CN112948595A - Method, system and equipment for building urban group operation state knowledge graph - Google Patents
Method, system and equipment for building urban group operation state knowledge graph Download PDFInfo
- Publication number
- CN112948595A CN112948595A CN202110337746.1A CN202110337746A CN112948595A CN 112948595 A CN112948595 A CN 112948595A CN 202110337746 A CN202110337746 A CN 202110337746A CN 112948595 A CN112948595 A CN 112948595A
- Authority
- CN
- China
- Prior art keywords
- index
- data
- urban
- city
- group
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000000605 extraction Methods 0.000 claims abstract description 34
- 238000010276 construction Methods 0.000 claims description 26
- 238000004364 calculation method Methods 0.000 claims description 13
- 239000000284 extract Substances 0.000 claims description 7
- 238000012351 Integrated analysis Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 238000009499 grossing Methods 0.000 claims description 3
- 230000006872 improvement Effects 0.000 abstract description 4
- 238000004445 quantitative analysis Methods 0.000 abstract description 3
- 238000009412 basement excavation Methods 0.000 abstract 2
- 238000007796 conventional method Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 238000005065 mining Methods 0.000 description 5
- 238000007405 data analysis Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000007418 data mining Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 235000019580 granularity Nutrition 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 239000010865 sewage Substances 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
- Y02A30/60—Planning or developing urban green infrastructure
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention belongs to the technical field of big data, and particularly relates to a method, a system and equipment for constructing an urban group operation state knowledge graph, aiming at solving the problems that the conventional method lacks comprehensive quantitative analysis on the operation state of each field of an urban group and cannot meet the requirement of deep excavation of the urban group operation rule. The invention comprises the following steps: the method comprises the steps of obtaining multi-source heterogeneous city group operation data, converting the multi-source heterogeneous city group operation data into city group operation space data sets and dividing subsets, determining general city group operation state indexes based on the space data subsets, calculating index weights based on the general city group operation state indexes and city characteristics, and constructing a city group operation state index system so as to construct a city group operation state knowledge graph. The method realizes extraction of potential relations among all operation elements in the urban group, and provides technical improvement for deep excavation of the urban group operation rules.
Description
Technical Field
The invention belongs to the technical field of big data, and particularly relates to a method, a system and equipment for constructing a knowledge graph of an urban group operation state.
Background
The urban group is an important development direction of urbanization, and the construction and operation decision of the urban group needs to analyze multi-source heterogeneous data in various fields. With the rise of the internet of things, the data scale is increasing day by day, and how to extract, organize, store, analyze and display the massive data is a problem to be solved in order to mine the operation rule of the urban population. In the prior art, the running state of the urban group is analyzed, usually, a certain specific field of the urban group is selected for data mining analysis, or qualitative description is carried out on a plurality of fields, and the requirement of deep mining of the running rule of the urban group cannot be met due to the lack of comprehensive quantitative analysis on the running state of each field of the urban group. In addition, the analysis of the running state of the urban group is related, and the problems of neglecting cooperation among cities and coupling in a cross-domain mode often exist.
A knowledge-graph is a knowledge-base that uses a graphical model to describe associations between knowledge and modeled things. In one aspect, knowledge graphs can employ more canonical and standard conceptual models, ontology terms, and grammatical formats to model and describe data of different sources, types, and structures; on the other hand, the knowledge graph can enhance the association between data through semantic linkage, so that the knowledge data is systematized and related. The data with the expression specification and strong relevance can play an important role in multiple aspects of improving retrieval, data analysis, assisting decision making, supporting reasoning and the like. However, at present, in the field of smart urban groups, no mature research for constructing a knowledge graph of the operation state of the urban group appears.
Disclosure of Invention
In order to solve the problems in the prior art, namely the prior art usually only carries out data mining analysis in a specific field or qualitative description on a plurality of fields, lacks comprehensive quantitative analysis on the running states of each field of an urban group, cannot meet the requirement of deep mining of the urban group running rules, neglects the problems of cooperation among cities, cross-field coupling and the like, the invention provides a method for constructing an urban group running state knowledge graph, which comprises the following steps:
step S100, obtaining multi-source heterogeneous city group operation data;
step S200, converting the multi-source heterogeneous city group operation data into an empty city group operation data set; the metropolitan run-time spatio-temporal data set comprises a plurality of spatio-temporal data subsets;
wherein k is 1, 2, …, n; n represents the number of city group operation service fields; i is 1, 2, …, m; j is 1, 2, …, m; m represents the number of cities in the city group; when i ≠ j,
a subset of data representing the mutual flow between city i and city j in the data in the domain of city group k; when the value of i is equal to j,
representing a subset of data within city i in the data of the k domain of the city group;
step S300, constructing a general urban group operation state index framework based on the spatio-temporal data subset;
step S400, calculating index weight by combining characteristics of the urban group based on the general urban group operation state index framework and the spatio-temporal data subset;
step S500, expressing the running state of the urban group based on the index framework of the running state of the general urban group and each index weight of the urban group running index system, and constructing the urban group running state index system;
and step S600, based on the city group running state index system and the time-space data set, performing entity extraction, entity relation extraction, entity attribute extraction and relation attribute extraction, and constructing a city group running state knowledge graph.
In some preferred embodiments, the acquiring the multi-source heterogeneous city group operation data further includes performing padding, smoothing, merging, normalizing, and consistency checking on the acquired data.
In some preferred embodiments, the index of the operating state of the urban group includes q primary indexes, each primary index includes p secondary indexes, each secondary index sets a forward label or a reverse label, the larger the value of the forward label is, the better the operating state of the urban group is, the larger the value of the reverse label is, the worse the operating state of the urban group is, p and q represent natural numbers, and the specific value is not specifically limited.
In some preferred embodiments, the specific steps of step S400 include:
step S410, carrying out standardization processing on data with different properties in an operating state index system to obtain a standardized value of the index, wherein the method comprises the following steps:
wherein, IabRepresenting the true value of the b index in the a city,
the normalized value of the b index in the a city is shown, and e represents the base number of the natural logarithm;
step S420, based on the standard value of the index, calculating the contribution value of m cities in the city group to each index:
wherein, PabRepresenting the contribution degree of the a-th city to the b-th index;
step S430, calculating the total contribution amount of m cities in the city group to all indexes based on the contribution values of the m cities in the city group to each index:
wherein S isbRepresenting the total contribution amount of m cities in the city group to the index b;
step S440, calculating each index weight of the city group operation index system based on the total contribution amount of the m cities to all indexes;
wherein, WbAnd (3) representing the weight of an index b in the urban group operation index system, wherein the value of r is 1 when the index is a forward index, otherwise, the value of r is-1, and z represents the total number of urban operation indexes.
In some preferred embodiments, the operating status of the urban group is represented as:
wherein, I'bThe normalized value of the running index b of the city group is represented by the value I of the index bbThe value I of the index b is obtained after standardization treatmentbObtained by performing integrated analysis on the urban grouping operation data.
In some preferred embodiments, the constructing the city group operation state knowledge graph specifically comprises the following steps:
determining each entity as a node, determining the entity relationship as a directed line segment, setting the attribute corresponding to the entity as a node attribute, and constructing an urban group operation state knowledge graph according to each node, the directed line segment and the node attribute.
In some preferred embodiments, the entity extraction comprises the following specific steps: writing an entity template by a rule and dictionary based method, and matching the linguistic data of the city group operation index system by combining a heuristic algorithm to obtain an index entity;
identifying the data entity based on a natural language model and artificial rules by a knowledge extraction method combining manual operation and automatic operation, and extracting knowledge of the time-space data subset to obtain the data entity;
the entity relationship extraction is carried out by a rule-based method based on the index entity and the data entity; the method comprises the following steps of (1) including the relationship between entities, the relationship between an index entity and a data entity and the relationship between data entities; also includes the relation between the upper and lower indexes;
the entity attribute extraction and the relation attribute extraction are used for constructing an entity attribute list and a relation attribute list of the city group operation, and comprise names, definitions, calculation methods, numerical values and forward and reverse directions.
In another aspect of the present invention, a system for constructing a knowledge graph of an operation state of an urban group is provided, the system comprising: the system comprises a data acquisition module, a time-space data division module, an index frame construction module, a weight calculation module, an operation state index system construction module and a knowledge map construction module;
the data acquisition module is configured to acquire multi-source heterogeneous city group operation data;
the space-time data dividing module is configured to convert the multi-source heterogeneous city group operation data into a city group operation space-time data set; the metropolitan run-time spatio-temporal data set comprises a plurality of spatio-temporal data subsets;
wherein k is 1, 2, …, n; n represents the number of city group operation service fields; i is 1, 2, …, m; j is 1, 2, …, m; m represents the number of cities in the city group; when i ≠ j,
a subset of data representing the mutual flow between city i and city j in the data in the domain of city group k; when the value of i is equal to j,
representing a subset of data within city i in the data of the k domain of the city group;
the index framework construction module is configured to determine a general urban group operation state index based on the spatio-temporal data subset;
the weight calculation module is configured to calculate index weight based on the general urban group operation state index and by combining urban group characteristics;
the operation state index system construction module is configured to express an urban operation state and construct an urban group operation state index system based on the standardized value of the actual index value and each index weight of the urban group operation index system;
and the knowledge graph construction module is configured to extract entities, extract entity relationships and extract attributes corresponding to the entities based on the urban group operation state index system and the spatio-temporal data subsets, and construct the urban group operation state knowledge graph.
In a third aspect of the present invention, an electronic device is provided, including: at least one processor; and a memory communicatively coupled to at least one of the processors; the memory stores instructions executable by the processor, and the instructions are used for being executed by the processor to realize the city group operation state knowledge graph construction method.
In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions for being executed by the computer to implement the above-mentioned method for building an urban grouping operation state knowledge graph.
The invention has the beneficial effects that:
(1) according to the method for constructing the urban group operation state knowledge graph, the constructed knowledge graph realizes extraction of potential relations among operation elements in the urban group, and not only relates to interaction in different service fields, but also comprises operation rules such as inter-city competition and cooperation which cannot be embodied by a common urban knowledge graph, so that technical improvement is provided for deep mining of the urban group operation rules, and the problem of insufficient retrieval effect in large data analysis of the urban group is solved;
(2) according to the method for constructing the urban group operation state knowledge graph, the weight of each index is obtained through an improved weight calculation method, an urban group operation state index system is constructed, and the comprehensive quantitative evaluation of the operation state of an urban group is realized.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of a method for constructing a knowledge graph of an operating state of an urban grouping according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a general urban group operation state index framework established by the urban group operation state knowledge graph construction method in the embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the knowledge graph entities, relationships, and attributes of the method for constructing the knowledge graph of the operation status of the urban grouping according to the embodiment of the present invention;
fig. 4 is a block diagram of a system for constructing a knowledge map of operating states of a city group according to an embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The invention provides a method for constructing a knowledge graph of an urban group running state, which comprises the following steps:
step S100, obtaining multi-source heterogeneous city group operation data;
step S200, converting the multi-source heterogeneous city group operation data into an empty city group operation data set; the metropolitan run-time spatio-temporal data set comprises a plurality of spatio-temporal data subsets;
wherein k is 1, 2, …, n; n represents the number of city group operation service fields; i is 1, 2, …, m; j is 1, 2, …, m; m represents the number of cities in the city group; when i ≠ j,
a subset of data representing the mutual flow between city i and city j in the data in the domain of city group k; when the value of i is equal to j,
representing a subset of data within city i in the data of the k domain of the city group;
step S300, constructing a general urban group operation state index framework based on the spatio-temporal data subset;
step S400, calculating index weight by combining characteristics of the urban group based on the general urban group operation state index framework;
step S500, expressing the running state of the urban group based on the index framework of the running state of the general urban group and each index weight of the urban group running index system, and constructing the urban group running state index system;
and S600, performing entity extraction, entity relation extraction, entity attribute extraction and relation attribute extraction based on the urban group operation state index system and the space-time data subset, and constructing an urban group operation state knowledge graph.
According to the method for constructing the urban group operation state knowledge graph, an index system of the urban group operation state is constructed, the operation state of an urban group is evaluated in an all-around quantitative mode, the problem of poor searching effect in urban group big data analysis is solved, the extraction of potential relations among operation elements in the urban group is achieved, and technical improvement is provided for deep mining of the urban group operation rule.
In order to more clearly explain the method for constructing the knowledge graph of the operating state of the urban area, the following describes in detail the steps in the embodiment of the present invention with reference to fig. 1.
The method for constructing the urban group operation state knowledge graph comprises the following steps of S100-S600, and the steps are described in detail as follows:
in this embodiment, the method further includes performing padding, smoothing, merging, normalizing and consistency checking on the collected data so as to obtain high-quality operating data of the urban group.
Step S100, obtaining multi-source heterogeneous city group operation data;
in this embodiment, the remote heterogeneous city group operation data includes historical data and real-time data of at least two service fields. The data of the business fields including basic data of each business field generated in the operation of the urban group may be public data (such as social data and encyclopedia data), government data (such as land utilization data and public transportation data), business data (such as map POI data and enterprise directory) acquired through each data platform on the network, such as business data of education, medical treatment, transportation, industry, finance, culture, ecology, population, land, social media, etc., and the business fields referred to in the present invention may also include other field definitions, which are not specifically limited herein.
Step S200, converting the multi-source heterogeneous city group operation data into an empty city group operation data set; the metropolitan run-time spatio-temporal data set comprises a plurality of spatio-temporal data subsets;
wherein k is 1, 2, …, n; n represents the number of city group operation service fields; i is 1, 2, …, m; j is 1, 2, …, m; m represents the number of cities in the city group; when i ≠ j,
a subset of data representing the mutual flow between city i and city j in the data in the domain of city group k; when the value of i is equal to j,
representing a subset of data within city i in the data of the k domain of the city group;
in this embodiment, the spatio-temporal data may refer to data having both time and space dimensions, and the operating data of the urban grouping mostly includes geographical location information and time information with different granularities. The spatiotemporal data format may be a tuple format including data entity information, a timestamp, and spatial coordinates, e.g., a population number spatiotemporal data format may be represented as { population number, timestamp, spatial coordinates }. The spatial coordinates include real world geospatial coordinates and also include network addresses of the network world, such as physical addresses of computer devices. Because the urban group is composed of relatively independent cities with specific geographic and administrative boundaries, the urban group operation data can be divided into data in each city and data flowing between cities; the space-time data set during the urban mass runtime can be divided into subsets of space-time data. The city operation data converted by the space-time data format has time, space and field attributes, and also has a subset label after dividing the subset, for example, the space-time label data format of the traffic flow can be expressed as { traffic flow, timestamp and space coordinate } [ within a city group/between two cities within the city group ].
In the embodiment, the city is taken as a unit to divide the city group operation service field data into subsets, and the city labels are added to the time-space data, so that a more complete city group operation state knowledge graph is constructed. By adopting the method, effective technical improvement can be provided for mining of the operation rule of the urban groups and the potential relation between the cities, data support can be provided for relevant application services of the smart urban groups, and convenience is provided for maintenance and updating of subsequent knowledge maps.
The spatiotemporal data sets and the spatiotemporal data subsets may be used to extract the urban mass operational data entities.
Step S300, constructing a general urban group operation state index framework based on the spatio-temporal data subset;
in this embodiment, the city group operation state index framework and the spatio-temporal data subset include q primary indexes, each primary index branch has p secondary indexes, each secondary index sets a forward or reverse label, the larger the value represented by the forward label is, the better the city group operation state is, the larger the value represented by the reverse label is, the worse the city group operation state is, and p and q are natural numbers.
In this embodiment, as shown in fig. 2, the established operating state index system of the smart city group is composed of 8 primary index classifications and 46 secondary indexes according to the principles of scientificity, hierarchy, relevance, clarity and operability by comprehensively considering all the fields of smart city group operation and the collaborative characteristics across city areas. Each secondary index has an index label of 'forward' or 'reverse', the forward 'indicates that the larger the corresponding index value is, the better the running state of the urban group is, and the reverse' indicates that the larger the corresponding index value is, the worse the running state of the urban group is.
Specifically, the primary index may be education, medical, traffic, cultural, ecological, economic, safe, virtual space.
The first-level index 'education' is classified into 5 second-level indexes, which can be the high-quality education resource coverage degree, the per-capita education input amount, the balanced allocation degree of the education resources, the number of teachers in per-capita middle and primary schools and the number of students in ordinary high schools.
Wherein, the first-level index medical treatment classification has 5 second-level indexes, which can be the number of beds per person, the amount of medical investment per person, the number of doctors in each department per person, the degree of satisfaction of residents in hospitalization and the proportion of hospitalization in different places.
The first-level index 'traffic' is classified into 7 second-level indexes, which can be highway network density, per-capita road area, one-hour traffic circle coverage area, 20km radius coverage of high-speed rail stations, city-crossing public traffic mileage, highway passenger freight volume and traffic congestion index.
Wherein, the first-level index 'culture' classification has 8 second-level indexes, the number of patents invented by everybody, R & D (research and development), the expenditure of expenses, the number of personnel R & D, the number of high-level scientific research papers, the book collection amount of the public library of everybody, the coverage and utilization rate of entertainment facilities, the coverage and utilization rate of sports facilities and the number of tourist spots.
Wherein, the first-level index is classified into 7 second-level indexes under the ecological classification, the air quality index, the unit GDP (gross Domestic product) water and electricity consumption, the urban Domestic garbage treatment capacity, the urban Domestic sewage treatment rate, the greening coverage rate of the built-up area, the cross-city environmental protection treatment rate and the three-waste comprehensive utilization product output value.
Wherein, the first-level index has 6 second-level indexes under the economic classification, the industrial structure difference degree, the industrial carrier area distribution quantity, the two-industry and three-industry space coordination degree, the per-capita GDP, the average salary of workers and the cross-market investment amount.
Wherein, the first-level index 'safety' classification has 6 second-level indexes, crime rate, emergency event processing efficiency, cross-market case rate, natural disaster coping efficiency, food and medicine safety accident rate and integrity of public facilities.
Wherein, the classification of the first-level index 'virtual space' has 2 second-level indexes, network public opinion cooperation degree and network competitive index.
The general urban group operation state index system can be suitable for the evaluation of the operation states of most urban groups at home and abroad, and can provide general evaluation standards for the construction and operation management effects of smart cities and smart urban groups.
Step S400, calculating index weight by combining characteristics of the urban group based on the general urban group operation state index framework;
before the weight calculation of the urban group operation state index system, carrying out integrated analysis on urban group operation data by using a big data integrated analysis method according to the general urban group operation index system to obtain an index value.
The big data integration analysis method can be statistical inference, visualization analysis, classification and regression, naive Bayes, support vector machines, random forests, clustering and other methods.
The method comprises the following specific steps: step S410, carrying out standardization processing on data with different properties in an operating state index system to obtain a standardized value of the index, wherein the method comprises the following steps:
wherein, IabRepresenting the true value of the b index in the a city,
a normalized value representing the b index in the a city;
step S420, based on the standard value of the index, calculating the contribution value of m cities in the city group to each index:
wherein, PabRepresenting the contribution degree of the a-th city to the b-th index;
step S430, calculating the total contribution amount of m cities to all indexes in the group based on the contribution value of the city to each index:
wherein S isbRepresenting the total contribution amount of m cities in the city group to the index b;
step S440, calculating each index weight of the city group operation index system based on the total contribution amount of the m cities to all indexes;
wherein, WbAnd (3) representing the weight of an index b in the urban group operation index system, wherein the value of r is 1 when the index is a forward index, otherwise, the value of r is-1, and z represents the total number of urban operation indexes.
Step S500, expressing the running state of the urban group based on the index framework of the running state of the general urban group and each index weight of the urban group running index system, and constructing the urban group running state index system;
in this embodiment, the operation state of the urban group is represented as:
wherein, I'bThe normalized value of the running index b of the city group is represented by the value D of the index bbThe value I of the index b is obtained after standardization treatmentbObtained by performing integrated analysis on the urban grouping operation data.
And S600, performing entity extraction, entity relation extraction, entity attribute extraction and relation attribute extraction based on the urban group operation state index system and the space-time data subset, and constructing an urban group operation state knowledge graph. The invention extracts knowledge from the index system, can directly show the cooperation and competition relationship between cities, and can store the index value in the database while subsequently inquiring and retrieving information.
In this embodiment, as shown in fig. 3, the extracting entity specifically includes the steps of: writing an entity template by a method based on rules and the point, and matching the linguistic data of the urban grouping operation index system by combining a heuristic algorithm to obtain an index entity; for example, the first level index "traffic" and the second level index "highway network density" in the city group operation index system can be extracted as index entities.
Identifying the data entity based on a natural language model and artificial rules by a knowledge extraction method combining manual operation and automatic operation, and extracting knowledge of the time-space data subset to obtain the data entity; for example, traffic field data of the operation of the urban group, such as railway network data, rail transit operation data, and the like, such as population data of the operation of the urban group, and the like, obtained in the data acquisition and format conversion stage can be extracted as data entities.
For example, population data of the urban population in the major bay area of Guangdong hong and Australia can be divided into "urban population data in the major bay area of Guangdong hong ═ Guangzhou population data, Shenzhen population data, Buddha population data, Zhuhai population data, Huizhou population data, Dongguan population data, Zhongshan population data, Jiangmen population data, Zhaoqing population data, hong Kong population data, and Australian population data }, and all the subsets of the urban population operating population data in the major bay area can be extracted as data entities.
The entity relationship extraction is carried out by a rule-based method based on the index entity and the data entity; the method comprises the following steps of (1) including the relationship between entities, the relationship between an index entity and a data entity and the relationship between data entities; also includes the relation between the upper and lower indexes;
specifically, the relationship between the index entities may be a relationship between an upper index and a lower index, for example, the index entity "traffic" is an upper index of the index entity "pedestrian road area", and is a lower index of the index entity "city group operation state". The relationship between the index entity and the data entity is a use and used relationship, for example, a 'population data' entity is used by an index entity 'per-person road area'. The relationships between data entities are inclusive and inclusive, for example, the "Guangzhou demographic data subset" entity is included in "demographic data".
The entity attribute extraction and the relation attribute extraction are used for constructing an entity attribute list and a relation attribute list of the city group operation, and comprise names, definitions, calculation methods, numerical values and forward and reverse directions. For example, the attributes of the index entity include name, definition, calculation method, value, forward and backward directions, and the like. The attribute of the index entity can be the name, definition, value, calculation method, etc. of the index. The attributes of a data entity may be the name, data content, storage format, source, use, subset tag, etc. of the item of data. For the entity attributes of the knowledge graph of the operation state of the urban group, the attributes are mainly extracted from semi-structured databases and unstructured databases such as urban yearbook, government data open platform, relevant research literature of urban group operation and the like.
The difference between the construction of the urban group knowledge graph and the construction of the conventional urban knowledge graph is as follows: the urban group is not an administrative division, but the city is an administrative division;
the urban group is a higher regional level than a city, the urban group comprises independent information of a plurality of cities and the relation of indexes in the cities, and also comprises information laws of round-trip, cooperation, competition, cooperation and the like among the cities, and the urban group itself is regarded as a whole. Whereas the urban knowledge map contains only knowledge of different business areas (traffic, population, education, etc.).
In this embodiment, each entity is determined as a node, the entity relationship is determined as a directed line segment, the attribute corresponding to the entity is set as a node attribute, and an urban group operation state knowledge graph is constructed according to each node, directed line segment and node attribute.
As shown in fig. 4, the system for constructing an urban group operation state knowledge graph according to the second embodiment of the present invention includes: the system comprises a data acquisition module, a time-space data division module, an index frame construction module, a weight calculation module, an operation state index system construction module and a knowledge map construction module;
the data acquisition module is configured to acquire multi-source heterogeneous city group operation data;
the space-time data dividing module is configured to convert the multi-source heterogeneous city group operation data into a city group operation space-time data set; the metropolitan run-time spatio-temporal data set comprises a plurality of spatio-temporal data subsets;
wherein k is 1, 2, …, n; n represents the number of city group operation service fields; i is 1, 2, …, m; j is 1, 2, …, m; m represents the number of cities in the city group; when i ≠ j,
a subset of data representing the mutual flow between city i and city j in the data in the domain of city group k; when the value of i is equal to j,
representing a subset of data within city i in the data of the k domain of the city group;
the index framework construction module is configured to determine a general urban group operation state index based on the spatio-temporal data subset;
the weight calculation module is configured to calculate index weight based on the general urban group operation state index and by combining urban group characteristics;
the operation state index system construction module is configured to represent the city operation state and construct an urban group operation state index system based on the standardized value of the actual index value and each index weight of the city operation index system;
the knowledge graph construction module is configured to extract entities, extract entity relations and extract attributes corresponding to the entities based on the urban group operation state index system and the spatio-temporal data subsets, and construct the urban group operation state knowledge graph
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system for constructing an operating state knowledge graph of an urban group provided in the foregoing embodiment is only illustrated by the division of the foregoing functional modules, and in practical applications, the above functions may be allocated to different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the above described functions. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
An electronic device according to a third embodiment of the present invention is characterized by including: at least one processor; and a memory communicatively coupled to at least one of the processors; the memory stores instructions executable by the processor, and the instructions are used for being executed by the processor to realize the city group operation state knowledge graph construction method.
A computer-readable storage medium according to a fourth embodiment of the present invention is characterized in that the computer-readable storage medium stores computer instructions for being executed by the computer to implement the above-mentioned method for building a knowledge graph of operating states of urban groups.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term 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.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (10)
1. A method for constructing a knowledge graph of an operation state of an urban group is characterized by comprising the following steps:
step S100, obtaining multi-source heterogeneous city group operation data;
step S200, converting the multi-source heterogeneous city group operation data into an empty city group operation data set; the metropolitan run-time spatio-temporal data set comprises a plurality of spatio-temporal data subsets;
wherein k is 1, 2, …, n; n represents the number of city group operation service fields; i is 1, 2, …, m; j is 1, 2, …, m; m represents a city in the city groupThe number of (2); when i ≠ j,
a subset of data representing the mutual flow between city i and city j in the data in the domain of city group k; when the value of i is equal to j,
representing a subset of data within city i in the data of the k domain of the city group;
step S300, constructing a general urban group operation state index framework based on the spatio-temporal data subset;
step S400, calculating each index weight of the urban group operation index system by combining the characteristics of the urban group based on the general urban group operation state index framework and the spatio-temporal data subset;
step S500, expressing the running state of the urban group based on the index framework of the running state of the general urban group and each index weight of the urban group running index system, and constructing the urban group running state index system;
and step S600, based on the city group running state index system and the time-space data set, performing entity extraction, entity relation extraction, entity attribute extraction and relation attribute extraction, and constructing a city group running state knowledge graph.
2. The method of claim 1, wherein the obtaining of multi-source heterogeneous metropolitan area operational data further comprises padding, smoothing, merging, normalizing, and checking consistency of the collected data.
3. The method for constructing the urban grouping operation state knowledge graph according to claim 1, wherein the urban grouping operation state indexes comprise q primary indexes, each primary index is branched with p secondary indexes, each secondary index is provided with a forward label or a reverse label, the urban grouping operation state is better when the forward label represents the larger numerical value, the urban grouping operation state is worse when the reverse label represents the larger numerical value, and p and q are natural numbers.
4. The method for constructing an urban grouping operation state knowledge graph according to claim 3, wherein the specific steps of the step S400 comprise:
step S410, carrying out standardization processing on data with different properties in an operating state index system to obtain a standardized value of the index, wherein the method comprises the following steps:
wherein, IabRepresenting the true value of the b index in the a city,
the normalized value of the b index in the a city is shown, and e represents the base number of the natural logarithm;
step S420, based on the standard value of the index, calculating the contribution value of m cities in the city group to each index:
wherein, PabRepresenting the contribution degree of the a-th city to the b-th index;
step S430, calculating the total contribution amount of m cities in the group to each index based on the contribution values of the cities to the indexes:
wherein S isbRepresenting the total contribution amount of m cities in the city group to the index b;
step S440, calculating each index weight of the city group operation index system based on the total contribution amount of the m cities to all indexes;
wherein, WbAnd (3) representing the weight of an index b in the urban group operation index system, wherein the value of r is 1 when the index is a forward index, otherwise, the value of r is-1, and z represents the total number of urban operation indexes.
5. The method of claim 4, wherein the operating state of the urban grouping is expressed as:
wherein, I'bThe normalized value of the running index b of the city group is represented by the value I of the index bbThe value I of the index b is obtained after standardization treatmentbObtained by performing integrated analysis on the urban grouping operation data.
6. The method for constructing an urban grouping operation state knowledge graph according to claim 1, wherein the specific steps of constructing the urban grouping operation state knowledge graph comprise:
determining each entity as a node, determining the entity relationship as a directed line segment, setting the attribute corresponding to the entity as a node attribute, and constructing an urban group operation state knowledge graph according to each node, the directed line segment and the node attribute.
7. The method of constructing an urban grouping operation state knowledge graph according to claim 1, wherein:
the entity extraction comprises the following specific steps: writing an entity template by a method based on rules and the point, and matching the linguistic data of the urban grouping operation index system by combining a heuristic algorithm to obtain an index entity;
identifying the data entity based on a natural language model and artificial rules by a knowledge extraction method combining manual operation and automatic operation, and extracting knowledge of the time-space data subset to obtain the data entity;
the entity relationship extraction is based on the index entity and the data entity, and the relationship extraction is carried out by a rule-based method; the method comprises the following steps of (1) including the relationship between entities, the relationship between an index entity and a data entity and the relationship between data entities; also includes the relation between the upper and lower indexes;
the entity attribute extraction and the relation attribute extraction are used for constructing an entity attribute list and a relation attribute list of the city group operation, and comprise names, definitions, calculation methods, numerical values and forward and reverse directions.
8. An urban grouping operation state knowledge graph construction system, which is characterized by comprising: the system comprises a data acquisition module, a time-space data division module, an index frame construction module, a weight calculation module, an operation state index system construction module and a knowledge map construction module;
the data acquisition module is configured to acquire multi-source heterogeneous city group operation data;
the space-time data dividing module is configured to convert the multi-source heterogeneous city group operation data into a city group operation space-time data set; the metropolitan run-time spatio-temporal data set comprises a plurality of spatio-temporal data subsets;
wherein k is 1, 2, …, n; n represents the number of city group operation service fields; i is 1, 2, …, m; j is 1, 2, …, m; m represents the number of cities in the city group; when i ≠ j,
a subset of data representing the mutual flow between city i and city j in the data in the domain of city group k; when the value of i is equal to j,
representing a subset of data within city i in the data of the k domain of the city group;
the index frame construction module is configured to construct a general urban group operation state index frame based on the spatio-temporal data subset;
the weight calculation module is configured to calculate index weights by combining characteristics of the urban groups based on the general urban group operation state index framework and the spatio-temporal data subsets;
the operating state index system building module is configured to represent the operating state of the urban group and build an urban group operating state index system based on a general urban group operating state index frame and each index weight of the urban group operating index system;
and the knowledge graph construction module is configured to extract entities, extract entity relationships and extract attributes corresponding to the entities based on the urban group operation state index system and the spatio-temporal data subsets, and construct the urban group operation state knowledge graph.
9. An electronic device, comprising: at least one processor; and a memory communicatively coupled to at least one of the processors; wherein the memory stores instructions executable by the processor for execution by the processor to implement the method of operating state knowledge graph construction for an urban population according to any of claims 1-7.
10. A computer-readable storage medium storing computer instructions for execution by the computer to implement the method of building a city group operating state knowledge graph according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110337746.1A CN112948595B (en) | 2021-03-30 | 2021-03-30 | Urban group running state knowledge graph construction method, system and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110337746.1A CN112948595B (en) | 2021-03-30 | 2021-03-30 | Urban group running state knowledge graph construction method, system and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112948595A true CN112948595A (en) | 2021-06-11 |
| CN112948595B CN112948595B (en) | 2023-04-25 |
Family
ID=76227405
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110337746.1A Active CN112948595B (en) | 2021-03-30 | 2021-03-30 | Urban group running state knowledge graph construction method, system and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112948595B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657848A (en) * | 2021-07-19 | 2021-11-16 | 武汉理工大学 | Industrial three-waste treatment task shunting method, system, device and medium |
| CN113901238A (en) * | 2021-12-07 | 2022-01-07 | 武大吉奥信息技术有限公司 | City physical examination index knowledge graph construction method and system |
| CN114925833A (en) * | 2022-04-20 | 2022-08-19 | 中国人民解放军91977部队 | Target state rule knowledge mining method based on capability data base map |
| CN115099722A (en) * | 2022-08-24 | 2022-09-23 | 自然资源部第三航测遥感院 | Knowledge pedigree-based homeland space planning index model management and application method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160247088A1 (en) * | 2015-02-20 | 2016-08-25 | International Business Machines Corporation | Confidence weighting of complex relationships in unstructured data |
| US20170076206A1 (en) * | 2015-09-16 | 2017-03-16 | International Business Machines Corporation | Cognitive Operations Based on Empirically Constructed Knowledge Graphs |
| CN106934032A (en) * | 2017-03-14 | 2017-07-07 | 软通动力信息技术（集团）有限公司 | A kind of city knowledge mapping construction method and device |
| CN110472066A (en) * | 2019-08-07 | 2019-11-19 | 北京大学 | A kind of construction method of urban geography semantic knowledge map |
| CN111191040A (en) * | 2019-11-13 | 2020-05-22 | 腾讯科技（深圳）有限公司 | City operation knowledge graph construction method and device and computer equipment |
| CN112070304A (en) * | 2020-09-09 | 2020-12-11 | 深圳大学 | City group element interaction measuring method, equipment and storage medium |
| CN112131275A (en) * | 2020-09-23 | 2020-12-25 | 中国科学技术大学智慧城市研究院(芜湖) | Enterprise portrait construction method of holographic city big data model and knowledge graph |
-
2021
- 2021-03-30 CN CN202110337746.1A patent/CN112948595B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160247088A1 (en) * | 2015-02-20 | 2016-08-25 | International Business Machines Corporation | Confidence weighting of complex relationships in unstructured data |
| US20170076206A1 (en) * | 2015-09-16 | 2017-03-16 | International Business Machines Corporation | Cognitive Operations Based on Empirically Constructed Knowledge Graphs |
| CN106934032A (en) * | 2017-03-14 | 2017-07-07 | 软通动力信息技术（集团）有限公司 | A kind of city knowledge mapping construction method and device |
| CN110472066A (en) * | 2019-08-07 | 2019-11-19 | 北京大学 | A kind of construction method of urban geography semantic knowledge map |
| CN111191040A (en) * | 2019-11-13 | 2020-05-22 | 腾讯科技（深圳）有限公司 | City operation knowledge graph construction method and device and computer equipment |
| CN112070304A (en) * | 2020-09-09 | 2020-12-11 | 深圳大学 | City group element interaction measuring method, equipment and storage medium |
| CN112131275A (en) * | 2020-09-23 | 2020-12-25 | 中国科学技术大学智慧城市研究院(芜湖) | Enterprise portrait construction method of holographic city big data model and knowledge graph |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657848A (en) * | 2021-07-19 | 2021-11-16 | 武汉理工大学 | Industrial three-waste treatment task shunting method, system, device and medium |
| CN113657848B (en) * | 2021-07-19 | 2023-08-18 | 武汉理工大学 | Method, system, device and medium for shunting industrial three-waste treatment task |
| CN113901238A (en) * | 2021-12-07 | 2022-01-07 | 武大吉奥信息技术有限公司 | City physical examination index knowledge graph construction method and system |
| CN114925833A (en) * | 2022-04-20 | 2022-08-19 | 中国人民解放军91977部队 | Target state rule knowledge mining method based on capability data base map |
| CN114925833B (en) * | 2022-04-20 | 2023-07-21 | 中国人民解放军91977部队 | Target state rule knowledge mining method based on capability data base graph |
| CN115099722A (en) * | 2022-08-24 | 2022-09-23 | 自然资源部第三航测遥感院 | Knowledge pedigree-based homeland space planning index model management and application method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112948595B (en) | 2023-04-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108710625B (en) | Automatic thematic knowledge mining system and method | |
| CN107092680B (en) | Government affair information resource integration method based on geographic grids | |
| CN112948595B (en) | Urban group running state knowledge graph construction method, system and equipment | |
| Wu et al. | Mapping the knowledge domain of smart city development to urban sustainability: a scientometric study | |
| Li et al. | Notice of retraction: intelligent transportation system in Macao based on deep self-coding learning | |
| CN105183869B (en) | Building knowledge mapping database and its construction method | |
| CN111222661A (en) | Urban planning implementation effect analysis and evaluation method | |
| CN111680025A (en) | Method and system for intelligently assimilating space-time information of multi-source heterogeneous data oriented to natural resources | |
| Chaudhry et al. | Automatic identification of urban settlement boundaries for multiple representation databases | |
| Corrado et al. | Data structuring for the ontological modelling of urban energy systems: The experience of the SEMANCO project | |
| Shang et al. | Estimating building-scale population using multi-source spatial data | |
| CN102508871B (en) | Method for constructing geographic object ontology oriented to disaster event | |
| CN103838825A (en) | Global geographical name data integrating and encoding method | |
| CN115099722B (en) | Knowledge pedigree-based homeland space planning index model management and application method | |
| CN105427001A (en) | Optimal route of school bus of regional middle and primary school | |
| Kuznichenko et al. | Development of a multi-criteria model for making decisions on the location of solid waste landfills | |
| Samadi Alinia et al. | Tehran’s seismic vulnerability classification using granular computing approach | |
| CN112508332B (en) | Gradual rural settlement renovation partitioning method considering multidimensional characteristics | |
| CN115098696A (en) | Method and device for constructing urban physical examination knowledge graph and storage medium | |
| CN114819589A (en) | Urban space high-quality utilization determination method, system, computer equipment and terminal | |
| Duan et al. | Urban flood vulnerability Knowledge-Graph based on remote sensing and textual bimodal data fusion | |
| Vasconcelos et al. | The utility of open-access biodiversity information in representing anurans in the Brazilian Atlantic Forest and Cerrado | |
| Colucci et al. | Minor historical centres ontology enrichment and population: An hamlet case study | |
| CN110096506B (en) | Tree cell structure description and storage method for multi-layer requirements | |
| Du et al. | A novel semantic recognition framework of urban functional zones supporting urban land structure analytics based on open‐source data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |