Smart city situation awareness recognition system based on artificial intelligence
By establishing a data standard library and user-end platform, allowing users to customize analysis functions and collaborate with enterprises in development, the high cost and limited benefits of smart city situational awareness and identification systems have been solved. This has enabled low-cost data integration and high-value mining, meeting personalized needs and improving system profitability.
Patent Information
- Application Number
- CN202511691155.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-18
- Publication Date
- 2026-02-10
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
AI-based smart city situational awareness and identification systems face high costs and limited returns during research, development, deployment, and operation, and over-reliance on subsidies affects companies' enthusiasm for research and development and investment.
By establishing a data standard library and user-end platform, users can customize user analysis functions, collaborate with enterprises in development, achieve low-cost integration and high-value mining of multi-source heterogeneous urban data, meet users' personalized needs, and utilize data while ensuring data privacy and compliance.
It effectively reduced costs, increased revenue, and achieved full utilization of data and satisfaction of personalized needs, thus breaking the dilemma of high cost and limited revenue in smart city situational awareness and recognition systems.
Smart Images
Figure CN121503905A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of smart city technology, specifically a smart city situational awareness and recognition system based on artificial intelligence. Background Technology
[0002] Driven by the wave of smart city construction, AI-based smart city situational awareness and recognition systems have become a key technological support for refined urban management and efficient operation. Leveraging the powerful data processing, pattern recognition, and deep learning capabilities of AI, this system integrates diverse and heterogeneous data resources within the city to achieve comprehensive, real-time perception and precise analysis of the city's operational status, providing city managers with a scientific basis for decision-making.
[0003] However, AI-based smart city situational awareness and recognition systems face high costs in their research, deployment, and operation. Optimizing and innovating AI algorithms requires a large number of specialized AI experts, resulting in extremely high human resource costs. Simultaneously, training accurate and efficient situational awareness models necessitates the labeling and training of massive amounts of urban data. Data collection, cleaning, and labeling not only consume significant time and manpower but may also involve compliance costs related to data privacy and security. Although AI-based smart city situational awareness and recognition systems have significant potential to improve urban governance capabilities and enhance residents' quality of life, current practical applications show less than optimistic returns. The profitability of AI-based smart city situational awareness and recognition systems largely depends on subsidies. However, over-reliance on subsidies also presents problems; the uncertainty of subsidies can negatively impact companies' R&D and investment enthusiasm. Summary of the Invention
[0004] To address the problems of the above solutions, this invention provides a smart city situational awareness and recognition system based on artificial intelligence.
[0005] The objective of this invention can be achieved through the following technical solutions: An AI-based smart city situational awareness and identification system, including platform and user terminals; The platform includes a data acquisition module, a situation analysis module, a data standard library, a reserve analysis module, and a platform analysis module. The acquisition module is used to collect real-time data from the target city according to a preset acquisition scheme to obtain urban monitoring data.
[0006] The situation analysis module is used to perform situation analysis based on urban monitoring data, obtain situation analysis results, and display the situation analysis results according to a preset display method.
[0007] The data standard library is used to store various data types available on the platform and the corresponding data application standards; it also stores various reserve application requirements.
[0008] Furthermore, the establishment of a data standard library includes: Identify the various data types available on the platform, set data application standards for each data type by the platform provider, integrate the data application standards corresponding to each data type, and establish a data standard library. The system acquires various candidate application requirements from users in real time, determines the data types needed to analyze these candidate application requirements, and integrates one or more data types corresponding to the candidate application requirements into candidate requirement features. Based on the characteristics of the candidate requirements, data application standards for each data type are matched from the data standard library, and the various data application standards are integrated into the requirement application standards for the candidate application requirements. The selected requirement characteristics are calibrated by applying the requirement standard to obtain the corresponding requirement calibration results, which include requirement calibration qualified and requirement calibration unqualified. Candidate application requirements whose requirements calibration results are qualified are marked as reserve application requirements, and the reserve application requirements are stored in the data standard library.
[0009] Furthermore, the characteristics of the selected requirements are calibrated using the application standards, including: Establish a demand calibration model, the expression of which is: ; In the formula: (q, p) are the input data, q represents the candidate requirement feature, p represents the requirement application standard; q→p means that the candidate requirement feature meets the requirement application standard, and the output data is the requirement calibration value XQ(q, p), which is 1 or 0. The requirement calibration model is used to analyze the characteristics of the candidate requirements and the application standards of the candidate application requirements to obtain the requirement calibration value. When the requirement calibration value is 1, the requirement calibration result is that the requirement calibration is qualified. When the required calibration value is 0, the required calibration result is that the required calibration is unqualified.
[0010] The reserve analysis module is used to perform reserve analysis. Based on the platform's profit expectations, it analyzes the reserve application requirements stored in the data standard library to obtain reserve analysis results for each reserve application requirement. These results include qualified and unqualified reserve analysis. Qualified reserve application requirements are marked as platform auxiliary requirements. Corresponding requirement analysis functions are set up based on these auxiliary requirements. The remaining reserve application requirements in the data standard library are calibrated based on these auxiliary requirements to obtain calibration results. Reserve application requirements that fail calibration are marked as unqualified.
[0011] The platform analysis module is used to process user needs based on each user's user needs information table, obtain corresponding needs processing data, including needs material data and needs analysis results; and send the needs processing data to the needs display module of the corresponding user terminal.
[0012] The user terminal includes a demand management module, a user analysis module, and a demand display module; The requirement management module is used to manage users' situational application requirements, acquire users' application requirements in real time, connect to the data standard library on the platform, calibrate users' application requirements, and obtain requirement calibration results, including requirement calibration qualified and requirement calibration unqualified; display the requirement calibration results to users, and mark the initial application requirements that have passed the calibration as users' situational application requirements; generate a user requirement information table based on the user's various situational application requirements; and send the user requirement information table to the platform analysis module on the platform.
[0013] Furthermore, users can dynamically mark situational application requirements that do not require requirement analysis in the user requirement information table.
[0014] Furthermore, the user application requirements are calibrated, including: Match user application requirements with the various reserve application requirements in the data standard library; When a reserve application requirement is matched, the requirement calibration is deemed satisfactory. When no matching reserve application requirements are found, the various data types required by the user application requirements are analyzed, and application standards are generated based on the data types and data standard library. Calibration is then performed based on the application standards to obtain the application calibration results.
[0015] The requirement display module is used to display the received requirement processing data, identify the requirement material data and requirement analysis results in the requirement processing data, send the requirement material data to the user analysis module, and display it accordingly based on the requirement analysis results.
[0016] The user analysis module is used to perform personalized user needs analysis, identify the situational application needs corresponding to the needs material data, analyze the needs material data according to the preset user analysis function based on the situational application needs, obtain the needs analysis results, and send the needs analysis results to the needs display module.
[0017] Furthermore, user analytics features can be shared with other users.
[0018] Compared with the prior art, the beneficial effects of the present invention are: This invention effectively addresses the dual challenges of high costs and limited returns faced by AI-based smart city situational awareness and recognition systems during research, development, deployment, and operation. By allowing users to customize user analysis functions for demand analysis, it facilitates meeting users' personalized needs while enabling collaborative development and improvement with enterprises, thereby reducing costs. Under the premise of ensuring data privacy and compliance, it achieves low-cost integration and high-value mining of multi-source heterogeneous urban data, realizing full utilization of data and increasing returns. Attached Figure Description
[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0020] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation
[0021] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.
[0022] like Figure 1 As shown, the AI-based smart city situational awareness and recognition system includes a platform and a user terminal. The platform establishes communication connections with each user terminal.
[0023] The platform includes a data acquisition module, a situation analysis module, a data standard library, a reserve analysis module, and a platform analysis module. The acquisition module is used to collect real-time data from the target city according to a preset acquisition scheme to obtain urban monitoring data.
[0024] Data is collected by connecting with various systems and equipment in the city, such as transportation and security, and the collected data is processed in terms of formatting and other aspects.
[0025] Specifically, data will be collected using existing smart city data monitoring methods.
[0026] For example, deploying IoT devices (sensors, cameras, smart terminals), 5G communication networks, and edge computing nodes can enable real-time data collection for urban infrastructure (transportation, energy, environment, public safety).
[0027] The situation analysis module is used to perform situation analysis based on urban monitoring data, obtain corresponding situation analysis results, and display the situation analysis results according to a preset display method.
[0028] In one embodiment, situational analysis is performed on urban monitoring data. This analysis is conducted using existing situational analysis methods to identify and optimize anomalies in energy, environment, public safety, transportation, and other areas. Specifically, the analysis is performed according to preset analysis requirements.
[0029] Exemplary, typical technologies include: Feature extraction: Identifying threat characteristics such as attack source IP and abnormal traffic patterns; Predictive models: Predict risks such as traffic congestion and equipment failure through time series analysis; Knowledge graphs: Constructing a network of connections between urban elements to aid causal reasoning (such as the relationship between fires and weather, and the flow of people).
[0030] In one embodiment, the situation analysis results are displayed according to a preset display method, which is to set the display method according to display requirements.
[0031] The situation analysis results can be transformed into visual dashboards, early warning signals, and automated response commands, supporting scenarios such as traffic dispatching, emergency command, and resource allocation.
[0032] The data standard library is used to store various data types available on the platform and the corresponding data application standards; it also stores various reserve application requirements, which are application requirements that have been determined to meet the application standards.
[0033] In one embodiment, the establishment of a data standard library includes: Identifying the various data types available on the platform refers to all data types that users might use, or even all data types. The platform sets data application standards for each data type, based on the platform's usage requirements for various data types, such as urban monitoring data and situational analysis results. For example, some data can be shared directly with users without privacy or security issues; other data requires authorization before sharing, such as employee travel identification, which requires authorization from the employee to share profile data of their travel route; some data cannot be shared but can be processed and analyzed on the platform, and the resulting analysis can be shared with users; and some data can be shared after de-hiding. The data application standards corresponding to each data type are integrated to establish a data standard library. The system acquires various possible situational application requirements in real time, marks them as candidate application requirements, analyzes the data types needed to implement each candidate application requirement, and integrates one or more corresponding data types into the candidate application requirement's candidate requirement features. Multiple implementation methods result in multiple candidate requirement features; for example, preprocessing data A into data B creates different data types. Only one candidate requirement feature needs to meet the application requirement standard. Based on the candidate requirement features, the system matches the corresponding data application standards for each data type from the data standard library and integrates them into the candidate application requirement's application standard. The selected requirement characteristics are calibrated by applying the requirement standard to obtain the corresponding requirement calibration results, which include requirement calibration qualified and requirement calibration unqualified. Candidate application requirements whose requirements calibration results are qualified are marked as reserve application requirements, and the reserve application requirements are stored in the data standard library.
[0034] In one embodiment, the selected requirement characteristics are calibrated by applying a requirement standard, which is a calibration based on existing calibration methods.
[0035] In one embodiment, calibrating the characteristics of the selected requirement by applying a requirement standard includes: Establish a demand calibration model, the expression of which is: ; In the formula: (q, p) represents the input data, q represents the candidate requirement feature, and p represents the requirement application standard; q→p means that the candidate requirement feature meets the requirement application standard, that is, under the premise of meeting the requirement application standard, it can obtain the collection permission of the data corresponding to the candidate requirement feature from the platform, which refers to all data; the output data is the requirement calibration value XQ(q, p), and the requirement calibration value is 1 or 0; the corresponding training set is set using the corresponding historical data for training; The requirements calibration model is used to analyze the characteristics and application standards of the corresponding candidate application requirements to obtain the corresponding requirements calibration values. When the requirement calibration value is 1, the requirement calibration result is that the requirement calibration is qualified. When the required calibration value is 0, the required calibration result is that the required calibration is unqualified.
[0036] In one embodiment, the data types required to implement the requirements of the candidate application can be determined based on existing methods, such as identifying the various data required based on the historical implementation methods of the candidate application requirements, and then determining the required data types; alternatively, a corresponding requirements analysis model can be established based on machine learning, deep learning algorithms, etc., and the analysis can be performed through the successfully trained requirements analysis model.
[0037] The reserve analysis module is used to perform reserve analysis on the reserve application requirements stored in the data standard library, determine whether the platform needs to establish an analysis function to implement the reserve application requirement, and obtain reserve analysis results, including qualified and unqualified reserve analysis results. Reserve application requirements with qualified reserve analysis results are marked as platform auxiliary requirements. Corresponding requirement analysis functions are set according to the platform auxiliary requirements. The remaining reserve application requirements are then recalibrated according to the platform auxiliary requirements to determine whether they still meet the requirement application standards. Since the remaining reserve application requirements cannot be used as platform auxiliary requirements, it means that some requirement implementation methods cannot be implemented. If the analysis shows that the requirement meets the requirement application standards as a platform auxiliary requirement, but it is not used as a platform auxiliary requirement, then the user end needs to perform analysis, which involves the risk of data transmission leakage. The requirement application standards need to be updated. Previously, as long as there was one possibility to implement the candidate application requirement, it would be used as a reserve application requirement. At this time, recalibration is required. Recalibration is performed again according to the updated requirement application standards in the above manner. The corresponding calibration results are obtained, and reserve application requirements with unqualified calibration results are marked as prohibited, indicating that they cannot be matched.
[0038] In one embodiment, the platform provider can perform intermediate processing based on the requirements of each reserve application. That is, the platform data required by the reserve application requirements is preprocessed on the platform side, and the preprocessed intermediate features are transmitted to meet the application requirements standards and remove the prohibition mark. Similarly, the evaluation can be carried out in the manner of platform-assisted requirements.
[0039] In one embodiment, it is determined whether the platform needs to build an analysis function to meet the requirements of the reserve application. The evaluation is conducted based on existing methods, mainly from the perspectives of cost and expected profitability. If there is a certain market demand and profit expectation, the reserve analysis is considered qualified. The specific selection is based on the platform's requirements.
[0040] For example, the analysis of reserve application requirements stored in the data standard library is performed based on the platform's profit expectations, including: Estimate the implementation cost and estimated profit for meeting the reserve application needs within a preset period, which can be a quarter, half a year, or a year; subtract the two and compare the difference with the profit expectation to determine whether the reserve analysis is qualified.
[0041] In one embodiment, corresponding requirements analysis functions are set up according to the platform's auxiliary needs, and these functions are developed by the platform's staff.
[0042] The platform analysis module is used to process user requirements based on each user's user requirement information table, obtain corresponding requirement processing data, including requirement material data and requirement analysis results, and send the requirement processing data to the corresponding user terminal.
[0043] In one embodiment, if the user requirement information table contains platform-assisted requirements, the requirements are analyzed according to the preset requirement analysis function to obtain the requirement analysis results; the remaining situational application requirements are collected and preprocessed according to the corresponding data to obtain requirement material data; and the requirement material data and requirement analysis results are integrated into requirement processing data.
[0044] The user terminal is mainly for use by enterprises and other users, and includes a demand management module and a user analysis module; The demand management module manages users' situational application needs, namely, user needs for business management and analysis based on the city situational awareness and identification system, such as tracking cargo transportation and analyzing pedestrian flow in store areas. When a user has a new situational application need, it is uploaded and marked as a user application need. The module connects to the data standard library on the platform side to calibrate the user application needs, determine whether they meet the application standards, and obtain the corresponding demand calibration results, including qualified and unqualified demands. The demand calibration results are displayed to the user, and the initial application needs that have passed calibration are marked as the user's situational application needs. A user demand information table is generated based on the user's various situational application needs. The user demand information table is then sent to the platform analysis module on the platform side.
[0045] In one embodiment, a user can dynamically mark situational application requirements that do not require requirement analysis in the user requirement information table as needed, and these situational application requirements will not be analyzed subsequently.
[0046] In one embodiment, calibrating user application requirements includes: Match user application requirements with the various reserve application requirements in the data standard library; When a reserve application requirement is matched, the requirement calibration is deemed satisfactory. When no matching reserve application requirements are found, the various data types required by the user's application requirements are analyzed. The user can provide the required data types, and application standards are generated based on the data types and data standard library. Each data type is then calibrated according to the application standards to obtain the corresponding requirement calibration results.
[0047] The requirement display module is used to display the received requirement processing data, identify the requirement material data and requirement analysis results in the requirement processing data, send the requirement material data to the user analysis module, and display the requirements according to the requirement analysis results, including the requirement analysis results sent by the user analysis module later.
[0048] The specific display method can be set according to user needs, or it can be uniformly set by the platform.
[0049] The user analysis module is used to load user-configured requirement analysis functions. Users can add requirement analysis functions to analyze and process application requirements for corresponding situations, mark them as user analysis functions, analyze the corresponding requirement material data according to the preset user analysis functions, obtain requirement analysis results, and send the requirement analysis results to the requirement display module.
[0050] In one embodiment, user-required functions are developed and loaded by the user themselves; otherwise, they can only be analyzed through the platform's analysis module.
[0051] In one embodiment, a user can, with authorization, share user analytics features with other users for application.
[0052] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.
[0053] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.
Claims
1. An AI-based smart city situational awareness and identification system, comprising a platform, wherein the platform includes a data acquisition module and a situational analysis module; the data acquisition module is used to acquire real-time data of a target city according to a preset data acquisition scheme to obtain urban monitoring data; the situational analysis module is used to perform situational analysis based on the urban monitoring data to obtain situational analysis results, and display the situational analysis results according to a preset display method; characterized in that, The platform terminal communicates with the user terminals of each user; the platform terminal also includes a data standard library, a reserve analysis module, and a platform analysis module; the user terminal includes a demand management module, a user analysis module, and a demand display module. The data standard library is used to store various data types available on the platform and the corresponding data application standards for those data types; it also stores various reserve application requirements. The reserve analysis module is used to perform reserve analysis on the reserve application requirements stored in the data standard library, obtain platform auxiliary requirements, and set up requirement analysis functions based on the platform auxiliary requirements. The platform analysis module is used to process the requirements based on the user requirement information table of each user, and obtain the corresponding requirement processing data, which includes requirement material data and requirement analysis results; and sends the requirement processing data to the requirement display module of the corresponding user terminal. The requirement management module is used to manage users' situational application requirements, acquire users' application requirements in real time, connect to the data standard library on the platform side to calibrate users' application requirements, obtain requirement calibration results, display the requirement calibration results to users, and mark the initial application requirements that have passed the requirement calibration as users' situational application requirements; generate a user requirement information table based on the user's various situational application requirements; and send the user requirement information table to the platform analysis module on the platform side accordingly. The requirement display module is used to display the received requirement processing data, identify the requirement material data and requirement analysis results in the requirement processing data, send the requirement material data to the user analysis module, and display it accordingly based on the requirement analysis results. The user analysis module is used to perform personalized user needs analysis, identify the situational application needs corresponding to the needs material data, analyze the needs material data according to the preset user analysis function based on the situational application needs, obtain the needs analysis results, and send the needs analysis results to the needs display module.
2. The smart city situational awareness and identification system based on artificial intelligence according to claim 1, characterized in that, The establishment of a data standard library includes: Identify the various data types available on the platform, set data application standards for each data type by the platform provider, integrate the data application standards corresponding to each data type, and establish a data standard library. The system acquires various candidate application requirements from users in real time, determines the data types needed to analyze these candidate application requirements, and integrates one or more data types corresponding to the candidate application requirements into candidate requirement features. Based on the characteristics of the candidate requirements, data application standards for each data type are matched from the data standard library, and the various data application standards are integrated into the requirement application standards for the candidate application requirements. The selected requirement characteristics are calibrated by applying the requirement standard to obtain the corresponding requirement calibration results, which include requirement calibration qualified and requirement calibration unqualified. Candidate application requirements whose requirements calibration results are qualified are marked as reserve application requirements, and the reserve application requirements are stored in the data standard library.
3. The AI-based smart city situational awareness and identification system according to claim 2, characterized in that, The characteristics of the selected requirements are calibrated by applying the required standards, including: Establish a demand calibration model, the expression of which is: ； In the formula: (q, p) are the input data, q represents the candidate requirement feature, p represents the requirement application standard; q→p means that the candidate requirement feature meets the requirement application standard, and the output data is the requirement calibration value XQ(q, p), which is 1 or 0. The requirement calibration model is used to analyze the characteristics of the candidate requirements and the application standards of the candidate application requirements to obtain the requirement calibration value. When the requirement calibration value is 1, the requirement calibration result is that the requirement calibration is qualified. When the required calibration value is 0, the required calibration result is that the required calibration is unqualified.
4. The smart city situational awareness and identification system based on artificial intelligence according to claim 1, characterized in that, Users can dynamically mark situational application requirements that do not require requirement analysis in the user requirement information table.
5. The smart city situational awareness and recognition system based on artificial intelligence according to claim 1, characterized in that, Calibrate user application requirements, including: Match user application requirements with the various reserve application requirements in the data standard library; When a reserve application requirement is matched, the requirement calibration is deemed satisfactory. When no matching reserve application requirements are found, the various data types required by the user application requirements are analyzed, and application standards are generated based on the data types and data standard library. Calibration is then performed based on the application standards to obtain the application calibration results.
6. The smart city situational awareness and identification system based on artificial intelligence according to claim 1, characterized in that, The reserve analysis module calibrates the remaining reserve application requirements in the data standard library according to the platform's auxiliary requirements, obtains the calibration results of the reserve application requirements, and marks the reserve application requirements that fail the calibration as unqualified.
7. The smart city situational awareness and identification system based on artificial intelligence according to claim 1, characterized in that, Perform reserve analysis on the reserve application requirements stored in the data standard library, including: Based on the platform's profit expectations, the reserve application requirements stored in the data standard library are analyzed to obtain the reserve analysis results for each reserve application requirement. Reserve application requirements that pass the reserve analysis are marked as platform auxiliary requirements.
8. The smart city situational awareness and identification system based on artificial intelligence according to claim 1, characterized in that, User analytics features enable sharing applications with other users.