A method and system for urban governance scheduling based on dynamic evaluation

Abstract

The present invention discloses a method and system for urban governance scheduling based on dynamic evaluation, which relates to the field of urban management and governance technology. The method includes: obtaining the first spatial area divided by the target city and identifying its functional characteristics; analyzing the characteristic information of the area to determine its governance adaptability. If the governance adaptability meets the predetermined threshold, the density database is retrieved for prediction to obtain a first predicted density. The prediction is calibrated according to the dynamic characteristic information of the area to obtain a first actual density; if the actual density is not within the predetermined threshold, the governance scheduling is adjusted. The technical problem that the existing technology cannot respond to the dynamic changes of the environment and personnel flow in a timely manner is solved, and the technical effect of real-time dynamic analysis of the environment and personnel flow is achieved.

Description

Technical Field

[0001] The present application relates to the field of urban management and governance technology, and in particular to a method and system for urban governance scheduling based on dynamic evaluation. Background Art

[0002] With the development of information technology and big data analytics, the role of dynamic assessment and real-time analysis in regional planning is gradually increasing. Against this backdrop, regional planning and scheduling methods based on dynamic assessment have emerged. These methods comprehensively consider multiple factors, such as topography and human activity, to dynamically assess and rapidly adjust governance adaptability. They can also collect and analyze multidimensional information about urban space in real time, providing scientific support for decision-making.

[0003] At the current stage, relevant technologies have the technical problem of being unable to respond to dynamic changes in the environment and personnel flow in a timely manner. Summary of the Invention

[0004] This application provides a method and system for urban governance scheduling based on dynamic evaluation. The method obtains the first spatial area divided by the target city and identifies its functional characteristics; analyzes the characteristic information of the area and determines its governance adaptability. If the governance adaptability meets the predetermined threshold, the density database is retrieved for prediction to obtain a first predicted density. The prediction is calibrated according to the dynamic characteristic information of the area to obtain a first actual density; if the actual density is not within the predetermined threshold, the governance scheduling is adjusted, thereby achieving the technical effect of real-time dynamic analysis of the environment and personnel flow.

[0005] This application provides an urban governance scheduling method based on dynamic evaluation, including:

[0006] Obtain a first spatial area, where the first spatial area refers to any area in the set of spatial areas obtained by dividing the target city, and the first spatial area has an identifier of a first functional characteristic; analyze the collected first area characteristic information of the first spatial area to obtain a first governance fitness of the first spatial area determined as the first functional characteristic; when the first governance fitness meets a predetermined fitness threshold, retrieve the density database to perform density prediction analysis on the first spatial area to obtain a first predicted density; calibrate and analyze the first predicted density according to the first dynamic characteristic information of the first spatial area to obtain a first actual density; when the first actual density is not within the predetermined density threshold, perform governance scheduling adjustment on the first spatial area.

[0007] This application also provides an urban governance scheduling system based on dynamic evaluation, including:

[0008] The first spatial area acquisition module, the first spatial area acquisition module is used to acquire the first spatial area, the first spatial area refers to any area in the set of spatial areas obtained by dividing the target city, and the first spatial area has an identifier of a first functional characteristic; the first governance fitness acquisition module, the first governance fitness acquisition module is used to analyze the collected first area characteristic information of the first spatial area, and obtain the first governance fitness of the first spatial area determined as the first functional characteristic; the first predicted density acquisition module, the first predicted density acquisition module is used to retrieve the density database to perform density prediction analysis on the first spatial area when the first governance fitness meets a predetermined fitness threshold, and obtain a first predicted density; the first actual density acquisition module, the first actual density acquisition module is used to calibrate and analyze the first predicted density according to the first dynamic characteristic information of the first spatial area, and obtain a first actual density; the governance scheduling adjustment module, the governance scheduling adjustment module is used to perform governance scheduling adjustment on the first spatial area when the first actual density is not within the predetermined density threshold.

[0009] This application proposes a method and system for urban governance scheduling based on dynamic assessment. First, the first spatial region of the target city is obtained and its functional characteristics are identified. The characteristic information of the region is analyzed to determine its governance adaptability. If the governance adaptability meets a predetermined threshold, a density database is retrieved for prediction, resulting in a first predicted density. This prediction is calibrated based on the region's dynamic characteristic information to obtain a first actual density. If the actual density is not within the predetermined threshold, governance scheduling adjustments are made, achieving the technical effect of real-time dynamic analysis of the environment and personnel flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0011] Figure 1 A flow chart of a method for urban governance scheduling based on dynamic evaluation provided in an embodiment of the present application.

[0012] Figure 2 A structural diagram of a city governance scheduling system based on dynamic evaluation provided in an embodiment of the present application.

[0013] Explanation of the accompanying drawings: first spatial area acquisition module 10, first governance fitness acquisition module 20, first predicted density acquisition module 30, first actual density acquisition module 40, governance scheduling adjustment module 50. DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0017] The embodiment of the present application provides a method for urban governance scheduling based on dynamic evaluation, such as Figure 1 As shown, the method includes:

[0018] Step S100, obtaining the first spatial area, the first spatial area refers to any one area in the set of spatial areas obtained by dividing the target city, and the first spatial area has an identification of the first functional characteristics. Specifically, first, determine the basis for dividing the target city, and comprehensively consider factors such as geographical features, administrative regions, and functional requirements. Use tools such as geographic information system technology and urban planning software to implement the division operation, such as demarcating different areas based on natural boundaries, administrative boundaries or functional requirements. After the division is completed, a set of spatial areas is obtained, from which an area is selected as the first spatial area, and a first functional characteristic identification is given according to the specific functions and characteristics of the area. For example, a commercial area can be identified as having frequent commercial activities and a large flow of people, a residential area can be identified as having concentrated residents and complete living service facilities, and an industrial area can be identified as having a concentration of factories and enterprises and a large volume of cargo transportation.

[0019] Step S200 analyzes the collected first regional characteristic information of the first spatial area to obtain a first governance fitness for the first spatial area, which is determined as the first functional characteristic. Specifically, the first spatial area's characteristic information types are determined, including topography, traffic conditions, population distribution, buildings, and public service facilities. This information is collected through methods such as geographic information systems, traffic monitoring equipment, and census data. Next, an evaluation index system is established based on the requirements of the first functional characteristic. For example, for commercial areas, factors such as transportation convenience, population density, spending power, and the availability of commercial facilities are considered; for residential areas, factors such as environmental quality, the abundance of educational and medical resources, and the availability of leisure and entertainment facilities are considered. Based on the characteristic indicators, a combination of quantitative and qualitative analysis is used to weight the scores of each indicator and sum them to obtain a final governance fitness score. This fitness score can be presented as a numerical score (e.g., 1-100) or as a categorized scale (e.g., low, medium, high). This analysis yields the first governance fitness and helps determine the fitness level.

[0020] In one possible implementation, the collected first area characteristic information of the first spatial area is analyzed to obtain the first governance adaptability of the first spatial area determined as the first functional characteristic, and step S200 further includes step S210, reading predetermined topographic indicators. Specifically, the content of the predetermined topographic indicators is determined, and it is clarified that the predetermined topographic indicators include specific elements such as altitude, basins, and peaks. The indicators can reflect the natural geographical characteristics of the first spatial area. For example, altitude can affect climate, environment, and architectural design; basin terrain may affect air circulation and drainage systems; and peaks may impose restrictions on transportation routes and landscape planning. Combined with satellite images, topographic maps, and field surveys, satellite images can provide macroscopic topographic features, topographic maps can display detailed information such as contour lines, and field surveys can verify and supplement information from other data sources.

[0021] Step S220 , collecting multi-dimensional features of the first spatial area based on the predetermined terrain and topography indicators to obtain first terrain and topography feature information. Specifically, analyze the impact of altitude on the first spatial area, consider the climate differences between areas at different altitudes. The higher the altitude, the lower the temperature and the lower the air pressure. For example, more heating and insulation measures are needed in high-altitude areas. Evaluate the requirements of altitude on building design. Analyze the impact of altitude on the environment. Vegetation types and animal distribution will be different at different altitudes. Analyze the impact of basin terrain on the first spatial area, consider the impact of basin terrain on air circulation, analyze the restrictions of basin terrain on transportation planning. The mountains around the basin will affect the layout and construction cost of transportation routes, and evaluate the impact of peaks on transportation routes. Mountains will become obstacles to transportation and need to be overcome by building tunnels, bridges and other engineering facilities. Through tools such as geographic information systems (GIS) and remote sensing technology, summarize and organize the analysis results of terrain indicators such as altitude, basins, and peaks to form the first terrain feature information, including descriptions of different terrain features, impact assessments, and identification of potential problems. Historical data, satellite images, and ground field surveys can be combined to help quantify and accurately analyze terrain features.

[0022] Step S230: Read the predetermined flow indicators. Specifically, the content of the predetermined flow indicators is determined, and it is clarified that the predetermined flow indicators include factors such as the flow of people and vehicles, distance, road conditions, and traffic convenience. The indicators can reflect the flow of people and vehicles in the first spatial area. For example, the flow of people reflects the population density and activity intensity of the area; the flow of vehicles can reflect traffic congestion and road carrying capacity; distance can measure the accessibility between different locations; road conditions include factors such as road width and road surface quality; and traffic convenience takes into account factors such as public transportation coverage and traffic light settings. Vehicle flow data is obtained through traffic monitoring equipment.

[0023] Step S240 collects multi-dimensional features of the first spatial area based on the predetermined flow indicators to obtain first flow characteristic information. Specifically, the impact of the flow of people and vehicles in different areas on the region is evaluated by analyzing the temporal distribution, spatial distribution, and flow volume of personnel and vehicle flows. Road conditions, traffic accessibility, and public transportation coverage are then considered to assess their impact on mobility and traffic efficiency. These analysis results are weighted and normalized to comprehensively evaluate the influence of each flow characteristic, resulting in the final first flow characteristic information. This information can be presented in the form of a numerical value, a ranking, or a weighted score. For example, indicators such as personnel flow, vehicle flow, road conditions, and traffic accessibility are assigned different weights and weighted and aggregated based on a preset scoring standard (e.g., a score of 1-10, or a low, medium, or high rating) to ultimately determine the flow characteristic score for the region. This comprehensive analysis provides a comprehensive assessment of personnel flow, vehicle flow, road conditions, and road conditions.

[0024] Step S250: Analyze the first topographic and topographic feature information and the first flow feature information to obtain the first governance adaptability. Specifically, analyze the impact of topographic and topographic features and flow characteristics, compare and analyze the first topographic and topographic feature information and the first flow feature information to find out the relationship and impact between the two. In order to achieve a quantitative assessment of governance adaptability, a governance adaptability assessment model can be established based on the analyzed first topographic and topographic feature information and the first flow feature information. The model can be constructed using the analytic hierarchy process (AHP) or the fuzzy comprehensive evaluation method, combining multi-dimensional analysis factors such as transportation convenience, environmental quality, resource utilization efficiency, etc. to determine the weight and evaluation criteria of each factor. First, construct respective evaluation dimensions based on the first topographic and topographic feature information and the first flow feature information, and assign different weights. Next, use the analytic hierarchy process to compare the degree of influence of different indicators, calculate the relative importance of each factor, and determine the weight coefficient. For the fuzzy comprehensive evaluation method, the evaluation dimensions can be fuzzified by constructing fuzzy sets and membership functions, and the governance adaptability of the first spatial area can be comprehensively evaluated by combining expert judgment and actual data. The comprehensive evaluation of the first spatial area is to input the evaluation results of topography, flow characteristics and other economic factors (such as transportation convenience, environmental resource endowment, etc.) into the governance adaptability evaluation model, and use the weight coefficients of each dimension to perform weighted summation to calculate an overall fitness score.

[0025] In one possible implementation, the first topographic feature information and the first flow feature information are analyzed to obtain the first governance adaptability. Step S250 further includes step S251, calling the city governance database and extracting the first historical governance record in the city governance database. The first historical governance record refers to the urban planning scheduling record for scheduling the governance of the first historical spatial area to the first functional characteristic. Specifically, the role of the urban governance database is clarified, which contains the governance records of different spatial areas in different periods. For example, the database records the planning and scheduling process of a certain area from an industrial area to a commercial area in the past, including information such as the measures taken, the problems encountered, and the final effect. The first historical governance record is extracted, and the urban governance record for scheduling the governance of the first historical spatial area to the same first functional characteristic as the current first spatial area, that is, the first historical governance record, is screened out from the urban governance database.

[0026] Step S252: First historical topographic and relief feature information for the first historical spatial region is compiled based on the predetermined topographic and relief indicators. Specifically, the first historical spatial region is analyzed using previously determined predetermined topographic and relief indicators, such as altitude, basins, and peaks. For example, the topographic and relief features of the first historical spatial region, including the presence of mountains, rivers, and terrain, are determined by consulting historical maps, geographic data, and conducting field surveys. The first historical topographic and relief feature information specifically includes: 1) numerical information, such as the specific altitude of the region, the slope of the terrain, the direction and drainage area of ​​rivers, etc.; 2) hierarchical information, such as whether the region is mountainous, plain, or hilly, and classified as "simple" or "complex" based on terrain complexity; and 3) descriptive information, such as the presence of special geographical phenomena (e.g., areas with abundant water resources or areas susceptible to flooding). Finally, the analyzed topographic and relief feature information is organized and summarized to form the first historical topographic and relief feature information.

[0027] Step S253: Perform a similarity analysis on the first topographic and relief feature information and the first historical topographic and relief feature information to obtain a first situation similarity. Specifically, feature information comparison is performed, where the first topographic and relief feature information of the current first spatial region is compared with the first historical topographic and relief feature information of the first historical spatial region one by one. For example, the two are compared in terms of altitude range, presence of basins or peaks, and other features. Similarity calculation is performed, and appropriate similarity calculation methods, such as cosine similarity and Euclidean distance, are used to quantify the two topographic and relief feature information. The first historical topographic and relief feature information is obtained by quantifying the topographic features of the current first spatial region and the first historical spatial region, including altitude, terrain type, slope, and other aspects. Altitude can be represented by the difference between the maximum altitude and the minimum altitude, the terrain type can be encoded as different numerical values, and the slope is quantified by calculating the average slope of the area. The quantified terrain feature data is converted into a feature vector. The first situation similarity uses a similarity calculation method such as cosine similarity or Euclidean distance to compare the terrain features of the current area and the historical area. The first situation similarity is obtained by calculating the angle or distance between the feature vectors of the two. The first situation similarity reflects the similarity between the current area and the historical area in terrain features. The larger the value or the smaller the distance, the higher the similarity between the two.

[0028] In step S254, when the first situation similarity meets a predetermined similarity threshold, the first historical governance difficulty level is extracted from the first historical governance record. Specifically, the first historical governance difficulty level refers to a comprehensive assessment of the various difficulties and challenges encountered when implementing governance measures in a specific spatial area during past governance processes. This difficulty level includes, but is not limited to, the following categories: 1) Geographic and environmental factors, such as complex terrain, extreme weather conditions, and environmental protection requirements within the region, which may increase project costs or delay implementation; 2) Social factors, such as demographic changes, population structure, and industrial development; and 3) Economic and technical challenges, such as excessive construction costs, technical difficulties, and infrastructure bottlenecks. Each difficulty level can be represented using a numerical scoring scale, typically using a scale from 1 to 5 or a specific quantitative indicator (such as the percentage of increased construction costs or the length of delay) to indicate the severity of the difficulty. Finally, a weighted summation is used to produce a comprehensive value, which serves as the first historical governance difficulty level.

[0029] Step S255: Take the inverse of the first historical governance difficulty as the first governance situation coupling degree. Specifically, determine the relationship between difficulty and coupling degree. Difficulty and coupling degree are inversely proportional, that is, the higher the governance difficulty, the lower the governance situation coupling degree; conversely, the lower the governance difficulty, the higher the governance situation coupling degree. For example, if the first historical governance difficulty is 0.6, then the first governance situation coupling degree is 1 / 0.6≈1.67. To determine the first governance situation coupling degree, calculate the inverse of the first historical governance difficulty to obtain the first governance situation coupling degree. The coupling degree reflects the governance similarity and reference value between the current first spatial region and the first historical spatial region in terms of topography and terrain. For example, if the first governance situation coupling degree is high, it means that the current region is similar to the historical region in terms of topography and terrain.

[0030] Step S256, performing variation weighting processing on the multiple flow characteristic parameters in the first flow characteristic information to obtain the first governance flow coupling degree. Specifically, multiple key flow characteristic parameters are selected from the first flow characteristic information, such as personnel flow, vehicle flow, traffic distance, traffic convenience, etc. For example, the personnel flow is expressed by the number of people passing through the area every day, and the vehicle flow is expressed by the number of vehicles passing through per hour. Different weights are assigned to each parameter based on the importance and degree of change of the flow characteristic parameters. The basis for determining the weight includes the relative importance of the flow characteristic to the current regional governance goals and the difference in changes compared with historical areas. For example, if the degree of traffic convenience has a greater impact on the area and there are significant differences from the historical area, a higher weight can be assigned to the parameter. Through weighted calculation, the values ​​of the flow characteristic parameters are combined to obtain the first governance flow coupling degree, that is, the comprehensive coupling degree between the flow characteristics and the governance effect.

[0031] Step S257: Take the average of the first governance situation coupling degree and the first governance flow coupling degree as the first governance fitness. Specifically, taking into account the topography and flow characteristics, the first governance situation coupling degree reflects the governance similarity in topography, and the first governance flow coupling degree reflects the governance similarity in flow characteristics. Taking the average of the two can comprehensively consider these two factors to obtain the first governance fitness. For example, if the first governance situation coupling degree is 1.67 and the first governance flow coupling degree is 1.2, then the first governance fitness is (1.67+1.2) / 2=1.435, and the first governance fitness is determined. The first governance fitness can be used to measure the feasibility and adaptability of the current first spatial area as the first functional characteristic of the governance. The higher the fitness, the more similar the area is to the historically successful governance area in terms of topography and flow characteristics, and the difficulty of governance is relatively low.

[0032] Step S300, when the first governance fitness meets the predetermined fitness threshold, the density database is retrieved to perform density prediction analysis on the first spatial area to obtain the first predicted density. Specifically, first, the predetermined fitness threshold is determined according to the target and various factors, and the calculated first governance fitness is compared with it. If it is greater than or equal to the threshold, relevant data is retrieved from the density database according to the identification and functional characteristics of the first spatial area. The database contains historical density records and related influencing factor records of multiple historical spatial areas consistent with the first functional characteristics, and the data structure is organized by time, regional identification, etc. Next, a suitable prediction method is selected, such as time series analysis, regression analysis or machine learning algorithm, and the data is preprocessed, including cleaning missing values ​​and outliers, standardization and feature engineering. Then, the selected method and preprocessed data are used for prediction, such as predicting future density by inputting time and historical data into a time series model. Finally, the first predicted density including personnel predicted density and vehicle predicted density is obtained, which can be expressed as a numerical value or level.

[0033] In one possible implementation, when the first governance fitness meets a predetermined fitness threshold, a density database is retrieved to perform density prediction analysis on the first spatial area to obtain a first predicted density. Step S300 further includes step S310, extracting a first density time series from the density database, wherein the density database includes historical density record data for multiple historical spatial areas consistent with the first functional characteristics, and the first density time series includes a first vehicle density time series and a first personnel density time series. Specifically, the density database is a collection of historical density records for spatial areas associated with different functional characteristics, covering personnel and vehicle density data for multiple historical spatial areas at different time points. This data is crucial for predicting the density of a first spatial area with the same functional characteristics. For example, for the functional characteristics of a city's commercial district, the database contains personnel and vehicle density data for multiple mature commercial districts at different time periods, including weekdays, holidays, and seasonal changes. By extracting the first density time series corresponding to the historical density record data, the historical changes in the number of vehicles and personnel in the relevant areas over time are recorded. The first vehicle density time series is collected through traffic monitoring equipment and parking records, while the first person density time series is obtained through census data, pedestrian flow monitoring equipment, and mobile device location data. Based on this time series data, using a prediction algorithm (such as time series analysis or regression models), the first predicted density is the prediction of the density of people and vehicles in the area over a period of time in the future based on historical density data.

[0034] Step S320: activating the intelligent prediction model, which includes a vehicle prediction layer and a personnel prediction layer.Specifically, an intelligent prediction model is constructed to collect data related to vehicle and personnel density. The collected data include but are not limited to: traffic flow data in different time periods, geometric characteristics of roads, land use types in surrounding areas, weather conditions. For personnel density prediction, the collected data include: demographic information in the area, building function information, time characteristics, public transportation routes and station information, and event arrangements. The collected data are sorted and cleaned, outliers and missing values ​​are removed, and the quality and integrity of the data are ensured. Feature engineering processing is performed on the sorted data to extract useful features to improve the prediction ability of the model. In terms of vehicle density, time features are constructed, such as dividing a day into different time periods and a week into work hours. For weekdays and weekends, the time information is quantified and encoded so that the model can learn the impact of different time periods on vehicle density, extract spatial features, such as calculating the road congestion index, road connectivity, etc., and classify and encode weather conditions, such as sunny days, cloudy days, rainy days, snowy days, etc., represented by different numerical values. In terms of population density, a population feature vector is constructed, including statistics such as the mean and standard deviation of the age distribution, the proportion of occupational structure, etc., and the commercial activity index and educational resource concentration are calculated based on the building function information. Activity events are quantified, such as setting different levels according to the scale of the activity and the expected number of participants. According to the characteristics of the data and the requirements of the prediction task, the appropriate model algorithm is selected. For vehicle density prediction, time series is used Analytical algorithms such as ARIMA (autoregressive integrated moving average model) can capture the trend, seasonality and cyclical characteristics in time series data; for personnel density prediction, classification and regression algorithms based on machine learning are used. For example, the decision tree algorithm branches according to different feature conditions to build a personnel density prediction model with good interpretability. The sorted data set is divided into training set, validation set and test set. The training set is used for model learning and parameter adjustment. The validation set is used to evaluate the performance of the model and select the best hyperparameters during the training process. The test set is used to finally evaluate the generalization ability of the model. Taking vehicle density prediction as an example, the LSTM model is used. First, the parameters of the model are initialized and the first vehicle in the training set is Multi-domain feature information is input into the LSTM model in sequence according to time. The model continuously adjusts parameters through the back-propagation algorithm to minimize the error between the predicted vehicle density and the actual vehicle density. For personnel density prediction, similarly, the multi-domain feature information of the first person in the training set is input into the selected model for training. For example, in decision tree training, the model continuously splits according to different feature values ​​to construct a tree structure so that each leaf node corresponds to the same personnel density level or a similar range of personnel numbers as much as possible. By continuously adjusting the splitting conditions and parameters, the prediction error of the model on the training set is minimized, and it is verified and optimized on the validation set. The trained model is finally evaluated using the test set.

[0035] In step S330, the vehicle prediction layer analyzes the first vehicle multi-domain feature information of the first vehicle density time series to obtain a first predicted vehicle density. Specifically, the first vehicle multi-domain feature information is comprehensive data used to describe vehicle density-related features, which is obtained through the previous feature engineering processing, including time features, spatial features, traffic-related features, and weather features. The vehicle prediction layer uses the trained vehicle density prediction model to analyze the first vehicle multi-domain feature information. For example, if an LSTM model is used, the first vehicle multi-domain feature information at the current moment is input into the model. The model will calculate the vehicle density prediction value for the future time period based on the previously learned time series pattern and feature relationship, that is, the first predicted vehicle density. The LSTM model can effectively process the long-term dependencies in the time series data through its internal memory unit and gating mechanism, accurately capture the changing trend of vehicle density over time and the influence of various factors on it. During the prediction process, the model will gradually update its internal state based on the input feature information, and finally output the prediction result.

[0036] Step S340: The first personnel multi-domain feature information of the first personnel density time series is analyzed by the personnel prediction layer to obtain the first predicted personnel density. Specifically, the first personnel multi-domain feature information is also data related to personnel density that has been processed by feature engineering, including time features, spatial features, demographic features, public transportation features, and activity event features. The personnel prediction layer processes the first personnel multi-domain feature information based on the trained personnel density prediction model. Taking the random forest model as an example, the first personnel multi-domain feature information is input into multiple decision trees for parallel prediction. Each decision tree will branch and judge according to different combinations of features, and finally obtain a prediction result of personnel density. Then, by synthesizing the prediction results of multiple decision trees, the final first predicted personnel density is obtained. The random forest model can reduce the overfitting risk of a single decision tree and improve the accuracy and stability of the prediction by integrating the prediction capabilities of multiple decision trees.

[0037] In step S350, the first predicted vehicle density and the first predicted person density together constitute the first predicted density. Specifically, the first predicted vehicle density obtained by the vehicle prediction layer and the first predicted person density obtained by the person prediction layer are combined. The two prediction results reflect the density of the first spatial area from the perspectives of vehicles and people, respectively, and together constitute the first predicted density. They can be simply combined into a vector form, such as [first predicted vehicle density, first predicted person density], or a comprehensive density index can be calculated based on actual needs, such as taking a weighted sum of the number of vehicles and people to obtain a single value to represent the first predicted density. The first predicted density can be represented and applied in various ways, depending on actual needs. In terms of traffic management, if the first predicted density of the first spatial area is predicted to be high during a certain time period, the traffic management department can formulate a traffic diversion plan in advance, such as adjusting the time configuration of traffic lights, increasing the number of traffic police on duty, and guiding vehicle diversion, to alleviate traffic congestion. At the same time, based on the first predicted vehicle density, the use of parking lots can be rationally planned, sufficient parking spaces can be reserved in advance, or vehicles can be directed to vacant parking lots in the surrounding area.

[0038] In one possible implementation, an intelligent prediction model is activated, the intelligent prediction model including a vehicle prediction layer and a personnel prediction layer, and step S320 further includes step S321, reading a predetermined unit time zone. Specifically, the predetermined unit time zone is determined. The selection of the predetermined unit time zone needs to be determined based on the specific research object and prediction target. Different unit time zones are suitable for different scenarios and analysis requirements. If the weekly cyclical changes in the area are being studied, such as the difference between weekends and weekdays, then a weekly time zone is more appropriate. The density characteristics of different days of the week are observed. For some long-term trend analysis, a monthly or quarterly time zone is more helpful in discovering seasonal or cyclical patterns.

[0039] Step S322, slicing the first density time series with the predetermined unit time zone as a constraint to obtain a first time series slice set. Specifically, the first density time series is a record sequence of changes in the density of people and vehicles in the first spatial area over time, which contains density data arranged in chronological order and is recorded in smaller time intervals such as hours and minutes. For example, if it is the first density time series data of a month, there is a corresponding record of people density and vehicle density every hour, forming a long sequence of time data. The first density time series is divided into multiple segments with the predetermined unit time zone as a constraint. If the predetermined unit time zone is a day, then 24 consecutive hours are taken as a slice, and the entire first density time series is divided into multiple day segments. If it is a week, 7 consecutive days are taken as a slice. For the monthly unit time zone, the data within a month is taken as a slice to obtain a set consisting of multiple time series slices, i.e., the first time series slice set, each slice represents the density time series data within a specific unit time zone.

[0040] Step S323, extract the first slice in the first time series slice set, where the first slice refers to the first density time series segment. Specifically, a slice is extracted from the first time series slice set as an example for further analysis and processing. The slice is called the first slice, which is part of the first density time series segment. The selection of the first slice can be used for subsequent model construction and data analysis demonstration. The processing method for the first slice can be extended to the entire first time series slice set to achieve unified processing and analysis of data in all time periods. The first slice is extracted from the first time series slice set by indexing or random selection. If it is processed sequentially, the first slice can be used as the first slice; or a slice can be randomly selected for analysis to verify the versatility and stability of the processing method.

[0041] Step S324, construct a first data group based on the multi-domain feature information of the first segment of the first density time series segment and the first density mode group of the first density time series segment. Specifically, the multi-domain feature information of the first segment is a feature description of the first density time series segment, which contains multiple aspects of information, used to reflect the relevant factors of density changes in the time period, time characteristics, including the specific time range to which the segment belongs, the classification of time periods, spatial characteristics, involving the geographical location characteristics of the area, the distribution and function of buildings in the area, historical trend characteristics, analysis of the density change trend of the segment in similar time periods in historical data, external factor characteristics, including weather conditions, and organize and quantify the multi-domain feature information to form a feature vector or data set for subsequent data group formation, for the first density time series segment. The mode of the personnel density and vehicle density data in the data set is calculated respectively. The mode is the data value that appears the most times in a set of data, reflecting the most common density level in the time period. A similar calculation is performed for the vehicle density. The first fragment of multi-domain feature information and the first density mode are combined to form a first data group. The multi-domain feature information is used as the input variable and the density mode is used as the output variable to construct a data pair form similar to (x, y), where x is the multi-domain feature information vector and y is the corresponding density mode. For example, x includes time features, spatial features, and external factor features, and y is the personnel density mode or the vehicle density mode in the time period.

[0042] Step S325: supervised learning is performed on the first data set to obtain the intelligent prediction model. Specifically, according to the characteristics of the data and the requirements of the prediction task, a suitable supervised learning algorithm is selected. For density prediction problems, if the data has a linear relationship and the features are relatively simple, a linear regression algorithm can be selected. If the data has a complex nonlinear relationship and multiple feature variables, a neural network can be selected. Before supervised learning, some preprocessing operations need to be performed on the first data set, for example, standardizing or normalizing the feature data so that the numerical ranges of different features are on a similar scale to improve the training efficiency and accuracy of the model. For classification problems, if the features are text or category data, encoding processing is required to convert them into numerical form, and the selected supervised learning algorithm is used to perform the first data set. Training, taking the feature information (x) in the first data group as input and the density mode (y) as the target output, allowing the model to learn the relationship between them, initializing the structure and parameters of the neural network, inputting the data in the first data group into the neural network in batches, calculating the predicted value through forward propagation, performing back propagation based on the error between the predicted value and the actual value, adjusting the parameters of the neural network, and after multiple iterative training until the model error reaches a smaller level or meets the predetermined training stop condition, evaluating the performance of the model, and obtaining a model with better performance, namely the intelligent prediction model, which can predict the density mode of people or vehicles in the area within a specific time period based on the relevant feature information of the input first spatial area.

[0043] In one possible implementation, a first data group is constructed based on the multi-domain feature information of the first segment of the first density time series segment and the first density mode group of the first density time series segment, and step S324 further includes step S3241, extracting the first vehicle density time series segment in the first density time series segment. Specifically, the first density time series segment is a data subset of a time period divided from the first density time series according to a predetermined unit time zone, and contains the density information of people and vehicles in the time period. The segment is the basis for further analyzing the density characteristics of vehicles and people. For example, if the predetermined unit time zone is one day, then the first density time series segment is the data on the changes in the density of people and vehicles over time within this day. The data part specifically about vehicle density is separated from the first density time series segment, that is, the first vehicle density time series segment. Because vehicle density has its own unique characteristics and patterns, it needs to be analyzed separately and extracted through data identification or indexing. Assuming that the first density time series segment is stored in the form of a two-dimensional array or data table, where one column represents personnel density and the other column represents vehicle density, then the first vehicle density time series segment can be extracted by selecting the data corresponding to the vehicle density column.

[0044] Step S3242: Time domain features are collected for the first vehicle density time series segment to obtain time domain feature information for the first vehicle segment. Specifically, time domain features directly reflect the changes in vehicle density over time. These features can help us understand the aggregation and dispersion patterns of vehicles in different time periods. The average value of the vehicle density data in the first vehicle density time series segment is calculated. The average value reflects the overall level of vehicle density within that time period. For example, if the average value of vehicle density data within a day is high, it indicates that the overall vehicle density on that day is relatively dense. The maximum value in the first vehicle density time series segment, i.e., the peak value of vehicle density, is found. The peak value may occur during rush hour. The standard deviation of the vehicle density data is calculated. The standard deviation measures the degree of data dispersion. A large standard deviation indicates that the vehicle density has changed significantly within that time period, possibly indicating large fluctuations in traffic flow. The trend of vehicle density over time is observed to determine whether it is gradually increasing, decreasing, or remaining relatively stable. This trend is roughly described by fitting a trend line. For example, linear regression is used to fit a straight line to represent the long-term trend of vehicle density. The time domain feature information is integrated to form the time domain feature information for the first vehicle segment.

[0045] Step S3243 performs frequency domain feature collection on the first vehicle density spectrum obtained by fast Fourier transforming the first vehicle density time series segment to obtain frequency domain feature information of the first vehicle segment. Specifically, a fast Fourier transform (FFT) converts the first vehicle density time series segment in the time domain to the frequency domain to obtain a first vehicle density spectrum. In the frequency domain, the frequency components of the vehicle density data can be analyzed to discover some patterns and features that are not obvious in the time domain. For example, certain periodic traffic patterns may appear as peaks at specific frequencies in the frequency domain. The periodicity is related to daily commuting patterns, weekly weekday and weekend cycles, etc. In the first vehicle density spectrum, the frequency component with the highest energy, i.e., the dominant frequency, is found. The dominant frequency reflects the most important periodic changes in the vehicle density data.

[0046] In step S3244, the time-domain feature information of the first vehicle segment and the frequency-domain feature information of the first vehicle segment are combined to form multi-domain feature information of the first vehicle segment. Specifically, the time-domain feature information and the frequency-domain feature information are combined to more comprehensively describe the characteristics of the first vehicle density time series segment. The time-domain features reflect the direct changes in vehicle density over time, while the frequency-domain features reveal the periodicity and frequency components therein. The two complement each other and can provide richer information for subsequent analysis and model building. The previously obtained time-domain feature information of the first vehicle segment and the frequency-domain feature information of the first vehicle segment are combined to form a comprehensive feature set, namely, the multi-domain feature information of the first vehicle segment. The multi-domain feature information can be represented by a data structure, such as a vector or structure containing time-domain and frequency-domain feature values.

[0047] Step S3245, obtaining the multi-domain feature information of the first personnel segment of the first personnel density time series segment in the first density time series segment. Specifically, for the personnel density portion in the first density time series segment, an analysis method similar to that for the vehicle density is adopted. First, the first personnel density time series segment is extracted, and then time domain features and frequency domain features are collected respectively. The time domain features include the mean, peak value, valley value, standard deviation, and trend of the personnel density. The frequency domain features are obtained by performing a fast Fourier transform on the personnel density time series segment and analyzing the features, such as the main frequency, bandwidth, and harmonic components. The time domain and frequency domain features are integrated to obtain the multi-domain feature information of the first personnel segment. The representation method of the multi-domain feature information is also similar to that of the first vehicle segment, and can be a vector or structure containing various feature values.

[0048] In step S3246, the multi-domain feature information of the first vehicle segment and the multi-domain feature information of the first person segment are combined to form the multi-domain feature information of the first segment. Specifically, the multi-domain feature information of the first segment is intended to comprehensively describe the overall situation of the first density time series segment, including density characteristics of both vehicles and people. The flow of vehicles and people is interrelated and needs to be considered simultaneously to more accurately analyze and predict the density of the area. The multi-domain feature information of the first vehicle segment and the multi-domain feature information of the first person segment are combined to form a more comprehensive feature set, namely the multi-domain feature information of the first segment.

[0049] Step S400, calibrate and analyze the first predicted density based on the first dynamic feature information of the first spatial area to obtain the first actual density. Specifically, first, obtain the first dynamic feature information of the first spatial area through multiple channels, including sensor data, mobile device data, social media data, and real-time event data, and integrate the data from different sources to form a data set. Then, analyze the impact of the dynamic feature information on the first predicted density, such as the impact of changes in traffic flow and personnel flow. Then, establish a calibration model or method. Input the first dynamic feature information into the calibration model or process it according to the rules, adjust the first predicted density, obtain the calibrated vehicle density and personnel density, and then integrate them into the first actual density according to actual needs. Finally, verify and evaluate the accuracy and rationality of the first actual density by comparing with the actual observation data and analyzing the time series. If the error is large or does not conform to the trend of change, the entire process needs to be adjusted and optimized.

[0050] In a possible implementation, the first predicted density is calibrated and analyzed based on the first dynamic feature information of the first spatial area to obtain a first actual density. Step S400 further includes step S410, analyzing the first dynamic feature information to obtain a first dynamic density time series. Specifically, the first dynamic feature information includes various data about the real-time changes in the first spatial area, covering multiple aspects of information, such as the real-time flow of people and vehicles, the real-time changes in traffic conditions, etc., which are continuously updated over time and can reflect the current dynamic state of the first spatial area; the real-time flow of vehicles can be obtained through traffic monitoring equipment, such as the number of vehicles, vehicle speed, road occupancy, etc.; changes in traffic conditions include whether the road is congested, the increase or decrease in traffic flow, etc.; information related to the density of people and vehicles is extracted from the first dynamic feature information. For people, based on the data from the pedestrian flow sensor, the density of people at different time points and in different areas is calculated. For vehicles, based on the data from the traffic monitoring equipment, the density of vehicles in different sections and time periods on the road is calculated. The information on the density of people and vehicles that changes with time is integrated to form a first dynamic density time series. The time series records the dynamic changes in the density of people and vehicles in the first spatial area over time, and is a density data sequence arranged in chronological order.

[0051] Step S420, based on the principle of spectral density function, a trend line analysis is performed on the first dynamic density time series to obtain the first future density at the first future time. Specifically, the spectral density function is used to analyze the frequency characteristics of time series data and identify the periodic and trend components in the data. For the personnel density time series, the periodic law can reflect changes such as peak periods; for the vehicle density time series, there is a similar peak law. By fitting the results of the spectral density function, a linear regression model is selected as the trend line analysis method. Linear regression fits the historical density data to obtain the best fitting straight line, and uses this straight line to make a prediction to obtain the first future density. The specific prediction time point is determined according to actual needs and data resolution. By substituting the future time point into the fitted linear regression equation, the corresponding personnel and vehicle density values ​​are calculated to obtain the first future density.

[0052] Step S430: Adjust the first predicted density based on the first correspondence between the first future time and the first future density to obtain the first actual density. Specifically, since the first predicted density is based on historical data and model prediction, and the first future density obtained by trend line analysis is a future value predicted based on the current dynamic change trend of the first spatial area, in order to obtain a more accurate first actual density, it is necessary to perform a weighted average of the two to determine a weight coefficient. ( 1), used to balance the contribution of the first predicted density and the first future density, for example, if =0.7, it means that it is more inclined to believe the first prediction density, while =0.3 means that more emphasis is placed on the first future density obtained based on trend line analysis. Assuming that the first predicted density is (including the predicted population density and vehicle density), the first future density is (also including the future density of people and vehicles), the adjustment for the actual density of people is calculated as follows: Actual density of people = .in is the first prediction of population density, is the first future population density. For the adjustment of the actual vehicle density, the calculation formula is: actual vehicle density = .in is the first prediction of population density, It is the first future population density. Through the above calculation, the first predicted density and the first future density obtained based on trend line analysis are integrated to obtain the first actual density that is more in line with the actual situation of the first spatial area. The first actual density not only takes into account the prediction results of historical data and models, but also combines the current dynamic change trend for future predictions.

[0053] Step S500: When the first actual density is not within the predetermined density threshold, the management and scheduling of the first spatial area are adjusted. Specifically, first, the predetermined density threshold is determined. This requires comprehensive consideration of factors such as the functional characteristics, resource carrying capacity, and urban planning objectives of the first spatial area. It is set by analyzing historical data, expert evaluations, and urban development plans, and different standards are applied to different functional areas. Then, the first actual density is evaluated, which is obtained by analyzing the dynamic characteristic information of the area to reflect the actual congestion and flow conditions. The actual density of people and vehicles is measured in various ways and compared with the predetermined threshold. When the first actual density is not within the threshold, scheduling adjustments are made. In terms of traffic management, this includes traffic flow control, such as dynamic adjustment of traffic light timing and other public transportation optimization. In terms of public facility configuration, there are parking space planning and public service facility adjustments. In terms of personnel evacuation guidance, guidance signs and information releases are set up. Emergency drills and training are also conducted to enhance personnel's response capabilities and safety awareness.

[0054] In the above, refer to Figure 1 A method for urban governance scheduling based on dynamic evaluation according to an embodiment of the present invention is described in detail. Figure 2 An urban governance scheduling system based on dynamic evaluation according to an embodiment of the present invention is described.

[0055] According to an embodiment of the present invention, a dynamic assessment-based urban governance and scheduling system is used to address the technical problem that existing technologies cannot promptly respond to dynamic changes in the environment and personnel flow, thereby achieving the technical effect of real-time dynamic analysis of the environment and personnel flow. The dynamic assessment-based urban governance and scheduling system includes: a first spatial area acquisition module 10, a first governance fitness acquisition module 20, a first predicted density acquisition module 30, a first actual density acquisition module 40, and a governance and scheduling adjustment module 50.

[0056] The first spatial region acquisition module 10 is used to acquire a first spatial region, where the first spatial region refers to any region in a set of spatial regions obtained by dividing the target city, and the first spatial region has an identifier of a first functional characteristic.

[0057] The first governance fitness acquisition module 20 is used to analyze the collected first area feature information of the first spatial area to obtain the first governance fitness of the first spatial area determined as the first functional characteristic.

[0058] The first predicted density acquisition module 30 is used to retrieve the density database to perform density prediction analysis on the first spatial area to obtain a first predicted density when the first governance fitness meets a predetermined fitness threshold.

[0059] The first actual density acquisition module 40 is configured to perform calibration analysis on the first predicted density according to the first dynamic feature information of the first spatial region to obtain a first actual density.

[0060] The governance scheduling adjustment module 50 is used to perform governance scheduling adjustment on the first spatial area when the first actual density is not within a predetermined density threshold.

[0061] The specific configuration of the first governance fitness acquisition module 20 will be described in detail below. As described above, the collected first regional feature information of the first spatial region is analyzed to obtain the first governance fitness of the first spatial region determined as the first functional characteristic. The first governance fitness acquisition module 20 further includes: a predetermined terrain and terrain indicator reading unit, the predetermined terrain and terrain indicator reading unit is used to read the predetermined terrain and terrain indicator; a first terrain and terrain feature information acquisition unit, the first terrain and terrain feature information acquisition unit is used to collect multi-dimensional features of the first spatial region based on the predetermined terrain and terrain indicator to obtain first terrain and terrain feature information; a predetermined flow indicator reading unit, the predetermined flow indicator reading unit is used to read the predetermined flow indicator; a multi-dimensional feature collection unit, the multi-dimensional feature collection unit is used to collect multi-dimensional features of the first spatial region based on the predetermined flow indicator to obtain first flow feature information; a first governance fitness acquisition unit, the first governance fitness acquisition unit is used to analyze the first terrain and terrain feature information and the first flow feature information to obtain the first governance fitness.

[0062] Wherein, the first topographic and terrain feature information and the first flow feature information are analyzed to obtain the first governance fitness, and the first governance fitness acquisition unit further includes: an urban governance database retrieval subunit, the urban governance database retrieval subunit is used to retrieve the urban governance database and extract the first historical governance record in the urban governance database, the first historical governance record refers to the urban governance record of scheduling the governance of the first historical spatial area as the first functional characteristic; a first historical topographic and terrain feature information assembling subunit, the first historical topographic and terrain feature information assembling subunit is used to assemble the first historical topographic and terrain feature information of the first historical spatial area based on the predetermined topographic and terrain indicators; a first situation similarity acquisition subunit, the first situation similarity acquisition subunit is used to compare the first topographic and terrain feature information with the first historical topographic and terrain feature information. The information is analyzed for similarity to obtain a first situation similarity; a first historical governance difficulty extraction subunit is used to extract the first historical governance difficulty in the first historical governance record when the first situation similarity meets a predetermined similarity threshold; a first governance situation coupling degree acquisition subunit is used to take the inverse of the first historical governance difficulty as the first governance situation coupling degree; a variation weighted processing subunit is used to perform variation weighted processing on multiple flow feature parameters in the first flow feature information to obtain a first governance flow coupling degree; a first governance fitness acquisition subunit is used to take the average of the first governance situation coupling degree and the first governance flow coupling degree as the first governance fitness degree.

[0063] The specific configuration of the first predicted density acquisition module 30 will be described in detail below. As described above, when the first governance fitness meets the predetermined fitness threshold, the density database is retrieved to perform density prediction analysis on the first spatial area to obtain the first predicted density. The first predicted density acquisition module 30 further includes: a first density time series extraction unit, the first density time series extraction unit is used to extract the first density time series in the density database, wherein the density database includes historical density record data of multiple historical spatial areas consistent with the first functional characteristics, the first density time series includes a first vehicle density time series and a first personnel density time series; an intelligent prediction model activation unit, the intelligent prediction model activation unit is used to activate the intelligent prediction model, the intelligent The prediction model includes a vehicle prediction layer and a personnel prediction layer; a first predicted vehicle density acquisition unit, the first predicted vehicle density acquisition unit is used to analyze the first vehicle multi-domain feature information of the first vehicle density time series through the vehicle prediction layer to obtain a first predicted vehicle density; a first personnel multi-domain feature information analysis unit, the first personnel multi-domain feature information analysis unit is used to analyze the first personnel multi-domain feature information of the first personnel density time series through the personnel prediction layer to obtain a first predicted personnel density; a first predicted density composition unit, the first predicted density composition unit is used to combine the first predicted vehicle density and the first predicted personnel density to form the first predicted density.

[0064] Among them, the intelligent prediction model is activated, and the intelligent prediction model includes a vehicle prediction layer and a personnel prediction layer. The intelligent prediction model activation unit further includes: a predetermined unit time zone reading subunit, and the predetermined unit time zone reading subunit is used to read the predetermined unit time zone; a first time series slice set acquisition subunit, and the first time series slice set acquisition subunit is used to slice the first density time series with the predetermined unit time zone as a constraint to obtain a first time series slice set; a first slice extraction subunit, and the first slice extraction subunit is used to extract the first slice in the first time series slice set, and the first slice refers to the first density time series segment; a first data group assembly subunit, and the first data group assembly subunit is used to construct a first data group based on the multi-domain feature information of the first segment of the first density time series segment and the first density mode group of the first density time series segment; a data supervised learning subunit, and the data supervised learning subunit is used to perform supervised learning on the first data group to obtain the intelligent prediction model.

[0065] Among them, based on the first segment multi-domain feature information of the first density time series segment and the first density mode group of the first density time series segment, a first data group is constructed, and the first data group construction sub-unit further includes: a density time series segment extraction micro-unit, the density time series segment extraction micro-unit is used to extract the first vehicle density time series segment in the first density time series segment; a time domain feature collection micro-unit, the time domain feature collection micro-unit is used to collect time domain features of the first vehicle density time series segment to obtain the time domain feature information of the first vehicle segment; a frequency domain feature collection micro-unit, the frequency domain feature collection micro-unit is used to collect frequency domain features of the first vehicle density spectrum obtained by fast Fourier transforming the first vehicle density time series segment The first vehicle segment frequency domain characteristic information; the first vehicle segment multi-domain characteristic information composition micro-unit, the first vehicle segment multi-domain characteristic information composition micro-unit is used for the first vehicle segment time domain characteristic information and the first vehicle segment frequency domain characteristic information to form the first vehicle segment multi-domain characteristic information; the first personnel segment multi-domain characteristic information acquisition micro-unit, the first personnel segment multi-domain characteristic information acquisition micro-unit is used to obtain the first personnel segment multi-domain characteristic information of the first personnel density time series segment in the first density time series segment; the first segment multi-domain characteristic information composition micro-unit, the first segment multi-domain characteristic information composition micro-unit is used for the first vehicle segment multi-domain characteristic information and the first personnel segment multi-domain characteristic information to form the first segment multi-domain characteristic information.

[0066] The specific configuration of the first actual density acquisition module 40 will be described in detail below. As described above, the first predicted density is calibrated and analyzed according to the first dynamic feature information of the first spatial area to obtain the first actual density. The first actual density acquisition module 40 further includes: a first dynamic density time series analysis unit, the first dynamic density time series analysis unit is used to analyze the first dynamic feature information to obtain the first dynamic density time series; a first future density acquisition unit, the first future density acquisition unit is used to perform a trend line analysis on the first dynamic density time series based on the principle of spectral density function to obtain the first future density at the first future time; a first actual density acquisition unit, the first actual density acquisition unit is used to adjust the first predicted density according to the first corresponding relationship between the first future time and the first future density to obtain the first actual density.

[0067] An urban governance and scheduling system based on dynamic evaluation provided by an embodiment of the present invention can execute an urban governance and scheduling method based on dynamic evaluation provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0068] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0069] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for urban governance scheduling based on dynamic evaluation, characterized in that: include: Obtaining a first spatial area, where the first spatial area refers to any area in a set of spatial areas obtained by dividing the target city, and the first spatial area has an identifier of a first functional characteristic; Analyzing the collected first region characteristic information of the first spatial region to obtain a first governance fitness of the first spatial region determined as the first functional characteristic; When the first governance fitness meets a predetermined fitness threshold, the density database is retrieved to perform density prediction analysis on the first spatial area to obtain a first predicted density; the first predicted vehicle density and the first predicted personnel density together constitute the first predicted density; Calibrate and analyze the first predicted density based on first dynamic feature information of the first spatial area to obtain a first actual density; the first dynamic feature information includes sensor data, mobile device data, social media data, and real-time event data; When the first actual density is not within a predetermined density threshold, performing a governance scheduling adjustment on the first spatial area; Reading predetermined terrain indicators; Based on the predetermined terrain and topography indicators, multi-dimensional features of the first spatial area are collected to obtain first terrain and topography feature information; Reading a predetermined flow index; wherein the flow index reflects the flow of people and vehicles in the first spatial area; performing multi-dimensional feature collection on the first spatial region based on the predetermined flow index to obtain first flow feature information; Analyzing the first topographic feature information and the first flow feature information to obtain the first governance adaptability; Retrieving a city governance database and extracting a first historical governance record from the city governance database, where the first historical governance record refers to a city governance record that schedules the governance of the first historical spatial area to be the first functional characteristic; forming first historical topographic and relief feature information of the first historical spatial area based on the predetermined topographic and relief indicators; Performing a similarity analysis on the first topographic and relief feature information and the first historical topographic and relief feature information to obtain a first situation similarity; When the first situation similarity meets a predetermined similarity threshold, extracting a first historical governance difficulty from the first historical governance record; The inverse of the first historical governance difficulty is taken as the first governance situation coupling degree; performing a variation weighted processing on a plurality of flow characteristic parameters in the first flow characteristic information to obtain a first control flow coupling degree; Taking the average of the first governance situation coupling degree and the first governance flow coupling degree as the first governance adaptability; Analyzing the first dynamic feature information to obtain a first dynamic density time series; Performing a trend line analysis on the first dynamic density time series based on the spectral density function principle to obtain a first future density at a first future time; The first predicted density is adjusted according to a first corresponding relationship between the first future time and the first future density to obtain the first actual density.

2. The urban governance scheduling method based on dynamic evaluation according to claim 1 is characterized in that: include: Extracting a first density time series from the density database, wherein the density database includes historical density record data of multiple historical spatial areas consistent with the first functional characteristics, and the first density time series includes a first vehicle density time series and a first person density time series; activating an intelligent prediction model, wherein the intelligent prediction model includes a vehicle prediction layer and a personnel prediction layer; Analyzing the first vehicle multi-domain feature information of the first vehicle density time series by the vehicle prediction layer to obtain a first predicted vehicle density; Analyzing the first personnel multi-domain feature information of the first personnel density time series by the personnel prediction layer to obtain a first predicted personnel density; The first predicted vehicle density and the first predicted personnel density together constitute the first predicted density.

3. The urban governance scheduling method based on dynamic evaluation according to claim 2 is characterized in that: include: Read the preset unit time zone; Slicing the first dense time series based on the predetermined unit time zone as a constraint to obtain a first time series slice set; Extracting a first slice from the first time series slice set, where the first slice refers to a first density time series segment; Creating a first data group based on the first segment multi-domain feature information of the first dense time series segment and the first density mode group of the first dense time series segment; Supervised learning is performed on the first data group to obtain the intelligent prediction model.

4. The urban governance scheduling method based on dynamic evaluation according to claim 3 is characterized in that: include: extracting a first vehicle density time series segment from the first density time series segment; Collecting time domain features of the first vehicle density time series segment to obtain time domain feature information of the first vehicle segment; Performing frequency domain feature collection on a first vehicle density spectrum obtained by fast Fourier transforming the first vehicle density time series segment to obtain frequency domain feature information of the first vehicle segment; The time domain feature information of the first vehicle segment and the frequency domain feature information of the first vehicle segment constitute the multi-domain feature information of the first vehicle segment; Acquire multi-domain feature information of a first personnel segment of a first personnel density time series segment in the first density time series segment; The first vehicle segment multi-domain feature information and the first person segment multi-domain feature information constitute the first segment multi-domain feature information.

5. An urban governance and dispatching system based on dynamic evaluation, characterized in that: The system is used to implement the urban governance scheduling method based on dynamic evaluation according to any one of claims 1 to 4, and the system includes: a first spatial region acquisition module, configured to acquire a first spatial region, wherein the first spatial region refers to any region in a set of spatial regions obtained by dividing the target city, and the first spatial region has an identifier of a first functional characteristic; a first governance fitness acquisition module, configured to analyze the collected first region feature information of the first spatial region to obtain a first governance fitness of the first spatial region determined as the first functional characteristic; a first predicted density acquisition module, configured to retrieve a density database to perform density prediction analysis on the first spatial area to obtain a first predicted density when the first governance fitness meets a predetermined fitness threshold; a first actual density acquisition module, configured to perform calibration analysis on the first predicted density according to first dynamic feature information of the first spatial region to obtain a first actual density; A governance scheduling adjustment module is used to perform governance scheduling adjustment on the first spatial area when the first actual density is not within a predetermined density threshold.

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