Urban behavior influence maximization analysis method oriented to timeliness constraint

Abstract

The invention relates to the technical field of data mining and intelligent analysis, and discloses a timeliness constraint-oriented city behavior influence maximization analysis method, which comprises the steps of acquiring real-time behavior data, judging key nodes, acquiring association strength, extracting a feature set, adjusting propagation parameters and the like. According to the method, the states of the key nodes can be dynamically monitored, the propagation strategy is optimized, the information propagation efficiency is improved, and the problems of quickly identifying the key nodes and optimizing propagation in a dynamic network environment are solved.

Description

Technical Field

[0001] This invention relates to the field of data mining and intelligent analysis technology, specifically to a method for maximizing the influence of urban behaviors under time constraints. Background Technology

[0002] With the acceleration of urbanization and the deepening of smart city construction, the real-time analysis and optimization of the dissemination of social behavioral data have become key challenges. In scenarios such as emergency management, public safety, and commercial promotion, maximizing influence under time constraints is particularly important. Traditional analytical methods have three major limitations: Static network assumption: Most studies construct static networks based on historical data (such as classic greedy algorithms and PageRank derivatives), ignoring dynamic changes in node activity and connection relationships (such as topological changes caused by population movement or sudden events). Lack of timeliness: Existing methods (such as the IC / LT model) do not consider the time window limit of information dissemination, resulting in a lag in the identification of key nodes and failing to meet the needs of real-time decision-making; Weak environmental adaptability: The propagation parameters are fixed, making it difficult to dynamically adjust strategies based on node status (such as sudden changes in activity) and environmental feedback (such as changes in population flow).

[0003] Therefore, there is an urgent need for a method to dynamically perceive key nodes and optimize dissemination strategies in real time in order to solve the problem of maximizing the influence of urban behavior under time constraints. Summary of the Invention

[0004] This invention provides a method for maximizing the influence of urban behaviors under time constraints, which solves the problem in existing technologies that it is difficult to quickly identify key nodes and optimize propagation strategies in dynamic network environments.

[0005] This invention provides a method for maximizing the influence of urban behaviors under time constraints, including: Collect real-time behavioral data within the target area, and determine whether there are key nodes that meet preset conditions based on the real-time behavioral data; When a key node exists within the target area and the key node is active, the first real-time association strength between the key node and the target behavior object is obtained. When the first real-time association strength is less than or equal to the first preset threshold, the first feature set of the key node is extracted based on the real-time behavior data, and it is determined whether the first feature set conforms to the first preset rule. The first feature set includes the node's activity feature and connectivity feature. If the first feature set conforms to the first preset rule, then the propagation parameters of the target behavior object are adjusted, and the propagation mode of the target behavior object is switched to the intermittent propagation mode; In the intermittent propagation mode, the second feature set of the key node is obtained based on the real-time behavior data, and the environmental feedback information of the target behavior object is obtained. Based on the second feature set and the environmental feedback information, it is determined whether the key node conforms to the second preset rule. If the key node meets the second preset rule, the propagation parameters of the target behavior object are adjusted to the maximum value, and the auxiliary function module of the target behavior object is paused.

[0006] The method for maximizing the influence of urban behavior under time constraints provided by the present invention collects real-time behavioral data within a target area and determines whether there are key nodes that meet preset conditions based on the real-time behavioral data, including: When an object to be analyzed is detected in the real-time behavior data, the core attributes of the object to be analyzed are extracted, and the core attributes are matched with the corresponding attributes in the preset key node feature library; The similarity between the core attribute and the corresponding attribute in the preset key node feature library is determined by a similarity calculation formula. When the similarity is greater than or equal to a preset similarity threshold, the object to be analyzed is identified as a key node.

[0007] According to the method for maximizing the influence of urban behavior under time constraints provided by the present invention, when there are key nodes in the target area and the key nodes are active, the method obtains the first real-time association strength between the key nodes and the target behavior object, including: Based on the real-time behavior data, the behavior direction of the key node is determined, and the angle between the behavior direction and the direction of the target behavior object is obtained with the location of the key node as the starting point. Multiple sets of real-time behavioral data are continuously collected, and the correlation strength between the key node and the target behavioral object is determined based on the multiple sets of real-time behavioral data. If the included angle is less than or equal to a preset angle and the association strength between the key node and the target behavior object changes, then the key node is considered to be in an active state. When the key node is active, the first real-time association strength is obtained based on the real-time behavior data.

[0008] According to the method for maximizing the influence of urban behavior under time constraints provided by the present invention, when the first real-time correlation strength is less than or equal to a first preset threshold, a first feature set of the key node is extracted based on the real-time behavior data, and it is determined whether the first feature set conforms to a first preset rule. The first feature set includes the node's activity feature and connectivity feature, including: The activity and connectivity characteristics of the key nodes are obtained based on the real-time behavioral data. Determine whether the activity feature conforms to the activity rule, and determine whether the connectivity feature conforms to the connectivity rule; When the activity feature conforms to the activity rule and the connectivity feature conforms to the connectivity rule, then the first feature set conforms to the first preset rule.

[0009] According to the method for maximizing the influence of urban behavior under time constraints provided by the present invention, determining whether the activity characteristics conform to activity rules includes: The activity features are used to determine the behavior frequency of the key nodes, and the behavior frequency is compared with the standard behavior frequency in the preset key node feature library. When the difference between the behavior frequency and the standard behavior frequency is greater than or equal to a first preset difference, the activity feature is considered to conform to the activity rule.

[0010] According to the method for maximizing the influence of urban behavior under time constraints provided by the present invention, determining whether the connectivity feature conforms to the connectivity rule includes: Based on the connectivity features, the number of neighboring nodes, interaction frequency, centrality index, and path length of the key node are obtained. Based on the number of adjacent nodes, determine whether the connection range of the key node exceeds a preset range; based on the interaction frequency, determine whether the interaction intensity of the key node is higher than a preset intensity; based on the centrality index, determine whether the centrality of the key node is higher than a preset centrality; and based on the path length, determine whether the propagation path of the key node is shorter than a preset path length. When the connection range of the key node exceeds a preset range, the interaction strength of the key node is higher than a preset strength, the centrality of the key node is higher than a preset centrality, and the propagation path of the key node is shorter than a preset path length, then the connectivity feature conforms to the connectivity rule.

[0011] According to the method for maximizing the influence of urban behaviors under time constraints provided by the present invention, if the first feature set meets the first preset rule, the propagation parameters of the target behavior object are adjusted and the propagation mode of the target behavior object is switched to an intermittent propagation mode. This includes: when the first feature set meets the first preset rule, the propagation parameter value of the target behavior object is increased by 20%, and the propagation mode of the target behavior object is switched to an intermittent propagation mode.

[0012] According to the method for maximizing the influence of urban behavior under time constraints provided by the present invention, in an intermittent propagation mode, a second feature set of the key node is obtained based on the real-time behavior data, and environmental feedback information of the target behavior object is obtained. Based on the second feature set and the environmental feedback information, it is determined whether the key node conforms to a second preset rule. The second feature set specifically comprises behavior frequency features, including: In the intermittent propagation mode, the real-time behavior frequency is compared with the behavior frequency at the last moment before switching to the intermittent propagation mode based on the behavior frequency characteristics. If the real-time behavior frequency is greater than the behavior frequency at the last moment before switching to the intermittent propagation mode, then it is determined whether the source direction of the environmental feedback information is consistent with the behavior direction of the key node. If the source direction of the environmental feedback information is consistent with the behavior direction of the key node, then the target behavior object is controlled to maintain the current propagation parameters and propagation mode; If the source direction of the environmental feedback information is inconsistent with the behavior direction of the key node, then the key node conforms to the second preset rule.

[0013] According to the method for maximizing the influence of urban behaviors under time constraints provided by the present invention, the method further includes: when the correlation strength between the target behavior object and the key node is greater than a first preset threshold, controlling the target behavior object to run under the initial propagation parameters and the initial propagation mode.

[0014] According to the time-constrained urban behavior influence maximization analysis method provided by the present invention, under the intermittent propagation mode, the real-time behavior frequency is compared with the behavior frequency at the last moment before switching to the intermittent propagation mode based on the behavior frequency characteristics, including: taking the average behavior frequency of the target behavior object within a first time period after switching to the intermittent propagation mode as the real-time behavior frequency based on the behavior frequency characteristics; taking the average behavior frequency within the last first time period before switching to the intermittent propagation mode as the behavior frequency at the last moment before switching to the intermittent propagation mode based on the behavior frequency characteristics; and comparing the real-time behavior frequency with the behavior frequency at the last moment before switching to the intermittent propagation mode.

[0015] This invention significantly improves the efficiency and accuracy of dissemination under time constraints through a dynamic perception-decision closed-loop system. Specific effects include: By dynamically matching core attributes and verifying both behavior direction and association strength, the problem of node state drift in dynamic networks is solved, and the accuracy of key node identification is improved. Dual threshold control mechanism: When the association strength is less than or equal to the threshold, the intermittent propagation mode is switched based on the activity-connectivity feature to avoid resource waste; Environmental feedback linkage: By comparing behavior frequency and judging directional consistency, the propagation parameters are dynamically adjusted to the maximum value or reverted to the initial mode, thereby improving the response speed; Intermittent propagation mode reduces ineffective exposures, and dynamic adjustment of propagation parameters improves effective coverage. The intelligent pause function for assistive functions reduces the computational load and meets the needs of second-level decision-making (such as traffic congestion management).

[0016] In summary, this invention is compatible with scenarios such as urban management (traffic flow, emergency response), social networks (hotspot tracking), and public safety (crowd evacuation), and its average propagation efficiency is improved in dynamic network environments. Attached Figure Description

[0017] Figure 1 This is a flowchart of the overall method of the present invention; Figure 2 This is a flowchart illustrating the key node identification process of the present invention; Figure 3 This is a block diagram of the first real-time correlation strength acquisition method of the present invention; Figure 4 This is a flowchart of the first feature set extraction and rule judgment of the present invention; Figure 5 This is a flowchart illustrating the rule-based judgment process under the intermittent propagation mode of the present invention. Figure 6This is a control block diagram of the propagation parameter adjustment and auxiliary function module of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention provides a method for maximizing the influence of urban behaviors under time constraints, and its specific implementation process is described in conjunction with the appendix. Figures 1 to 6 A detailed explanation will follow. First, as... Figure 1 As shown, the entire method starts with the collection of real-time behavioral data, and then proceeds through steps such as the identification of key nodes, the acquisition of the first real-time association strength, the extraction of the first feature set and rule judgment, the adjustment of propagation parameters, and the switching of intermittent propagation modes, ultimately completing the optimization of the propagation strategy for the target behavioral object.

[0020] In practice, the first step is to collect real-time behavioral data within the target area using sensor networks or social media platforms. This data can include crowd movement trajectories, interaction records, and other dynamic information. Subsequently, based on... Figure 2 The key node identification process, as shown, detects the object to be analyzed from real-time behavioral data and extracts its core attributes. These core attributes typically encompass key indicators such as location information, behavioral frequency, and connectivity. These core attributes are then matched with corresponding attributes in a pre-defined key node feature library, and the similarity between the two is calculated using a similarity calculation formula. When the similarity is greater than or equal to a pre-defined similarity threshold, the object to be analyzed is identified as a key node. This process ensures high accuracy and specificity in the selection of key nodes, thus laying the foundation for subsequent analysis.

[0021] Next, as Figure 3As shown, when a key node exists within the target area and is active, it is necessary to obtain the first real-time association strength between the key node and the target behavior object. Specifically, the behavior direction of the key node is determined based on real-time behavior data, and the angle between its behavior direction and the direction of the target behavior object is obtained, starting from the location of the key node. Multiple real-time behavior data are continuously collected, and the association strength between the key node and the target behavior object is judged based on these data to see if it changes. If the angle is less than or equal to a preset angle and the association strength changes, the key node is considered to be active. On this basis, the first real-time association strength is further obtained based on the real-time behavior data. This process dynamically evaluates the activity level of the key node through changes in behavior direction and association strength, ensuring that the analysis results can reflect the actual situation in a timely manner.

[0022] When the first real-time correlation strength is less than or equal to the first preset threshold, proceed to the next step, which involves extracting the first feature set of key nodes based on real-time behavioral data and determining whether it conforms to the first preset rule. For example... Figure 4 As shown, the first feature set includes node activity features and connectivity features. Activity features are primarily extracted through behavior frequency, which is compared with standard behavior frequencies in a preset key node feature library. If the difference between the behavior frequency and the standard behavior frequency is greater than or equal to a first preset difference, the activity feature is considered to conform to the activity rule. Connectivity features are extracted through multiple dimensions, including the number of adjacent nodes, interaction frequency, centrality index, and path length. The number of adjacent nodes is used to determine whether the connection range of a key node exceeds a preset range; interaction frequency is used to determine whether its interaction strength is higher than a preset strength; centrality index is used to determine whether its centrality is higher than a preset centrality; and path length is used to determine whether its propagation path is shorter than a preset path length. Only when all the above conditions are met is the connectivity feature considered to conform to the connectivity rule. If both the activity feature and connectivity feature conform to their respective rules, then the first feature set conforms to the first preset rule.

[0023] If the first feature set conforms to the first preset rule, such as Figure 1 As shown, it is necessary to adjust the propagation parameters of the target behavior object and switch its propagation mode to intermittent propagation mode. Specifically, when the first feature set meets the first preset rule, the propagation parameter value of the target behavior object is increased by 20%, and the intermittent propagation mode is switched. This adjustment aims to optimize the propagation strategy, enabling the target behavior object to more effectively spread information within the sphere of influence of key nodes.

[0024] Subsequently, under the intermittent transmission pattern, such as Figure 5As shown, it is necessary to obtain the second feature set of key nodes based on real-time behavioral data, and combine it with the environmental feedback information of the target behavior object to determine whether the key node conforms to the second preset rule. The second feature set is specifically a behavioral frequency feature, and its processing includes comparing the real-time behavioral frequency with the behavioral frequency at the last moment before switching to the intermittent propagation mode. Specifically, based on the behavioral frequency feature, the average behavioral frequency during the first time period after the target behavior object switches to the intermittent propagation mode is taken as the real-time behavioral frequency, and the average behavioral frequency during the last time period before switching to the intermittent propagation mode is taken as the behavioral frequency at the last moment before the intermittent propagation mode. Comparing the two, if the real-time behavioral frequency is greater than the behavioral frequency at the last moment before the intermittent propagation mode, it is further determined whether the source direction of the environmental feedback information is consistent with the behavioral direction of the key node. If the source direction is consistent, the target behavior object is controlled to maintain its current propagation parameters and propagation mode; if the source direction is inconsistent, the key node is considered to conform to the second preset rule.

[0025] When the critical node meets the second preset rule, such as Figure 6 As shown, the propagation parameters of the target behavior object need to be adjusted to their maximum value, and the auxiliary function modules need to be paused. This operational logic aims to maximize the propagation capability of the target behavior object while avoiding interference from the auxiliary function modules. Furthermore, if the association strength between the target behavior object and the key node is greater than a first preset threshold, the target behavior object is controlled to operate with the initial propagation parameters and initial propagation mode. This mechanism ensures the flexibility of the propagation strategy, enabling dynamic adjustment of propagation behavior based on the state of the key node.

[0026] Throughout the process, the various components collaborated closely through real-time behavioral data. For example, the identification of key nodes relied on the collection and analysis of real-time behavioral data, while the acquisition of the first real-time correlation strength was based on the dynamic relationship between the key node and the target behavioral object. The extraction of the first and second feature sets further refined the feature description of the key nodes, providing a scientific basis for adjusting the propagation parameters of the target behavioral object. The introduction of environmental feedback information enhanced the system's adaptability to changes in the external environment, making the propagation strategy more intelligent and efficient. The pause operation of the auxiliary function modules reflected the refined management of the propagation strategy, ensuring the rational allocation and utilization of resources.

[0027] Through the specific implementation methods described above, this invention effectively solves the problem in existing technologies of the difficulty in quickly identifying key nodes and optimizing propagation strategies in dynamic network environments. Whether in urban traffic management, social network analysis, or public safety early warning systems, this method can significantly improve information dissemination efficiency and provide strong support for urban management and social governance.

[0028] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for maximizing the influence of urban behaviors under time constraints, characterized in that: include: Collect real-time behavioral data within the target area, and determine whether there are key nodes that meet preset conditions based on the real-time behavioral data; When a key node exists within the target area and the key node is active, the first real-time association strength between the key node and the target behavior object is obtained. When the first real-time association strength is less than or equal to the first preset threshold, the first feature set of the key node is extracted based on the real-time behavior data, and it is determined whether the first feature set conforms to the first preset rule. The first feature set includes the node's activity feature and connectivity feature. If the first feature set conforms to the first preset rule, then the propagation parameters of the target behavior object are adjusted, and the propagation mode of the target behavior object is switched to the intermittent propagation mode; In the intermittent propagation mode, the second feature set of the key node is obtained based on the real-time behavior data, and the environmental feedback information of the target behavior object is obtained. Based on the second feature set and the environmental feedback information, it is determined whether the key node conforms to the second preset rule. If the key node meets the second preset rule, the propagation parameters of the target behavior object are adjusted to the maximum value, and the auxiliary function module of the target behavior object is paused.

2. The method for maximizing the influence of urban behavior under time constraints as described in claim 1, characterized in that, Collect real-time behavioral data within the target area, and determine whether there are key nodes that meet preset conditions based on the real-time behavioral data, including: When an object to be analyzed is detected in the real-time behavior data, the core attributes of the object to be analyzed are extracted, and the core attributes are matched with the corresponding attributes in the preset key node feature library; The similarity between the core attribute and the corresponding attribute in the preset key node feature library is determined by a similarity calculation formula. When the similarity is greater than or equal to a preset similarity threshold, the object to be analyzed is identified as a key node.

3. The method for maximizing the influence of urban behavior under time constraints as described in claim 1, characterized in that, When a key node exists within the target area and the key node is active, the first real-time association strength between the key node and the target behavior object is obtained, including: Based on the real-time behavior data, the behavior direction of the key node is determined, and the angle between the behavior direction and the direction of the target behavior object is obtained with the location of the key node as the starting point. Multiple sets of real-time behavioral data are continuously collected, and the correlation strength between the key node and the target behavioral object is determined based on the multiple sets of real-time behavioral data. If the included angle is less than or equal to a preset angle and the association strength between the key node and the target behavior object changes, then the key node is considered to be in an active state. When the key node is active, the first real-time association strength is obtained based on the real-time behavior data.

4. The method for maximizing the influence of urban behavior under time constraints as described in claim 1, characterized in that, When the first real-time association strength is less than or equal to the first preset threshold, a first feature set of the key node is extracted based on the real-time behavior data, and it is determined whether the first feature set conforms to the first preset rule. The first feature set includes the node's activity feature and connectivity feature, including: The activity and connectivity characteristics of the key nodes are obtained based on the real-time behavioral data. Determine whether the activity feature conforms to the activity rule, and determine whether the connectivity feature conforms to the connectivity rule; When the activity feature conforms to the activity rule and the connectivity feature conforms to the connectivity rule, then the first feature set conforms to the first preset rule.

5. The method for maximizing the influence of urban behavior under time constraints as described in claim 4, characterized in that, Determining whether the activity feature conforms to the activity rule includes: The activity features are used to determine the behavior frequency of the key nodes, and the behavior frequency is compared with the standard behavior frequency in the preset key node feature library. When the difference between the behavior frequency and the standard behavior frequency is greater than or equal to a first preset difference, the activity feature is considered to conform to the activity rule.

6. The method for maximizing the influence of urban behavior under time constraints as described in claim 4, characterized in that, Determining whether the connectivity feature conforms to the connectivity rule includes: Based on the connectivity features, the number of neighboring nodes, interaction frequency, centrality index, and path length of the key node are obtained. Based on the number of adjacent nodes, determine whether the connection range of the key node exceeds a preset range; based on the interaction frequency, determine whether the interaction intensity of the key node is higher than a preset intensity; based on the centrality index, determine whether the centrality of the key node is higher than a preset centrality; and based on the path length, determine whether the propagation path of the key node is shorter than a preset path length. When the connection range of the key node exceeds a preset range, the interaction strength of the key node is higher than a preset strength, the centrality of the key node is higher than a preset centrality, and the propagation path of the key node is shorter than a preset path length, then the connectivity feature conforms to the connectivity rule.

7. The method for maximizing the influence of urban behavior under time constraints as described in claim 1, characterized in that, In intermittent propagation mode, a second feature set of the key node is obtained based on the real-time behavior data, and environmental feedback information of the target behavior object is obtained. Based on the second feature set and the environmental feedback information, it is determined whether the key node conforms to a second preset rule. The second feature set specifically comprises behavior frequency features, including: In the intermittent propagation mode, the real-time behavior frequency is compared with the behavior frequency at the last moment before switching to the intermittent propagation mode based on the behavior frequency characteristics. If the real-time behavior frequency is greater than the behavior frequency at the last moment before switching to the intermittent propagation mode, then it is determined whether the source direction of the environmental feedback information is consistent with the behavior direction of the key node. If the source direction of the environmental feedback information is consistent with the behavior direction of the key node, then the target behavior object is controlled to maintain the current propagation parameters and propagation mode; If the source direction of the environmental feedback information is inconsistent with the behavior direction of the key node, then the key node conforms to the second preset rule.

8. The method for maximizing the influence of urban behavior under time constraints as described in claim 1, characterized in that, Also includes: When the association strength between the target behavior object and the key node is greater than a first preset threshold, the target behavior object is controlled to run in the initial propagation parameters and initial propagation mode.

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