Data processing method and data processing platform for auxiliary decision-making in grassroots governance

Abstract

The present invention relates to the technical field of grassroots governance and intelligent decision support systems, and in particular to a grassroots governance auxiliary decision data processing method and a data processing platform, including event data collection and preprocessing, recursive call and collaborative optimization based on optimization algorithms, path simulation and optimization through virtual environments and multidimensional probability networks, and adaptive decision adjustment based on real-time feedback. Through the present invention, the system can achieve efficient event processing and global optimization, enhance the decision-making ability and response speed to deal with complex events, and significantly improve the overall efficiency of grassroots governance.

Description

Technical Field

[0001] The present invention relates to the technical field of grassroots governance and intelligent decision support systems, and in particular to a grassroots governance auxiliary decision data processing method and a data processing platform. Background Art

[0002] Traditional manual processing methods are no longer able to cope with the increasingly complex needs of grassroots governance, especially in modern cities, where events are diverse and frequent, requiring timely and accurate decision support. To this end, modern grassroots governance has gradually relied on intelligent and data-driven technical means to help improve management efficiency and decision-making accuracy.

[0003] Existing grassroots governance technologies (Chinese invention patent, publication number: CN117271847A, name: grassroots governance business auxiliary processing method and system based on large models) mainly rely on business auxiliary processing methods based on large models. These technologies build grassroots governance event models, combine labeled event data for model training, and classify and analyze events within the trained model range. Although this technology can improve the efficiency of event processing to a certain extent, there are still some obvious shortcomings:

[0004] Model training in existing technologies relies on predefined rules and labels, which makes the system perform poorly when faced with unforeseen complex events. Due to the limitations of model training cycles and data annotation, when the event type or data exceeds the scope of model training, the system's response accuracy will drop significantly.

[0005] For security reasons, existing technologies deploy models in an intranet environment, limiting the model's ability to access Internet content. Although this improves data security, it also leads to a lack of flexibility and update capabilities when the system handles events related to external data.

[0006] Existing technologies mainly rely on the reasoning of static rules and models and lack the ability to dynamically adjust and optimize, which is particularly insufficient when events develop rapidly or situations are complex and changeable. Summary of the invention

[0007] In view of the many problems existing in the above-mentioned prior arts, the present invention provides a grassroots governance auxiliary decision-making data processing method and a data processing platform. The present invention forms a dynamic and highly flexible decision support system through multi-level recursive optimization and real-time feedback mechanism, combined with virtual environment simulation and multi-dimensional probability network; its core lies in the use of advanced algorithms and data processing technologies to realize the full process intelligence from event data collection, processing, optimization to execution; ultimately, this system significantly improves the decision-making efficiency and accuracy in grassroots governance, and ensures efficient response to complex events.

[0008] A method for processing grassroots governance auxiliary decision-making data, comprising the following steps:

[0009] Collect event data, pre-process the data, and classify and prioritize the pre-processed event data according to event type and urgency to generate high-priority event data;

[0010] High-priority event data is distributed to multiple distributed intelligent nodes. Each distributed intelligent node performs event classification, feature extraction and preliminary decision-making on the high-priority event data, generates preliminary decision data through transformation data model and hybrid reasoning system, and then optimizes the preliminary decision data through nonlinear decision feedback mechanism to generate optimized preliminary decision data;

[0011] Distribute the optimization preliminary decision data to models at multiple levels, and recursively call and collaboratively optimize the optimization results of models at each level through a cross-level recursive optimization algorithm to generate cross-level recursive optimization data;

[0012] Based on cross-level recursive optimization data, a virtual environment is created to simulate the event processing path, and the path is optimized through a multi-dimensional probabilistic network to finally generate the optimal path data;

[0013] Perform event processing based on the optimal path data, analyze the execution results and make adaptive adjustments, update the optimized decision data to the global knowledge base, and synchronize it to the distributed intelligent nodes.

[0014] Preferably, the data preprocessing includes removing noise from the event data, filtering out environmental noise in the event data using a denoising algorithm; converting the format of the event data, unifying data from different sources into a standardized format; and extracting features from the event data, identifying and extracting key features through a feature extraction algorithm.

[0015] Preferably, the classification and priority setting steps are implemented through an algorithm based on event type and urgency, wherein the urgency is set by calculating the potential impact of the event and the processing time requirement, and the generated high-priority event data contains a comprehensive indicator of time sensitivity and processing urgency.

[0016] Preferably, the transformation data model dynamically adjusts the event data features through the following calculation expression:

[0017]

[0018] in, Represents the transformed feature data; represents the transformation function associated with the event type; Represents the initial features of event data; Represents parameters related to event type characteristics.

[0019] Preferably, the hybrid reasoning system includes a rule reasoning module and a statistical reasoning module. The rule reasoning module performs logical reasoning on structured data according to a predefined rule set, and the statistical reasoning module performs statistical analysis on unstructured data based on a probability model. The two are combined to generate preliminary decision data.

[0020] Preferably, the preliminary decision data is optimized by the following nonlinear mapping function:

[0021]

[0022] in, Represents the optimized preliminary decision data; Represents a nonlinear mapping function; Indicates preliminary decision data; Represents the cumulative weight factor in the feedback loop.

[0023] Preferably, the optimization preliminary decision data is distributed to models at multiple levels, and the optimization results of the models at multiple levels are recursively called and collaboratively optimized through the following recursive optimization expression:

[0024]

[0025] in, Indicates Recursively optimize data; Represents department model optimization data; Represents event type model optimization data; , and They represent the weight coefficients in the recursive optimization process respectively.

[0026] Preferably, the virtual environment is created, the virtual environment performs simulation-driven learning according to historical data and real-time data, and performs probability inference and path optimization on the simulation results of the event processing path through a multidimensional probability network, wherein the path optimization is selected by the following expression:

[0027]

[0028] in, Represents the probability value of the optimal path; Indicates The probability of the simulation results of each path.

[0029] Preferably, the event processing is performed based on the optimal path data, the execution result data is collected in real time, the execution result is analyzed by an adaptive algorithm, correction decision data is generated, and the correction decision data is input into the global knowledge base, the decision rules and models in the global knowledge base are updated, and the updated global knowledge base data is synchronized to all distributed intelligent nodes.

[0030] A system for implementing the grassroots governance auxiliary decision-making data processing method, comprising:

[0031] A data collection module is used to collect event data, and to remove noise, convert formats and extract features from the event data to generate standardized event data;

[0032] A data classification and priority setting module is used to classify and prioritize standardized event data according to event type and urgency, and generate high-priority event data;

[0033] A distributed intelligent node module, including a plurality of distributed intelligent nodes, is used to receive high-priority event data, classify and extract features of the high-priority event data, generate preliminary decision data through a transformation data model and a hybrid reasoning system, and then optimize the preliminary decision data through a nonlinear decision feedback mechanism to generate optimized preliminary decision data;

[0034] The cross-level recursive optimization module is used to distribute the optimization preliminary decision data to models at multiple levels, and recursively call and collaboratively optimize the optimization results of models at each level through the cross-level recursive optimization algorithm to generate cross-level recursive optimization data;

[0035] The event recognition and simulation module is used to create a virtual environment based on cross-level recursive optimization data, simulate the event processing path, and optimize the simulation results through a multi-dimensional probability network to generate the optimal path data;

[0036] The execution and feedback module is used to execute event processing based on the optimal path data, collect and analyze the execution result data, make adaptive adjustments, generate correction decision data, and update the correction decision data to the global knowledge base;

[0037] The global knowledge base module is used to store and update optimized decision data and synchronize the updated data to multiple distributed intelligent nodes to ensure that the system can continuously optimize and respond to future event processing needs.

[0038] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0039] The present invention achieves coordination between models at different levels and global optimal decision-making through a cross-level recursive optimization algorithm; this technology overcomes the problem of model limitations in the prior art and can flexibly adjust and optimize the decision path when facing complex events;

[0040] The present invention realizes dynamic optimization and continuous improvement of decision-making through real-time feedback mechanism and adaptive algorithm; this enables the system to continuously optimize decision-making according to actual execution situation during event processing, thereby improving the accuracy of decision-making and execution effect;

[0041] The present invention realizes efficient optimization of event processing paths through virtual environment simulation and multi-dimensional probabilistic networks; this technology not only solves the limitations of data interaction in the prior art, but also greatly improves the system's ability to perform multi-dimensional analysis and optimal path selection in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the process of the present invention;

[0043] Figure 2 It is a schematic diagram of cross-level recursive optimization in the present invention;

[0044] Figure 3 A schematic diagram of a virtual environment and a multi-dimensional probabilistic network in the present invention;

[0045] Figure 4 It is a schematic diagram of synchronization between the global knowledge base and the distributed intelligent nodes in the present invention;

[0046] Figure 5 It is a structural block diagram of the data processing platform of the present invention. DETAILED DESCRIPTION

[0047] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0048] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0049] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0050] like Figure 1 As shown, a method for processing grassroots governance auxiliary decision-making data includes the following steps:

[0051] Collect event data, pre-process the data, and classify and prioritize the pre-processed event data according to event type and urgency to generate high-priority event data;

[0052] Preferably, the data preprocessing includes removing noise from the event data, filtering out environmental noise in the event data using a denoising algorithm; converting the format of the event data, unifying data from different sources into a standardized format; and extracting features from the event data, identifying and extracting key features through a feature extraction algorithm.

[0053] Noise removal is to clean up irrelevant or interfering information in the data to ensure the purity of the data. In the present invention, noise removal is mainly used to process the environmental noise in the event data. This is usually achieved by using denoising algorithms, which can automatically identify and filter out interfering components in the data, such as background noise in surveillance videos or external electromagnetic interference in sensor data. The main effect of using noise removal is to improve the signal-to-noise ratio of the data and ensure that subsequent processing steps are based on high-quality data. For example, when processing audio data with rich environmental noise, using Fourier transform for frequency domain analysis can effectively remove high-frequency noise and retain only event-related audio signals. This processing improves the accuracy of audio data feature extraction and provides a solid foundation for subsequent event classification and decision-making.

[0054] Format conversion is to unify data from different sources into a standardized format. In grassroots governance, data may come from a variety of heterogeneous sources, such as text, audio, video, and sensor data. The formats and structures of various types of data vary, and directly using these data for decision-making processing may lead to compatibility issues and low processing efficiency. Through format conversion, the system can convert all data into a standardized format to adapt it to the needs of subsequent processing modules. For example, sensor data is usually in a time series format, while text data may be a natural language description. Through format conversion, these different types of data can be converted into a unified structured data format, which is convenient for the system to perform unified processing and analysis in subsequent steps.

[0055] Feature extraction is to further process the preprocessed data to extract the key features in the data. The feature extraction algorithm analyzes the internal structure of the data to identify the most representative information for event classification and decision-making. For example, when processing video data, a convolutional neural network (CNN) can be used to automatically extract the outlines and motion trajectories of objects in the image. These features can help the system quickly identify the nature of the event and possible development trends. Through feature extraction, the system can convert massive amounts of raw data into a compact feature set, greatly reducing the complexity of data processing while retaining information that is critical to event judgment.

[0056] Preferably, the classification and priority setting steps are implemented through an algorithm based on event type and urgency, wherein the urgency is set by calculating the potential impact of the event and the processing time requirement, and the generated high-priority event data contains a comprehensive indicator of time sensitivity and processing urgency.

[0057] The core of the classification step is to classify events into different categories based on their characteristics. These categories are usually based on predefined event types, such as public security events, environmental events, public health events, etc. Each event type has its own unique processing flow and resource requirements, so the classification algorithm automatically assigns events to corresponding categories based on relevant features of the event, such as text description, geographic location, time of event occurrence, etc. For example, when processing a large amount of municipal alarm data, the system can use natural language processing (NLP) technology to extract keywords from event descriptions, combined with historical data analysis, to automatically identify whether this is a public security incident or a public safety hazard. This classification based on event type not only helps to improve the system's response speed, but also ensures the rational allocation of resources so that different types of events are handled in the most appropriate way.

[0058] After the classification is completed, the priority setting step further distinguishes the order of handling each event by calculating the urgency of the event. The assessment of urgency is mainly based on two core factors: the potential impact of the event and the processing time requirement. The potential impact assessment involves quantifying the possible consequences of the event, such as casualties, property losses, social stability, etc.; the processing time requirement is to estimate how long the event must be responded to in order to avoid adverse consequences based on the nature of the event and historical data. The urgency is usually calculated by a comprehensive algorithm that combines the potential impact and processing time requirements to generate a comprehensive urgency index. For example, for a fire alarm, the system will comprehensively consider the potential danger area of ​​the fire (such as whether it is close to residential areas), the current meteorological conditions (such as wind speed and direction), and the time it takes for rescue forces to reach, and quickly calculate the urgency index of the event. The system will then sort the events according to this index and prioritize those with the highest urgency index.

[0059] The generated high-priority event data contains comprehensive indicators of time sensitivity and processing urgency. Time sensitivity refers to the sensitivity of an event to changes in external conditions within a specific time window, which directly affects the timeliness of event processing. Processing urgency reflects the serious consequences that may arise if an event is not handled in a timely manner. The high-priority event data generated by these two indicators ensures that when facing multiple concurrent events, the system can prioritize those events that require immediate response and reduce the negative impact of delays.

[0060] In one embodiment, the application scenario of the grassroots governance system in responding to emergencies. For example, in an extreme weather warning system, the system received a large number of alarm messages, including waterlogging, fallen trees, power outages, etc. The system first classifies these events into different types based on the description of the alarm information, and then calculates the urgency of each event. For example, waterlogging events may be given a higher emergency index due to their potential risk of traffic paralysis and trapped people, while power outage events are set with an emergency index based on the population density and importance of power supply in the affected area. In the end, the system prioritizes waterlogging events and dispatches emergency rescue forces to the scene quickly, while the power department deals with power outages later.

[0061] High-priority event data is distributed to multiple distributed intelligent nodes. Each distributed intelligent node performs event classification, feature extraction and preliminary decision-making on the high-priority event data, generates preliminary decision data through transformation data model and hybrid reasoning system, and then optimizes the preliminary decision data through nonlinear decision feedback mechanism to generate optimized preliminary decision data;

[0062] The allocation of distributed intelligent nodes is the starting point of this step. High-priority event data is first distributed to multiple distributed intelligent nodes, which can be located in different physical locations or logically represent different processing units in the system. The benefit of distributed processing is that it can improve the parallel processing capability of the system and avoid overloading of a single node, thereby improving the overall processing speed and fault tolerance of the system. In an actual application scenario, such as a city's emergency response system, distributed intelligent nodes may be distributed in different areas of the city, and each node is responsible for processing high-priority event data in its area. In this way, when multiple emergencies occur at the same time, the system can reasonably allocate tasks among the nodes to ensure a quick response.

[0063] At each distributed intelligent node, event classification and feature extraction are first performed. The classification step further refines the event, possibly reclassifying it based on more specific local conditions or more detailed event descriptions. Feature extraction analyzes event data at a higher dimension to extract the most useful feature information for subsequent decision-making. The feature extraction process may involve a variety of technical means, such as feature extraction algorithms based on deep learning, which can automatically identify key patterns and relationships from event data. For example, when processing surveillance video data, intelligent nodes can extract the action features of people in the event through convolutional neural networks (CNNs), so as to better judge the nature of the event and possible subsequent developments.

[0064] After completing classification and feature extraction, the intelligent node enters the preliminary decision-making stage. The preliminary decision is based on the feature data extracted in the previous step, using the transformation data model and hybrid reasoning system to make preliminary judgments and response suggestions for the event. The transformation data model refers to dynamically adjusting the parameters of the model according to different types of event characteristics to adapt to different event types and situations. This dynamic adjustment can make the model more flexible and adaptable, thereby improving the accuracy of preliminary decisions. The hybrid reasoning system combines the advantages of rule reasoning and statistical reasoning. The former can make logical judgments based on a predefined set of rules, while the latter performs uncertainty analysis based on historical data and probability models. This hybrid reasoning method enables the system to quickly process events with high certainty and make more reliable preliminary judgments when facing uncertainty.

[0065] After the initial decision data is generated, the system optimizes these preliminary decision data through a nonlinear decision feedback mechanism. The principle of the nonlinear decision feedback mechanism is to dynamically adjust the weight factors in the feedback loop and combine the deviation between the actual feedback and the model output to correct and optimize the preliminary decision data. This step uses a series of nonlinear mapping functions to fine-tune the decision path to reduce errors and improve the accuracy of the decision. For example, in the case of an emergency evacuation, the initial decision may recommend a certain evacuation route, but after feedback analysis, it is found that the actual evacuation time of the route exceeds expectations. The system will readjust the weight factors through the nonlinear decision feedback mechanism to optimize a new and more effective evacuation route.

[0066] In one embodiment, in a complex emergency event management system. When a high-priority fire alarm is triggered, the system will quickly distribute the alarm data to several intelligent nodes close to the fire location. These nodes further classify and extract features based on the specific location, time, weather conditions and other information of the fire, such as the speed of fire spread, the density of surrounding buildings, etc. Subsequently, the system will combine these features, dynamically adjust the parameters of the decision model by transforming the data model, and give preliminary decision suggestions through the hybrid reasoning system, such as formulating a preliminary evacuation plan. Then, the system will adjust the evacuation plan in real time through a nonlinear decision feedback mechanism based on on-site feedback, such as the actual evacuation time and personnel flow, to ensure the safety and efficiency of the final evacuation path.

[0067] Preferably, the transformation data model dynamically adjusts the event data features through the following calculation expression:

[0068]

[0069] in, Represents the transformed feature data; represents the transformation function associated with the event type; Represents the initial features of event data; Represents parameters related to event type characteristics.

[0070] The transformation data model enhances the adaptability of the decision model to different types of events by dynamically adjusting the data features. First, the initial features of the event data It is the basic information extracted from the original data. These features usually contain basic descriptions of the event, such as time, location, number of participants, etc. However, relying solely on these initial features is not enough to accurately deal with different types of complex events. Therefore, the transformation function is introduced , which weights and adjusts the initial features according to the specific type of event (such as natural disasters, social security events, public health events, etc.), so that these features show higher relevance and importance in different types of event processing.

[0071] Transformation function The design of the transformation function is usually based on historical data and expert knowledge. It can automatically adjust during the decision-making process. For example, for a fire event, the transformation function may amplify the characteristic data related to the spread of the fire (such as wind speed and humidity), while for a traffic accident, the transformation function may pay more attention to characteristics such as road conditions and traffic volume. Through such dynamic adjustments, the system can flexibly adjust data input according to different event types, ensuring that when processing each event, the system receives the most relevant and decision-making data.

[0072] In this model, the parameters It plays the role of a correction factor and is used to fine-tune the transformed feature data. Usually related to the specific characteristics of the event, it is possible to further refine the data based on the uniqueness of the event. For example, when dealing with an epidemic outbreak, in addition to the conventional number of infected people and transmission route characteristics, parameters It can also reflect the local distribution of medical resources or the public's awareness of epidemic prevention, and these characteristics can have an important impact on the final decision.

[0073] The introduction of the transformation data model greatly improves the flexibility and accuracy of the system. By dynamically adjusting the initial features, the system can generate more accurate feature data according to different event types. , thereby improving the decision-making model's processing effect on different events. This flexibility ensures that the system can maintain efficient and accurate response capabilities when facing complex and changing events.

[0074] In one embodiment, an emergency event handling scenario in a city management system. Assume that in a situation where multiple disasters occur simultaneously, the system needs to handle different types of events such as earthquakes, fires, and floods. For earthquake events, the transformation data model The system may increase the weight of geological features, such as earthquake intensity and focal depth, while for fire events, the system may pay more attention to features related to fire spread, such as wind speed and building density. It will be more in line with actual needs, ensuring that the system can base its analysis and decision-making on the most relevant data and ultimately formulate the most effective response measures.

[0075] Preferably, the hybrid reasoning system includes a rule reasoning module and a statistical reasoning module. The rule reasoning module performs logical reasoning on structured data according to a predefined rule set, and the statistical reasoning module performs statistical analysis on unstructured data based on a probability model. The two are combined to generate preliminary decision data.

[0076] The rule reasoning module is a component of the hybrid reasoning system, and its role is to perform logical reasoning on structured data. Structured data usually includes formatted and organized data information, such as records in a database, data read by sensors, or predefined event information. The rule reasoning module performs reasoning operations on this data based on a predefined set of rules. The rule set is usually formulated by domain experts based on years of experience and historical data, and contains the processing logic for specific events. For example, in emergency management, the rules for fire events may include: if the spread of the fire exceeds a certain threshold, the people in the affected area will be evacuated first. This rule-based reasoning method can quickly reach clear decisions, and is particularly suitable for event processing scenarios with clear logic and conditions.

[0077] Unlike the rule-based reasoning module, the statistical reasoning module mainly processes unstructured data, such as text, images, and videos. This type of data is usually complex and difficult to process with simple rules. The statistical reasoning module performs statistical analysis on unstructured data through a probability model, which can infer the probability or trend of certain events by learning a large amount of historical data. For example, when processing social media data, the system can use the statistical reasoning module to analyze the public's reaction trend to an event and determine the urgency of the event or its possible development direction. The advantage of statistical reasoning is that it can handle data with high uncertainty, provide a basis for decision-making through probability analysis, and enable the system to make reasonable judgments when facing ambiguous or unknown situations.

[0078] The core of the hybrid reasoning system is to combine the advantages of these two modules, which can not only perform precise rule reasoning on structured data, but also perform comprehensive statistical analysis on unstructured data, thereby generating more comprehensive preliminary decision data. This combination ensures the flexibility and accuracy of the system when processing different types of data. For example, in an emergency response scenario, when the system receives an alarm message about a potential public safety incident, the rule reasoning module can quickly determine whether the incident requires immediate action based on existing rules, while the statistical reasoning module can further analyze relevant social media data to assess the public's attention to the incident and the speed of its spread. By combining these two reasoning results, the system can come to a more comprehensive preliminary decision, such as whether to initiate an emergency response immediately or to conduct further observation first.

[0079] In one embodiment, a major public event handling scenario in urban management. For example, a city management system receives an alarm message about a subway accident. The rule reasoning module can quickly come up with a preliminary response plan based on the structured data of the accident, such as subway train numbers, the number of passengers affected, the location of the accident, etc., such as closing relevant lines and evacuating passengers. At the same time, the statistical reasoning module can evaluate the social impact and spread speed of the incident by analyzing relevant discussions and public opinion on social media. If the statistical analysis results show that the incident has attracted a lot of attention in a short period of time and the public reaction is strong, the system may adjust the initial decision and initiate a higher level of emergency response to prevent the incident from further fermenting.

[0080] Preferably, the preliminary decision data is optimized by the following nonlinear mapping function:

[0081]

[0082] in, Represents the optimized preliminary decision data; represents a nonlinear mapping function; Indicates preliminary decision data; Represents the cumulative weight factor in the feedback loop.

[0083] In the present invention, the introduction of nonlinear decision feedback mechanism plays a key role in improving the accuracy and response speed of decision making. The mechanism optimizes the preliminary decision data through nonlinear mapping function to ensure that the final decision can better adapt to the actual situation and environmental changes.

[0084] In this mechanism, preliminary decision data It is generated based on the system's previous decision-making module, and it contains preliminary response suggestions for the current event. However, this preliminary decision is often affected by various dynamic factors during the actual execution process, such as changes in the external environment, the real-time availability of resources, and the further development of the event. Therefore, relying solely on preliminary decision data for action may not be able to cope with complex and changeable real-world scenarios. At this time, the nonlinear mapping function Played a key role.

[0085] Nonlinear mapping function It is a complex mathematical model that performs nonlinear transformations on input data (i.e., the sum of preliminary decision data and cumulative weight factors). The purpose of this nonlinear transformation is to adjust decisions based on the nonlinear relationship of the data to make them more in line with actual needs. Compared with linear models, nonlinear mapping functions can handle more complex data relationships and can more accurately reflect changes in the real world. For example, when dealing with a public health emergency, the preliminary decision data may be a linear prediction based on the current number of infections, but the actual spread of infection often exhibits nonlinear characteristics (such as exponential growth). Through nonlinear mapping functions, the system can more accurately adjust predictions of the spread of infection and optimize resource allocation and response strategies.

[0086] Cumulative weight factor is the feedback component in the mechanism. The cumulative weight factor in the feedback loop represents the experience data accumulated by the system through real-time monitoring and analysis during the decision-making process. These experience data may include previous decision results, environmental changes, and the actual development of events. Over time, the cumulative weight factor gradually reflects the importance of these feedback information, thereby playing a role in correction and optimization in the nonlinear mapping function. For example, in an urban emergency evacuation plan, the initial decision may be based on standard traffic flow data, but as the actual evacuation proceeds, the system will continuously collect actual traffic data through the feedback loop and adjust the cumulative weight factor , so that decisions can be optimized in real time, ultimately resulting in a more reasonable and effective evacuation plan.

[0087] Through the nonlinear decision feedback mechanism, the system can make real-time decision optimization when facing complex and dynamic environments. This process ensures that the system not only relies on preliminary static decisions, but can also make dynamic adjustments based on feedback in actual operations. Ultimately, this mechanism can greatly improve the system's response speed and decision accuracy, avoiding inefficient or even wrong decisions caused by the initial decision not matching the actual situation.

[0088] In one embodiment, in an application of an urban flood emergency management system, when the system receives a flood warning, the generated preliminary decision data This may include recommended evacuation areas and required resource allocation. However, as the actual development of the flood, the initial decision may need to be adjusted. Through the nonlinear decision feedback mechanism, the system can monitor the flood water level changes, wind speed, rainfall and other factors in real time, and continuously adjust the cumulative weight factor through the feedback loop. At this time, the nonlinear mapping function The system will re-optimize the evacuation decision based on the latest data, such as adjusting the evacuation route, increasing or decreasing the allocation of resources, etc. Guide flood emergency operations to ensure the city can respond most effectively to sudden floods.

[0089] Distribute the optimization preliminary decision data to models at multiple levels, and recursively call and collaboratively optimize the optimization results of models at each level through a cross-level recursive optimization algorithm to generate cross-level recursive optimization data;

[0090] Preferably, the optimization preliminary decision data is distributed to models at multiple levels, and the optimization results of the models at multiple levels are recursively called and collaboratively optimized through the following recursive optimization expression:

[0091]

[0092] in, Indicates Recursively optimize data; Represents department model optimization data; Represents event type model optimization data; , and They represent the weight coefficients in the recursive optimization process respectively.

[0093] Cross-level recursive optimization is a highly complex and critical step used to integrate and optimize decision models at multiple levels. The main purpose of this step is to coordinate models at different levels through recursive calls and collaborative optimization, thereby generating more accurate and globally optimal decision data.

[0094] The cross-level recursive optimization algorithm distributes the optimization preliminary decision data to models at multiple levels and performs recursive calls and collaborative optimization among these models to gradually improve the overall effect of the decision. Figure 2 As shown in the figure. The "level" here can be understood as different decision dimensions or modules in the system, such as department model and event type model. Each hierarchical model is optimized independently and generates its corresponding optimization data. However, relying solely on the optimization results of a certain level may lead to a local optimum in decision making rather than a global optimum. Therefore, the cross-level recursive optimization algorithm repeatedly adjusts and optimizes the results of each level by recursively calling between models at different levels to ensure the global consistency and optimality of the decision.

[0095] exist middle, As the core data of the recursive call, it is constantly adjusted and optimized in each round of recursion. Department model optimization data Optimizing data with event type models Represents the output results of different level models in the current optimization round. These results are calculated by weight coefficients , and are combined to generate new recursive optimization data The weight coefficients may be dynamically adjusted during the recursive process to adapt to the importance and influence of models at different levels. For example, in some cases, the results of the department model may be more important than the event type model, and the system will adjust the weight coefficients accordingly. and To highlight the impact of the departmental model.

[0096] The main advantage of cross-level recursive optimization is that it can optimize decisions on a global scale, ensuring that models at different levels can work together to avoid the limitations of local optimal solutions. For example, in grassroots governance, one department may focus on optimizing resource allocation, while another department focuses on the classification and priority setting of events. Through cross-level recursive optimization, these different levels of decisions can be coordinated with each other, and ultimately generate a global optimal decision that comprehensively considers the effective use of resources, time urgency, and event impact.

[0097] In terms of effect, cross-level recursive optimization ensures global consistency and overall optimization of decisions, which is especially important when dealing with complex events. The system can not only achieve optimization at the local level, but also integrate the optimization results of each level into a more sophisticated and comprehensive decision-making solution through recursive calls. This recursive optimization process improves the decision-making efficiency and accuracy of the system, especially when facing multi-dimensional and multi-variable complex events, which can significantly enhance the system's responsiveness and decision-making effect.

[0098] In one embodiment, an integrated emergency management system needs to coordinate multiple departments (such as firefighting, medical care, and transportation) and event types (such as fire, flood, and earthquake) to respond to large-scale natural disasters. After being optimized, it is distributed to models at all levels, such as the resource allocation model of the fire department and the evacuation model of the traffic management department. Each department performs optimization independently and generates optimization results. and Through a cross-level recursive optimization algorithm, the system gradually adjusts these results so that the decisions of all departments are coordinated with each other to ensure that resources and actions are optimally configured globally. As the recursive calls proceed, the system eventually generates a comprehensive response plan to ensure that all departments can work together efficiently and minimize the losses caused by the disaster.

[0099] like Figure 3 As shown, based on cross-level recursive optimization data, a virtual environment is created to simulate the event processing path, and the path is optimized through a multi-dimensional probability network, and finally the optimal path data is generated;

[0100] Preferably, the virtual environment is created, the virtual environment performs simulation-driven learning according to historical data and real-time data, and performs probability inference and path optimization on the simulation results of the event processing path through a multidimensional probability network, wherein the path optimization is selected by the following expression:

[0101]

[0102] in, Represents the probability value of the optimal path; Indicates The probability of the simulation results of each path.

[0103] In the present invention, event processing path simulation and path optimization based on cross-level recursive optimization data are key steps for the system to determine the optimal action plan. This process creates a virtual environment for simulation and combines a multidimensional probabilistic network to infer and optimize the event processing path, thereby generating optimal path data to support the execution of decisions.

[0104] This process first relies on cross-level recursive optimization data, which is generated by the recursive optimization algorithm in the previous step and represents the optimal combination output of models at different levels. Based on this data, the system creates a virtual environment, which is not just a simple simulation space, but a dynamic, digital scene based on real-world conditions. The virtual environment will drive learning based on historical data and real-time data, that is, it will extract patterns from the processing history of past events, and combine the current real-time data to simulate the possible development path of events in the future.

[0105] Simulation-driven learning in a virtual environment is achieved through advanced machine learning algorithms that can continuously adjust environmental parameters based on data input in order to more accurately simulate multiple possible development scenarios of an event. For example, when simulating a flood event, the virtual environment combines historical flood data (such as water level, rainfall, terrain features, etc.) with current real-time meteorological data to predict the possible development path and impact range of the flood. This simulation not only provides foresight on the possible outcomes of the event, but also provides rich data support for subsequent path optimization.

[0106] The role of multidimensional probabilistic networks in this process is crucial. Multidimensional probabilistic networks are complex mathematical models used to calculate and infer the success probabilities of different event processing paths. The simulation results of multiple event processing paths generated in a virtual environment will be input into the multidimensional probabilistic network. The system will perform probabilistic analysis based on the characteristics of these paths to evaluate the possible success rate of each path in actual execution. The core of path optimization is to ensure that the final decision plan can maximize the achievement of the expected goal by selecting the optimal path, that is, the path with the highest probability of success.

[0107] The meaning of is to select the path with the highest probability value as the optimal path among all simulation results. This optimization process makes full use of the advantages of probability analysis. By evaluating the feasibility and potential success rate of different paths under actual conditions, the system can select the path with the highest probability of success. For example, in urban emergency evacuation decision-making, the system may generate multiple evacuation route simulation results. Path optimization will select the route with the lowest probability of obstruction and the fastest speed as the final evacuation path to maximize the safety of personnel.

[0108] Through this path optimization based on cross-level recursive optimization data, virtual environment simulation and multi-dimensional probability network, the system can greatly improve the accuracy and operability of decision-making. It not only provides a systematic method to predict and select the best action plan, but also can cope with complex and changing real-world conditions, ensuring that the decision has a high success rate during execution.

[0109] In one embodiment, an emergency response scenario in a large-scale public emergency. Assume that a city faces a large-scale public safety threat, such as a terrorist attack, and the system needs to develop an optimal evacuation plan. First, the system generates simulation results of multiple evacuation paths based on cross-level recursive optimization data. The simulation of these paths takes into account the historical experience of handling similar events, as well as the current real-time collected environmental and personnel flow data. Subsequently, the system inputs these simulation results into a multidimensional probability network to perform a probabilistic analysis of the path. Finally, an evacuation path with the highest success rate is selected by optimizing the expression, and the system pushes this path as the optimal decision data to the emergency response team to ensure that the evacuation operation can be carried out quickly and efficiently, minimizing casualties.

[0110] Perform event processing based on the optimal path data, analyze the execution results and make adaptive adjustments, update the optimized decision data to the global knowledge base, and synchronize it to the distributed intelligent nodes.

[0111] like Figure 4 As shown, preferably, the event processing is performed based on the optimal path data, the execution result data is collected in real time, the execution result is analyzed by an adaptive algorithm, correction decision data is generated, and the correction decision data is input into the global knowledge base, the decision rules and models in the global knowledge base are updated, and the updated global knowledge base data is synchronized to all distributed intelligent nodes.

[0112] In the framework of the present invention, executing event processing based on optimal path data, as well as real-time analysis and adaptive adjustment are important steps to ensure that the system is continuously optimized in actual operation. The core of this process is to use the optimal path data generated by the previous optimization to perform specific event processing, and at the same time, through real-time monitoring and analysis of the execution results, dynamically adjust the decision, and update the optimized decision to the global knowledge base to ensure that the system can continuously learn and improve.

[0113] After the system generates the optimal path data, it first uses the path data to guide the specific execution of event processing. This step means that the system puts the previously derived optimal decision plan into practice, such as evacuating people along the optimized route in an emergency evacuation. The execution process at this stage involves multiple real-time variables, such as personnel movement speed, environmental changes, resource consumption, etc. These variables may deviate from the initial expectations, so the system needs to monitor them in real time.

[0114] Real-time collection of execution result data is a key part of this process. The system obtains various data generated during event processing in real time through sensors, monitoring equipment, communication systems and other channels. For example, during the evacuation of personnel, the system will collect information such as crowd density, traffic flow, and evacuation speed in real time. These data provide the basis for subsequent analysis and decision-making adjustments.

[0115] The system uses an adaptive algorithm to analyze the execution result data collected in real time. The core of the adaptive algorithm is to dynamically adjust the decision parameters according to the real-time data to optimize the current decision path. The algorithm will adjust the weights and modify the model according to the difference between the actual execution results and the expected results, and then generate corrected decision data. For example, if the system finds that the actual passing time of a certain evacuation path is longer than expected, it may be caused by a sudden traffic jam. The adaptive algorithm will automatically analyze the cause and recommend changing the evacuation route or adjusting the evacuation speed to ensure that the overall evacuation plan can proceed smoothly.

[0116] After generating the correction decision data, the system will input this data into the global knowledge base. The global knowledge base is the core data storage and knowledge management platform of the system, which records all historical decisions, model parameters, rule sets, and related optimization data. By inputting the latest correction decision data into the global knowledge base, the system can continuously enrich and improve its knowledge system, so that the system can better utilize past experience data when facing similar events, and improve the quality and efficiency of decision-making.

[0117] In addition, synchronizing the updated global knowledge base data to all distributed intelligent nodes is a key step to ensure the coordination of the entire system. Distributed intelligent nodes need to rely on the latest data and models in the global knowledge base when performing tasks. Therefore, after each knowledge base update, the system will synchronize these updates to each intelligent node, so that they can operate based on the latest optimization decisions when processing similar events in the future. This synchronization process ensures the overall consistency and coordination of the system, avoiding decision errors or inefficiencies caused by inconsistent data between different nodes.

[0118] Through this process, the system can collect and analyze data in real time and make adaptive adjustments during actual event processing to ensure the accuracy and effectiveness of decision-making. This continuous feedback and adjustment mechanism not only improves the success rate of current event processing, but also continuously optimizes the overall performance of the system, enabling the system to cope with increasingly complex and changing actual situations in long-term operation.

[0119] In one embodiment, an application scenario of a city traffic management system in response to sudden natural disasters. Assume that after an earthquake, the system generates the optimal traffic evacuation route and uses this path data to guide the traffic evacuation in the urban area. As the evacuation proceeds, the system monitors the traffic flow, congestion and average speed of each road in real time. When the system detects that a main road is severely congested due to a traffic accident, the adaptive algorithm will immediately analyze and generate correction decision data, and recommend replanning the evacuation route or diverting part of the traffic. Subsequently, these correction data are entered into the global knowledge base and synchronized to other distributed intelligent nodes, so that the entire traffic system can adjust its strategy in a short time to ensure the smooth implementation of the evacuation plan and the safety of personnel.

[0120] like Figure 5 As shown, a data processing platform for implementing the grassroots governance decision-making auxiliary data processing method includes:

[0121] The data collection module is used to collect event data, remove noise, convert formats and extract features from the event data to generate standardized event data; this module is responsible for collecting event data from multiple sources and performing preliminary processing on these data. First, noise removal technology is used to filter out interference information to ensure the purity of the data. Next, format conversion technology is used to unify data from different sources into a standardized format for subsequent processing. Finally, feature extraction algorithms are used to identify and extract key features to generate standardized event data. Through these steps, the system can ensure the quality and consistency of input data, laying the foundation for subsequent decision-making processing.

[0122] The data classification and priority setting module is used to classify and prioritize standardized event data according to event type and urgency, and generate high-priority event data; this module classifies and prioritizes standardized event data according to event type and urgency. The classification process relies on predefined rules and algorithms to classify events into different types, such as natural disasters, social security events, etc. Priority setting generates high-priority event data by calculating the potential impact of events and processing time requirements. In this way, the system can prioritize those events that have the greatest impact on grassroots governance and ensure that resources are reasonably allocated and used.

[0123] The distributed intelligent node module includes multiple distributed intelligent nodes, which are used to receive high-priority event data, classify and extract features of the high-priority event data, and generate preliminary decision data through transformation data models and hybrid reasoning systems, and then optimize the preliminary decision data through nonlinear decision feedback mechanisms to generate optimized preliminary decision data; the module includes multiple intelligent nodes, which are used to receive and process high-priority event data. Each node independently classifies events and extracts features, and generates preliminary decision data through transformation data models and hybrid reasoning systems. Subsequently, the system optimizes the preliminary decision data using nonlinear decision feedback mechanisms to generate more effective optimized preliminary decision data. The core of this module lies in the ability of distributed processing, which can quickly respond and make preliminary decisions in large-scale, multi-event concurrency situations.

[0124] The cross-level recursive optimization module is used to distribute the optimized preliminary decision data to models at multiple levels, and recursively call and co-optimize the optimization results of each level model through the cross-level recursive optimization algorithm to generate cross-level recursive optimization data; this module distributes the optimized preliminary decision data to models at multiple levels through the recursive optimization algorithm, and recursively calls and co-optimizes between these models. Finally, cross-level recursive optimization data is generated. The key to this process is the collaborative work of models at all levels to ensure the global optimality of the decision and avoid the limitation of local optimality.

[0125] The event recognition and simulation module is used to create a virtual environment based on cross-level recursive optimization data, simulate the event processing path, and optimize the simulation results through a multidimensional probability network to generate optimal path data; This module creates a virtual environment based on cross-level recursive optimization data and simulates the event processing path in this environment. The simulation results are optimized through a multidimensional probability network to generate optimal path data. The virtual environment simulation combines historical data and real-time data to accurately predict the development trend of events, and selects the processing path with the highest success rate through probabilistic inference, providing strong support for the final decision.

[0126] The execution and feedback module is used to perform event processing based on the optimal path data, collect and analyze the execution result data, make adaptive adjustments, generate correction decision data, and update the correction decision data to the global knowledge base; after obtaining the optimal path data, the system performs event processing based on these data and collects the execution result data in real time. By analyzing the execution results through adaptive algorithms, the system generates correction decision data and updates these data to the global knowledge base. The core of this module is real-time feedback and adaptive adjustments to ensure that the system can dynamically optimize decisions during event processing and improve the accuracy and response speed of decisions.

[0127] The global knowledge base module is used to store and update optimized decision data, and synchronize the updated data to multiple distributed intelligent nodes to ensure that the system can continuously optimize and respond to future event processing needs. The global knowledge base is used to store and update all optimized decision data, and synchronize these data to multiple distributed intelligent nodes. Through this synchronization mechanism, the system can continuously optimize and ensure that each node can use the latest decision models and rules when responding to future events, thereby improving the overall responsiveness and decision quality of the system.

[0128] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0129] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for processing grass-roots governance decision-making assistance data, characterized in that: The following steps are involved: Collect event data, pre-process the data, and classify and prioritize the pre-processed event data according to event type and urgency to generate high-priority event data; High-priority event data is distributed to multiple distributed intelligent nodes. Each distributed intelligent node classifies, extracts features, and makes preliminary decisions on the high-priority event data. Preliminary decision data is generated through a transformation data model and a hybrid reasoning system. The preliminary decision data is then optimized through a nonlinear decision feedback mechanism to generate optimized preliminary decision data. The transformation data model is used to dynamically adjust the parameters of the model according to different types of event features to adapt to different event types and scenarios. The nonlinear decision feedback mechanism modifies and optimizes the preliminary decision data by dynamically adjusting the weight factors in the feedback loop and combining the deviation between the actual feedback and the model output; Distribute the optimization preliminary decision data to models at multiple levels, and recursively call and collaboratively optimize the optimization results of models at each level through a cross-level recursive optimization algorithm to generate cross-level recursive optimization data; the cross-level recursive optimization algorithm repeatedly adjusts and optimizes the results of each level by recursively calling models at different levels; Based on cross-level recursive optimization data, a virtual environment is created to simulate the event processing path, and the event processing path is inferred and optimized through a multi-dimensional probability network, ultimately generating the optimal path data; Perform event processing based on the optimal path data, analyze the execution results and make adaptive adjustments, update the optimized decision data to the global knowledge base, and synchronize it to the distributed intelligent nodes.

2. The method for processing grass-roots governance decision-making assistance data according to claim 1 is characterized in that: The data preprocessing includes removing noise from the event data, using a denoising algorithm to filter out environmental noise in the event data; converting the format of the event data, unifying data from different sources into a standardized format; and extracting features from the event data, identifying and extracting key features through a feature extraction algorithm.

3. The method for processing grass-roots governance auxiliary decision-making data according to claim 1 is characterized in that: The classification and priority setting steps are implemented through an algorithm based on event type and urgency, wherein the urgency is set by calculating the potential impact of the event and the processing time requirement, and the generated high-priority event data contains comprehensive indicators of time sensitivity and processing urgency.

4. The method for processing grass-roots governance auxiliary decision-making data according to claim 1 is characterized in that: The transformation data model dynamically adjusts event data features through the following calculation expressions: in, Represents the transformed feature data; represents the transformation function associated with the event type; Represents the initial features of event data; Represents parameters related to event type characteristics.

5. The method for processing grass-roots governance auxiliary decision-making data according to claim 1 is characterized in that: The hybrid reasoning system includes a rule reasoning module and a statistical reasoning module. The rule reasoning module performs logical reasoning on structured data according to a predefined rule set, and the statistical reasoning module performs statistical analysis on unstructured data based on a probability model. The two are combined to generate preliminary decision data.

6. The method for processing grass-roots governance auxiliary decision-making data according to claim 1 is characterized in that: The preliminary decision data is optimized through the following nonlinear mapping function: in, Represents the optimized preliminary decision data; represents a nonlinear mapping function; Indicates preliminary decision data; Represents the cumulative weight factor in the feedback loop.

7. The method for processing grass-roots governance auxiliary decision-making data according to claim 1 is characterized in that: The optimization preliminary decision data is distributed to models at multiple levels, and the optimization results of the models at multiple levels are recursively called and collaboratively optimized through the following recursive optimization expressions: in, Indicates Recursively optimize data; Represents department model optimization data; Represents event type model optimization data; , and They represent the weight coefficients in the recursive optimization process respectively.

8. The method for processing grass-roots governance auxiliary decision-making data according to claim 1 is characterized in that: The virtual environment is created, and the virtual environment performs simulation-driven learning based on historical data and real-time data, and performs probability inference and path optimization on the simulation results of the event processing path through a multidimensional probability network, wherein the path optimization is selected through the following expression: in, Represents the probability value of the optimal path; Indicates The probability of the simulation results of each path.

9. The method for processing grass-roots governance auxiliary decision-making data according to claim 1 is characterized in that: The method performs event processing based on the optimal path data, collects execution result data in real time, analyzes the execution results through an adaptive algorithm, generates correction decision data, inputs the correction decision data into a global knowledge base, updates the decision rules and models in the global knowledge base, and synchronizes the updated global knowledge base data to all distributed intelligent nodes.

10. A data processing platform for implementing the grassroots governance decision-making support data processing method according to any one of claims 1 to 9, characterized in that: include: A data collection module is used to collect event data, and to remove noise, convert formats and extract features from the event data to generate standardized event data; A data classification and priority setting module is used to classify and prioritize standardized event data according to event type and urgency, and generate high-priority event data; A distributed intelligent node module, including a plurality of distributed intelligent nodes, is used to receive high-priority event data, classify and extract features of the high-priority event data, generate preliminary decision data through a transformation data model and a hybrid reasoning system, and then optimize the preliminary decision data through a nonlinear decision feedback mechanism to generate optimized preliminary decision data; The cross-level recursive optimization module is used to distribute the optimization preliminary decision data to models at multiple levels, and recursively call and collaboratively optimize the optimization results of models at each level through the cross-level recursive optimization algorithm to generate cross-level recursive optimization data; The event recognition and simulation module is used to create a virtual environment based on cross-level recursive optimization data, simulate the event processing path, and optimize the simulation results through a multi-dimensional probability network to generate the optimal path data; The execution and feedback module is used to execute event processing based on the optimal path data, collect and analyze the execution result data, make adaptive adjustments, generate correction decision data, and update the correction decision data to the global knowledge base; The global knowledge base module is used to store and update optimized decision data and synchronize the updated data to multiple distributed intelligent nodes to ensure that the system can continuously optimize and respond to future event processing needs.

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