A dynamic adjustment method for an urban renewal planning scheme and related devices

Abstract

This application belongs to the technical field of urban planning, and discloses a dynamic adjustment method and related equipment for an urban renewal planning scheme. The method includes: obtaining urban street view video data through normal drone cruising and shooting; 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 preset building facade damage level thresholds, traffic impact level thresholds, building facade damage risk weights, and traffic impact risk weights, and calculating a comprehensive risk score according to the building facade damage level and traffic impact level; determining an adjustment strategy according to the comprehensive risk score for dynamically adjusting the urban renewal planning scheme; thereby being able to improve the adjustment efficiency of the urban renewal planning scheme.

Description

Technical Field

[0001] This application relates to the technical field of urban planning. Specifically, it relates to a method for dynamically adjusting an urban renewal planning scheme and related devices. Background Art

[0002] During the renovation of old urban areas and urban renewal, urban planning departments face the need to evaluate the conditions of blocks and dynamically adjust planning schemes. Traditionally, relying on manual on-site surveys and data collection is inefficient and time-consuming, resulting in a lag in the update of planning schemes and difficulty in meeting the changing needs of urban blocks. Regularly cruising and shooting street view image data with drones equipped with high-definition cameras provides a new data source for urban planning, enabling more efficient acquisition of building facade and traffic information in blocks.

[0003] However, the key lies in how to utilize this data to achieve dynamic adjustment of urban renewal planning schemes. Existing urban renewal planning methods, even with the introduction of drone data, 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 while 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 urban planning field 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 during urban renewal.

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

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

[0006] In a first aspect, this application provides a method for dynamically adjusting an urban renewal planning scheme. The steps of this method include:

[0007] A1. Obtain urban street view video data through regular cruising and shooting with drones;

[0008] A2. Preprocess the urban street view video data, identify 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;

[0009] A3. Based on preset building facade damage level thresholds, traffic impact level thresholds, building facade damage risk weights, and traffic impact risk weights, construct a weighted risk assessment model, and calculate a comprehensive risk score according to the building facade damage level and the traffic impact level;

[0010] A4. Determine an adjustment strategy based on the comprehensive risk score for dynamically adjusting the urban renewal planning scheme.

[0011] This method utilizes drone street view video data to simplify the data analysis process, achieve dynamic adjustment of the planning scheme, and improve the efficiency of adjusting the urban renewal planning scheme.

[0012] Preferably, step A2 includes:

[0013] A201. Detect the weather conditions corresponding to the urban street view video data;

[0014] A202. According to the detected weather conditions, adaptively adjust the image preprocessing parameters, perform image preprocessing on the urban street view video data, and obtain the preprocessed street view video data adapted to the weather conditions;

[0015] A203. Identify the building facade damage information and traffic information from the preprocessed street view video data adapted to the weather conditions, and evaluate the building facade damage level and traffic impact level.

[0016] Since the influence of weather conditions has been considered in the preprocessing process and targeted optimization has been carried out, the accuracy and reliability of information identification and level evaluation can be effectively improved.

[0017] Preferably, step A3 includes:

[0018] A301. Obtain the current urban renewal stage information and block feature information;

[0019] A302. Generate a weight adjustment instruction according to the current urban renewal stage information and the block feature information, and the weight adjustment instruction includes a building facade damage risk weight adjustment parameter and a traffic impact risk weight adjustment parameter;

[0020] A303. Adjust the preset building facade damage risk weight and traffic impact risk weight according to the building facade damage risk weight adjustment parameter and the traffic impact risk weight adjustment parameter to obtain the adjusted building facade damage risk weight and the adjusted traffic impact risk weight;

[0021] A304. 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, construct a weighted risk assessment model;

[0022] A305. Calculate the comprehensive risk score according to the building facade damage level and the traffic impact level, and the constructed weighted risk assessment model.

[0023] Thus, the adaptive adjustment of the risk weight is realized, 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 the actual situation, thereby providing a more reliable basis for the dynamic adjustment of the urban renewal planning scheme and improving the flexibility and effectiveness of the urban renewal planning.

[0024] Preferably, step A302 includes:

[0025] A302a. Information reliability verification:

[0026] A302a1. Identify the data sources of the current urban renewal stage information and the block feature information;

[0027] A302a2. Evaluate the credibility level of the data sources;

[0028] A302a3. Compare the credibility level of the data sources with a preset credibility threshold;

[0029] A302a4. If the credibility level of the data sources is lower than the credibility threshold, start the information correction processing flow, and the information correction processing flow includes:

[0030] Use the preset default urban renewal stage information and default block feature information to replace the current urban renewal stage information and the block feature information; or

[0031] Query the expert knowledge base to obtain the historical urban renewal stage information and historical block feature information related to the current urban renewal area, and based on the historical urban renewal stage information and historical block feature information, correct the current urban renewal stage information and the block feature information;

[0032] A302b. Generate a weight adjustment instruction based on the current urban renewal stage information and block feature information after information reliability verification or information correction processing.

[0033] Preferably, step A3 includes:

[0034] A311. Collect historical cruise data within a preset time period, and the historical cruise data includes historical building facade damage level data and historical traffic impact level data;

[0035] A312. Based on the historical building facade damage level data and the historical traffic impact level data, use a threshold optimization algorithm to calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold;

[0036] 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, construct a weighted risk assessment model;

[0037] A314. According to the building facade damage level and the traffic impact level, as well as the constructed weighted risk assessment model, calculate the comprehensive risk score.

[0038] Preferably, step A312 includes:

[0039] Based on the historical building facade damage level data and the historical traffic impact level data, use the grid search method to calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold.

[0040] Preferably, the step A312 includes:

[0041] B12. Calculate the change rates of the historical building facade damage level data and the historical traffic impact level data;

[0042] B13. Compare the change rates with the preset change rate threshold;

[0043] B14. When at least one of the change rates 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, decrease the execution frequency of the threshold optimization algorithm;

[0044] 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.

[0045] In a second aspect, the present application provides a dynamic adjustment device for an urban renewal planning scheme, and the device includes:

[0046] A data acquisition module, configured to obtain urban street view video data through regular drone cruising and shooting;

[0047] An information recognition and evaluation module, configured to preprocess the urban street view video data, identify building facade damage information and traffic information from the preprocessed urban street view video data, and evaluate the building facade damage level and the traffic impact level;

[0048] A risk score calculation module, configured 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;

[0049] A planning scheme adjustment module, configured to determine an adjustment strategy according to the comprehensive risk score for dynamically adjusting the urban renewal planning scheme.

[0050] In a third aspect, the present application provides an electronic device, including a processor and a memory. The memory stores a computer program executable by the processor. When the processor executes the computer program, it runs the steps in the dynamic adjustment method of the urban renewal planning scheme as described above.

[0051] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it runs the steps in the dynamic adjustment method of the urban renewal planning scheme as described above.

[0052] Beneficial effects: The dynamic adjustment method and related devices of the urban renewal planning scheme provided by the present application utilize drone street view video data to simplify the data analysis process, realize the dynamic adjustment of the planning scheme, and improve the adjustment efficiency of the urban renewal planning scheme. Description of the Drawings

[0053] Figure 1 It is a flowchart of the dynamic adjustment method of the urban renewal planning scheme provided by the embodiment of the present application.

[0054] Figure 2 It is a schematic structural diagram of the dynamic adjustment device of the urban renewal planning scheme provided by the embodiment of the present application.

[0055] Figure 3 It is a schematic structural diagram of the electronic device provided by the embodiment of the present application.

[0056] Reference numerals: 1. Data acquisition module; 2. Information recognition and evaluation module; 3. Risk score calculation module; 4. Planning scheme adjustment module; 301. Processor; 302. Memory; 303. Communication bus. Detailed Embodiments

[0057] Next, the technical solutions in the embodiments of the present application will be clearly and completely described 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 the embodiments. Usually, the components of the embodiments of the present application described and shown 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 present application to be protected, but only 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 creative efforts belong to the scope of protection of the present application.

[0058] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, 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 the present application, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0059] With the acceleration of the urbanization process, the importance of urban planning has become increasingly prominent. In the urban planning system, the regulatory detailed planning, as a key link connecting the upper and lower levels, directly affects the sustainable development of the city. However, the traditional evaluation methods of regulatory detailed planning often have problems such as strong subjectivity and insufficiently objective and comprehensive evaluation results. To solve these problems, the present application proposes an innovative evaluation method for regulatory detailed planning, aiming to improve the scientificity and objectivity of the evaluation.

[0060] In urban planning practice, we often encounter such a situation: the overall planning scheme of a city has been formulated, and then a more detailed regulatory detailed planning scheme needs to be developed. However, how to ensure the consistency between the regulatory detailed planning scheme and the overall planning scheme while meeting the actual needs of specific areas is a complex problem. The evaluation method proposed in the present application is designed to solve this problem.

[0061] Please refer to Figure 1 , Figure 1 which is a method for dynamically adjusting the urban renewal planning scheme in some embodiments of the present application. The steps of this method include:

[0062] A1. Obtain urban street view video data through normal unmanned aerial vehicle (UAV) cruising and shooting;

[0063] 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;

[0064] A3. Based on the preset building facade damage level threshold, traffic impact level threshold, building facade damage risk weight, and traffic impact risk weight, construct a weighted risk assessment model, and calculate the comprehensive risk score according to the building facade damage level and traffic impact level;

[0065] A4. Determine the adjustment strategy according to the comprehensive risk score to dynamically adjust the urban renewal planning scheme.

[0066] Among them, in step A1, the drone is configured to perform regular cruise shooting over the urban blocks according to a preset cruise route and frequency. Thereby, urban street view video data can be continuously and regularly obtained, overcoming the problem of low data collection efficiency in the traditional manual method, and providing a timely data basis for the dynamic adjustment of the urban renewal planning scheme.

[0067] Among them, in step A2, preprocessing operations are performed on the obtained urban street view video data, such as image enhancement, denoising, etc., to improve the accuracy of subsequent information recognition. Then, building facade damage information and traffic information are automatically identified from the preprocessed video data. The building facade damage information can include information indicating the safety of the building structure such as cracks and peeling; the traffic information can include information indicating the traffic operation status such as traffic flow and congestion level. Then, the system evaluates the building facade damage level and the traffic impact level, and converts the identified information into a quantitative level for subsequent risk assessment and decision-making.

[0068] Among them, in step A3, a weighted risk assessment model is constructed. This model uses the building facade damage level threshold, the traffic impact level threshold, the building facade damage risk weight, and the 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, according to the building facade damage level and the traffic impact level evaluated in step A2, a comprehensive risk score is calculated. The comprehensive risk score realizes the quantitative assessment of the urban renewal risk and provides a quantitative basis for the formulation of subsequent adjustment strategies.

[0069] Among them, in step A4, according to the comprehensive risk score calculated in step A3, the corresponding adjustment strategy is determined, and the urban renewal planning scheme is dynamically adjusted with the determined adjustment strategy. The higher the comprehensive risk score, the higher the urban renewal risk, and the greater the adjustment range of the adjustment strategy to be taken, so as to ensure that the urban renewal planning scheme can be adjusted in a timely manner according to the actual changes in the urban blocks.

[0070] Specifically, the working principle of the dynamic adjustment method for the urban renewal planning scheme proposed in this application is as follows: First, the drone is used for regular cruising shooting to efficiently and regularly obtain urban street view video data, providing data support for the evaluation of the 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, realizing the quantitative evaluation of the building safety and traffic operation conditions in the urban block. Further, a weighted risk assessment model is constructed, comprehensively considering the building facade damage risk and traffic impact risk, and the evaluation results are converted into a comprehensive risk score to realize the quantitative evaluation of the urban renewal risk. 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 a timely manner according to the actual changes in the urban block, realizing the rapid dynamic adjustment of the urban renewal planning scheme. Thus, the problems of low efficiency and data lag in the traditional manual exploration method are overcome, the efficiency and response speed of the urban renewal planning are improved, and the effectiveness and timeliness of the urban renewal planning scheme are ensured.

[0071] In some specific embodiments, the urban planning department can use drones to conduct regular cruise shootings in specific urban blocks. For example, it conducts a cruise shooting once a week. During the cruise, the high-definition camera carried by the drone continuously shoots street view video data. The obtained street view video data is transmitted to the data processing center. The data processing center first performs preprocessing of image denoising and enhancement on the video data to improve the image quality. Then, using image recognition algorithms, it identifies building facade damage information such as the number of cracks on the building facade, crack width, and peeling area, as well as traffic information such as the traffic flow and average vehicle speed on the road, from the preprocessed video data. The system quantifies the building facade damage information into building facade damage levels according to preset evaluation criteria. For example, it is divided into three levels: slight damage, moderate damage, and severe damage, and numerical values can also be used to represent the building facade damage level; the traffic information is quantified into traffic impact levels, such as divided into three levels: unobstructed, congested, and severely congested, and numerical values can also be used to represent the traffic impact level. Further, thresholds for the building facade damage level and traffic impact level are preset. For example, the threshold for the building facade damage level is moderate damage, and the threshold for the traffic impact level is congestion. At the same time, the risk weight for building facade damage is preset as 0.6, and the risk weight for traffic impact is 0.4. The constructed weighted risk assessment model can be: Comprehensive risk score = Risk weight for building facade damage * (Building facade damage level / Threshold for building facade damage level) + Risk weight for traffic impact * (Traffic impact level / Threshold for traffic impact level). According to the calculated comprehensive risk score, the risk levels are divided. For example, a comprehensive risk score greater than 0.8 is a high risk, 0.5 - 0.8 is a medium risk, and less than 0.5 is a low risk. Corresponding adjustment strategies are formulated for different risk levels. For example, the high-risk level corresponds to a substantial adjustment of the urban renewal planning scheme, including increasing the frequency of building structure safety inspections, optimizing the traffic organization plan, etc.; the medium-risk level corresponds to a minor adjustment, such as fine-tuning traffic signal timing, strengthening building facade maintenance, etc.; the low-risk level corresponds to maintaining the existing planning scheme. Through the above steps, the dynamic adjustment of the urban renewal planning scheme is achieved.

[0072] In some preferred embodiments, step A2 includes:

[0073] A201. Detect the weather conditions corresponding to the urban street view video data;

[0074] A202. According to the detected weather conditions, adaptively adjust the image preprocessing parameters, perform image preprocessing on the urban street view video data, and obtain the preprocessed street view video data adapted to the weather conditions;

[0075] A203. Identify the building facade damage information and traffic information from the preprocessed street view video data adapted to the weather conditions, and evaluate the building facade damage level and traffic impact level.

[0076] Among them, in step A201, the detection of weather conditions can be achieved in various ways. For example, the urban meteorological data interface can be accessed to obtain the weather information of the location where the drone takes pictures in real time. Or, the weather features contained in the urban street view video data itself can be analyzed. For example, by parameters such as the brightness, color temperature, and texture clarity of the image, it can be judged whether the video is taken under weather conditions such as sunny, cloudy, or rainy days.

[0077] Among them, in step A202, the adaptive adjustment of image preprocessing parameters means that different image preprocessing methods and parameters are adopted for different weather conditions. Specifically, under the condition of sufficient sunlight on sunny days, the focus can be on image sharpening and contrast enhancement to highlight the damage of building facades and traffic details; under cloudy or insufficient light conditions, the focus can be on image brightness improvement and noise suppression to improve the overall quality of the image; under poor visibility conditions such as rainy or foggy days, a defogging and de-raining 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, and different combinations of image preprocessing parameters corresponding to different weather conditions are stored in the parameter configuration table.

[0078] Among them, in step A203, after the preprocessing adapted to the weather conditions, the damage information of building facades and traffic information in the urban street view video data are identified, and the damage level of building facades and the traffic impact level are evaluated. Since the preprocessing process has considered the influence of weather conditions and carried out targeted optimization, the accuracy and reliability of information identification and level evaluation can be effectively improved.

[0079] 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.

[0080] 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.

[0081] For example, when step A201 detects that the weather condition is rainy, step A202 selects a rainy-day preprocessing algorithm combination, adjusts the algorithm parameters, and preprocesses the urban street view video data. After the preprocessed street view video data, the rain interference 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.

[0082] In some embodiments, step A3 includes:

[0083] A301. Obtain the current urban renewal stage information and block feature information;

[0084] A302. Generate a weight adjustment instruction according to the current urban renewal stage information and block feature information, where the weight adjustment instruction includes a building facade damage risk weight adjustment parameter and a traffic impact risk weight adjustment parameter;

[0085] A303. Adjust the preset building facade damage risk weight and traffic impact risk weight according to the building facade damage risk weight adjustment parameter and the traffic impact risk weight adjustment parameter to obtain the adjusted building facade damage risk weight and the adjusted traffic impact risk weight;

[0086] A304. 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, construct a weighted risk assessment model;

[0087] A305. Calculate the comprehensive risk score according to the building facade damage level and traffic impact level, and the constructed weighted risk assessment model.

[0088] 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 feature information can be obtained by querying through the GIS geographic information system. For example, the urban renewal stage information can be divided into three levels: "initial stage", "mid-stage", and "mature stage". The block feature information can be divided into three categories: "commercial block", "residential block", and "industrial block"; but it is not limited to this.

[0089] Among them, in step A302, the generation of the weight adjustment instruction can be implemented through a preset rule library, and the rule library stores the corresponding weight adjustment parameters under different stage and block feature combinations. For example, when the urban renewal is in the initial stage and the block feature is a residential block, the rule library 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%.

[0090] Among them, in step A303, the preset risk weights of building facade damage and traffic impact can be default values set during system initialization. The adjustment process is to perform addition and subtraction operations on the default weight values according to the parameters in the weight adjustment instruction.

[0091] Among them, in step A304, the weighted risk assessment model can be: Comprehensive risk score = Risk weight of building facade damage * (Building facade damage level / Threshold of building facade damage level) + Risk weight of traffic impact * (Traffic impact level / Threshold of traffic impact level); In the formula, the risk weights of building facade damage and traffic impact adopt the adjusted risk weights.

[0092] Among them, in step A305, the calculation of the comprehensive risk score is to substitute the building facade damage level and traffic impact level evaluated in step A2 into the weighted risk assessment model constructed in step A304 for calculation.

[0093] Specifically, aiming at the problem that fixed risk weights may lead to distorted evaluation results in different urban renewal stages and different block characteristics, a technical solution for dynamically adjusting risk weights is proposed. During the dynamic adjustment process of the urban renewal planning scheme, different stages and different blocks may have different degrees of attention to the risks of building facade damage and traffic impact. 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 the urban renewal planning scheme, 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 obtained information, and the adjustment parameters of the risk weights of building facade damage and traffic impact are specified in this instruction. Step A303 adjusts the preset risk weights according to the adjustment parameters to obtain weight values that are more in line with the current situation. Step A304 constructs a weighted risk assessment model using the adjusted risk weights to ensure that the model can perform risk assessment according to the latest weight configuration. Finally, step A305 calculates the comprehensive risk score using the risk assessment model with dynamically adjusted weights, combined with the building facade damage level and traffic impact level evaluated in the previous steps. Thus, the adaptive adjustment of risk weights is realized, enabling the risk assessment model to better adapt to the different demands of different stages and blocks during the urban renewal process, making the comprehensive risk score more accurate and in line with the actual situation, thereby providing a more reliable basis for the dynamic adjustment of the urban renewal planning scheme and improving the flexibility and effectiveness of urban renewal planning.

[0094] In some specific embodiments, the urban renewal stage information is divided into three levels: "initial stage", "mid-stage", and "mature stage". The block feature 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 that the building facade damage risk weight adjustment parameter is increased by 0.1, and the traffic impact risk weight adjustment parameter is decreased by 0.05. When the system obtains that the current urban renewal stage information is "initial stage" and the block feature information is "residential block", a weight adjustment instruction is generated. According to this 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 is used for subsequent comprehensive risk score calculation. In this way, for different stages and block features, the risk assessment model can dynamically adjust the weights to more accurately reflect the actual risk situation, making the adjustment of the urban renewal planning scheme more targeted and effective.

[0095] In some embodiments, step A302 includes:

[0096] A302a. Information reliability verification:

[0097] A302a1. Identify the data sources of the current urban renewal stage information and block feature information;

[0098] A302a2. Evaluate the credibility level of the data sources;

[0099] A302a3. Compare the credibility level of the data sources with the preset credibility threshold;

[0100] A302a4. If the credibility level of the data sources is lower than the credibility threshold, start the information correction processing flow, and the information correction processing flow includes:

[0101] Adopt the preset default urban renewal stage information and default block feature information to replace the current urban renewal stage information and block feature information; or

[0102] Query the expert knowledge base to obtain the historical urban renewal stage information and historical block feature information related to the current urban renewal area, and based on the historical urban renewal stage information and historical block feature information, correct the current urban renewal stage information and block feature information;

[0103] A302b. Generate a weight adjustment instruction based on the current urban renewal stage information and block feature information after information reliability verification or information correction processing.

[0104] That is, before generating a weight adjustment instruction based on the current urban renewal stage information and block feature information, the current urban renewal stage information and block feature information are first subjected to a verification process.

[0105] Among them, through the information reliability verification step, the accuracy of the urban renewal stage information and block feature information on which the weight adjustment instruction is generated is ensured. Specifically, first identify the information source, such as data platforms from authoritative departments, web crawlers, professional sensor networks, manual input, or other systems. Subsequently, evaluate the credibility of the data source. For example, if the information comes from a data platform of an authoritative department, the credibility is relatively high; if it comes from a web crawler, the credibility may be relatively low. Set a credibility threshold for judging whether the information source is reliable. If the credibility of the information source is lower than this threshold, the information may be inaccurate, and thus information correction processing is performed. The information correction processing provides two methods: one is to directly replace it with preset default information, which is a fast but possibly less accurate method; 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 refined and can improve the accuracy of the information. Finally, the weight adjustment instruction is generated based on the information that has passed the reliability verification or correction, so as to ensure that subsequent risk assessments and planning scheme adjustments 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 are improved.

[0106] Specifically, in the information reliability verification process, when implementing the data source identification operation, it can be completed 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". Conversely, if the data comes from a web crawler program, the data source is identified as "web crawler", and the credibility level is rated as "low". The credibility level assessment operation can adopt a preset scoring standard or a credibility assessment model. For example, a credibility level table can be preset, classifying common data sources and assigning corresponding credibility levels, such as "official database API - high", "professional sensor network - medium", "web crawler - low", "manual entry - medium". The credibility threshold can be set to "medium", and when the credibility level of the data source is lower than "medium", the information correction process is initiated. In the information correction process, the preset default information is used for replacement. As a quick correction measure, the default information can be set with the urban renewal stage as "initial" and the block feature as "residential block". When querying the expert knowledge base for the information correction process, the expert knowledge base can be constructed as a database containing historical urban renewal project information. By retrieving 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 feature information of this block in past urban renewal projects are retrieved from the knowledge base and used to correct the current information (for example, using the retrieved block feature information as the corrected block feature information. If the time interval between the update time corresponding to the retrieved latest stage information and the current time does not exceed the preset interval threshold, or the retrieved latest stage information is the last stage, such as the mature stage, then 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 process, 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.

[0107] 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 feature 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 as "high". In the information reliability verification process, since the credibility level of the data source of the geographic information system, which is "medium", is lower than the credibility threshold of "high", the information correction processing flow is initiated. In the information correction processing flow, the expert knowledge base is queried. The expert knowledge base stores the file information of historical urban renewal projects. Through querying the expert knowledge base, it is obtained that the historical renewal stage of this block is "mid-term", and the historical block feature is "residential area". The current block feature information is corrected to "residential area". A weight adjustment instruction is generated based on the corrected block feature information and urban renewal stage information. The weight adjustment instruction includes that the weight adjustment parameter for the building facade damage risk is increased by 0.15, and the weight adjustment parameter for the traffic impact risk is decreased by 0.05. Thus, the accuracy of the weight adjustment instruction is ensured, and the effectiveness of the dynamic adjustment of the urban renewal planning scheme is improved.

[0108] In some embodiments, step A3 includes:

[0109] A311. Collect historical cruise data within a preset time period. The historical cruise data includes historical building facade damage level data and historical traffic impact level data;

[0110] A312. Based on the historical building facade damage level data and historical traffic impact level data, use a threshold optimization algorithm to calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold;

[0111] 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, construct a weighted risk assessment model;

[0112] A314. According to the building facade damage level and traffic impact level, as well as the constructed weighted risk assessment model, calculate the comprehensive risk score.

[0113] 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 a sufficient amount of 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's regular cruise shooting of the urban block at different time points through the information recognition and evaluation module. The historical building facade damage level data and historical traffic impact level data are the quantitative values output by the information recognition and evaluation module after analyzing and evaluating the historical street view video data.

[0114] Among them, in step A312, the threshold optimization algorithm is an algorithm that can automatically adjust the threshold according to historical data. For example, the grid search algorithm, genetic algorithm, or particle swarm optimization algorithm can be used. The grid search algorithm traverses all possible threshold combinations within a preset threshold search range at a set step size, calculates the model performance metrics for each set of thresholds, and finally selects the threshold combination with the optimal performance metrics 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 cooperation and information sharing among particles.

[0115] Among them, in step A313, the weighted risk assessment model is a model used to calculate the comprehensive risk score. This model takes the building facade damage level, traffic impact level, building facade damage level threshold, and traffic impact level threshold weight as inputs and calculates the comprehensive risk score through weighted summation (the specific model can refer to the previous text).

[0116] Among them, in step A314, the comprehensive risk score is an indicator used to measure the necessity of adjusting the urban renewal planning scheme. 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 planning scheme is required.

[0117] Specifically, the working principle of this solution is 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 urban block risk changes. Then, in step A312, the system uses a threshold optimization algorithm to automatically calculate the optimized building facade damage level threshold and traffic impact level threshold based on the collected historical data. The threshold optimization algorithm can find the optimal threshold setting according to historical data, making the risk assessment based on these thresholds more accurate. For example, if historical data shows that when the building facade damage level threshold is set to level 3 and the traffic impact level threshold is set to level 4, the prediction performance of the risk assessment model is the best, 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. Next, in step A313, the system constructs a weighted risk assessment model based on the optimized threshold and the preset risk weights. Compared with using a fixed threshold in the prior art, the risk assessment model constructed using the optimized threshold 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 urban blocks, providing a more reliable basis for subsequent dynamic adjustment of the urban renewal planning scheme according to the risk score. Thus, through the above steps, the adaptive optimization of the threshold is achieved, overcoming the defect that the preset threshold is fixed and difficult to accurately reflect the dynamic changes of urban renewal, and improving the accuracy and adaptability of the dynamic adjustment of the urban renewal planning scheme.

[0118] 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 the dynamic adjustment of the urban renewal planning scheme. At this time, step A3 includes:

[0119] A301. Obtain the current urban renewal stage information and block feature information;

[0120] A302. Generate a weight adjustment instruction according to the current urban renewal stage information and the block feature information, where the weight adjustment instruction includes a building facade damage risk weight adjustment parameter and a traffic impact risk weight adjustment parameter;

[0121] Adjust the preset building facade damage risk weight and traffic impact risk weight according to the building facade damage risk weight adjustment parameter and the traffic impact risk weight adjustment parameter, so as to obtain the adjusted building facade damage risk weight and the adjusted traffic impact risk weight;

[0122] A311. Collect historical cruise data within a preset time period, where the historical cruise data includes historical building facade damage level data and historical traffic impact level data;

[0123] A312. Based on the historical building facade damage level data and the historical traffic impact level data, use a threshold optimization algorithm to calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold;

[0124] A315. 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, construct a weighted risk assessment model;

[0125] A316. Calculate the comprehensive risk score according to the building facade damage level, the traffic impact level, and the constructed weighted risk assessment model.

[0126] In some specific embodiments, step A312 includes:

[0127] Based on the historical building facade damage level data and the historical traffic impact level data, use a grid search method to calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold.

[0128] Through the grid search algorithm, the optimal threshold combination can be automatically found, enabling the risk assessment model to more accurately identify the situations that require adjustment of the urban renewal planning scheme, thereby improving the efficiency and quality of the urban renewal planning.

[0129] Specifically, the steps of calculating the optimized building facade damage level threshold and the optimized traffic impact level threshold based on the historical building facade damage level data and the historical traffic impact level data by using a grid search method include:

[0130] B1. Initialize the upper limit of the evaluation value as -1, initialize the optimized building facade damage level threshold D_1 as empty, and initialize the optimized traffic impact level threshold T_1 as empty;

[0131] B2. Set the search range D_r of the building facade damage level threshold and the search range T_r of the traffic impact level threshold, as well as the threshold search step size st;

[0132] B3. Within the search range \(D_r\) of the building facade damage level threshold, select the current building facade damage level threshold \(D_0\) at intervals of the threshold search step size \(st\).

[0133] B4. Within the search range \(T_r\) of the traffic impact level threshold, select the current traffic impact level threshold \(T_0\) at intervals of the threshold search step size \(st\).

[0134] B5. Initialize the confusion matrix parameters: true positive parameter \(TP = 0\), false positive parameter \(FP = 0\), false negative parameter \(FN = 0\), true negative parameter \(TN = 0\).

[0135] 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:

[0136] 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 predict that the urban renewal planning scheme needs to be adjusted, and set the \(i\)-th predicted adjustment decision \(A_i = 1\). Otherwise, predict that the urban renewal planning scheme does not need to be adjusted, and set the \(i\)-th predicted adjustment decision \(A_i = 0\).

[0137] 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 \(TP\), false positive parameter \(FP\), false negative parameter \(FN\), and true negative parameter \(TN\). Here, \(a_i\) is 1 or 0, \(a_i = 1\) indicates that the situation corresponding to the \(i\)-th historical data point was determined to require adjustment of the urban renewal planning scheme historically, and \(a_i = 0\) indicates that no adjustment is required.

[0138] B7. Calculate the precision \(Pr\), recall \(Rc\), and evaluation value. The specific calculation methods are: \(Pr = TP / (TP + FP)\), when \(TP + FP = 0\), \(Pr = 0\); \(Rc = TP / (TP + FN)\), when \(TP + FN = 0\), \(Rc = 0\); evaluation value \(= 2*(Pr*Rc) / (Pr + Rc)\), when \(Pr + Rc = 0\), the evaluation value \(= 0\).

[0139] B8. If the current evaluation value is greater than the evaluation value upper limit, then update the evaluation value upper limit to the current evaluation value, update the optimized building facade damage level threshold \(D_1\) to the current building facade damage level threshold \(D_0\), and update the optimized traffic impact level threshold \(T_1\) to the current traffic impact level threshold \(T_0\).

[0140] B9. Repeat B4 to B8 until all the thresholds within the traffic impact level threshold search range \(T_r\) are traversed.

[0141] B10. Repeat B3 to B9 until all the thresholds within the building facade damage level threshold search range \(D_r\) are traversed.

[0142] B11. Take 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.

[0143] Among them, the initialization process of step B1 provides the initial state for the subsequent threshold optimization. The upper limit of the evaluation value is set to -1, ensuring 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 as empty, indicating that the optimal threshold has not been determined before the optimization starts.

[0144] 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 values of the thresholds. The threshold search step size \(st\) determines the fineness of the threshold adjustment within the search range and can be adjusted according to actual needs.

[0145] Among them, steps B3 and B4 describe the threshold selection method. Within their 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.

[0146] Among them, step B5 initializes the confusion matrix parameters. The true positive parameter \(TP\), false positive parameter \(FP\), false negative parameter \(FN\), and true negative parameter \(TN\) are initialized to 0 to prepare for evaluating the performance of the current threshold combination based on historical data later.

[0147] Among them, step B6 is the core evaluation step. By traversing the historical data, for each historical data point, predictions are made using the currently selected threshold combination (\(D_0\) and \(T_0\)), and the prediction results are compared with the historical adjustment decision data to update the confusion matrix parameters. The confusion matrix provides the data basis for calculating the precision and recall rate later.

[0148] Among them, step B7 calculates the evaluation value. The evaluation value 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 selection of the optimal threshold.

[0149] Among them, in step B8, the threshold is updated. If the evaluation value of the current threshold combination is better than the historical optimal value (the upper limit of the evaluation value), the upper limit of the evaluation value is updated, and the current threshold combination is set as the optimized building facade damage level threshold D_1 and the optimized traffic impact level threshold T_1, ensuring that the algorithm can find the optimal threshold combination.

[0150] Among them, steps B9 and B10 control the traversal of the search range. Step B9 ensures that all thresholds within the traffic impact level threshold search range T_r are traversed under the current building facade damage level threshold D_0, and step B10 further controls the traversal of all thresholds within the building facade damage level threshold search range D_r, completing the full coverage of the entire search space.

[0151] Among them, step B11 takes the optimal thresholds D_1 and T_1 found as the output result of step A312, which is used for the subsequent construction of the risk assessment model, providing accurate threshold parameters for the dynamic adjustment of the urban renewal planning scheme. Through the above steps, the complete process of the threshold optimization algorithm is realized, providing technical support for improving the effectiveness of the dynamic adjustment of the urban renewal planning scheme.

[0152] Specifically, the above method provides a threshold optimization algorithm based on grid search and evaluation value for determining the optimal building facade damage level threshold and traffic impact level threshold. 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 the historical adjustment decisions, and the precision rate, recall rate, and evaluation value are calculated. 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 optimally on historical data. This method guides the threshold optimization through the quantitative index evaluation value, ensuring the objectivity and quantifiability of the optimization process. Through grid search, the threshold search space is covered as much as possible, increasing 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 the actual urban renewal scenario, thereby improving the accuracy and effectiveness of the dynamic adjustment of the urban renewal planning scheme, solving the problems of lag in the adjustment of the urban renewal planning scheme and difficulty in adapting to the rapid changes in urban blocks mentioned in the background technology, and realizing the real-time response and adjustment of the planning scheme.

[0153] In some specific embodiments, the search range D_r of the building facade damage level threshold is set to [1, 5], the search range T_r of the traffic impact level threshold is set to [1, 5], and the threshold search step size st is set to 1. The historical data contains 1000 data points, and each data point 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 upper limit of the evaluation value is initialized to -1, and the optimized building facade damage level threshold D_1 and the optimized traffic impact level threshold T_1 are empty. First, the algorithm traverses within the search range [1, 5] of the building facade damage level threshold with a step size of 1 to select the current building facade damage level threshold D_0. For example, first select D_0 = 1. Then, within the search range [1, 5] of the traffic impact level threshold, also traverse with a step size of 1 to select the current traffic impact level threshold T_0. For example, first select T_0 = 1. 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, then predict A_i = 1, otherwise A_i = 0. Compare A_i with a_i and update the confusion matrix. After completing the traversal, calculate the precision Pr, recall Rc, and evaluation value. If the current evaluation value is greater than the upper limit of the evaluation value, then update the upper limit of the evaluation value to the current evaluation value, and update D_1 = D_0, T_1 = T_0. Then, continue to select the next T_0 value within the search range [1, 5] of the traffic impact level threshold and repeat the above process until the search range of the traffic impact level threshold is traversed. After that, select the next D_0 value and repeat the process of traversing the search range of the traffic impact level threshold until the search range of the building facade damage level threshold is traversed. Finally, obtain the optimized building facade damage level threshold D_1 and the optimized traffic impact level threshold T_1. For example, after calculation, the finally 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 calculate the comprehensive risk score more accurately, so as to more effectively guide the dynamic adjustment of the urban renewal planning scheme.

[0154] In some preferred embodiments, step A312 further includes:

[0155] B12, calculating the change rate of the historical building facade damage level data and the historical traffic impact level data;

[0156] B13, comparing the change rate with a preset change rate threshold;

[0157] 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 all change rates are less than the change rate threshold, decrease the execution frequency of the threshold optimization algorithm.

[0158] 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.

[0159] Among them, in step B12, the change rates of the historical building facade damage level data and the historical traffic impact level data can be calculated as the change amount of the average building facade damage level or the average traffic impact level within a preset detection period, for example, one month or one quarter.

[0160] Among them, in step B13, the preset change rate threshold is a reference value for judging the speed of change of the urban street view and traffic conditions, and this threshold can be set according to the actual application scenario and requirements. For example, it can be set to 10% or 20%. Among them, the change rates of the historical building facade damage level data and 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.

[0161] Among them, in step B14, increasing the execution frequency of the threshold optimization algorithm can be changing the original execution of the optimization algorithm once a month to once a week, and decreasing the execution frequency can be changing the original execution once a month to once a quarter. It should be noted that if in step B13 the change rates of the historical building facade damage level data and the historical traffic impact level data are compared with two different change rate thresholds respectively, then step B14 actually means: when at least one of the change rates of the historical building facade damage level data and the historical traffic impact level data is greater than or equal to the corresponding change rate threshold, increase the execution frequency of the threshold optimization algorithm; when the change rates of the historical building facade damage level data and the historical traffic impact level data are both less than the corresponding change rate threshold, decrease the execution frequency of the threshold optimization algorithm.

[0162] Among them, in step B15, the threshold optimization algorithm is the algorithm described above. According to the adjusted execution frequency, this algorithm is executed, so as to calculate the optimized building facade damage level threshold and the optimized traffic impact level threshold suitable for the current urban change speed.

[0163] Specifically, in response to the problem that the execution frequency of the threshold optimization algorithm remains fixed, the present application proposes a technical means of dynamically adjusting the execution frequency of the threshold optimization algorithm according to the change rate of historical data. First, in step B12, the change rates of the historical building facade damage level data and the historical traffic impact level data are calculated, which reflect the change amplitude of the urban renewal-related data over a period of time. Then, in step B13, the change rate is compared with a preset change rate threshold to determine the speed of change of the urban block condition. When at least one change rate is greater than or equal to the change rate threshold, it indicates that the urban block condition 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 a timely manner, so as to more accurately evaluate the current risk condition. On the contrary, when the change rate is less than the change rate threshold, it indicates that the urban block condition changes slowly. At this time, in step B14, the execution frequency of the threshold optimization algorithm is decreased to reduce unnecessary consumption of computing resources. Finally, in step B15, the threshold optimization algorithm is executed according to the adjusted execution frequency, and the optimized building facade damage level threshold and the optimized traffic impact level threshold are calculated. Thus, the execution frequency of the threshold optimization algorithm matches the actual situation of urban changes, improving the resource utilization efficiency and ensuring the timeliness and accuracy of risk assessment.

[0164] The change rates of the historical building facade damage level data and the historical traffic impact level data

[0165] In some specific embodiments, the preset detection period is set to one month. The change rate of the historical building facade damage level data is calculated as the ratio of the difference between the average building facade damage level this month and last month to the average building facade damage level last month. The change rate of the historical traffic impact level data is calculated as the ratio of the difference between the average traffic impact level this month and last month to the average traffic impact level last month. The preset change rate threshold is set to 15%. Assume that the average building facade damage level last month was 2, and the average building facade damage level this month is 2.5. Then the corresponding change rate is (2.5 - 2) / 2 = 25%. The average traffic impact level last month was 2.3, and the average traffic impact level this month is 2.8. Then the corresponding change rate is (2.8 - 2.3) / 2.8 = 17.9%. Since both 25% and 17.9% are greater than 15%, the execution frequency of the threshold optimization algorithm is increased. For example, the execution period of the threshold optimization algorithm is adjusted from once a month to once every two weeks. Conversely, if the average building facade damage level this month is 2.1, the corresponding change rate is (2.1 - 2) / 2 = 5%. The average traffic impact level this month is 2.5, and 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 decreased. For example, the execution period 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 change of the urban block conditions.

[0166] In some embodiments, step A4 includes:

[0167] A401. Determine the risk level according to the comprehensive risk score;

[0168] A402. According to the determined risk level, select the corresponding adjustment strategy to dynamically adjust the urban renewal planning scheme; wherein, the higher the risk level, the greater the adjustment amplitude of the adjustment strategy.

[0169] Among them, in step A401, the comprehensive risk score calculated in step A3 is mapped to the risk level. The technical effect brought by this is that the continuous risk score values can be converted into discrete risk levels, which is convenient for subsequent step A402 to select appropriate adjustment strategies according to the risk levels. For example, the mapping relationship between the risk score and the risk level can be preset. For example, the comprehensive risk score is divided into three levels: low risk, medium risk, and high risk. The score from 0 to 30 is divided into the low risk level, the score from 31 to 70 is divided into the medium risk level, and the score from 71 to 100 is divided into the high risk level. Thus, through the risk level division, the hierarchical processing of the risk score is realized, providing a basis for the formulation of subsequent adjustment strategies.

[0170] Among them, in step A402, according to the risk level determined in step A401, the 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 a low risk level, a minor adjustment strategy can be selected, such as increasing the collection frequency of street view video data; for a medium risk level, a medium adjustment strategy can be selected, such as fine-tuning the priority of building facade renovation in the urban renewal planning scheme; for a high risk level, a major adjustment strategy can be selected, such as immediately starting a partial revision of the urban renewal planning scheme and giving priority to dealing with the problems of building facade damage and traffic congestion in the risk area. Through the above method, the matching of the adjustment strategy and the risk level is achieved, ensuring that the adjustment range of the adjustment strategy is adapted to the risk degree, and making the dynamic adjustment of the urban renewal planning scheme more refined and effective.

[0171] Specifically, when the urban planning department uses the drone street view video data for the dynamic adjustment of the urban renewal planning scheme, it first obtains the comprehensive risk score through the risk scoring calculation module. Then, step A401 is executed. According to the preset risk level division standard, the comprehensive risk score is converted into the corresponding risk level. For example, when the comprehensive risk score is 65 points, according to the preset score level division standard, it is determined as the medium risk level. Subsequently, step A402 is executed. According to the determined medium risk level, the adjustment strategy corresponding to the medium risk level is searched in the adjustment strategy library. For example, the adjustment strategy corresponding to the medium risk level can be "fine-tuning the priority of building facade renovation 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 renovation originally planned to be carried out three months later is advanced to next month. Thus, a dynamic adjustment of the urban renewal planning scheme based on the risk level is completed. Through the linkage of 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.

[0172] In some specific embodiments, in the adjustment strategy library, the correspondence between the risk level and the adjustment strategy is pre-configured. For example, a low risk level corresponds to the strategy of "only monitoring, no adjustment of the planning scheme for the time being"; a medium risk level corresponds to the strategy of "slightly adjusting the planning scheme, for example, optimizing traffic signal timing, fine-tuning the building facade maintenance plan"; a high risk level corresponds to the strategy of "substantially adjusting the planning scheme, for example, starting a road renovation project, advancing the building facade repair project, adjusting the block function layout". As a preferred embodiment, the adjustment strategies in the adjustment strategy library can be further refined, and each risk level can correspond to multiple adjustment strategies with different amplitudes, and can be flexibly selected and applied according to the actual situation. For example, the medium risk level can be subdivided into sub-levels such as slightly lower than medium risk, medium risk, and slightly higher than medium risk, and respectively correspond to adjustment strategies with different amplitudes to achieve more refined adjustment control.

[0173] Reference Figure 2 , this application also provides a dynamic adjustment device for an urban renewal planning scheme, and the device includes:

[0174] A data acquisition module 1, configured to obtain urban street view video data through normal unmanned aerial vehicle (UAV) cruising and shooting (the specific process refers to step A1 in the foregoing text);

[0175] An information recognition and evaluation module 2, configured to preprocess the urban street view video data, identify building facade damage information and traffic information from the preprocessed urban street view video data, and evaluate the building facade damage level and the traffic impact level (the specific process refers to step A2 in the foregoing text);

[0176] A risk score calculation module 3, configured 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 specific process refers to step A3 in the foregoing text);

[0177] A planning scheme adjustment module 4, configured to determine an adjustment strategy according to the comprehensive risk score, so as to dynamically adjust the urban renewal planning scheme (the specific process refers to step A4 in the foregoing text).

[0178] Please refer to Figure 3 , Figure 3A schematic structural diagram of an electronic device provided by 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 marked). The memory 302 stores a computer program executable by the processor 301. When the electronic device runs, the processor 301 executes the computer program to perform the dynamic adjustment of the urban renewal planning scheme in any optional implementation manner of the above embodiment, so as to achieve the following functions: obtaining urban street view video data through normal unmanned aerial vehicle (UAV) cruising shooting; 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 the traffic impact level; constructing a weighted risk assessment model based on preset building facade damage level thresholds, traffic impact level thresholds, building facade damage risk weights, and traffic impact risk weights, 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 for dynamically adjusting the urban renewal planning scheme.

[0179] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it performs the dynamic adjustment of the urban renewal planning scheme in any optional implementation manner of the above embodiment, so as to achieve the following functions: obtaining urban street view video data through normal unmanned aerial vehicle (UAV) cruising shooting; 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 the traffic impact level; constructing a weighted risk assessment model based on preset building facade damage level thresholds, traffic impact level thresholds, building facade damage risk weights, and traffic impact risk weights, 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 for dynamically adjusting the urban renewal planning scheme.

[0180] 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 (SRAM for short), electrically erasable programmable read-only memory (EEPROM for short), erasable programmable read-only memory (EPROM for short), programmable read-only memory (PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, magnetic disk or optical disk.

[0181] 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 illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another 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 displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0182] In addition, the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0183] Furthermore, in each embodiment of the present application, the various functional modules can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0184] In this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0185] The above are only embodiments of the present application and are not intended to limit the protection scope of the present application. For those skilled in the art, various modifications and changes can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within 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; 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, and the constructed weighted risk assessment model; 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.

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. 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; A planning scheme adjustment module, used to determine an adjustment strategy according to the comprehensive risk score, so as to dynamically adjust the urban renewal planning scheme; The risk score calculation module constructs 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 calculates the comprehensive risk score according to the building facade damage level and the traffic impact level, and executes: 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, and the constructed weighted risk assessment model; 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.

4. 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 1-2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the method for dynamically adjusting the urban renewal planning scheme as described in any one of claims 1-2 are performed.

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