CN117151555A - Smart city service system - Google Patents
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Abstract
The application provides a smart city service system, which belongs to the technical field of big data and comprises the following components: the index acquisition module is used for acquiring and analyzing the service demands of the urban users to obtain multidimensional indexes aiming at the service demands; the list construction module is used for calling each dimension information matched with the multidimensional index from the data collection library and constructing an initial dimension list; the feedback evaluation module is used for acquiring feedback service information in the latest period and carrying out feedback evaluation on the feedback service information to determine a feedback factor in the corresponding dimension; the first updating module is used for carrying out first updating on the corresponding dimension information; the second updating module is used for carrying out second updating on the dimension information in the initial dimension list based on the non-single optimization factor; and the information arrangement module is used for carrying out information arrangement based on the updated dimension list in a personalized way according to the requirements of urban users and outputting the information to the user side. The timely effectiveness of the information is guaranteed, and the user experience is improved.
Description
Technical Field
The application relates to the technical field of big data, in particular to a smart city service system.
Background
The smart city is a new theory and a new mode for promoting city planning, construction, management and service intelligence by applying new generation information integration technologies such as Internet of things, cloud computing, big data, space geographic information integration and the like.
With more and more urban services, users generally search according to a set number of fixed dimensions under the condition of acquiring wanted information, the acquired information is old and cannot be perfectly matched with the demands, and the user experience is reduced.
Accordingly, the present application proposes a smart city service system.
Disclosure of Invention
The application provides a smart city service system which is used for constructing a dimension list by acquiring dimension indexes, updating the list by acquiring feedback factors and potential association analysis in the latest period, ensuring the timely effectiveness of information and meeting the requirements of users as far as possible.
The present application provides a smart city service system, comprising:
the system comprises an index acquisition module, a service request acquisition module and a service request analysis module, wherein the index acquisition module is used for acquiring service demands of urban users, analyzing the service demands and obtaining multidimensional indexes aiming at the service demands, and the service demands are related to network retrieval information of the urban users based on an urban service platform;
the list construction module is used for retrieving each piece of dimension information matched with the multi-dimension index from the data collection library and constructing an initial dimension list;
the feedback evaluation module is used for acquiring feedback service information in the latest period, and carrying out feedback evaluation on the feedback service information to determine a feedback factor in the corresponding dimension;
the first updating module is used for carrying out first updating on corresponding dimension information in the initial dimension list based on the feedback factors;
the second updating module is used for carrying out potential association analysis on all the determined feedback factors to obtain a non-single optimization factor, and carrying out second updating on the dimension information in the initial dimension list based on the non-single optimization factor;
and the information arrangement module is used for carrying out information arrangement based on the updated dimension list according to the requirement individuation of the urban users and outputting the information to a user side.
Preferably, the index obtaining module includes:
the vector construction unit is used for analyzing the service requirement to obtain a plurality of analysis words, carrying out clustering treatment on the analysis words to obtain a plurality of cluster clusters and associated words based on each cluster, and constructing and obtaining a cluster vector based on each cluster according to the distance between the cluster and the associated word;
the importance determining unit is used for locking the target object under the mutation distance in the clustering vector and determining the clustering importance of the corresponding cluster;
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing the cluster importance of the ith cluster; n3 represents satisfaction->Related vocabulary number of (a); />Indicating the occurrence number of mutation distances; />Represents the i2 nd mutation distance; />Representing the distance between the target object corresponding to the i2 th mutation distance and the cluster; />Representing the distance between the target object corresponding to the i2 th mutation distance and the cluster; />Representing the distance between the i 1-th associated vocabulary and the cluster; n1 represents the total number of words in the corresponding cluster vector;
the importance optimization unit is used for optimizing the clustering importance of each cluster to obtain the optimized importance;
the screening unit is used for screening the cluster clusters with the optimized importance larger than the preset importance as a first cluster, and carrying out similar classification on the rest clusters to obtain the classification quantity;
a dimension number determining unit configured to take a sum of the number of existence of the first cluster and the number of classification as a dimension number;
and the index construction unit is used for respectively matching the dimension numbers with the vocabulary descriptions of the corresponding central vocabularies to construct and obtain the multi-dimension index.
Preferably, the importance optimization unit is configured to:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing the optimal importance of the ith cluster; ln represents the sign of the logarithmic function; />Representing a distance variance corresponding to n 3; />Represents a distance average value corresponding to n 3; />Representing the minimum distance under the ith cluster.
Preferably, the list construction module includes:
the relation establishing unit is used for establishing a matching relation between the region description of each blank region in the blank list and the dimension index and determining a designated region with the matching degree of each dimension index being 1;
the filling unit is used for filling the fetched dimension information into the designated area respectively and constructing an initial dimension list.
Preferably, the feedback evaluation module includes:
the disassembly unit is used for disassembling the feedback service information according to the dimension indexes to obtain disassembly information under each dimension index;
the association degree determining unit is used for determining a first association degree of each piece of disassembling information and the central vocabulary of the corresponding dimension index, and meanwhile, acquiring a second association degree of each piece of disassembling information and the central vocabulary of each piece of residual dimension index except the corresponding dimension index;
the index extraction unit is used for screening the maximum association degree from the second association degrees and extracting a first index corresponding to the maximum association degree;
and the factor determining unit is used for determining the feedback factor under the corresponding dimension based on the maximum association degree, the first index, the first association degree and the central vocabulary of the corresponding dimension.
Preferably, the first updating module includes:
the information extraction unit is used for extracting key information from final information matched with the feedback factors according to the feedback factors in each dimension;
the similarity matching unit is used for performing similarity matching on the key information and the dimension information in the corresponding dimension, and determining whether similar information with the similarity larger than a preset degree exists in the corresponding dimension information;
if the similar information exists, replacing the similar information according to the key information, and displaying significance;
if the key information does not exist, determining a residual space of a designated area corresponding to the corresponding dimension information, and judging whether the residual space can accommodate the corresponding key information;
if the information can be accommodated, filling the corresponding key information into a residual space, and distinguishing the original filling information from the latest filling information in a remarkable way;
if the non-critical information cannot be accommodated, locking non-critical information in the dimension information in the corresponding execution area, and compressing the non-critical information according to the following formula to fill the critical information;
the method comprises the steps of carrying out a first treatment on the surface of the Wherein m1 represents the number of appearance positions in the non-critical information;space required for representing key information; />Representing the remaining space; />Representing the footprint of non-critical information; />Representing a multiple adjustment function; />Representing an adjustment amount of non-critical information for the j1 st appearance position; />Representing the occupied space of the j1 st appearance position; />A compression attribute indicating a corresponding appearance position, wherein when the value is 1, compression is determined to be allowed, and when the value is 0, compression is determined not to be allowed;
based on the replacement results and the filling results, a first update of the initial dimension list is achieved.
Preferably, the second updating module includes:
the combination determining unit is used for inputting all feedback factors into the association analysis model to obtain a factor combination with association, wherein the factor combination comprises at least two factors, which are collectively called as non-single optimization factors;
establishing a quantity index for the position points of the corresponding dimension information stored in the initial dimension list according to the dimension information related to each factor combination;
and screening the updating modes matched with the quantity indexes from the index-updating mapping table to carry out second updating on the corresponding dimension information.
Preferably, the information arrangement module includes:
acquiring an ordering scheme arrangement scheme which is individually matched with the demands of the urban users from an arrangement database;
and arranging the updated dimension list according to the arrangement scheme of the ordering scheme, and outputting the updated dimension list to a user side.
Compared with the prior art, the application has the following beneficial effects:
the dimension index is obtained to construct a dimension list, and then the feedback factor and potential association analysis under the latest period are obtained to update the list, so that the timely effectiveness of information is ensured, and the user requirement is met as far as possible.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the application is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, serve to explain the application. In the drawings:
fig. 1 is a block diagram of a smart city service system according to an embodiment of the present application.
Detailed Description
The preferred embodiments of the present application will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present application only, and are not intended to limit the present application.
The present application relates to a smart city service system, as shown in fig. 1, comprising:
the system comprises an index acquisition module, a service request acquisition module and a service request analysis module, wherein the index acquisition module is used for acquiring service demands of urban users, analyzing the service demands and obtaining multidimensional indexes aiming at the service demands, and the service demands are related to network retrieval information of the urban users based on an urban service platform;
the list construction module is used for retrieving each piece of dimension information matched with the multi-dimension index from the data collection library and constructing an initial dimension list;
the feedback evaluation module is used for acquiring feedback service information in the latest period, and carrying out feedback evaluation on the feedback service information to determine a feedback factor in the corresponding dimension;
the first updating module is used for carrying out first updating on corresponding dimension information in the initial dimension list based on the feedback factors;
the second updating module is used for carrying out potential association analysis on all the determined feedback factors to obtain a non-single optimization factor, and carrying out second updating on the dimension information in the initial dimension list based on the non-single optimization factor;
and the information arrangement module is used for carrying out information arrangement based on the updated dimension list according to the requirement individuation of the urban users and outputting the information to a user side.
In this embodiment, the service requirement refers to a requirement that the user needs to solve the problem, for example, the service requirement is search information of obtaining preferential strength of different aspects in the house purchase policy, which is input by the user in the city service platform.
At this time, the preferential strength of different aspects in the house purchase policy is analyzed to obtain multidimensional indexes, for example, a plurality of indexes such as public accumulation, pay-per-view, meeting the optimization setting standard and the like.
It should be noted that, different search information is input into the platform, and after the search information is analyzed, the obtained dimension index is different.
In this embodiment, the multi-dimensional index means that at least 2 indexes are included.
In this embodiment, the data collection library is a database of various newly generated data for the smart city, and the data is directly obtained based on the internet of things platform.
In this embodiment, the dimension information may be directly extracted from the database according to the index, and the corresponding information is placed at a suitable position according to the index placement position, so as to obtain the initial dimension list.
In this embodiment, the latest period refers to a period from the time of last update to the time of current update of the database.
In this embodiment, the feedback service information refers to one feedback information for various services uploaded based on the internet of things platform, and is feedback conditions of other users for the services.
In this embodiment, the purpose of the feedback evaluation is to determine the reference value of the corresponding feedback information.
In this embodiment, the first update is to update the original information in the initial dimension list, because the effective preference is changed and then a timely change is needed.
In this embodiment, for example, if there is a relationship between the feedback factor 1 and the feedback factor 2, then the feedback factor 1 and the feedback factor 2 are non-single optimization factors, for example, the information 1 is updated according to the feedback factor 1, and at this time, the feedback factor 2 and the feedback factor 1 are related, and then the information related to the feedback factor 2 may also be acquired to update the information reflected in the information 1, that is, the second update.
In this embodiment, the requirement personalization refers to a display requirement of a user, for example, the user likes a display with a five-color, and performs display after dyeing and rendering on the dimension list, for example, the user likes a display with logic, and performs display after processing a logic frame on the dimension list.
In this embodiment, the user side refers to a mobile phone side, a computer side, and the like, and may receive the electronic device that outputs the result.
The beneficial effects of the technical scheme are as follows: the dimension index is obtained to construct a dimension list, and then the feedback factor and potential association analysis under the latest period are obtained to update the list, so that the timely effectiveness of information is ensured, and the user requirement is met as far as possible.
The application relates to a smart city service system, which comprises an index acquisition module, a control module and a control module, wherein the index acquisition module comprises:
the vector construction unit is used for analyzing the service requirement to obtain a plurality of analysis words, carrying out clustering treatment on the analysis words to obtain a plurality of cluster clusters and associated words based on each cluster, and constructing and obtaining a cluster vector based on each cluster according to the distance between the cluster and the associated word;
the importance determining unit is used for locking the target object under the mutation distance in the clustering vector and determining the clustering importance of the corresponding cluster;
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing the cluster importance of the ith cluster; n3 represents satisfaction->Related vocabulary number of (a); />Indicating the occurrence number of mutation distances; />Represents the i2 nd mutationA distance; />Representing the distance between the target object corresponding to the i2 th mutation distance and the cluster; />Representing the distance between the target object corresponding to the i2 th mutation distance and the cluster;representing the distance between the i 1-th associated vocabulary and the cluster; n1 represents the total number of words in the corresponding cluster vector;
the importance optimization unit is used for optimizing the clustering importance of each cluster to obtain the optimized importance;
the screening unit is used for screening the cluster clusters with the optimized importance larger than the preset importance as a first cluster, and carrying out similar classification on the rest clusters to obtain the classification quantity;
a dimension number determining unit configured to take a sum of the number of existence of the first cluster and the number of classification as a dimension number;
and the index construction unit is used for respectively matching the dimension numbers with the vocabulary descriptions of the corresponding central vocabularies to construct and obtain the multi-dimension index.
In this embodiment, the clustering process is directly processed according to the existing clustering algorithm, for example, the cluster 01 includes 01 itself and elements 010 and 012, and at this time, the clustering vector is: [01 010 012], wherein the distance of 01 to 010 is closer than the distance of 01 to 012.
In this embodiment, the preset importance is preset.
In this embodiment, the similarity classification is obtained by classifying the remaining clusters according to the cluster parameter description based on a similarity function, and the number of classifications refers to the number of classification groups corresponding to the classified clusters.
In this embodiment, dimension number = number of classifications + number of first clusters.
The beneficial effects of the technical scheme are as follows: the clustering process is convenient for directly and simply acquiring the clustering clusters, the first clusters are conveniently obtained through screening through importance calculation and comparison, the dimension number is obtained by combining the classifying number, a basis is provided for constructing a list, and the indirect data use value is provided for meeting the user demands.
The application discloses a smart city service system, which is characterized in that the importance optimizing unit is used for:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing the optimal importance of the ith cluster; ln represents the sign of the logarithmic function; />Representing a distance variance corresponding to n 3; />Represents a distance average value corresponding to n 3; />Representing the minimum distance under the ith cluster.
The beneficial effects of the technical scheme are as follows: by optimizing the clustering importance, the reliability of the result acquisition can be ensured, and a reasonable basis is provided for subsequent list construction and data matching.
The application relates to a smart city service system, which comprises a list construction module, a service module and a service module, wherein the list construction module comprises:
the relation establishing unit is used for establishing a matching relation between the region description of each blank region in the blank list and the dimension index and determining a designated region with the matching degree of each dimension index being 1;
the filling unit is used for filling the fetched dimension information into the designated area respectively and constructing an initial dimension list.
In this embodiment, the blank list includes the region description and the index description matched with the region description, so that the blank list can be directly matched to obtain a corresponding designated region.
The beneficial effects of the technical scheme are as follows: the region description is matched with the dimension index to determine the designated region, so that the dimension information is effectively filled, and an initial dimension list is conveniently constructed.
The application relates to a smart city service system, which comprises a feedback evaluation module, wherein the feedback evaluation module comprises:
the disassembly unit is used for disassembling the feedback service information according to the dimension indexes to obtain disassembly information under each dimension index;
the association degree determining unit is used for determining a first association degree of each piece of disassembling information and the central vocabulary of the corresponding dimension index, and meanwhile, acquiring a second association degree of each piece of disassembling information and the central vocabulary of each piece of residual dimension index except the corresponding dimension index;
the index extraction unit is used for screening the maximum association degree from the second association degrees and extracting a first index corresponding to the maximum association degree;
and the factor determining unit is used for determining the feedback factor under the corresponding dimension based on the maximum association degree, the first index, the first association degree and the central vocabulary of the corresponding dimension.
In this embodiment, the disassembling information refers to performing disassembling according to an index, for example, feedback service information is information 1, information 2, information 3, and information 4, where there are 2 dimension indexes, and information 2 is obtained after disassembling according to index 1, and information 1, information 3, and information 4 are obtained after disassembling according to index 2.
In this embodiment, the central vocabulary refers to the vocabulary of the first cluster corresponding to the corresponding index.
In this embodiment, the first degree of association refers to association between the vocabulary of the first cluster and the disassembly information, and the more consistent the vocabulary of the first cluster and the disassembly information, the greater the corresponding degree of association.
In this embodiment, for example, the first association degree is the dimension index 1 and the dimension index 1, and then the second association degree is the dimension index 2 and the dimension index 1.
In this embodiment, the first index corresponds to the maximum degree of association.
In this embodiment, the feedback factor is obtained by analyzing the maximum association degree, the first index, the first association degree and the central vocabulary of the corresponding dimension based on an analysis model, where the model is obtained by training the neural network model according to the association degree, the index, the central vocabulary and the feedback result matched with the neural network model as samples, so that the corresponding feedback factor can be directly obtained through the model.
The beneficial effects of the technical scheme are as follows: according to the method, the relevance between different disassembled information and the index is obtained according to the disassembly of the index to the information, and further feedback information is effectively obtained through model analysis, so that the first update of the list is ensured, and the experience of a user is further met.
The application relates to a smart city service system, the first updating module comprises:
the information extraction unit is used for extracting key information from final information matched with the feedback factors according to the feedback factors in each dimension;
the similarity matching unit is used for performing similarity matching on the key information and the dimension information in the corresponding dimension, and determining whether similar information with the similarity larger than a preset degree exists in the corresponding dimension information;
if the similar information exists, replacing the similar information according to the key information, and displaying significance;
if the key information does not exist, determining a residual space of a designated area corresponding to the corresponding dimension information, and judging whether the residual space can accommodate the corresponding key information;
if the information can be accommodated, filling the corresponding key information into a residual space, and distinguishing the original filling information from the latest filling information in a remarkable way;
if the non-critical information cannot be accommodated, locking non-critical information in the dimension information in the corresponding execution area, and compressing the non-critical information according to the following formula to fill the critical information;
the method comprises the steps of carrying out a first treatment on the surface of the Wherein m1 represents the number of appearance positions in the non-critical information;space required for representing key information; />Representing the remaining space; />Representing the footprint of non-critical information; />Representing a multiple adjustment function; />Representing an adjustment amount of non-critical information for the j1 st appearance position; />Representing the occupied space of the j1 st appearance position; />A compression attribute indicating a corresponding appearance position, wherein when the value is 1, compression is determined to be allowed, and when the value is 0, compression is determined not to be allowed;
based on the replacement results and the filling results, a first update of the initial dimension list is achieved.
In this embodiment, for example, the final information matched by the feedback factor 1 in dimension 1 is information 02, and at this time, the key information 021 is extracted from the information 02, and at this time, the key information is highly matched with the feedback factor.
In this embodiment, the purpose of the similarity matching is to determine whether there is information that can be directly replaced.
For example, the dimension information has information 020 and key information 021 which are highly matched, and the information content of the key information 021 is not more preferable than the information 020 when the information 020 is not included, and at this time, the key information 021 replaces the information 020.
In this embodiment, the purpose of the saliency display is to facilitate visual understanding by the user.
In this embodiment, the remaining space refers to a space in the designated area except for a position occupied by the dimensional information corresponding to the original.
In this embodiment, the non-critical information refers to the remaining information except the critical information in the dimension information, and in the process of compressing the non-critical information, it is necessary to consider that some positions cannot be compressed, that is, the data representing the positions cannot be compressed, and then the compression multiple needs to be set reasonably to ensure that the critical information can be effectively accommodated.
In this embodiment, the first update is mainly embodied in the replacement result and the filling result.
The beneficial effects of the technical scheme are as follows: the key information is extracted through acquiring the dimension information to facilitate similarity matching with the dimension information, whether replacement or filling is carried out later is effectively determined, reasonable compression multiples are set through combining compression attributes of different positions with the residual space, effective accommodation of the key information is guaranteed, experience of a user is further improved, and service requirements of the user are comprehensively met.
The application relates to a smart city service system, the second updating module comprises:
the combination determining unit is used for inputting all feedback factors into the association analysis model to obtain a factor combination with association, wherein the factor combination comprises at least two factors, which are collectively called as non-single optimization factors;
establishing a quantity index for the position points of the corresponding dimension information stored in the initial dimension list according to the dimension information related to each factor combination;
and screening the updating modes matched with the quantity indexes from the index-updating mapping table to carry out second updating on the corresponding dimension information.
In this embodiment, the correlation analysis model is trained in advance, and the neural network model is trained based on different feedback factors and the correlation between the different factors by an expert as a sample.
In this embodiment, the location point refers to any location point of the designated area of the corresponding list, and it is only necessary to conveniently establish an index location point, where the number index is established to obtain and implement effective update for the update mode of the area.
In this embodiment, the index-update mapping table includes different index numbers and update manners, for example, the index number is 2, and then the update is performed on the position 1 to be updated in the dimension information, and after the update is completed, the update is performed on the position 2 to be updated.
For example, if the index number is 6, then the random three positions to be updated in the dimension information are synchronously updated, and after the update is completed, the rest three positions are synchronously updated.
The beneficial effects of the technical scheme are as follows: and the factors are subjected to association analysis based on the model, so that factor combination is facilitated, the quantity index is conveniently established, an effective updating mode is obtained for updating, and the updating efficiency is ensured.
The application relates to a smart city service system, which comprises an information arrangement module, a service module and a service module, wherein the information arrangement module comprises:
acquiring an ordering scheme arrangement scheme which is individually matched with the demands of the urban users from an arrangement database;
and arranging the updated dimension list according to the arrangement scheme of the ordering scheme, and outputting the updated dimension list to a user side.
In this embodiment, the arrangement database includes a plurality of various arrangement schemes of the ranking scheme, such as a rendering ranking scheme arrangement scheme, a logic ranking scheme arrangement scheme, etc., mainly for ranking the list, and ensuring effective output.
The beneficial effects of the technical scheme are as follows: the dimension list is arranged by acquiring a scheme matched with individuation from the database, so that the experience effect of a user is further improved.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A smart city service system, comprising:
the system comprises an index acquisition module, a service request acquisition module and a service request analysis module, wherein the index acquisition module is used for acquiring service demands of urban users, analyzing the service demands and obtaining multidimensional indexes aiming at the service demands, and the service demands are related to network retrieval information of the urban users based on an urban service platform;
the list construction module is used for retrieving each piece of dimension information matched with the multi-dimension index from the data collection library and constructing an initial dimension list;
the feedback evaluation module is used for acquiring feedback service information in the latest period, and carrying out feedback evaluation on the feedback service information to determine a feedback factor in the corresponding dimension;
the first updating module is used for carrying out first updating on corresponding dimension information in the initial dimension list based on the feedback factors;
the second updating module is used for carrying out potential association analysis on all the determined feedback factors to obtain a non-single optimization factor, and carrying out second updating on the dimension information in the initial dimension list based on the non-single optimization factor;
and the information arrangement module is used for carrying out information arrangement based on the updated dimension list according to the requirement individuation of the urban users and outputting the information to a user side.
2. The smart city service system of claim 1, wherein the index acquisition module comprises:
the vector construction unit is used for analyzing the service requirement to obtain a plurality of analysis words, carrying out clustering treatment on the analysis words to obtain a plurality of cluster clusters and associated words based on each cluster, and constructing and obtaining a cluster vector based on each cluster according to the distance between the cluster and the associated word;
the importance determining unit is used for locking the target object under the mutation distance in the clustering vector and determining the clustering importance of the corresponding cluster;
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing the cluster importance of the ith cluster; n3 represents satisfaction->Related vocabulary number of (a); />Indicating the occurrence number of mutation distances;represents the i2 nd mutation distance; />Representing the distance between the target object corresponding to the i2 th mutation distance and the cluster; />Representing the distance between the target object corresponding to the i2 th mutation distance and the cluster; />Representing the distance between the i 1-th associated vocabulary and the cluster; n1 represents the total number of words in the corresponding cluster vector;
the importance optimization unit is used for optimizing the clustering importance of each cluster to obtain the optimized importance;
the screening unit is used for screening the cluster clusters with the optimized importance larger than the preset importance as a first cluster, and carrying out similar classification on the rest clusters to obtain the classification quantity;
a dimension number determining unit configured to take a sum of the number of existence of the first cluster and the number of classification as a dimension number;
and the index construction unit is used for respectively matching the dimension numbers with the vocabulary descriptions of the corresponding central vocabularies to construct and obtain the multi-dimension index.
3. The smart city service system of claim 2, wherein the importance optimization unit is configured to:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing the optimal importance of the ith cluster; ln represents the sign of the logarithmic function; />Representing a distance variance corresponding to n 3; />Represents a distance average value corresponding to n 3; />Representing the minimum distance under the ith cluster.
4. The smart city service system of claim 1, wherein the list construction module comprises:
the relation establishing unit is used for establishing a matching relation between the region description of each blank region in the blank list and the dimension index and determining a designated region with the matching degree of each dimension index being 1;
the filling unit is used for filling the fetched dimension information into the designated area respectively and constructing an initial dimension list.
5. The smart city service system of claim 1, wherein the feedback evaluation module comprises:
the disassembly unit is used for disassembling the feedback service information according to the dimension indexes to obtain disassembly information under each dimension index;
the association degree determining unit is used for determining a first association degree of each piece of disassembling information and the central vocabulary of the corresponding dimension index, and meanwhile, acquiring a second association degree of each piece of disassembling information and the central vocabulary of each piece of residual dimension index except the corresponding dimension index;
the index extraction unit is used for screening the maximum association degree from the second association degrees and extracting a first index corresponding to the maximum association degree;
and the factor determining unit is used for determining the feedback factor under the corresponding dimension based on the maximum association degree, the first index, the first association degree and the central vocabulary of the corresponding dimension.
6. The smart city service system of claim 1, wherein the first update module comprises:
the information extraction unit is used for extracting key information from final information matched with the feedback factors according to the feedback factors in each dimension;
the similarity matching unit is used for performing similarity matching on the key information and the dimension information in the corresponding dimension, and determining whether similar information with the similarity larger than a preset degree exists in the corresponding dimension information;
if the similar information exists, replacing the similar information according to the key information, and displaying significance;
if the key information does not exist, determining a residual space of a designated area corresponding to the corresponding dimension information, and judging whether the residual space can accommodate the corresponding key information;
if the information can be accommodated, filling the corresponding key information into a residual space, and distinguishing the original filling information from the latest filling information in a remarkable way;
if the non-critical information cannot be accommodated, locking non-critical information in the dimension information in the corresponding execution area, and compressing the non-critical information according to the following formula to fill the critical information;
the method comprises the steps of carrying out a first treatment on the surface of the Wherein m1 represents the number of appearance positions in the non-critical information; />Space required for representing key information; />Representing the remaining space; />Representing the footprint of non-critical information; />Representing a multiple adjustment function; />Representing an adjustment amount of non-critical information for the j1 st appearance position; />Representing the occupied space of the j1 st appearance position; />A compression attribute indicating a corresponding appearance position, wherein when the value is 1, compression is determined to be allowed, and when the value is 0, compression is determined not to be allowed;
based on the replacement results and the filling results, a first update of the initial dimension list is achieved.
7. The smart city service system of claim 1, wherein the second update module comprises:
the combination determining unit is used for inputting all feedback factors into the association analysis model to obtain a factor combination with association, wherein the factor combination comprises at least two factors, which are collectively called as non-single optimization factors;
establishing a quantity index for the position points of the corresponding dimension information stored in the initial dimension list according to the dimension information related to each factor combination;
and screening the updating modes matched with the quantity indexes from the index-updating mapping table to carry out second updating on the corresponding dimension information.
8. The smart city service system of claim 1, wherein the information arrangement module comprises:
acquiring an ordering scheme arrangement scheme which is individually matched with the demands of the urban users from an arrangement database;
and arranging the updated dimension list according to the arrangement scheme of the ordering scheme, and outputting the updated dimension list to a user side.
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