verification item set W, W *=Wj+1-Wj, when W * is not equal to ∅, submitting a verification item included in W * by a user, supplementing identity verification information to a user identity verification rule meeting the j+1st database, and extracting a data sequence in the j+1st database;
step S504: after acquiring all data corresponding to the first type information retrieval targets, acquiring the data corresponding to the second type information retrieval targets, and filling data in all data filling positions in the information template K.
The system comprises: the system comprises a storage management module, a template management module, a sequencing management module, a data cache area management module and a verification information management module;
The storage management module is used for managing the data sequence of the database;
The template management module is used for generating and managing an information template, wherein the template management module comprises: the system comprises a classification unit, an index entry management unit and an information index management unit, wherein the classification unit is used for classifying data filling positions, the index entry management unit is used for screening index entries, and the information index management unit is used for managing information indexes;
The sorting management module is used for managing the retrieval sequence of the data, and comprises: the system comprises a first search target management unit, a second search target management unit, a search parameter calculation unit and a sequencing unit, wherein the first search target management unit is used for acquiring a data filling position corresponding to a first type of information search target, the second search target management unit is used for acquiring a data filling position corresponding to a second type of information search target, the search parameter calculation unit is used for calculating search parameters of the first type of information search target, and the sequencing unit is used for sequencing data acquisition sequences;
The data cache area management module is used for managing the data cache area, wherein the data cache area management module comprises: the device comprises a data extraction unit, a sequence number management unit and a data transmission unit, wherein the data extraction unit is used for extracting a data sequence, the sequence number management unit is used for managing a data position needing to be hidden, and the data transmission unit is used for returning the extracted data;
The verification information management module is used for classifying and managing the user information verification rules of different databases.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The city data model management method based on multi-feature fusion is characterized by comprising the following steps of:
Step S100: in a city information management system, which comprises a plurality of databases for storing city information data, the data stored in each database by the same user are respectively ordered to obtain data sequences of which the data in each database are arranged, and the positions of each piece of data are marked in the data sequences;
Step S200: the user performs data collection on city information data stored in different databases, acquires a history record of a user calling the databases, creates an information template, marks a data sequence number of the data in a data sequence in the template, and creates an information index according to the data included in the same data item in the template;
step S300: classifying the data to be filled in the information template, and obtaining the data acquisition sequence of different data types in the information template according to the information index quantity among different types of data from at least more;
Step S400: establishing a data cache area, extracting a complete data sequence corresponding to certain type of data in an information target, storing the complete data sequence in the data cache area, acquiring a data sequence number in an information template, hiding data corresponding to the data sequence number which is not included in the information template in the data cache area, extracting data corresponding to a position of the data sequence number which is included in the information template, and filling the data in the information template;
Step S500: and carrying out classification management on user information verification rules of different databases to obtain identity verification items which are required to be supplemented by users, releasing a data sequence in a data cache area after the current type of data is filled into the information template according to a data acquisition sequence, acquiring a data sequence corresponding to the latter type of data, storing the data sequence in the data cache area, and repeating the data extraction process until filling of all data filling positions in the information template is completed.
2. The urban data model management method based on multi-feature fusion according to claim 1, wherein: the step S100 includes:
Step S101: in a database A in a plurality of databases, acquiring all data stored in the database A by the user ID1, and arranging the data according to the time sequence of each piece of data creation to obtain a data sequence L A 1 of the user ID1 in the database A;
Step S102: marking the serial numbers of all pieces of data in the data sequence L A 1 in L A 1, wherein each marking position corresponds to one piece of data;
Step S103: and traversing the data stored in each database by the user ID1, and respectively arranging the user ID1 into a data sequence corresponding to the corresponding database in each database.
3. The urban data model management method based on multi-feature fusion according to claim 2, wherein: step S200 includes:
Step S201: a plurality of data entries are arranged in the information template, each data entry comprises a plurality of data filling positions, the serial numbers of the data to be filled in the corresponding data sequences in the plurality of data filling positions are obtained, and the serial numbers are marked on the corresponding data filling positions in the information template;
Step S202: setting data stored in the same database as a data type, wherein the classification mode of the data filling position is consistent with that of the data, and screening out data entries at least comprising two data filling positions with different types from all data entries, and marking the data entries as index entries;
Step S203: and setting information indexes between two different types of data filling positions in the index entry, wherein the information indexes are connected with serial numbers corresponding to the two data filling positions.
4. The urban data model management method based on multi-feature fusion according to claim 3, wherein: step S300 includes:
Step S301: classifying the data filling positions in the information template K according to the mode in the step S202 to obtain information indexes between two different types of data filling positions;
Step S302: acquiring a class corresponding to a data filling position with the largest quantity of connection information indexes, setting the class as a first class data position, selecting the class corresponding to the data filling position with the largest quantity of information indexes from the classes corresponding to the data filling positions without information indexes connected with the first class data position, setting the class corresponding to the data filling position with the largest quantity of information indexes as a second class data position, and the like until no data position with information indexes connected with the first class data position, the second class data position, the third class data position, … … or the N class data position exists, wherein N represents the maximum quantity of the classes corresponding to the selected data position;
Step S303: recording the first type data position, the second type data position, the third type data position, … … and the N type data position into a first type information retrieval target, and recording the category corresponding to the residual data filling position in the information template K into a second type information retrieval target;
Step S304: judging the position relation of any two types of data positions connected by the existing information indexes as an adjacent relation, and acquiring the total number M k of the adjacent relation in the information template K and the number of the information indexes connected among the various data positions in the information template K;
Step S305: calculating a retrieval parameter Q Ni of the Ni-th class data position in the first class information retrieval target, wherein Q Ni=（MNi/Mk）×RNi and Ni is less than or equal to N, M Ni represents the number of data positions with adjacent relation with the Ni-th class data position, and R Ni represents the number of information indexes connected with the Ni-th class data position;
Step S306: and calculating the retrieval parameters corresponding to all N types of data positions in the first type of information retrieval targets, obtaining the data acquisition sequence of the first type of information retrieval targets according to the arrangement sequence of the retrieval parameters from large to small, and obtaining the data acquisition sequence of the second type of information retrieval targets according to the number of information indexes connected with each data filling position in the second type of information retrieval targets from large to small.
5. The method for managing the urban data model based on multi-feature fusion according to claim 4, wherein: step S400 includes:
step S401: acquiring a p-th type data position K p in the information template K, wherein data in K p are stored in a database B p;
Step S402: acquiring a data sequence L Bp 1 of the user ID1 in a database B p, and forming a data sequence number corresponding to each data filling position in K p into an acquisition sequence number set E kp;
Step S403: acquiring all data sequence numbers of the L Bp 1 to form a set E 1, and calculating a hidden sequence number set E hide, wherein E hide= E1-Ekp;
Step S404: in L Bp 1, the data corresponding to the sequence number included in E hide is hidden, the data of which L Bp 1 is not hidden is extracted, and the data is filled into the information template K according to the data sequence number correspondence, where the data sequence number correspondence indicates that when the data sequence number in L Bp 1 is consistent with the data sequence number in the information template K, it is determined that the data sequence number has a correspondence.
6. The method for managing the urban data model based on multi-feature fusion according to claim 5, wherein: step S500 includes:
step S501: acquiring user authentication rules corresponding to different databases, and classifying according to authentication items used in the user authentication rules;
Step S502: according to the data acquisition sequence, all verification items corresponding to the jth database are composed into a verification item set W j, all verification items corresponding to the (j+1) th database are composed into a verification item set W j+1, when When the user submits the authentication information, extracting the authentication information corresponding to the authentication item included in the W j+1 from the authentication information submitted by the user, and forwarding the authentication information to the (j+1) th database for authentication;
step S503: calculating a supplementary verification item set W *,W*=Wj+1-Wj, when W * is not equal to ∅, submitting a verification item included in W * by a user, supplementing the identity verification information to the user identity verification rule meeting the j+1th database, and extracting a data sequence in the j+1th database;
step S504: after acquiring all data corresponding to the first type information retrieval targets, acquiring the data corresponding to the second type information retrieval targets, and filling data in all data filling positions in the information template K.
7. A city data model management system applied to the city data model management method based on multi-feature fusion of any one of claims 1-6, characterized in that the system comprises the following modules:
The system comprises a storage management module, a template management module, a sorting management module, a data cache area management module and a verification information management module, wherein the storage management module is used for managing data sequences of databases, the template management module is used for generating and managing information templates, the sorting management module is used for managing the retrieval sequence of data, the data cache area management module is used for managing the data cache area, and the verification information management module is used for classifying and managing user information verification rules of different databases.
8. The urban data model management system according to claim 7, wherein: the template management module comprises: the system comprises a classification unit, an index entry management unit and an information index management unit, wherein the classification unit is used for classifying data filling positions, the index entry management unit is used for screening index entries, and the information index management unit is used for managing information indexes.
9. The urban data model management system according to claim 8, wherein: the ordering management module comprises: the system comprises a first search target management unit, a second search target management unit, a search parameter calculation unit and a sequencing unit, wherein the first search target management unit is used for acquiring a data filling position corresponding to a first type of information search target, the second search target management unit is used for acquiring a data filling position corresponding to a second type of information search target, the search parameter calculation unit is used for calculating search parameters of the first type of information search target, and the sequencing unit is used for sequencing data acquisition sequences.
10. The urban data model management system according to claim 9, wherein: the data cache area management module comprises: the device comprises a data extraction unit, a sequence number management unit and a data sending unit, wherein the data extraction unit is used for extracting a data sequence, the sequence number management unit is used for managing a data position needing to be hidden, and the data sending unit is used for returning the extracted data.
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