A method and system for calculating urban physical examination evaluation indicators for national land space planning

Abstract

The present invention discloses a method and system for calculating indicators of physical examination and evaluation of a city in land space planning, which specifically relates to the field of real-time updating of urban event information, and is used to solve the problem of real-time data processing in urban event evaluation, and is achieved by combining graph theory models and information theory principles to improve the response efficiency and accuracy of urban event management. By constructing an event graph, the Laplace matrix and feature space are used to analyze event similarity, taking into account differences in geographical location, time and event type. In addition, by calculating information entropy and conditional information volume, the ability of historical events to explain current events is evaluated. The system generates a dynamic emergency response effectiveness index, a spatial impact domain index and a time domain duration index, and further calculates a data insertion priority coefficient to optimize the data insertion priority. It can dynamically adapt to environmental changes, improve the processing speed of emergency events and the accuracy of the evaluation of urban operation status.

Description

Technical Field

[0001] The present invention relates to the field of real-time updating of urban event information, and more specifically, to a method and system for calculating urban physical examination evaluation indicators for national land space planning. Background Art

[0002] In existing national land space planning and urban physical examination assessments, common challenges include insufficient real-time data processing, limitations in analytical methods, and insufficient flexibility in responding to complex urban events. Traditional assessment systems often rely on static data sets and lack real-time updates, which makes it impossible for relevant decision support systems to respond quickly when emergencies occur, resulting in resource allocation and emergency response measures that may not be timely or effective.

[0003] In addition, existing analytical methods often do not fully exploit the deep information contained in historical data. For example, when dealing with complex events such as natural disasters or large-scale social events, conventional methods may not be able to accurately predict the scope and duration of the event, which limits the ability of city managers to formulate specific and targeted measures. Therefore, there is a need for an advanced method and system that can comprehensively consider the similarities between events, the ability to interpret historical data, and adjust data processing priorities in real time.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for calculating the evaluation index of the city physical examination of national land space planning, which combines the graph theory model and the information theory principle to improve the response efficiency and accuracy of urban event management. By constructing an event graph, the Laplace matrix and the feature space are used to analyze the similarity of events, taking into account the differences in geographical location, time and event type. In addition, by calculating the information entropy and the amount of conditional information, the explanatory power of historical events on current events is evaluated. The system generates a dynamic emergency response effectiveness index, a spatial impact domain index and a time domain duration index, and further calculates the data insertion priority coefficient to optimize the data insertion priority. It can dynamically adapt to environmental changes, improve the processing speed of emergency events and the accuracy of the evaluation of urban operation status, so as to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: S1, connecting to multiple data sources in the city to detect and classify emergency events in real time, and using natural language processing and image recognition technology to parse and format unstructured data to meet the needs of subsequent analysis;

[0007] S2, based on the structural characteristics and similarity calculation of the event graph, uses the Laplacian matrix and feature space to build a similarity analysis model to measure the similarity between current events and historical events;

[0008] S3, using the information diversity and explanatory power of historical events to assess the extent to which they explain current events;

[0009] S4, after it is clear that the historical event has the ability to explain the current event, the similarity score in step S2 is used to perform parameter fitting, and the response of the current event and the breadth and depth of the impact are analyzed as a basis to give the corresponding dynamic allocation data insertion priority prompt signal.

[0010] In a preferred embodiment, S2 includes the following contents:

[0011] Treat events as nodes in a graph and use the characteristics of the graph to measure the similarity between nodes;

[0012] Every historical event and current events are considered as images A node in

[0013] Strength of edge The calculation formula is:

[0014] ;

[0015] in, and The differences are geographical and temporal. and is a parameter normalized for geographical and temporal differences, is an exponential function, which is 0 if the event type matches, otherwise it is 1;

[0016] Using the Laplacian Matrix of the Event Graph As the structure of the graph, , is the degree matrix, is the adjacency matrix, containing ;

[0017] Calculate the eigenvalues ​​and eigenvectors of the Laplace matrix, select the previous The eigenvectors corresponding to the smallest non-zero eigenvalues ​​construct a eigenspace;

[0018] In the feature space defined by the eigenvectors, the position of each event is given by the coordinates of its eigenvector;

[0019] The similarity between the current event and the historical event is calculated by the inverse of the Euclidean distance between the two in the feature space: ;

[0020] in and are the coordinates of historical events and current events in the feature space respectively.

[0021] In a preferred embodiment, S3 includes the following contents:

[0022] First, the similarity scores between the current event and all historical events are calculated through step S2;

[0023] Use information entropy to assess the diversity and information content of historical events in explaining current events: ;

[0024] in is the total number of historical events, Represents the similarity score between the current event and all historical events, is the normalized probability distribution of the similarity of historical events to current events;

[0025] Calculate the conditional information, that is, the amount of information provided by the similarity score of historical events under the condition that the current event is known: ;

[0026] in represents the maximum entropy value if all events are equally likely;

[0027] The ratio of the conditional information content and the information threshold is calculated and marked as the degree of explanation, which is used to quantify the degree to which historical events explain current events.

[0028] In a preferred embodiment, S4 includes the following contents:

[0029] When the explanation degree value is greater than or equal to 1, response information, breadth information and depth information are obtained based on the similarity between the current event and the historical event, where the response information includes the dynamic emergency response effectiveness index, the breadth information includes the spatial impact domain index, and the depth information includes the time domain duration index.

[0030] In a preferred embodiment, the process of obtaining the dynamic emergency response effectiveness index is as follows:

[0031] Collect data about the current event, including the time the event started and timing of intervention implementation ;

[0032] Convert time data into continuous time series;

[0033] Calculate the time difference between each event ;

[0034] Applying a dynamic weighting function , automatically adjust the weight based on the urgency of the event and the processing time of similar events in historical data: ;

[0035] in is an adjustment parameter used to adjust the decay and periodic changes of weights over time;

[0036] Using weighted event difference To adjust the original time difference;

[0037] A nonlinear function is used to transform the weighted time difference to calculate the dynamic emergency response effectiveness index:

[0038] ;

[0039] in Represents the hyperbolic sine function.

[0040] In a preferred embodiment, the process of obtaining the spatial influence domain diameter index is as follows:

[0041] A regression model based on the logarithmic logic function is used to predict the spatial impact domain of an event: ;

[0042] in:

[0043] is the spatial influence domain diameter index;

[0044] is the maximum spatial influence domain diameter;

[0045] is the similarity score obtained from the similarity calculation;

[0046] is the similarity score of the inflection point of the influence range, that is, when hour, ;

[0047] It is a parameter that controls the slope of the function curve and is used to determine the sensitivity of the similarity to the impact range.

[0048] In a preferred embodiment, the process of obtaining the time domain duration index is as follows:

[0049] The exponential decay model is used to calculate the duration to obtain the time domain duration index:

[0050] ;

[0051] in:

[0052] is the time domain duration index;

[0053] and are the minimum and maximum durations, respectively;

[0054] is the similarity score obtained from the similarity calculation;

[0055] is a baseline similarity score, when When , the duration drops rapidly from the maximum value;

[0056] is the decay coefficient, which controls the speed at which the time caused by the similarity difference decreases.

[0057] In a preferred embodiment, the dynamic emergency response effectiveness index, the spatial impact domain diameter index and the time domain duration index are comprehensively analyzed and calculated to generate a dynamic response data insertion priority coefficient;

[0058] For each real-time event, calculate the dynamic response data insertion priority coefficient;

[0059] If the dynamic response data insertion priority coefficient is less than the priority threshold of one, the event is considered to have the lowest priority and is processed later or according to the regular data update cycle, and a low-level signal is issued;

[0060] If the dynamic response data insertion priority coefficient is greater than or equal to priority threshold 1 and less than priority threshold 2, it is assigned a lower priority, the insertion demand is second, and a secondary signal is issued;

[0061] If the dynamic response data insertion priority coefficient is greater than or equal to the priority threshold 2, this type of event is considered the most urgent and needs to be processed immediately, and a high-level signal is issued.

[0062] A system for calculating city physical examination evaluation indicators for national land space planning, comprising: a data collection hub module, a similarity spectrum analysis module, a historical interpretation evaluation module and a priority decision engine module;

[0063] The data collection hub module connects to multiple data sources in the city to detect and classify emergency events in real time, uses natural language processing and image recognition technology to parse and format unstructured data to meet the needs of subsequent analysis, and sends the collected data to the similarity spectrum analysis module;

[0064] The similarity spectrum analysis module builds a similarity analysis model based on the structural characteristics and similarity calculation of the event graph using the Laplace matrix and feature space, measures the similarity between the current event and the historical event, and sends the similarity measurement results to the historical interpretation evaluation module;

[0065] The historical explanation evaluation module uses the information diversity and explanatory power of historical events to evaluate their degree of explanation for current events and sends the evaluation results to the priority decision engine module;

[0066] After it is clear that historical events have the ability to explain the current event, the priority decision engine module performs parameter fitting through the similarity score in step S2, analyzes the response of the current event and the breadth and depth of impact as a basis, and gives the corresponding dynamic allocation data insertion priority prompt signal.

[0067] The technical effects and advantages of the method and system for calculating the evaluation index of a city physical examination in land space planning of the present invention are as follows:

[0068] 1. The present invention makes comprehensive use of graph theory models and information theory principles. First, events are converted into nodes in a graph, and the associations between events are represented by edges, which helps to quantify the similarities between events. Secondly, the historical events and current events are located in the feature space using the method of Laplace matrix and feature space construction, and then the similarities between them are calculated. This method takes into account the differences in geography, time, and event types, and converts them into real values ​​to measure the similarities between events. In addition, through the calculation of information entropy and conditional information, the diversity and information content of historical events in explaining current events are evaluated. Such a comprehensive analysis not only takes into account the similarity, but also quantifies the explanatory power of historical events on current events, thereby providing a more comprehensive and reliable basis for prediction.

[0069] 2. After clarifying that the current event and the historical event have similar dependency analysis, the present invention calculates the response, impact breadth and depth information of the event, and generates a dynamic emergency response effectiveness index, a spatial impact domain diameter index and a time domain duration index based on this, and generates a dynamic response data insertion priority coefficient through comprehensive analysis and calculation to optimize the priority of data insertion, ensuring that key data can be processed with priority when facing emergencies or major changes. This not only speeds up the response to emergencies, but also improves the accuracy of the city's operating status assessment. It can also dynamically adapt to the ever-changing environment and event characteristics, thereby maintaining efficient operation in a changing urban environment. This makes it easier to enhance the efficiency of urban management and emergency response, so that urban planning and decision-making can make decisions more wisely, and optimize the overall function of the city and the living environment of residents. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of a method for calculating a city physical examination evaluation index for national land space planning according to the present invention;

[0071] Figure 2 It is a structural schematic diagram of a system for calculating urban physical examination evaluation indicators for national land space planning according to the present invention. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] Embodiment 1: Figure 1 The present invention provides a method for calculating the evaluation index of a city physical examination of national land space planning, including:

[0074] S1, which connects to multiple data sources in the city to detect and classify emergency events in real time, and uses natural language processing and image recognition technology to parse and format unstructured data to meet the needs of subsequent analysis;

[0075] S2, based on the structural characteristics and similarity calculation of the event graph, uses the Laplacian matrix and feature space to build a similarity analysis model to measure the similarity between current events and historical events;

[0076] S3, using the information diversity and explanatory power of historical events to assess the extent to which they explain current events;

[0077] S4, after it is clear that the historical event has the ability to explain the current event, the similarity score in step S2 is used to perform parameter fitting, and the response of the current event and the breadth and depth of the impact are analyzed as a basis to give the corresponding dynamic allocation data insertion priority prompt signal.

[0078] S1 includes the following processing contents:

[0079] Connect to multiple data sources such as city management systems, social media platforms, public safety monitoring systems, and traffic management systems through APIs.

[0080] Connecting to multiple data sources through APIs is flexible and scalable, and can achieve real-time data access, standardized interfaces, data integration and consistency, thereby improving the system's integration, interoperability and data quality, and adapting to scenario requirements of connecting to multiple data sources such as urban management systems, social media platforms, public safety monitoring systems and traffic management systems.

[0081] City Management System Connection: Use the agreed authentication credentials (such as API keys) to send requests to the API endpoint of the city management system through HTTP requests. These requests may include obtaining urban planning data, demographic information, land use data, etc. In the request, you need to specify the required data types and parameters, as well as the expected response format. For example, you can use a GET request to obtain real-time traffic information or location data of public facilities in a specific area.

[0082] Social media platform connection: You need to register on the relevant social media platform and obtain authentication credentials such as developer keys and access tokens. Then, access the data by sending HTTP requests to the API endpoints of the social media platform. These requests may include obtaining user profile information, published posts or tweets, and discussions related to specific topics. For example, you can use a GET request to retrieve instant events or opinions posted by residents of a certain area on social media.

[0083] Public safety monitoring system connection: You need to use the agreed authentication credentials to send requests to the API endpoint of the public safety monitoring system through HTTP requests. These requests may include obtaining real-time surveillance camera video streams, alarm event data, or police department statistics. For example, you can use a GET request to obtain crime rate data or real-time surveillance video in a certain area.

[0084] Traffic management system connection: You need to use the agreed authentication credentials and communicate with the traffic management system's API endpoints through HTTP requests. These requests may include obtaining traffic flow data, road condition information, public transportation operation status, etc. For example, you can use a GET request to obtain traffic congestion on a specific road or bus arrival information.

[0085] Use long polling technology to achieve real-time streaming of data to ensure the real-time and integrity of data.

[0086] Using long polling technology to achieve real-time streaming of data can ensure the real-time and integrity of data. Long polling is a communication method between a client and a server. The client sends a request to the server. After receiving the request, the server will remain connected and continue to wait for data updates or specified events to occur. Once new data is available or an event occurs, the server immediately sends the data to the client, and then the client immediately sends a new request to maintain the connection. This method can achieve instant data transmission, ensure the real-time nature of the data, and maintain the connection in the event of network delays or instability, ensuring the integrity of the data.

[0087] Conduct a preliminary screening of the accessed data and exclude irrelevant data.

[0088] Preliminary screening of the incoming data and exclusion of irrelevant data is to ensure that the system only processes relevant and urgent data, mainly based on the content, source, timestamp, etc. of the data. For example, in the data of the city management system, filtering rules can be set to retain only data related to traffic accidents, natural disasters or emergencies, while excluding updates on daily traffic conditions or ordinary municipal work.

[0089] Specifically, suppose the system needs to connect to the city management system to obtain traffic information. First, set a filtering rule to retain only data related to traffic accidents. This rule is based on matching keywords in the data. For example, only data containing keywords such as "traffic accident", "roadblock", "car accident" and so on are retained. Secondly, set a time window to only retain data on accidents or emergencies that occurred in the recent period of time to ensure that the system processes the latest and urgent information. Finally, set a priority rule to ensure that only data with high urgency is processed. For example, the data of traffic accidents is set to the highest priority, while the data of ordinary traffic congestion is set to a lower priority.

[0090] Through the set screening mechanism, irrelevant data can be effectively excluded and only information related to emergency events can be processed, thereby improving processing efficiency and response speed, and ensuring that important events and emergencies can be responded to and handled in a timely manner.

[0091] Convert data from all sources into a common format, such as a common timestamp format, location representation, and standard metrics for incident severity.

[0092] For example, when processing urban fire data, you can follow the steps below to convert the data format:

[0093] Unify timestamp format: Convert all timestamps in the data to the same time format so that the system can consistently parse and compare time information. For example, convert all timestamps to ISO8601 format (for example: YYYY-MM-DDTHH:MM:SSZ).

[0094] Standardization of location representation: Standardize the description of fire locations in all data to ensure that the system can accurately identify and locate the location of the fire. Fire locations can be represented using geographic coordinates (such as longitude and latitude) or using standard geographic location descriptions (such as city names, street addresses, etc.).

[0095] Standardized measures of incident severity: Standardize the measures of fire incident severity in all data so that the system can compare and classify different fire incidents. Consistent standard measures can be used, such as the scale of the fire, the scope of impact, the intensity of burning, etc. to represent the severity of the fire incident.

[0096] For example, suppose the system needs to process urban fire data from different sources. First, for timestamps, convert the time representation in all data to ISO8601 format, such as converting "2024-05-0815:30:00" to "2024-05-08T15:30:00Z". Second, for location representation, standardize the fire locations in all data into geographic coordinates, such as using longitude and latitude to represent the specific location where the fire occurred. Finally, for the measurement of event severity, a unified standard can be used, such as grading the intensity of the fire from 1 to 5 to indicate the size and severity of the fire.

[0097] By converting urban fire data into a unified format, the system can more conveniently process, analyze and visualize data, thereby improving the ability to monitor, respond to and prevent fire incidents.

[0098] Unstructured data processing involves natural language processing (NLP) techniques, including text classification algorithms, which are used to identify text information related to emergency events, and sentiment analysis, which is used to assess the urgency and negativity of public reactions.

[0099] Text classification algorithms are machine learning techniques used to automatically assign text data to predefined categories or labels. In terms of identifying text information related to emergency events, machine learning-based text classification models such as Naive Bayes, Support Vector Machines (SVM), deep learning models such as recurrent neural networks or convolutional neural networks, etc. can be used. These models are able to learn key features in text and assign text to emergency-related and non-emergency-related categories.

[0100] Sentiment analysis is a technique used to automatically analyze the emotional tendencies in text, such as positive, negative, or neutral emotions. In terms of assessing the urgency and negativity of public reactions, sentiment analysis can be used to analyze the emotions and attitudes expressed by the public in text. Common sentiment analysis methods include rule-based methods, dictionary-based methods, and machine learning-based methods. These methods can help the system automatically identify and evaluate the public's attitudes and emotions towards emergency events, and determine the urgency and negative impact of the event.

[0101] Image recognition technology involves object recognition, scene analysis, pattern recognition and other technologies, which are used to identify visual evidence of emergency events from videos or pictures and detect abnormal activities or potentially dangerous scenes.

[0102] Object recognition technology uses deep learning models, such as convolutional neural networks (CNNs), to identify and classify objects in images. Scene parsing technology is used to understand the overall scene and environment in an image. These technologies can be used in combination to extract key information from images or videos, such as visual evidence of emergencies such as fires and traffic accidents. Common object recognition models include YOLO (YouOnlyLookOnce), SSD (SingleShotMultiBoxDetector), etc., while common scene parsing models include VGG, ResNet, etc.

[0103] Pattern recognition technology is used to detect specific patterns or behaviors in images to find abnormal activities or potential dangerous scenes. For example, models such as recurrent neural networks (RNN) or long short-term memory networks (LSTM) can be used to analyze dynamic behaviors in video streams to detect abnormal situations such as sudden gathering of people and smoke from fires. In addition, machine learning-based anomaly detection algorithms such as support vector machines (SVM) and clustering algorithms can be used to extract features from images or videos and identify potential dangerous scenes.

[0104] Combine data from different sources and use data fusion technology to improve data accuracy and reliability.

[0105] First, data from different sources need to be matched and aligned to ensure data consistency in time, space or other aspects. For example, social media reports and real-time images from surveillance cameras are time-stamped so that data from both can be compared in the same time period. Next, key information is extracted from data from different sources and associations are established between the data. For example, descriptions and location information of events are extracted from social media reports, and images or videos of the location of events are extracted from real-time images from surveillance cameras. Data fusion technology is used to integrate and fuse data from different sources to generate more comprehensive and accurate information. For example, the event description in social media reports is compared and verified with the scene in the real-time images of surveillance cameras to verify the consistency of the occurrence and description of the event. Through data fusion technology, data from different sources can be used to improve the accuracy and reliability of data, so as to more effectively identify and respond to emergency events, reduce false alarms and missed alarms, and improve the efficiency and effectiveness of emergency response.

[0106] Advanced formatting of filtered and processed data is performed to prepare it for subsequent in-depth analysis. This includes two main steps:

[0107] a. Convert the data into an input format that the machine learning model can accept, i.e., feature vector. This involves extracting, transforming, and encoding the data so that it can be represented as a numerical feature vector. For example, the bag-of-words model or word embedding technology is used to convert text data into a vector representation; convolutional neural networks (CNNs) are used to extract features from image data and convert them into vector form.

[0108] b. Normalize the feature vectors to ensure uniform scales between different features and avoid excessive influence of certain features on model training. Normalization methods include min-max scaling, standardization, etc.

[0109] Add metadata to each piece of data, including information such as the source of the data, the time when the data was acquired, and the processing timestamp, for subsequent tracking and analysis. Metadata is added by adding new fields to the data set or adding additional information to the data record. For example, add a new field to the data set to store the source of the data, or add additional fields to each data record to store information such as the acquisition time and processing timestamp.

[0110] S2 includes the following:

[0111] Events are considered as nodes in a graph, and the characteristics of the graph are used to measure the similarity between nodes.

[0112] Every historical event and current events are considered as images A node in .

[0113] Edges between nodes Indicates potential connections between events.

[0114] Strength of edge The calculation formula is:

[0115] ;

[0116] in, and The differences are geographical and temporal. and is a parameter normalized for geographical and temporal differences, is an exponential function that is 0 if the event type matches, and 1 otherwise.

[0117] Using the Laplacian Matrix of the Event Graph As the structure of the graph, , is the degree matrix, is the adjacency matrix, containing .

[0118] To make it easier to understand " is the adjacency matrix, containing The following explanation is now given:

[0119] The adjacency matrix is ​​used in graph theory to represent the existence and strength of edges between events in a graph. Each element of the adjacency matrix Represents the strength of the edge between the historical event and the current event. This strength is used to quantify the association or similarity between the two events. It is defined by an exponential decay function, combining the differences in geographic location, time, and event type, thereby converting the geographic and temporal differences of two events and whether the type is the same into a real value. The larger the value, the more similar the two events are. Therefore, in the adjacency matrix, The value of It is directly given, and higher values ​​indicate stronger connections between nodes, that is, higher similarity between events. In this way, the adjacency matrix directly reflects the connection strength between nodes (events) in the event graph, which is the basis for subsequent graph analysis (such as spectral clustering).

[0120] Calculate the eigenvalues ​​and eigenvectors of the Laplace matrix, select the previous A feature space is constructed by using the eigenvectors corresponding to the smallest non-zero eigenvalues.

[0121] The choice of reflects the complexity of the main structure in the graph and the inherent dimensionality of the data. It is determined by analyzing the eigenvalue spectrum of the Laplacian matrix The specific operation is: calculate all the eigenvalues ​​of the Laplace matrix, arrange them in ascending order, observe the intervals of the eigenvalues, and select the sequence number corresponding to the eigenvalue at the first significantly increased interval as This “gap” indicates that the eigenvalue transitions from the part representing smaller noise perturbations to the part reflecting the main structure of the graph. Therefore, choosing this gap as the split point can capture the main cluster structure while avoiding overfitting and noise interference.

[0122] In the feature space defined by the eigenvectors, the position of each event is given by the coordinates of its eigenvector.

[0123] The similarity between the current event and the historical event is calculated by the inverse of the Euclidean distance between the two in the feature space: ;

[0124] in and are the coordinates of historical events and current events in the feature space respectively.

[0125] The greater the similarity between current events and historical events, the higher the similarity and overlap between current events and historical events, which may mean that they have similar characteristics, backgrounds or influencing factors, or are different instances of the same type of events. This may lead to similar impacts and consequences, and the lessons learned from historical events can guide the response and handling of current events. Conversely, the smaller the similarity between current events and historical events, the greater the difference and uniqueness between them, which may be new or rare events that require independent thinking and response strategies.

[0126] A similarity evaluation method based on graph theory is used to analyze the degree to which historical events explain current events. This is because judging the similarity between historical and current events only by similarity scores, although the correspondence in structure and features can be identified, this similarity only indicates the degree of similarity between the two events in certain dimensions, and is not sufficient to directly reflect the practical value of historical events in predicting the specific scope and duration of the impact of current events. For example, two events with similar geographical locations may have very different scopes of impact. If predictions are made based solely on the similarity of locations, significant prediction errors may result.

[0127] Further analysis through conditional information volume is to quantify the contribution of historical events in reducing the uncertainty of current event prediction. Conditional information volume can measure the degree of reduction of unknown information about current events when the similarity of historical events is known. This quantitative indicator helps to judge the value of historical data in practical applications, that is, whether it can provide enough information based on historical events to support effective prediction of new events. This method improves the scientificity and accuracy of predictions because it not only considers the similarities between events, but also evaluates the conversion efficiency of these similarities in practical applications.

[0128] For example, if two events have a high degree of connectivity in the graph theory model, but in fact the socioeconomic context or specific influencing factors when they occurred are very different, then a simple similarity score may overestimate the actual correlation between them. By analyzing the amount of conditional information, we can further understand the limitations and potential of this similarity in actual prediction, thereby providing decision makers with more accurate and in-depth analysis results to ensure the effectiveness and reliability of decision-making.

[0129] S3 includes the following:

[0130] First, step S2 is used to calculate the similarity scores between the current event and all historical events.

[0131] Use information entropy to assess the diversity and information content of historical events in explaining current events:

[0132] ;

[0133] in is the total number of historical events, Represents the similarity score between the current event and all historical events, It is the normalized probability distribution of the similarity of historical events to current events.

[0134] Calculate the conditional information, that is, the amount of information provided by the similarity score of historical events under the condition that the current event is known: ;

[0135] in Represents the maximum entropy value if all events are equally likely.

[0136] The ratio of the conditional information content and the information threshold is calculated and marked as the degree of explanation, which is used to quantify the degree to which historical events explain current events.

[0137] If the explanation value is greater than or equal to 1, it indicates that historical events provide sufficient information to support effective predictions of the scope and duration of the impact of current events.

[0138] If the explanation value is less than 1, it means that the information about historical events is insufficient to provide reliable predictions, and other data sources or analytical methods may be needed to enhance the accuracy of the predictions.

[0139] The concepts of information entropy and conditional information are used to quantify the extent to which historical events explain current events. Information entropy can help us gain a deeper understanding of the diversity and uncertainty of historical events in describing current events, while conditional information makes it easier to measure how much uncertainty about current events is reduced after knowing these historical events. Combining these two metrics can more scientifically evaluate the effectiveness and reliability of historical events in predicting current events.

[0140] The present invention makes comprehensive use of graph theory models and information theory principles. First, events are converted into nodes in a graph, and the associations between events are represented by edges, which helps to quantify the similarity between events. Secondly, the historical events and current events are located in the feature space using the method of Laplace matrix and feature space construction, and then the similarity between them is calculated. This method takes into account the differences in geography, time, and event type, and converts them into real values ​​to measure the similarity between events. In addition, the diversity and information content of historical events in explaining current events are evaluated by calculating information entropy and conditional information. Such a comprehensive analysis not only takes into account similarity, but also quantifies the explanatory power of historical events on current events, thereby providing a more comprehensive and reliable basis for prediction.

[0141] In national spatial planning and urban physical examination and assessment, it is crucial to accurately analyze the scope and duration of the impact of existing events, because this information helps urban planners and policymakers make more informed decisions. First, the analysis of the scope of impact can clarify the geographical area and breadth that the event may affect, which is extremely critical for the allocation of resources, optimization of emergency response, and adjustment of future plans. For example, after a natural disaster occurs, by predicting its scope of impact, rescue routes and resource allocation can be effectively planned, while providing guidance for reconstruction work and ensuring the priority restoration of critical infrastructure, thereby reducing long-term socioeconomic losses. Second, understanding the duration of the event helps predict the recovery time of urban functions and potential lasting impacts, which is crucial to maintaining the continued operation of the city and the quality of life of residents. For example, the duration prediction of traffic jams can help traffic management departments adjust signal light control and bus running times to alleviate congestion problems. Therefore, through accurate model prediction and historical data analysis, real-time and effective data support can be provided to urban managers, enabling them to better respond to emergencies and optimize the overall functions of the city and the living environment of residents.

[0142] S4 includes the following:

[0143] When the explanation degree value is greater than or equal to 1, response information, breadth information and depth information are obtained based on the similarity between the current event and the historical event, where the response information includes the dynamic emergency response effectiveness index, the breadth information includes the spatial impact domain index, and the depth information includes the time domain duration index.

[0144] The main purpose of calculating the dynamic emergency response effectiveness index is to quantify and evaluate the time efficiency of the response team in implementing effective intervention measures after an emergency occurs. This indicator can help emergency management departments understand the actual performance of the response system in real crisis situations and provide real-time feedback so that emergency response strategies can be adjusted and improved in a timely manner. Evaluating response time through quantitative methods can help relevant departments identify potential weaknesses, optimize resource allocation, and improve response speed, thereby reducing the losses and impacts that may be caused by the incident.

[0145] The Dynamic Emergency Response Effectiveness Index provides a specific metric for assessing the time delay between the occurrence of an incident and the implementation of emergency response measures. A high value for this indicator indicates a rapid and effective response that is able to intervene before the incident causes serious damage, while a low value may indicate that response measures are deployed slowly and inefficiently. By monitoring and analyzing this indicator, decision makers can better understand and improve the overall performance of the emergency response system, ensuring faster and more effective action in future incidents.

[0146] The process of obtaining the dynamic emergency response effectiveness index is as follows:

[0147] Collect data about the current event, including the time the event started and timing of intervention implementation ;

[0148] Convert temporal data into a continuous time series, taking into account the environmental impact of events and other relevant temporal factors;

[0149] Calculate the time difference between each event ;

[0150] Applying a dynamic weighting function , automatically adjust the weight based on the urgency of the event and the processing time of similar events in historical data: ;

[0151] in It is a parameter obtained by fitting historical data and is used to adjust the attenuation and cyclical changes of weights over time.

[0152] Using weighted event difference To adjust the original time difference so that it reflects the time efficiency in actual operation.

[0153] A nonlinear function is used to transform the weighted time difference to calculate the dynamic emergency response effectiveness index: ;

[0154] in, Represents the hyperbolic sine function.

[0155] The nonlinear part in the dynamic emergency response effectiveness index calculation formula increases the sensitivity to small time differences through logarithmic and hyperbolic sine functions, while maintaining a moderate response to long time differences.

[0156] The dynamic emergency response effectiveness index is used to indicate and reflect the timeliness and effectiveness of emergency response. The larger the index, the faster and more effective the response measures are implemented, and the more rapid action can be taken before the incident causes serious damage, thereby minimizing losses and impacts. A higher index value means that the emergency response system is able to manage the crisis in a timely manner, reflecting efficient crisis handling capabilities and good preparedness. On the contrary, the smaller the index value, the longer the time from the occurrence of the incident to effective intervention, the poorer the timeliness and effectiveness of the response measures, and may lead to more serious consequences and losses. Therefore, a low index value indicates the need to improve emergency response plans and strategies to improve their response speed and efficiency.

[0157] When it is determined that historical events have explanatory power for current events, the reason why the spatial impact domain index and the temporal duration index still need to be calculated separately is that these two indicators provide an in-depth understanding of the spatial and temporal dimensions of the impact of the event, respectively. The spatial impact domain index measures the spatial breadth of the impact of the event, reflecting the geographical spread of the event, which is crucial for resource allocation, emergency response, and geographical positioning of preventive measures. The temporal duration index describes the duration of the impact of the event, which is important for understanding the short-term and long-term consequences of the event and planning for sustained response or recovery efforts. Even if historical events can explain the occurrence of current events, the specific values ​​of these two indices still guide the implementation of different strategies because they help decision makers distinguish which resources and response measures are most suitable for the current specific situation, ensuring that each response is as effective and precise as possible.

[0158] The starting point and significance of calculating these two indices is to ensure that targeted actions can be taken in the face of complex and ever-changing emergency events. For example, an emergency event with a wide spatial impact and short duration (such as a fire or explosion) requires rapid and concentrated emergency response measures to control the impact and quickly restore normal conditions. On the contrary, an event with a small spatial impact but long duration (such as a persistent flood) requires long-term resource investment and continuous monitoring. By calculating these two indices separately, decision makers can better understand the nature and needs of the event and develop a response strategy that is more suitable for the specific situation, thereby playing a key role in ensuring the safety of people and reducing economic losses.

[0159] The Spatial Impact Domain Index is an indicator that quantifies the breadth of an event's spatial impact, and is calculated based on the similarity between the current event and historical events. This index is evaluated by comparing the spatial extent of historical events. The calculation process first involves identifying historical events similar to the current event and analyzing the spatial diffusion patterns of these events. Then, based on this historical data, a mathematical model is used to predict the spatial impact domain of the current event. The model considers the type, severity, and historical spatial extension data of the event to generate a quantitative spatial impact domain index, which helps decision makers understand the geographic scope of the event's possible impact and allocate resources and prepare for emergencies accordingly.

[0160] The process of obtaining the spatial influence domain diameter index is as follows:

[0161] A regression model based on the logarithmic logic function is used to predict the spatial impact domain of an event:

[0162] ;

[0163] in:

[0164] is the spatial influence domain diameter index;

[0165] is the maximum spatial impact domain diameter, which is determined by analyzing the maximum impact range in historical events;

[0166] is the similarity score obtained from the similarity calculation;

[0167] is the similarity score of the inflection point of the influence range, that is, when hour, ;

[0168] It is a parameter that controls the slope of the function curve, and its value determines the sensitivity of the similarity to the impact range.

[0169] and It can be obtained by fitting historical data, for example, using the least squares method or other optimization algorithms to determine these parameters to best fit the historical data.

[0170] The spatial impact domain index is used to represent and reflect the scope of an event's impact in geographic space. The larger the value of the index, the wider the spatial scope of the event's impact, which may involve more regions, cities or villages, requiring more extensive resource dispatch and more complex emergency response measures. On the contrary, if the spatial impact domain index is small, it means that the scope of the event's impact is relatively limited, and may only affect a small area or a specific location. The corresponding response measures may be more concentrated and targeted, and the resources and emergency response involved are relatively less.

[0171] The temporal duration index is used to quantify the duration of the impact of an event. The calculation of this index also depends on the similarity between the current event and historical events. In this process, historical events with similar conditions to the current event are first identified and the duration data of these events are analyzed. By collecting this data, a mathematical model is used to estimate the duration of the current event. Considering the impact intensity of the event, the duration of historical events, and the similarity score, the temporal duration index is finally output. This index is crucial for planning long-term response measures, resource allocation, and evaluating the possible long-term impact of events on socioeconomic activities.

[0172] The process of obtaining the time domain duration index is as follows:

[0173] The exponential decay model is used to calculate the duration to obtain the time domain duration index:

[0174] ;

[0175] in:

[0176] is the time domain duration index;

[0177] and are the minimum and maximum durations, respectively, based on the observed range of historical data;

[0178] is the similarity score obtained from the similarity calculation;

[0179] is a baseline similarity score, when When , the duration drops rapidly from the maximum value;

[0180] is the decay coefficient, which controls the speed at which the time caused by the similarity difference decreases.

[0181] and It is determined by analyzing the duration range of historical events;

[0182] and Parameters are fitted based on historical data.

[0183] The time domain duration index is used to indicate and reflect the duration of the impact of an event. When this index is large, it means that the impact of the event may last for a long time, which poses a long-term challenge to emergency management, resource allocation and recovery plans, and may require continuous monitoring and long-term support measures. On the contrary, if the time domain duration index is small, it means that the impact of the event is short, and the corresponding response and recovery measures can also be completed in a shorter time, with less overall demand for resources and management pressure. This helps decision makers to arrange emergency resources and recovery strategies in the short term more effectively.

[0184] The dynamic emergency response effectiveness index, the spatial impact domain diameter index and the time domain duration index are comprehensively analyzed and calculated to generate the dynamic response data insertion priority coefficient. For example, the following calculation formula can be used: ;

[0185] in Indicates the dynamic response data insertion priority coefficient, , and They are the dynamic emergency response effectiveness index, the spatial impact domain index and the time domain duration index. , and They are the preset proportional coefficients of the dynamic emergency response effectiveness index, the spatial impact domain diameter index and the time domain duration index, and all are greater than zero.

[0186] The dynamic response data insertion priority coefficient is mainly used to indicate and reflect the priority and urgency of the national land space planning urban physical examination system in terms of real-time and accuracy assessment. The core role of this index is to help the system dynamically adjust the priority of data insertion to ensure that in emergency or critical situations, the urban operation status of the affected area can be quickly reassessed and adjusted.

[0187] When the dynamic response data insertion priority coefficient value is large, it indicates that the current event has a very significant impact on the city's operation or changes rapidly, and needs to be handled with priority. This situation usually involves major emergencies or events with wide-ranging impacts, such as natural disasters, important public events, etc., and their data must be processed and updated with priority to ensure the timeliness and effectiveness of response measures.

[0188] On the contrary, when the dynamic response data insertion priority coefficient value is small, it means that the urgency of the event is low, the impact may be limited or the change is not large, and it can be processed according to the normal priority. This means that the system can arrange the data update of these events while processing more urgent data, without the need to immediately adjust the overall city operation status.

[0189] Through such an index, city managers can allocate resources more accurately, optimize response processes, and improve overall management efficiency and emergency response capabilities. This dynamic adjustment mechanism of priorities is particularly important for large-scale urban management systems, as it can greatly improve the speed and accuracy of response to emergencies, thereby reducing possible losses and negative impacts.

[0190] For each real-time event, calculate the dynamic response data insertion priority coefficient;

[0191] If the dynamic response data insertion priority coefficient is less than the priority threshold of one, the event is considered to have the lowest priority and can be processed later or in a regular data update cycle, sending a low-level signal;

[0192] If the dynamic response data insertion priority coefficient is greater than or equal to priority threshold 1 and less than priority threshold 2, it is assigned a lower priority and the insertion demand is second. A secondary level signal is issued;

[0193] If the dynamic response data insertion priority coefficient is greater than or equal to the priority threshold 2, this type of event is considered the most urgent and needs to be processed immediately, and a high-level signal is issued.

[0194] After clarifying that the current event and the historical event have similar dependency analysis, the present invention calculates the response, impact breadth and depth information of the event, and generates a dynamic emergency response efficiency index, a spatial impact domain diameter index and a time domain duration index based on this, and generates a dynamic response data insertion priority coefficient through comprehensive analysis and calculation to optimize the priority of data insertion, ensuring that key data can be processed with priority when facing emergencies or major changes. This not only speeds up the response to emergencies, but also improves the accuracy of the city's operating status assessment. And it can dynamically adapt to the ever-changing environment and event characteristics, so as to maintain efficient operation in a constantly changing urban environment. This is convenient for enhancing the efficiency of urban management and emergency response, enabling urban planning and decision-making to make decisions more wisely, and optimizing the overall function of the city and the living environment of residents.

[0195] Embodiment 2: Figure 2 The present invention provides a system for calculating the evaluation index of a city physical examination for land space planning, including: a data collection hub module, a similarity spectrum analysis module, a historical interpretation evaluation module and a priority decision engine module;

[0196] The data collection hub module connects to multiple data sources in the city to detect and classify emergency events in real time, uses natural language processing and image recognition technology to parse and format unstructured data to meet the needs of subsequent analysis, and sends the collected data to the similarity spectrum analysis module;

[0197] The similarity spectrum analysis module builds a similarity analysis model based on the structural characteristics and similarity calculation of the event graph using the Laplace matrix and feature space, measures the similarity between the current event and the historical event, and sends the similarity measurement results to the historical interpretation evaluation module;

[0198] The historical explanation evaluation module uses the information diversity and explanatory power of historical events to evaluate their degree of explanation for current events and sends the evaluation results to the priority decision engine module;

[0199] After it is clear that historical events have the ability to explain the current event, the priority decision engine module performs parameter fitting through the similarity score in step S2, analyzes the response of the current event and the breadth and depth of impact as a basis, and gives the corresponding dynamic allocation data insertion priority prompt signal.

[0200] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0201] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0202] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0203] In the several embodiments provided in the present application, it should be understood that the disclosed systems and devices can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0204] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0205] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0206] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0207] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0208] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calculating the evaluation index of a city physical examination in national land space planning, characterized by: S1, which connects to multiple data sources in the city to detect and classify emergency events in real time, and uses natural language processing and image recognition technology to parse and format unstructured data to meet the needs of subsequent analysis; S2, based on the structural characteristics and similarity calculation of the event graph, uses the Laplacian matrix and feature space to build a similarity analysis model to measure the similarity between current events and historical events; S3, using the information diversity and explanatory power of historical events to assess the extent to which they explain current events; S4, after it is clear that the historical event has the ability to explain the current event, the similarity score in step S2 is used to perform parameter fitting, and the response of the current event and the breadth and depth of the impact are analyzed as a basis, and the corresponding dynamic allocation data insertion priority prompt signal is given; S2 Includes the following: Treat events as nodes in a graph and use the characteristics of the graph to measure the similarity between nodes; Every historical event and current events are considered as images A node in Edges between nodes Indicates potential connections between events; Strength of edge The calculation formula is: ； in, and The differences are geographical and temporal. and is a parameter normalized for geographical and temporal differences, is an exponential function, which is 0 if the event type matches, otherwise it is 1; Using the Laplacian Matrix of the Event Graph As the structure of the graph, , is the degree matrix, is an adjacency matrix containing ; Calculate the eigenvalues ​​and eigenvectors of the Laplace matrix, select the previous The eigenvectors corresponding to the smallest non-zero eigenvalues ​​construct a eigenspace; In the feature space defined by the eigenvectors, the position of each event is given by the coordinates of its eigenvector; The similarity between the current event and the historical event is calculated by the inverse of the Euclidean distance between the two in the feature space: ; in and are the coordinates of historical events and current events in the feature space respectively; S3 includes the following: First, the similarity scores between the current event and all historical events are calculated through step S2; Use information entropy to assess the diversity and information content of historical events in explaining current events: ； in is the total number of historical events, Represents the similarity score between the current event and all historical events, is the normalized probability distribution of the similarity of historical events to current events; Calculate the conditional information, that is, the amount of information provided by the similarity score of historical events under the condition that the current event is known: ; in represents the maximum entropy value if all events are equally likely; Calculate the ratio of conditional information content to information threshold, and mark the ratio as explanation degree, which is used to quantify the degree to which historical events explain current events; S4 includes the following: When the explanation degree value is greater than or equal to 1, based on the similarity between the current event and the historical event, the response information, breadth information and depth information are obtained, where the response information includes the dynamic emergency response effectiveness index, the breadth information includes the spatial impact domain diameter index, and the depth information includes the time domain duration index; The process of obtaining the dynamic emergency response effectiveness index is as follows: Collect data about the current event, including the time the event started and timing of intervention implementation ; Convert time data into continuous time series; Calculate the time difference between each event ; Applying a dynamic weighting function , automatically adjust the weight based on the urgency of the event and the processing time of similar events in historical data: ; in is an adjustment parameter used to adjust the decay and periodic changes of weights over time; Using weighted event difference To adjust the original time difference; A nonlinear function is used to transform the weighted time difference to calculate the dynamic emergency response effectiveness index: ； in represents the hyperbolic sine function; The process of obtaining the spatial influence domain diameter index is as follows: A regression model based on the logarithmic logic function is used to predict the spatial impact domain of an event: ； in: is the spatial influence domain diameter index; is the maximum spatial influence domain diameter; is the similarity score obtained from the similarity calculation; is the similarity score of the inflection point of the influence range, that is, when hour, ; It is a parameter that controls the slope of the function curve and is used to determine the sensitivity of similarity to the impact range; The process of obtaining the time domain duration index is as follows: The exponential decay model is used to calculate the duration to obtain the time domain duration index: ； in: is the time domain duration index; and are the minimum and maximum durations, respectively; is the similarity score obtained from the similarity calculation; is a baseline similarity score, when When , the duration drops rapidly from the maximum value; is the decay coefficient, which controls the speed at which time decreases due to the similarity difference; The dynamic emergency response effectiveness index, spatial impact domain diameter index and time domain duration index are calculated and analyzed comprehensively to generate dynamic response data for insertion into priority coefficients; For each real-time event, calculate the dynamic response data insertion priority coefficient; If the dynamic response data insertion priority coefficient is less than the priority threshold of one, the event is considered to have the lowest priority and is processed later or according to the regular data update cycle, and a low-level signal is issued; If the dynamic response data insertion priority coefficient is greater than or equal to priority threshold 1 and less than priority threshold 2, it is assigned a lower priority, the insertion demand is second, and a secondary signal is issued; If the dynamic response data insertion priority coefficient is greater than or equal to the priority threshold 2, this type of event is considered the most urgent and needs to be processed immediately, and a high-level signal is issued.

2. A system for calculating the physical examination evaluation index of a city in national land space planning, used to implement the method for calculating the physical examination evaluation index of a city in national land space planning according to claim 1, comprising: Data collection hub module, similarity spectrum analysis module, historical interpretation evaluation module and priority decision engine module; The data collection hub module connects to multiple data sources in the city to detect and classify emergency events in real time, uses natural language processing and image recognition technology to parse and format unstructured data to meet the needs of subsequent analysis, and sends the collected data to the similarity spectrum analysis module; The similarity spectrum analysis module builds a similarity analysis model based on the structural characteristics and similarity calculation of the event graph using the Laplace matrix and feature space, measures the similarity between the current event and the historical event, and sends the similarity measurement results to the historical interpretation evaluation module; The historical explanation evaluation module uses the information diversity and explanatory power of historical events to evaluate their degree of explanation for current events and sends the evaluation results to the priority decision engine module; After it is clear that historical events have the ability to explain the current event, the priority decision engine module performs parameter fitting through the similarity score in step S2, analyzes the response of the current event and the breadth and depth of impact as a basis, and gives the corresponding dynamic allocation data insertion priority prompt signal.

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