Dynamic adjustment method for urban update planning scheme and related equipment

Abstract

The invention belongs to the technical field of city planning, and discloses a dynamic adjustment method for a city update planning scheme and related equipment, and the method comprises the steps: obtaining city street view video data through the normalized cruise shooting of an unmanned plane; preprocessing the urban street scene video data, identifying building facade damage information and traffic information from the preprocessed urban street scene video data, and evaluating a building facade damage level and a traffic influence level; based on a preset building facade damage grade threshold value, a traffic influence grade threshold value, a building facade damage risk weight and a traffic influence risk weight, constructing a weighted risk assessment model, and according to the building facade damage grade and the traffic influence grade, calculating a comprehensive risk score; determining an adjustment strategy according to the comprehensive risk score, so as to dynamically adjust the urban update planning scheme; therefore, the adjustment efficiency of the urban update planning scheme can be improved.

Description

Technical Field

[0001] The present application relates to the field of urban planning technology, and in particular to a method for dynamically adjusting an urban renewal planning scheme and related equipment. Background Art

[0002] Urban planning departments are faced with the need to evaluate the condition of neighborhoods and dynamically adjust planning schemes during the renovation of old urban areas and urban renewal. Traditionally, relying on manual on-site surveys and data collection is inefficient and time-consuming, resulting in delayed updates of planning schemes and difficulty in adapting to the needs of changes in urban neighborhoods. Drones equipped with high-definition cameras routinely cruise to capture street view image data, providing a new source of data for urban planning and enabling more efficient acquisition of neighborhood building facades and traffic information.

[0003] However, the key lies in how to use this data to achieve dynamic adjustment of urban renewal planning schemes. Even with the introduction of drone data, existing urban renewal planning methods still lack a mechanism to adjust planning schemes according to changes in street view data, especially to overcome the lag in data processing and scheme adjustment under the premise of ensuring building structure safety assessment and traffic impact analysis. How to achieve efficient response and adjustment of planning schemes has become a key bottleneck in improving the efficiency and quality of urban renewal planning. The field of urban planning urgently needs a technical solution that can make full use of drone street view data, simplify the data analysis process, and achieve dynamic adjustment of planning schemes to meet the changing needs in the process of urban renewal.

[0004] In view of the above problems, the existing technology needs to be improved urgently. Summary of the invention

[0005] The purpose of this application is to provide a dynamic adjustment method and related equipment for urban renewal planning schemes, which can improve the adjustment efficiency of urban renewal planning schemes.

[0006] In a first aspect, the present application provides a method for dynamically adjusting an urban renewal planning scheme, the method comprising the steps of: A1. Obtain urban street view video data through regular drone cruise shooting; A2. Preprocessing the urban street view video data, identifying building facade damage information and traffic information from the preprocessed urban street view video data, and evaluating the building facade damage level and traffic impact level; A3. Based on the preset building facade damage level threshold, traffic impact level threshold, building facade damage risk weight and traffic impact risk weight, a weighted risk assessment model is constructed, and a comprehensive risk score is calculated according to the building facade damage level and the traffic impact level; A4. Determine an adjustment strategy based on the comprehensive risk score to dynamically adjust the urban renewal planning scheme.

[0007] This method utilizes drone street view video data to simplify the data analysis process, realize dynamic adjustment of planning schemes, and improve the adjustment efficiency of urban renewal planning schemes.

[0008] Preferably, the step A2 comprises: A201. Detecting weather conditions corresponding to the city street view video data; A202. According to the detected weather conditions, the image preprocessing parameters are adaptively adjusted to preprocess the city street view video data to obtain preprocessed street view video data adapted to the weather conditions; A203. Identify building facade damage information and traffic information from the pre-processed street view video data adapted to weather conditions, and evaluate the building facade damage level and traffic impact level.

[0009] Since the preprocessing process has taken the impact of weather conditions into consideration and carried out targeted optimization, the accuracy and reliability of information recognition and grade assessment can be effectively improved.

[0010] Preferably, step A3 comprises: A301. Obtain information on the current urban renewal stage and block characteristics; A302. Generate a weight adjustment instruction based on the current urban renewal stage information and the block characteristic information, the weight adjustment instruction including a building facade damage risk weight adjustment parameter and a traffic impact risk weight adjustment parameter; A303. According to the building facade damage risk weight adjustment parameter and the traffic impact risk weight adjustment parameter, adjust the preset building facade damage risk weight and traffic impact risk weight to obtain an adjusted building facade damage risk weight and an adjusted traffic impact risk weight; A304. Construct a weighted risk assessment model based on the preset building facade damage level threshold, traffic impact level threshold, adjusted building facade damage risk weight and adjusted traffic impact risk weight; A305. Calculate a comprehensive risk score based on the building facade damage level and the traffic impact level, as well as the constructed weighted risk assessment model.

[0011] As a result, adaptive adjustment of risk weights is achieved, enabling the risk assessment model to better adapt to the differentiated needs of different stages and blocks in the urban renewal process, making the comprehensive risk score more accurate and in line with actual conditions, thereby providing a more reliable basis for the dynamic adjustment of urban renewal planning schemes and improving the flexibility and effectiveness of urban renewal planning.

[0012] Preferably, step A302 includes: A302a. Information reliability check: A302a1. Identify the data source of the current urban renewal stage information and the block feature information; A302a2. Evaluate the credibility level of the data source; A302a3. Compare the credibility level of the data source with a preset credibility threshold; A302a4. If the credibility level of the data source is lower than the credibility threshold, the information correction process is started, and the information correction process includes: Using preset default urban renewal stage information and default block characteristic information to replace the current urban renewal stage information and the block characteristic information; or Query the expert knowledge base to obtain historical urban renewal stage information and historical block characteristic information related to the current urban renewal area, and based on the historical urban renewal stage information and historical block characteristic information, correct the current urban renewal stage information and the block characteristic information; A302b. Generate weight adjustment instructions based on the current urban renewal stage information and block characteristic information after information reliability verification or information correction processing.

[0013] Preferably, step A3 comprises: A311. Collect historical cruise data within a preset time period, the historical cruise data including historical building facade damage level data and historical traffic impact level data; A312. Based on the historical building facade damage level data and the historical traffic impact level data, a threshold optimization algorithm is used to calculate an optimized building facade damage level threshold and an optimized traffic impact level threshold; A313. Based on the optimized building facade damage level threshold, the optimized traffic impact level threshold, the preset building facade damage risk weight and the traffic impact risk weight, a weighted risk assessment model is constructed; A314. Calculate a comprehensive risk score based on the building facade damage level and the traffic impact level, as well as the constructed weighted risk assessment model.

[0014] Preferably, step A312 includes: Based on the historical building facade damage level data and the historical traffic impact level data, a grid search method is used to calculate an optimized building facade damage level threshold and an optimized traffic impact level threshold.

[0015] Preferably, the step A312 includes: B12, calculating the change rate of the historical building facade damage level data and the historical traffic impact level data; B13, comparing the change rate with a preset change rate threshold; B14, when at least one of the change rates is greater than or equal to the change rate threshold, increasing the execution frequency of the threshold optimization algorithm; when the change rates are all less than the change rate threshold, reducing the execution frequency of the threshold optimization algorithm; B15, executing the threshold optimization algorithm according to the adjusted execution frequency, and calculating the optimized building facade damage level threshold and the optimized traffic impact level threshold.

[0016] In a second aspect, the present application provides a device for dynamically adjusting an urban renewal planning scheme, the device comprising: A data acquisition module is used to acquire urban street view video data through regular drone cruise shooting; An information identification and evaluation module, used to pre-process the urban street view video data, identify building facade damage information and traffic information from the pre-processed urban street view video data, and evaluate the building facade damage level and traffic impact level; A risk score calculation module is used to construct a weighted risk assessment model based on a preset building facade damage level threshold, a traffic impact level threshold, a building facade damage risk weight, and a traffic impact risk weight, and calculate a comprehensive risk score according to the building facade damage level and the traffic impact level; The planning scheme adjustment module is used to determine the adjustment strategy according to the comprehensive risk score, so as to dynamically adjust the urban renewal planning scheme.

[0017] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, it runs the steps in the method for dynamic adjustment of the urban renewal planning scheme as described above.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, runs the steps in the method for dynamic adjustment of urban renewal planning schemes as described above.

[0019] Beneficial effects: The present application provides a method and related equipment for dynamic adjustment of urban renewal planning schemes, which utilize drone street view video data to simplify the data analysis process, realize dynamic adjustment of planning schemes, and improve the adjustment efficiency of urban renewal planning schemes. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1A flowchart of a method for dynamically adjusting an urban renewal planning scheme provided in an embodiment of the present application.

[0021] Figure 2 A schematic diagram of the structure of a dynamic adjustment device for an urban renewal planning scheme provided in an embodiment of the present application.

[0022] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0023] Explanation of reference numerals: 1. Data acquisition module; 2. Information identification and evaluation module; 3. Risk score calculation module; 4. Planning scheme adjustment module; 301. Processor; 302. Memory; 303. Communication bus. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0025] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0026] With the acceleration of urbanization, the importance of urban planning has become increasingly prominent. In the urban planning system, the control detailed planning, as a key link between the upper and lower levels, directly affects the sustainable development of the city. However, the traditional control detailed planning evaluation method often has problems such as strong subjectivity and the evaluation results are not objective and comprehensive enough. In order to solve these problems, this application proposes an innovative control detailed planning evaluation method, which aims to improve the scientificity and objectivity of the evaluation.

[0027] In urban planning practice, we often encounter such a situation: a city's master plan has been completed, and a more detailed control detailed plan needs to be formulated next. However, how to ensure that the control detailed plan is consistent with the master plan while meeting the actual needs of the specific area is a complex issue. The evaluation method proposed in this application is designed to solve this problem.

[0028] Please refer to Figure 1 , Figure 1 The present invention is a method for dynamically adjusting the urban renewal planning scheme in some embodiments of the present application, and the steps of the method include: A1. Obtain urban street view video data through regular drone cruise shooting; A2. Preprocess the urban street view video data, identify the building facade damage information and traffic information from the preprocessed urban street view video data, and evaluate the building facade damage level and traffic impact level; A3. Based on the preset building facade damage level threshold, traffic impact level threshold, building facade damage risk weight and traffic impact risk weight, a weighted risk assessment model is constructed, and a comprehensive risk score is calculated based on the building facade damage level and traffic impact level; A4. Determine adjustment strategies based on comprehensive risk scores to dynamically adjust urban renewal planning schemes.

[0029] Among them, in step A1, the drone is configured to perform regular cruise shooting over the city blocks according to the preset cruise route and frequency. In this way, the city street view video data can be continuously and regularly obtained, overcoming the problem of low efficiency of traditional manual data collection, and providing a timely data basis for the dynamic adjustment of urban renewal planning schemes.

[0030] Among them, in step A2, preprocessing operations such as image enhancement and denoising are performed on the acquired urban street view video data to improve the accuracy of subsequent information recognition. After that, the building facade damage information and traffic information are automatically identified from the preprocessed video data. The building facade damage information may include cracks, detachment and other information that characterize the safety of the building structure; the traffic information may include traffic volume, congestion level and other information that characterizes the traffic operation status. Then, the system evaluates the building facade damage level and traffic impact level, and converts the identified information into a quantitative level to facilitate subsequent risk assessment and decision-making.

[0031] Among them, in step A3, a weighted risk assessment model is constructed. The model uses the building facade damage level threshold, traffic impact level threshold, building facade damage risk weight and traffic impact risk weight as input parameters. The level threshold is used to define the risk level, and the risk weight is used to adjust the proportion of different risk factors in the comprehensive risk assessment. Through this model, the comprehensive risk score is calculated based on the building facade damage level and traffic impact level evaluated in step A2. The comprehensive risk score realizes the quantitative assessment of urban renewal risks and provides a quantitative basis for the formulation of subsequent adjustment strategies.

[0032] Among them, in step A4, according to the comprehensive risk score calculated in step A3, the corresponding adjustment strategy is determined, and the determined adjustment strategy is used to dynamically adjust the urban renewal planning scheme. The higher the comprehensive risk score, the higher the urban renewal risk, and the greater the adjustment range of the adjustment strategy to be adopted, so as to ensure that the urban renewal planning scheme can be adjusted in time according to the actual changes in the urban blocks.

[0033] Specifically, the dynamic adjustment method of the urban renewal planning scheme proposed in this application has the following working principle: first, the normalized cruise shooting of drones is used to efficiently and regularly obtain urban street view video data to provide data support for the evaluation of urban block conditions. Then, the street view video data is preprocessed, and the building facade damage information and traffic information are automatically identified and evaluated from it to achieve the quantitative evaluation of urban block building safety and traffic operation conditions. Further, a weighted risk assessment model is constructed to comprehensively consider the risk of building facade damage and traffic impact, and the evaluation results are converted into a comprehensive risk score to achieve the quantitative evaluation of urban renewal risks. Finally, the adjustment strategy is determined according to the comprehensive risk score, and the urban renewal planning scheme is dynamically adjusted to ensure that the urban renewal planning scheme can be adjusted in time according to the actual changes in the urban blocks, and the rapid dynamic adjustment of the urban renewal planning scheme is achieved. As a result, the problems of low efficiency and data lag of traditional manual survey methods are overcome, the efficiency and response speed of urban renewal planning are improved, and the effectiveness and timeliness of urban renewal planning schemes are guaranteed.

[0034] In some specific implementations, the urban planning department can use drones to conduct regular patrol shooting in specific city blocks, for example, patrol shooting once a week. During the patrol process, the high-definition camera carried by the drone continuously shoots street view video data. The acquired street view video data is transmitted to the data processing center. The data processing center first performs image denoising and enhancement preprocessing on the video data to improve the image quality. Then, using the image recognition algorithm, the building facade damage information such as the number of cracks on the building facade, the width of the cracks, the detached area, and the traffic information such as the traffic volume and average speed of the road are identified from the preprocessed video data. According to the preset evaluation criteria, the system quantifies the building facade damage information into the building facade damage level, for example, it is divided into three levels of slight damage, moderate damage, and severe damage, and the building facade damage level can also be represented by a numerical value; the traffic information is quantified into the traffic impact level, for example, it is divided into three levels of smooth, congested, and severely congested, and the traffic impact level can also be represented by a numerical value. Furthermore, the threshold of building facade damage level and the threshold of traffic impact level are preset, for example, the threshold of building facade damage level is moderate damage, and the threshold of traffic impact level is congestion. At the same time, the preset building facade damage risk weight is 0.6, and the traffic impact risk weight is 0.4. The weighted risk assessment model can be constructed as follows: comprehensive risk score = building facade damage risk weight * (building facade damage level / building facade damage level threshold) + traffic impact risk weight * (traffic impact level / traffic impact level threshold). According to the calculated comprehensive risk score, the risk level is divided, for example, a comprehensive risk score greater than 0.8 is high risk, 0.5-0.8 is medium risk, and less than 0.5 is low risk. According to different risk levels, corresponding adjustment strategies are formulated. For example, a high risk level corresponds to a substantial adjustment of the urban renewal planning scheme, including increasing the frequency of building structure safety inspections, optimizing traffic organization schemes, etc.; a medium risk level corresponds to a minor adjustment, such as fine-tuning traffic signal timing, strengthening building facade maintenance, etc.; a low risk level corresponds to maintaining the existing planning scheme. Through the above steps, dynamic adjustment of the urban renewal planning scheme is achieved.

[0035] In some preferred embodiments, step A2 comprises: A201. Detect weather conditions corresponding to city street view video data; A202. According to the detected weather conditions, the image preprocessing parameters are adaptively adjusted to preprocess the urban street view video data to obtain preprocessed street view video data adapted to the weather conditions; A203. Identify building facade damage information and traffic information from pre-processed street view video data adapted to weather conditions, and assess the building facade damage level and traffic impact level.

[0036] Among them, in step A201, the detection of weather conditions can be achieved in a variety of ways. For example, the city meteorological data interface can be accessed to obtain real-time weather information of the drone shooting location. Alternatively, the weather characteristics contained in the city street view video data itself can be analyzed, for example, through parameters such as image brightness, color temperature, texture clarity, etc., to determine whether the video was shot under weather conditions such as sunny, cloudy or rainy.

[0037] Among them, in step A202, adaptively adjusting the image preprocessing parameters means that different image preprocessing methods and parameters are used for different weather conditions. Specifically, on sunny days with sufficient light, emphasis can be placed on image sharpening and contrast enhancement to highlight building facade damage and traffic details; on cloudy days or under insufficient light conditions, emphasis can be placed on image brightness enhancement and noise suppression to improve the overall image quality; on rainy or foggy days with poor visibility, a defogging and deraining algorithm can be used to reduce the impact of weather factors on image quality. The adaptive adjustment of preprocessing parameters can be achieved by means of a preset parameter configuration table, which stores image preprocessing parameter combinations corresponding to different weather conditions.

[0038] Among them, in step A203, after preprocessing to adapt to weather conditions, building facade damage information and traffic information in the urban street view video data are identified, and the building facade damage level and traffic impact level are evaluated. Since the preprocessing process has taken into account the impact of weather conditions and has been optimized in a targeted manner, the accuracy and reliability of information identification and level evaluation can be effectively improved.

[0039] Specifically, in the dynamic adjustment method of the urban renewal planning scheme, in order to ensure that the urban street view video data can be effectively utilized under various weather conditions, step A2 includes weather condition detection and adaptive image preprocessing. First, through step A201, the system can identify the weather conditions under which the currently acquired urban street view video data was shot. This step is the basis for the subsequent adjustment of preprocessing parameters. Then, in step A202, the system automatically selects and adjusts the parameters of image preprocessing according to the weather conditions detected in step A201. For example, if it is detected that the current video is shot on a rainy day, the system may automatically enable the rain removal algorithm and adjust the denoising and sharpening parameters to eliminate the adverse effects of rain on video quality as much as possible and improve the clarity of the video. On the contrary, if the video is shot on a sunny day, the system may use a different combination of preprocessing parameters, for example, focusing on enhancing the contrast and sharpness of the image to more clearly show the detailed information of the building facade. This adaptive preprocessing method ensures that urban street view video data can be optimally processed regardless of weather conditions, providing a high-quality data basis for the subsequent identification of building facade damage information and traffic information. Finally, in step A203, the system will accurately identify building facade damage information and traffic information from the adaptively preprocessed street view video data, and conduct a grade assessment of the degree of building facade damage and traffic impact. Due to the use of a preprocessing method adapted to weather conditions, the identified information and the assessment level will be more accurate and reliable, thereby providing a more reliable basis for the subsequent calculation of comprehensive risk scores and the dynamic adjustment of urban renewal planning schemes, thereby improving the scientificity and effectiveness of urban renewal planning.

[0040] In some specific embodiments, in step A201, the detection of weather conditions can be achieved by analyzing the ambient light intensity and color saturation when the drone collects the video. For example, a light intensity threshold and a color saturation threshold are set. When it is detected that the light intensity is lower than the threshold and the color saturation is reduced, it is determined to be a cloudy day; when it is detected that there are obvious raindrops or water mist features in the video, it is determined to be a rainy day. In step A202, the preset algorithm collection library stores a sunny day preprocessing algorithm combination, a cloudy day preprocessing algorithm combination, and a rainy day preprocessing algorithm combination. The sunny day preprocessing algorithm combination may include a sharpening filter and a contrast enhancement algorithm, the cloudy day preprocessing algorithm combination may include a brightness enhancement and a median filtering algorithm, and the rainy day preprocessing algorithm combination may include a rain removal algorithm, a non-local mean denoising, and an adaptive histogram equalization algorithm.

[0041] For example, when step A201 detects that the weather condition is rainy, step A202 selects the rain preprocessing algorithm combination and adjusts the algorithm parameters to preprocess the urban street view video data. After the preprocessing, the rain interference of the street view video data is effectively reduced, and the image clarity is improved, which is more conducive to the subsequent identification of building facade damage information and traffic information.

[0042] In some embodiments, step A3 comprises: A301. Obtain information on the current urban renewal stage and block characteristics; A302. Generate a weight adjustment instruction based on the current urban renewal stage information and block characteristic information. The weight adjustment instruction includes a building facade damage risk weight adjustment parameter and a traffic impact risk weight adjustment parameter; A303. According to the building facade damage risk weight adjustment parameter and the traffic impact risk weight adjustment parameter, adjust the preset building facade damage risk weight and the traffic impact risk weight to obtain the adjusted building facade damage risk weight and the adjusted traffic impact risk weight; A304. Construct a weighted risk assessment model based on the preset building facade damage level threshold, traffic impact level threshold, adjusted building facade damage risk weight and adjusted traffic impact risk weight; A305. Calculate the comprehensive risk score based on the building facade damage level and traffic impact level, as well as the constructed weighted risk assessment model.

[0043] Among them, in step A301, the current urban renewal stage information can be directly retrieved from the urban renewal project management system, and the block characteristic information can be obtained through a GIS geographic information system query. For example, the urban renewal stage information can be divided into three levels: "initial stage", "mid-term stage" and "mature stage". The block characteristic information can be divided into three categories: "commercial block", "residential block" and "industrial block", but is not limited to this.

[0044] Among them, in step A302, the generation of the weight adjustment instruction can be realized by a preset rule base, which stores the corresponding weight adjustment parameters under different stages and block feature combinations. For example, when urban renewal is in the early stage and the block feature is a residential block, the rule base sets the building facade damage risk weight adjustment parameter to increase by 10% and the traffic impact risk weight adjustment parameter to decrease by 5%.

[0045] In step A303, the preset building facade damage risk weight and traffic impact risk weight may be the default values ​​set when the system is initialized. The adjustment process is to perform addition and subtraction operations on the default weight values ​​according to the parameters in the weight adjustment instruction.

[0046] Among them, in step A304, the weighted risk assessment model can be: comprehensive risk score = building facade damage risk weight*(building facade damage level / building facade damage level threshold)+traffic impact risk weight*(traffic impact level / traffic impact level threshold); wherein, the building facade damage risk weight and the traffic impact risk weight adopt the adjusted risk weights.

[0047] Among them, in step A305, the calculation of the comprehensive risk score is based on the building facade damage level and traffic impact level evaluated in step A2, and is substituted into the weighted risk assessment model constructed in step A304 for calculation.

[0048] Specifically, in view of the problem that fixed risk weights may lead to distortion of assessment results under different urban renewal stages and different block characteristics, a technical solution for dynamically adjusting risk weights is proposed. In the dynamic adjustment process of urban renewal planning schemes, different stages and different blocks may pay different attention to the risk of building facade damage and traffic impact risk. In order to fully reflect the actual needs of urban renewal, ensure that the risk assessment results are consistent with the actual situation, and thus improve the effectiveness and accuracy of the adjustment of urban renewal planning schemes, the current urban renewal stage information and block characteristic information are obtained through step A301 as the basis for weight adjustment. Then, in step A302, a weight adjustment instruction is generated according to the acquired information, and the instruction clarifies the adjustment parameters of the building facade damage risk weight and the traffic impact risk weight. Step A303 adjusts the preset risk weight according to the adjustment parameters to obtain a weight value that is more in line with the current situation. Step A304 uses the adjusted risk weights to construct a weighted risk assessment model to ensure that the model can perform risk assessment according to the latest weight configuration. Finally, step A305 uses the risk assessment model with dynamically adjusted weights, combined with the building facade damage level and traffic impact level assessed in the previous steps, to calculate the comprehensive risk score. As a result, adaptive adjustment of risk weights is achieved, enabling the risk assessment model to better adapt to the differentiated needs of different stages and blocks in the urban renewal process, making the comprehensive risk score more accurate and in line with actual conditions, thereby providing a more reliable basis for the dynamic adjustment of urban renewal planning schemes and improving the flexibility and effectiveness of urban renewal planning.

[0049] In some specific implementations, the urban renewal stage information is divided into three levels: "initial stage", "mid-term stage" and "mature stage". The block characteristic information is divided into three categories: "commercial block", "residential block" and "industrial block". The preset initial value of the building facade damage risk weight is set to 0.6, and the initial value of the traffic impact risk weight is set to 0.4. In the rule base, for the combination of "initial stage" + "residential block", the set weight adjustment instruction is to increase the building facade damage risk weight adjustment parameter by 0.1 and reduce the traffic impact risk weight adjustment parameter by 0.05. When the system obtains the current urban renewal stage information as "initial stage" and the block characteristic information as "residential block", a weight adjustment instruction is generated. According to the instruction, the building facade damage risk weight is adjusted to 0.7, and the traffic impact risk weight is adjusted to 0.35. The weighted risk assessment model is constructed based on the adjusted weights and used for subsequent comprehensive risk score calculations. In this way, the risk assessment model can dynamically adjust the weights for different stages and block characteristics, more accurately reflect the actual risk situation, and make the adjustment of the urban renewal planning scheme more targeted and effective.

[0050] In some implementations, step A302 includes: A302a. Information reliability check: A302a1. Identify the data sources for information on the current urban renewal stage and block characteristics; A302a2. Evaluate the credibility level of data sources; A302a3. Compare the credibility level of the data source with a preset credibility threshold; A302a4. If the credibility level of the data source is lower than the credibility threshold, the information correction process is initiated, and the information correction process includes: Use the preset default urban renewal stage information and default block characteristic information to replace the current urban renewal stage information and block characteristic information; or Query the expert knowledge base to obtain historical urban renewal stage information and historical block characteristic information related to the current urban renewal area, and based on the historical urban renewal stage information and historical block characteristic information, correct the current urban renewal stage information and block characteristic information; A302b. Generate weight adjustment instructions based on the current urban renewal stage information and block characteristic information after information reliability verification or information correction processing.

[0051] That is, before generating the weight adjustment instruction based on the current urban renewal stage information and the block characteristic information, the current urban renewal stage information and the block characteristic information are first verified.

[0052] Among them, the accuracy of the urban renewal stage information and block characteristic information based on which the weight adjustment instruction is generated is guaranteed through the information reliability verification step. Specifically, the source of information is first identified, such as a data platform from an authoritative department, from a web crawler, from a professional sensor network, manual entry, or from other systems. Then the credibility of the data source is evaluated. For example, if the information comes from the data platform of an authoritative department, the credibility is high; if it comes from a web crawler, the credibility may be low. A credibility threshold is set to determine whether the information source is reliable. If the credibility of the information source is lower than this threshold, the information may have errors, so the information correction processing is performed. The information correction processing provides two ways: one is to directly replace it with the preset default information, which is a fast but may not be accurate enough; the other is to query the expert knowledge base and use historical data and expert experience to correct the current information. This method is more sophisticated and can improve the accuracy of the information. Finally, the weight adjustment instruction is generated based on the information that has been verified or corrected for reliability, thereby ensuring that the subsequent risk assessment and planning scheme adjustment are based on relatively reliable information. Through the information reliability verification step, the planning adjustment deviation can be effectively reduced, and the accuracy and effectiveness of the dynamic adjustment of the urban renewal planning scheme can be improved.

[0053] Specifically, in the information reliability verification link, the data source identification operation can be implemented by analyzing the header information of the data packet or querying the log of the data management system. For example, if the data comes from the official database API interface of the urban management department, the data source is identified as "official database API" and the credibility level is rated as "high". On the contrary, if the data comes from the web crawler program, the data source is identified as "web crawler" and the credibility level is rated as "low". The credibility level evaluation operation can adopt a preset scoring standard or credibility evaluation model. For example, a credibility level table can be preset to classify common data sources and assign corresponding credibility levels, such as "official database API-high", "professional sensor network-medium", "web crawler-low", and "manual entry-medium". The credibility threshold can be set to "medium", for example. When the credibility level of the data source is lower than "medium", the information correction process is started. In the information correction process, the preset default information is used for replacement. As a quick correction method, the default information can be set to the urban renewal stage as "initial" and the block feature as "residential block". In the process of querying the expert knowledge base for information correction, the expert knowledge base can be constructed as a database containing historical urban renewal project information. By searching the database, historical information related to the current urban renewal area is obtained. For example, if the current area is a historical and cultural block, the stage information and block characteristic information of the block in the past urban renewal project are retrieved from the knowledge base, and the current information is corrected accordingly (for example, the retrieved block characteristic information is used as the corrected block characteristic information. If the interval between the update time corresponding to the latest stage information retrieved and the current time does not exceed the preset interval threshold, or the latest stage information retrieved is the last stage, such as the mature stage, the retrieved stage information is used as the corrected current urban renewal stage information. Otherwise, the next stage of the retrieved stage information is used as the corrected current urban renewal stage information). In the weight adjustment instruction generation stage, the weight adjustment instruction can be encoded as an instruction signal containing weight adjustment parameters to adjust the weight parameters in the risk assessment model.

[0054] In some specific embodiments, the data source of the urban renewal stage information is identified as the urban renewal management system, and the data source of the block characteristic information is identified as the geographic information system. The credibility level of the urban renewal management system is evaluated as "high", and the credibility level of the geographic information system is evaluated as "medium". The credibility threshold is preset to "high". In the information reliability verification link, the credibility level of the data source of the geographic information system "medium" is lower than the credibility threshold "high", and the information correction process is started. In the information correction process, the expert knowledge base is queried, and the expert knowledge base stores the archival information of the historical urban renewal project. Through the expert knowledge base query, it is obtained that the historical renewal stage of the block is "mid-term" and the historical block characteristics are "residential area". The current block characteristic information is corrected to "residential area". The weight adjustment instruction is generated based on the corrected block characteristic information and urban renewal stage information. The weight adjustment instruction includes the building facade damage risk weight adjustment parameter of increasing 0.15 and the traffic impact risk weight adjustment parameter of decreasing 0.05. As a result, the accuracy of the weight adjustment instruction is guaranteed, and the effectiveness of the dynamic adjustment of the urban renewal planning scheme is improved.

[0055] In some embodiments, step A3 comprises: A311. Collect historical cruise data within a preset time period, including historical building facade damage level data and historical traffic impact level data; A312. Based on the historical building facade damage level data and historical traffic impact level data, the threshold optimization algorithm is used to calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold; A313. Construct a weighted risk assessment model based on the optimized building facade damage level threshold, the optimized traffic impact level threshold, the preset building facade damage risk weight and the traffic impact risk weight; A314. Calculate the comprehensive risk score based on the building facade damage level and traffic impact level, as well as the constructed weighted risk assessment model.

[0056] Among them, in step A311, the preset time period can be set to, for example, the most recent month, the most recent quarter, or the most recent year, so as to collect sufficient historical cruise data for subsequent threshold optimization calculations. The historical cruise data is obtained by processing the street view video data obtained by the drone through regular cruise shooting of urban blocks at different time points through the information recognition and evaluation module. The historical building facade damage level data and the historical traffic impact level data are quantitative values ​​output by the information recognition and evaluation module after analyzing and evaluating the historical street view video data.

[0057] Among them, in step A312, the threshold optimization algorithm is an algorithm that can automatically adjust the threshold according to historical data, for example, a grid search algorithm, a genetic algorithm or a particle swarm optimization algorithm can be used. The grid search algorithm is to traverse all possible threshold combinations with a set step size within a preset threshold search range, and calculate the model performance index under each set of thresholds, and finally select the threshold combination with the best performance index as the optimized threshold. The genetic algorithm is an optimization algorithm that simulates the biological evolution process, and gradually searches for the optimal threshold through operations such as selection, crossover and mutation. The particle swarm optimization algorithm is an optimization algorithm that simulates the foraging behavior of bird flocks, and searches for the optimal threshold through collaboration and information sharing between particles.

[0058] Among them, in step A313, the weighted risk assessment model is a model used to calculate the comprehensive risk score. The model takes the building facade damage level, traffic impact level, building facade damage level threshold and traffic impact level threshold as input, and calculates the comprehensive risk score by weighted summation (the specific model can be referred to in the previous text).

[0059] In step A314, the comprehensive risk score is an indicator used to measure the necessity of adjusting the urban renewal plan. The higher the comprehensive risk score, the higher the risk of the current state of the urban block, and the more dynamic adjustment of the urban renewal plan is needed.

[0060] Specifically, the scheme works as follows: First, in step A311, the system collects historical cruise data over a period of time, which includes the historical building facade damage level and the historical traffic impact level. The purpose of collecting historical data is to provide a data basis for subsequent threshold optimization, so that the optimized threshold can better reflect the law of risk changes in urban blocks. Then, in step A312, the system uses the threshold optimization algorithm to automatically calculate the optimized building facade damage level threshold and traffic impact level threshold using the collected historical data. The threshold optimization algorithm can find the optimal threshold setting based on historical data, so that risk assessment based on these thresholds can be more accurate. For example, if historical data shows that the risk assessment model has the best prediction performance when the building facade damage level threshold is set to level 3 and the traffic impact level threshold is set to level 4, then the threshold optimization algorithm will set the optimized building facade damage level threshold to level 3 and the optimized traffic impact level threshold to level 4. Then, in step A313, the system constructs a weighted risk assessment model based on the optimized threshold and the preset risk weight. Compared with the fixed thresholds used in the prior art, the risk assessment model constructed using the optimized thresholds can more accurately assess the comprehensive risk of urban blocks. Finally, in step A314, the system calculates the comprehensive risk score based on the building facade damage level and the traffic impact level using the constructed weighted risk assessment model. The calculated comprehensive risk score can more accurately reflect the actual risk level of the urban block, and provide a more reliable basis for the subsequent dynamic adjustment of the urban renewal planning scheme according to the risk score. Therefore, through the above steps, the adaptive optimization of the threshold is achieved, which overcomes the defect that the preset threshold is fixed and difficult to accurately reflect the dynamic changes of urban renewal, and improves the accuracy and adaptability of the dynamic adjustment of the urban renewal planning scheme.

[0061] It should be noted that in step A3, both risk weight adjustment and level threshold adjustment can be performed, so as to more accurately reflect the dynamic changes of urban renewal and further improve the accuracy and adaptability of dynamic adjustment of urban renewal planning schemes. At this time, step A3 includes: A301. Obtain information on the current urban renewal stage and block characteristics; A302. Generate a weight adjustment instruction based on the current urban renewal stage information and the block characteristic information, the weight adjustment instruction including a building facade damage risk weight adjustment parameter and a traffic impact risk weight adjustment parameter; A303. According to the building facade damage risk weight adjustment parameter and the traffic impact risk weight adjustment parameter, adjust the preset building facade damage risk weight and traffic impact risk weight to obtain an adjusted building facade damage risk weight and an adjusted traffic impact risk weight; A311. Collect historical cruise data within a preset time period, including historical building facade damage level data and historical traffic impact level data; A312. Based on the historical building facade damage level data and historical traffic impact level data, the threshold optimization algorithm is used to calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold; A315. Construct a weighted risk assessment model based on the optimized building facade damage level threshold, the optimized traffic impact level threshold, the adjusted building facade damage risk weight and the adjusted traffic impact risk weight; A316. Calculate the comprehensive risk score based on the building facade damage level and traffic impact level, as well as the constructed weighted risk assessment model.

[0062] In some specific implementations, step A312 includes: Based on the historical building facade damage level data and historical traffic impact level data, the grid search method is used to calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold.

[0063] Through the grid search algorithm, the optimal threshold combination can be automatically found, so that the risk assessment model can more accurately identify situations where urban renewal planning schemes need to be adjusted, thereby improving the efficiency and quality of urban renewal planning.

[0064] Specifically, based on the historical building facade damage level data and the historical traffic impact level data, the steps of calculating the optimized building facade damage level threshold and the optimized traffic impact level threshold by using the grid search method include: B1, the upper limit of the initial evaluation value is -1, the threshold value of the building facade damage level D_1 after initial optimization is empty, and the threshold value of the traffic impact level T_1 after initial optimization is empty; B2, set the threshold search range D_r of the building facade damage level and the threshold search range T_r of the traffic impact level, as well as the threshold search step length st; B3, within the building facade damage level threshold search range D_r, with the threshold search step length st as the interval, select the current building facade damage level threshold D_0; B4, within the traffic impact level threshold search range T_r, with the threshold search step length st as the interval, select the current traffic impact level threshold T_0; B5, initialize the confusion matrix parameters: true positive parameter TP=0, false positive parameter FP=0, false negative parameter FN=0, true negative parameter TN=0; B6, traverse the historical data (including historical building facade damage level data, historical traffic impact level data and corresponding historical adjustment decision data), for the i-th historical data point (including the i-th historical building facade damage level data d_i, the i-th historical traffic impact level data t_i and the i-th historical adjustment decision data a_i), where i ranges from 1 to n, and n is the total number of historical data points: If the i-th historical building facade damage level data d_i is greater than or equal to the current building facade damage level threshold D_0, or the i-th historical traffic impact level data t_i is greater than or equal to the current traffic impact level threshold T_0, then it is predicted that the urban renewal plan needs to be adjusted, and the i-th prediction adjustment decision A_i=1; otherwise, it is predicted that the urban renewal plan does not need to be adjusted, and the i-th prediction adjustment decision A_i=0; Compare the predicted adjustment decision A_i with the i-th historical adjustment decision data a_i, and update the values ​​of the true positive parameter TP, the false positive parameter FP, the false negative parameter FN, and the true negative parameter TN; where a_i is 1 or 0, a_i=1 means that the situation corresponding to the i-th historical data point was historically determined to require adjustment of the urban renewal planning scheme, and a_i=0 means that no adjustment is required; B7, calculate the precision rate Pr, recall rate Rc and evaluation value; the specific calculation method is: Pr = TP / (TP+FP), when TP+FP = 0, Pr = 0; Rc = TP / (TP+FN), when TP+FN = 0, Rc = 0; evaluation value = 2*(Pr*Rc) / (Pr+Rc), when Pr+Rc = 0, evaluation value = 0; B8, if the current evaluation value is greater than the upper limit of the evaluation value, the upper limit of the evaluation value is updated to the current evaluation value, the optimized building facade damage level threshold D_1 is updated to the current building facade damage level threshold D_0, and the optimized traffic impact level threshold T_1 is updated to the current traffic impact level threshold T_0; B9, repeat B4 to B8 until all thresholds in the traffic impact level threshold search range T_r are traversed; B10, repeat B3 to B9 until all thresholds within the building facade damage level threshold search range D_r are traversed; B11, taking the optimized building facade damage level threshold D_1 and the optimized traffic impact level threshold T_1 as the calculation results of step A312.

[0065] Among them, the initialization process in step B1 provides the initial state for the subsequent threshold optimization. The upper limit of the evaluation value is set to -1 to ensure that any calculated evaluation value in the first iteration will be higher than this initial value. The optimized building facade damage level threshold D_1 and the optimized traffic impact level threshold T_1 are initialized to empty, indicating that the optimal threshold has not been determined before the optimization begins.

[0066] Among them, step B2 sets the search range and step size. The building facade damage level threshold search range D_r and the traffic impact level threshold search range T_r define the interval of possible threshold values. The threshold search step size st determines the degree of refinement of the threshold adjustment within the search range and can be adjusted according to actual needs.

[0067] Among them, steps B3 and B4 describe the threshold selection method. Within the respective search ranges, the current building facade damage level threshold D_0 and the current traffic impact level threshold T_0 are cyclically selected at intervals of the set step size. This grid search method aims to traverse all possible threshold combinations.

[0068] Among them, step B5 initializes the confusion matrix parameters, and the true positive parameter TP, the false positive parameter FP, the false negative parameter FN and the true negative parameter TN are initialized to 0, in preparation for the subsequent evaluation of the performance of the current threshold combination based on historical data.

[0069] Among them, step B6 is the core evaluation step. By traversing the historical data, for each historical data point, prediction is made using the currently selected threshold combination (D_0 and T_0), the prediction results are compared with the historical adjustment decision data, and the confusion matrix parameters are updated. The confusion matrix provides a data basis for the subsequent calculation of precision and recall.

[0070] Among them, step B7 calculates the evaluation value, which comprehensively considers the precision and recall rate, and is used to quantify the quality of the current threshold combination, providing a basis for the subsequent optimal threshold selection.

[0071] Among them, step B8 executes threshold update. If the evaluation value of the current threshold combination is better than the historical optimal value (upper limit of the evaluation value), the upper limit of the evaluation value is updated, and the current threshold combination is set to the optimized building facade damage level threshold D_1 and the optimized traffic impact level threshold T_1 to ensure that the algorithm can find the optimal threshold combination.

[0072] Among them, steps B9 and B10 control the traversal of the search range. Step B9 ensures that under the current building facade damage level threshold D_0, all thresholds within the traffic impact level threshold search range T_r are traversed, and step B10 further controls the traversal of all thresholds within the building facade damage level threshold search range D_r to complete full coverage of the entire search space.

[0073] Among them, step B11 uses the searched optimal thresholds D_1 and T_1 as the output results of step A312 for the subsequent risk assessment model construction, providing accurate threshold parameters for the dynamic adjustment of urban renewal planning schemes. Through the above steps, the complete process of the threshold optimization algorithm is realized, providing technical guarantee for improving the effectiveness of dynamic adjustment of urban renewal planning schemes.

[0074] Specifically, the above method provides a threshold optimization algorithm based on grid search and evaluation value, which is used to determine the optimal threshold of building facade damage level and traffic impact level. The algorithm first initializes the optimal threshold and the upper limit of the evaluation value, and sets the search range and step size of the threshold. Then, all possible threshold combinations are traversed through nested loops. For each threshold combination, the evaluation process is simulated using historical cruise data, and compared with historical adjustment decisions to calculate precision, recall and evaluation value. The algorithm iteratively updates the optimal threshold by comparing the current evaluation value with the upper limit of the evaluation value, and finally obtains a set of threshold combinations that perform best on historical data. The method guides threshold optimization by quantitative index evaluation values, ensuring the objectivity and quantifiability of the optimization process. Through grid search, the threshold search space is covered as much as possible, which increases the possibility of finding the global optimal solution. By using historical data for evaluation and optimization, the optimized threshold can better adapt to the needs of actual urban renewal scenarios, thereby improving the accuracy and effectiveness of the dynamic adjustment of urban renewal planning schemes, solving the problem of delayed adjustment of urban renewal planning schemes mentioned in the background technology, and the difficulty in adapting to the rapid changes in urban blocks, and realizing real-time response and adjustment of planning schemes.

[0075] In some specific implementations, the building facade damage level threshold search range D_r is set to [1,5], the traffic impact level threshold search range T_r is set to [1,5], and the threshold search step st is set to 1. The historical data contains 1000 data points, each of which includes historical building facade damage level data d_i, historical traffic impact level data t_i, and historical adjustment decision data a_i. At the beginning of the algorithm, the initial evaluation value upper limit is -1, and the optimized building facade damage level threshold D_1 and the optimized traffic impact level threshold T_1 are empty. The algorithm first traverses the building facade damage level threshold search range [1,5] with a step size of 1, and selects the current building facade damage level threshold D_0. For example, D_0=1 is first selected. Then, in the traffic impact level threshold search range [1,5], the current traffic impact level threshold T_0 is also traversed with a step size of 1. For example, T_0=1 is first selected. Next, initialize the confusion matrix parameters TP, FP, FN, and TN to 0. Traverse 1000 historical data points. For each data point, if d_i≥D_0 or t_i≥T_0, predict A_i=1, otherwise A_i=0. Compare A_i with a_i and update the confusion matrix. After the traversal is completed, calculate the precision Pr, recall Rc, and evaluation value. If the current evaluation value is greater than the upper limit of the evaluation value, update the upper limit of the evaluation value to the current evaluation value, update D_1=D_0, and T_1=T_0. Then, continue to select the next T_0 value within the traffic impact level threshold search range [1,5], and repeat the above process until the traffic impact level threshold search range is traversed. After that, select the next D_0 value and repeat the process of traversing the traffic impact level threshold search range until the building facade damage level threshold search range is traversed. Finally, the optimized building facade damage level threshold D_1 and the optimized traffic impact level threshold T_1 are obtained. For example, after calculation, the final optimized building facade damage level threshold D_1 is 3, and the optimized traffic impact level threshold T_1 is 2. These two thresholds will be used in the subsequent weighted risk assessment model to more accurately calculate the comprehensive risk score, thereby more effectively guiding the dynamic adjustment of urban renewal planning schemes.

[0076] In some preferred embodiments, step A312 further includes: B12, calculate the rate of change of historical building facade damage level data and historical traffic impact level data; B13, comparing the change rate with a preset change rate threshold; B14, when at least one change rate is greater than or equal to the change rate threshold, increase the execution frequency of the threshold optimization algorithm; when the change rates are all less than the change rate threshold, reduce the execution frequency of the threshold optimization algorithm; B15, execute the threshold optimization algorithm according to the adjusted execution frequency, and calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold.

[0077] Among them, in step B12, the change rate of historical building facade damage level data and historical traffic impact level data can be calculated as the change in the average building facade damage level or the average traffic impact level within a preset detection period, for example, one month or one quarter.

[0078] In step B13, the preset change rate threshold is a reference value for judging the speed of change of urban street scenes and traffic conditions, and the threshold can be set according to actual application scenarios and requirements, for example, set to 10% or 20%. The change rate of the historical building facade damage level data and the change rate of the historical traffic impact level data can be compared with the same change rate threshold, or can be compared with two different change rate thresholds respectively.

[0079] Among them, in step B14, increasing the execution frequency of the threshold optimization algorithm can be to adjust the optimization algorithm originally executed once a month to once a week, and reducing the execution frequency can be to adjust the optimization algorithm originally executed once a month to once a quarter. It should be noted that if the change rate of the historical building facade damage level data and the change rate of the historical traffic impact level data in step B13 are respectively compared with two different change rate thresholds, then step B14 actually means: when at least one of the change rate of the historical building facade damage level data and the change rate of the historical traffic impact level data is greater than or equal to the corresponding change rate threshold, the execution frequency of the threshold optimization algorithm is increased; when the change rate of the historical building facade damage level data and the change rate of the historical traffic impact level data are both less than the corresponding change rate threshold, the execution frequency of the threshold optimization algorithm is reduced.

[0080] Among them, in step B15, the threshold optimization algorithm is the algorithm described above. Through the adjusted execution frequency, the algorithm is executed to calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold that are adapted to the current urban change speed.

[0081] Specifically, in response to the problem that the execution frequency of the threshold optimization algorithm is fixed, the present application proposes a technical means for dynamically adjusting the execution frequency of the threshold optimization algorithm according to the change rate of historical data. First, in step B12, the change rate of the historical building facade damage level data and the historical traffic impact level data is calculated, which reflects the change range of the urban renewal related data over a period of time. Then, in step B13, the change rate is compared with the preset change rate threshold to determine the speed of change of the urban block status. When at least one change rate is greater than or equal to the change rate threshold, it indicates that the urban block status changes rapidly. At this time, in step B14, the execution frequency of the threshold optimization algorithm is increased to ensure that the threshold can be updated in time, so as to more accurately assess the current risk status. On the contrary, when the change rate is less than the change rate threshold, it indicates that the urban block status changes slowly. At this time, in step B14, the execution frequency of the threshold optimization algorithm is reduced to reduce unnecessary computing resource consumption. Finally, in step B15, the threshold optimization algorithm is executed according to the adjusted execution frequency to calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold. As a result, the execution frequency of the threshold optimization algorithm matches the actual situation of urban changes, improving resource utilization efficiency and ensuring the timeliness and accuracy of risk assessment.

[0082] Change rate of historical building facade damage level data and historical traffic impact level data In some specific embodiments, the preset detection cycle is set to one month, and the change rate of the historical building facade damage level data is calculated as the ratio of the difference between the average value of the building facade damage level this month and last month to the average value of the building facade damage level last month, and the change rate of the historical traffic impact level data is calculated as the ratio of the difference between the average value of the traffic impact level this month and last month to the average value of the traffic impact level last month. The preset change rate threshold is set to 15%. Assuming that the average value of the building facade damage level last month is 2, and the average value of the building facade damage level this month is 2.5, the corresponding change rate is (2.5-2) / 2=25%, the average value of the traffic impact level last month is 2.3, and the average value of the traffic impact level this month is 2.8, then the corresponding change rate is (2.8-2.3) / 2.8=17.9%. Since 25% and 17.9% are both greater than 15%, the execution frequency of the threshold optimization algorithm is increased, for example, the execution cycle of the threshold optimization algorithm is adjusted from once a month to once every two weeks. On the contrary, if the average value of the building facade damage level this month is 2.1, the corresponding change rate is (2.1-2) / 2=5%, and the average value of the traffic impact level this month is 2.5, then the corresponding change rate is (2.5-2.3) / 2.5=8%. Since 5% and 8% are less than 15%, the execution frequency of the threshold optimization algorithm is reduced. For example, the execution cycle of the threshold optimization algorithm is adjusted from once a month to once a quarter. In this way, the execution frequency of the threshold optimization algorithm can be adaptively adjusted according to the speed of changes in the urban block conditions.

[0083] In some embodiments, step A4 comprises: A401. Determine the risk level based on the comprehensive risk score; A402. According to the determined risk level, select the corresponding adjustment strategy and dynamically adjust the urban renewal planning scheme; among them, the higher the risk level, the greater the adjustment range of the adjustment strategy.

[0084] Among them, in step A401, the comprehensive risk score calculated in step A3 is mapped to the risk level. The technical effect brought about by this is that the continuous risk score value can be converted into a discrete risk level, so that the subsequent step A402 can select a suitable adjustment strategy according to the risk level. For example, the mapping relationship between the risk score and the risk level can be preset, such as dividing the comprehensive risk score into three levels: low risk, medium risk and high risk. The score of 0-30 points is divided into a low risk level, the score of 31-70 points is divided into a medium risk level, and the score of 71-100 points is divided into a high risk level. Therefore, through the risk level division, the hierarchical processing of risk scores is realized, which provides a basis for the formulation of subsequent adjustment strategies.

[0085] Among them, in step A402, according to the risk level determined in step A401, a corresponding adjustment strategy is selected from the preset adjustment strategy library, and the urban renewal planning scheme is adjusted. The adjustment strategy library stores adjustment strategies corresponding to different risk levels, and the risk level is associated with the adjustment range of the adjustment strategy. The higher the risk level, the greater the adjustment range of the adjustment strategy. For example, for low risk levels, a slight adjustment strategy can be selected, such as increasing the frequency of street view video data acquisition; for medium risk levels, a medium adjustment strategy can be selected, such as fine-tuning the priority of building facade repair in the urban renewal planning scheme; for high risk levels, a substantial adjustment strategy can be selected, such as immediately starting a partial revision of the urban renewal planning scheme, and giving priority to dealing with building facade damage and traffic congestion problems in risk areas. In the above manner, the matching of the adjustment strategy and the risk level is achieved, ensuring that the adjustment range of the adjustment strategy is consistent with the risk level, making the dynamic adjustment of the urban renewal planning scheme more refined and effective.

[0086] Specifically, when the urban planning department uses drone street view video data to dynamically adjust the urban renewal planning scheme, it first obtains a comprehensive risk score through the risk score calculation module. After that, step A401 is executed to convert the comprehensive risk score into the corresponding risk level according to the preset risk level classification standard. For example, when the comprehensive risk score is 65 points, it is determined to be a medium risk level according to the preset score level classification standard. Subsequently, step A402 is executed to find the adjustment strategy corresponding to the medium risk level in the adjustment strategy library according to the determined medium risk level. For example, the adjustment strategy corresponding to the medium risk level can be "fine-tuning the priority of building facade repair in the urban renewal planning scheme". Finally, the planning scheme adjustment module applies the selected adjustment strategy to adjust the current urban renewal planning scheme, for example, the priority of building facade repair originally planned to be repaired three months later is advanced to the next month. Thus, a dynamic adjustment of the urban renewal planning scheme based on the risk level is completed. Through the linkage between the risk level and the adjustment strategy, the adjustment of the urban renewal planning scheme according to the risk level is realized, making the adjustment measures more accurate and efficient.

[0087] In some specific implementations, the correspondence between risk levels and adjustment strategies is pre-configured in the adjustment strategy library. For example, the low risk level corresponds to the strategy of "monitoring only, do not adjust the planning scheme for the time being"; the medium risk level corresponds to the strategy of "adjusting the planning scheme slightly, for example, optimizing traffic signal timing, fine-tuning the building facade maintenance plan"; the high risk level corresponds to the strategy of "adjusting the planning scheme significantly, for example, launching road reconstruction projects, advancing building facade repair projects, and adjusting the functional layout of the block". As a preferred implementation, the adjustment strategies in the adjustment strategy library can be further refined, and each risk level can correspond to multiple adjustment strategies of different magnitudes, and can be flexibly selected for application according to actual conditions. For example, the medium risk level can be subdivided into medium-low risk, medium risk, and medium-high risk sub-levels, and each corresponds to an adjustment strategy of different magnitudes to achieve more refined adjustment control.

[0088] refer to Figure 2 The present application also provides a dynamic adjustment device for an urban renewal planning scheme, the device comprising: Data acquisition module 1, used to acquire urban street view video data through regular drone cruise shooting (refer to step A1 above for the specific process); Information identification and evaluation module 2, used to pre-process the urban street view video data, identify building facade damage information and traffic information from the pre-processed urban street view video data, and evaluate the building facade damage level and traffic impact level (for the specific process, refer to step A2 above); The risk score calculation module 3 is used to construct a weighted risk assessment model based on the preset building facade damage level threshold, traffic impact level threshold, building facade damage risk weight and traffic impact risk weight, and calculate the comprehensive risk score according to the building facade damage level and the traffic impact level (for the specific process, refer to step A3 above); The planning scheme adjustment module 4 is used to determine the adjustment strategy according to the comprehensive risk score, so as to dynamically adjust the urban renewal planning scheme (for the specific process, please refer to step A4 above).

[0089] Please refer to Figure 3 , Figure 3A structural schematic diagram of an electronic device provided in an embodiment of the present application, the present application provides an electronic device, including: a processor 301 and a memory 302, the processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanisms (not shown), the memory 302 stores a computer program executable by the processor 301, and when the electronic device is running, the processor 301 executes the computer program to perform the dynamic adjustment of the urban renewal planning scheme in any optional implementation of the above-mentioned embodiment, so as to achieve the following functions: obtaining urban street view video data through normalized cruise shooting of unmanned aerial vehicles; preprocessing the urban street view video data, and identifying building facade damage information and traffic information from the preprocessed urban street view video data, and evaluating the building facade damage level and traffic impact level; constructing a weighted risk assessment model based on a preset building facade damage level threshold, a traffic impact level threshold, a building facade damage risk weight and a traffic impact risk weight, and calculating a comprehensive risk score according to the building facade damage level and the traffic impact level; determining an adjustment strategy according to the comprehensive risk score to dynamically adjust the urban renewal planning scheme.

[0090] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the dynamic adjustment of the urban renewal planning scheme in any optional implementation method of the above-mentioned embodiment is performed to achieve the following functions: obtaining urban street view video data through regular cruise shooting of an unmanned aerial vehicle; preprocessing the urban street view video data, and identifying building facade damage information and traffic information from the preprocessed urban street view video data, and evaluating the building facade damage level and traffic impact level; constructing a weighted risk assessment model based on a preset building facade damage level threshold, a traffic impact level threshold, a building facade damage risk weight, and a traffic impact risk weight, and calculating a comprehensive risk score based on the building facade damage level and the traffic impact level; determining an adjustment strategy based on the comprehensive risk score to dynamically adjust the urban renewal planning scheme.

[0091] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0092] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0093] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0095] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0096] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for dynamically adjusting an urban renewal planning scheme, characterized in that: The steps of the method include: A1. Obtain urban street view video data through regular drone cruise shooting; A2. Preprocessing the urban street view video data, identifying building facade damage information and traffic information from the preprocessed urban street view video data, and evaluating the building facade damage level and traffic impact level; A3. Based on the preset building facade damage level threshold, traffic impact level threshold, building facade damage risk weight and traffic impact risk weight, a weighted risk assessment model is constructed, and a comprehensive risk score is calculated according to the building facade damage level and the traffic impact level; A4. Determine an adjustment strategy based on the comprehensive risk score to dynamically adjust the urban renewal planning scheme.

2. The method for dynamically adjusting an urban renewal planning scheme according to claim 1, characterized in that: The step A2 comprises: A201. Detecting weather conditions corresponding to the city street view video data; A202. According to the detected weather conditions, the image preprocessing parameters are adaptively adjusted to preprocess the city street view video data to obtain preprocessed street view video data adapted to the weather conditions; A203. Identify building facade damage information and traffic information from the pre-processed street view video data adapted to weather conditions, and evaluate the building facade damage level and traffic impact level.

3. The method for dynamically adjusting an urban renewal planning scheme according to claim 1, characterized in that: Step A3 includes: A301. Obtain information on the current urban renewal stage and block characteristics; A302. Generate a weight adjustment instruction based on the current urban renewal stage information and the block characteristic information, the weight adjustment instruction including a building facade damage risk weight adjustment parameter and a traffic impact risk weight adjustment parameter; A303. According to the building facade damage risk weight adjustment parameter and the traffic impact risk weight adjustment parameter, adjust the preset building facade damage risk weight and traffic impact risk weight to obtain an adjusted building facade damage risk weight and an adjusted traffic impact risk weight; A304. Construct a weighted risk assessment model based on the preset building facade damage level threshold, traffic impact level threshold, adjusted building facade damage risk weight and adjusted traffic impact risk weight; A305. Calculate a comprehensive risk score based on the building facade damage level and the traffic impact level, as well as the constructed weighted risk assessment model.

4. The method for dynamically adjusting an urban renewal planning scheme according to claim 3, characterized in that: Step A302 includes: A302a. Information reliability check: A302a1. Identify the data source of the current urban renewal stage information and the block feature information; A302a2. Evaluate the credibility level of the data source; A302a3. Compare the credibility level of the data source with a preset credibility threshold; A302a4. If the credibility level of the data source is lower than the credibility threshold, the information correction process is started, and the information correction process includes: Using preset default urban renewal stage information and default block characteristic information to replace the current urban renewal stage information and the block characteristic information; or Query the expert knowledge base to obtain historical urban renewal stage information and historical block characteristic information related to the current urban renewal area, and based on the historical urban renewal stage information and historical block characteristic information, correct the current urban renewal stage information and the block characteristic information; A302b. Generate weight adjustment instructions based on the current urban renewal stage information and block characteristic information after information reliability verification or information correction processing.

5. The method for dynamically adjusting an urban renewal planning scheme according to claim 1, characterized in that: Step A3 includes: A311. Collect historical cruise data within a preset time period, the historical cruise data including historical building facade damage level data and historical traffic impact level data; A312. Based on the historical building facade damage level data and the historical traffic impact level data, a threshold optimization algorithm is used to calculate an optimized building facade damage level threshold and an optimized traffic impact level threshold; A313. Based on the optimized building facade damage level threshold, the optimized traffic impact level threshold, the preset building facade damage risk weight and the traffic impact risk weight, a weighted risk assessment model is constructed; A314. Calculate a comprehensive risk score based on the building facade damage level and the traffic impact level, as well as the constructed weighted risk assessment model.

6. A method for dynamically adjusting an urban renewal planning scheme according to claim 5, characterized in that: Step A312 includes: Based on the historical building facade damage level data and the historical traffic impact level data, a grid search method is used to calculate an optimized building facade damage level threshold and an optimized traffic impact level threshold.

7. The method for dynamically adjusting an urban renewal planning scheme according to claim 5, characterized in that: The step A312 includes: B12, calculating the change rate of the historical building facade damage level data and the historical traffic impact level data; B13, comparing the change rate with a preset change rate threshold; B14, when at least one of the change rates is greater than or equal to the change rate threshold, increasing the execution frequency of the threshold optimization algorithm; when the change rates are all less than the change rate threshold, reducing the execution frequency of the threshold optimization algorithm; B15, executing the threshold optimization algorithm according to the adjusted execution frequency, and calculating the optimized building facade damage level threshold and the optimized traffic impact level threshold.

8. A dynamic adjustment device for urban renewal planning scheme, characterized in that: The device includes: A data acquisition module is used to acquire urban street view video data through regular drone cruise shooting; An information identification and evaluation module, used to pre-process the urban street view video data, identify building facade damage information and traffic information from the pre-processed urban street view video data, and evaluate the building facade damage level and traffic impact level; A risk score calculation module is used to construct a weighted risk assessment model based on a preset building facade damage level threshold, a traffic impact level threshold, a building facade damage risk weight, and a traffic impact risk weight, and calculate a comprehensive risk score according to the building facade damage level and the traffic impact level; The planning scheme adjustment module is used to determine the adjustment strategy according to the comprehensive risk score, so as to dynamically adjust the urban renewal planning scheme.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, it runs the steps in the method for dynamic adjustment of the urban renewal planning scheme as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for dynamically adjusting an urban renewal planning scheme as described in any one of claims 1 to 7 are performed.

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