A cross-platform perception robot system for social governance based on a vertical big model

Abstract

The present invention discloses a social governance cross-platform perception robot system based on a vertical large model, which relates to the field of social governance systems based on vertical large models, including: a group management module for managing user group data, a perception configuration module for configuring rules for intelligent robot perception data, a data monitoring module for monitoring the generated data, a task management module for managing and coordinating various tasks performed by the robot, a data processing and analysis center, including a data storage unit, a data analysis unit and a model building unit, and a data display and early warning module for displaying the results of data processing and analysis in the form of intuitive charts and curves. The social governance cross-platform perception robot system of the present invention can accurately manage group data, customize perception, efficiently process tasks, quickly analyze data, meet personalized needs, facilitate remote management, and improve social governance efficiency through the optimization of various modules.

Description

Technical Field

[0001] The present invention relates to the technical field of social governance systems, and in particular to a social governance cross-platform perception robot system based on a vertical large model. Background Art

[0002] In the context of the country's vigorous promotion of digital transformation and acceleration of digital government construction, the level of social governance modernization has been greatly improved. The government actively promotes the construction of digital government, is committed to improving the level of government services, and makes government services efficient, convenient and intelligent. From the top-level design of social governance to grassroots governance, informatization construction has been fully launched. However, as the informatization transformation continues to deepen, in the process of moving into the deep waters, especially when going deep into the community and community property, the difficulties, bottlenecks and pain points of grassroots governance are gradually exposed.

[0003] Within the "one kilometer" closest to the residents, there is a lack of a fast and smooth terminal governance mechanism. Community workers often fail to discover the demands of mobile terminal groups within the jurisdiction in a timely manner, and the handling is not efficient enough, and there is no special team to handle related incidents. Grassroots functional departments are unable to respond in real time to the livelihood demands of the majority of residents in the jurisdiction. With the existing information construction mechanism, it is often the case that even small demands must be raised to the district or city level for processing, making it difficult to achieve "small matters do not leave the grid, big matters do not leave the community, and difficult matters do not leave the street."

[0004] This situation urgently requires an innovative governance model. Based on this, a cross-platform perception robot system for social governance based on a vertical large model came into being. The system includes multiple key modules, aiming to solve the problems in grassroots governance. The group management module manages user group data in a fine manner, can accurately identify the user identity and group members in the group, and uses JSON format and customized multiple group information to provide an accurate user data foundation for grassroots governance. The perception configuration module configures the rules for intelligent robots to perceive data, covering rule configurations such as keyword detection, question response, and information release, and ensures rapid perception and accurate response to grassroots demands through parallel execution of programs.

[0005] The data monitoring module monitors the generated data in real time and introduces intelligent robots in the group. When users send messages in the group, the intelligent robots can collect messages in JSON format and transmit the data to the control center for centralized storage to provide support for subsequent data processing and analysis. The task management module is responsible for managing and coordinating the various tasks performed by the robots, and reasonably allocating them according to the nature of the tasks, the robot's capabilities and the workload to ensure efficient execution of the tasks.

[0006] The data processing and analysis center uses natural language processing technology and statistical analysis methods to analyze and process the collected data in real time, build an intention recognition model, and improve the ability to understand and handle grassroots demands. The data display and early warning module displays the analysis results in intuitive charts and curves. When prohibited speech is detected or the alarm rules are triggered, the early warning information is automatically notified by SMS. The SMS push management module identifies the push plan of the target data business according to the user's personalized push needs to improve the accuracy of information transmission. The robot management module maintains and upgrades the robot functions to achieve efficient background management of the robot.

[0007] In short, the system extends grassroots governance to residents' daily mobile phones through innovative technical means, forming a new model with mobile phone groups as the hub and AI intelligent perception robots participating in grassroots governance online in real time. The AI ​​intelligent WeChat robot can quickly respond and push the acceptance status and processing progress of requests, providing strong technical support for improving the level of grassroots governance. Summary of the invention

[0008] The present invention proposes a social governance cross-platform perception robot system based on a vertical large model to solve the problems mentioned in the above-mentioned prior art.

[0009] In order to achieve the above purpose, the present invention adopts the following technical solution: a social governance cross-platform perception robot system based on a vertical large model, comprising:

[0010] A group management module is used to manage user group data, identify user identities and group members in the group, and use JSON format to customize multiple group information, including performing information entropy analysis on the historical group data sequence set and the historical business group usage sub-data sequence set, respectively, to obtain a historical group information entropy set and a historical business usage information entropy set, and to centrally analyze the historical group information entropy set and the historical business usage information entropy set to obtain M centralized information entropy sets, and import the obtained M centralized information entropy sets into the module as basic group information sets to finally obtain group data information;

[0011] The perception configuration module is used to configure the rules for intelligent robots to perceive data. The rule configuration includes robot keyword detection configuration, question reply rule configuration, and information release rule configuration. The rule configuration is executed in parallel through programs. For data services in the field of social governance, Q data perception rules are configured for N robot groups. For the obtained data perception rule configuration, the rule verification algorithm is applied to check the perception rules for errors. The specific verification algorithm is as follows:

[0012] in , m is the length of regular sequence A, n is the length of regular sequence B, It represents the distance or difference measurement between the i-th element in rule sequence A and the j-th element in rule sequence B (the appropriate measurement method is defined according to the specific characteristics of the rule elements). When the similarity score calculation result is equal to 1, it means that the data perception rule verification has passed;

[0013] The data monitoring module is used to monitor the generated data, including a data collection unit, a data transmission unit, and a data storage unit. An intelligent robot is introduced into the group. When a user sends a message to the group, the intelligent robot collects the message in the group in JSON format, and transmits the collected data to the control center through the data transmission unit by wired or wireless means. After receiving the transmitted data, the control center stores the data in a centralized manner to facilitate the next step of data processing and analysis. The data collection unit is a robot that automatically collects the data sent by the group users in real time based on the API interface, and parses the acquired data. The data transmission unit uploads the data parsed by the robot to the control center by wired or wireless means.

[0014] The task management module is used to manage and coordinate various tasks performed by the robot. According to the nature of the task, the robot's capabilities and the current workload, the task is assigned to the most suitable robot to achieve effective scheduling and optimization of robot resources. Control instructions can be sent to the robot as needed to ensure that the task is carried out in a predetermined manner and sequence, including task delivery and reception (tasks are delivered to the robot through the system interface or API interface, and the robot task management module is responsible for receiving and parsing task instructions to ensure the accuracy and completeness of task information), task monitoring (real-time monitoring of task progress and status during task execution, timely alarm and taking measures when abnormal situations or task failures occur), and task statistics (collecting task execution results after task completion and feeding them back to the user, generating statistical data and reports on task execution);

[0015] The data processing and analysis center includes a data storage unit, a data analysis unit and a model building unit. The data storage unit uses a large-capacity hard disk or solid-state hard disk to store the data uploaded by the data transmission unit to ensure that the data will not be lost. The data analysis unit uses natural language processing technology and statistical analysis methods to analyze and process the collected data in real time. The model building unit builds an intention recognition model based on machine learning algorithms and finite element analysis methods, and uses support vector machines (SVMs) and artificial neural network (ANN) machine learning algorithms to learn and train data information related to the field of social governance, establish a mapping relationship between intentions and entities, and combine finite element analysis methods to model and analyze user messages, optimize model parameters, and improve the accuracy and reliability of the model. The prediction accuracy of the model is not less than 90% on the test set. When determining the artificial neural network (ANN) machine learning algorithm, ,in is the input value, are their respective weights, and the final result y is the sum of their multiplications. The activation function is only related to the input value and the weight. When the input function remains unchanged and the output is equal to the activation function, the neuron is said to be linear, and the output function is an improved S-type function: , where x is the input variable, k, m, n are constant variables, and e is the base of the natural logarithm. For large positive variables, the value of the function gradually approaches 1, and for large negative variables, the value of the function gradually approaches 0. At zero, the value of the function is 0.5, giving a smooth transition between high and low neuron outputs;

[0016] The data display and warning module is used to display the results of data processing and analysis in the form of intuitive charts and curves, including user message growth curves and user consultation problem distribution cloud maps. When prohibited speech is detected or the preset alarm rules are triggered, SMS notifications are automatically sent to warn the user. The alarm rules are set and adjusted according to the actual operation situation. It supports multi-user access at the same time. Users can view the robot's operation status and alarm information in real time through a web browser or mobile terminal. Different users have different operating permissions. Administrator users have advanced permissions such as system settings, display settings, and user permission settings. Ordinary users only have the permission to view data and receive alarm information.

[0017] The SMS push management module is used to formulate a time node sequence set according to the user's personalized push requirements to identify the push plan of the target data service, obtain the user's personalized push plan set, and manage the SMS push of the target data service based on the user's personalized push plan;

[0018] The robot management module is used to maintain and upgrade the robot functions, send control instructions to the robot, and realize the background management function of the robot.

[0019] Furthermore, the JSON format and customized group information in the group management module include but are not limited to detailed information such as group name, group type, group member list, group permission settings, etc., so as to comprehensively and accurately manage group data.

[0020] Furthermore, the keyword detection configuration in the perception configuration module can be customized according to different fields and scenarios of social governance, including but not limited to keywords in security management, environmental sanitation, public order, etc., so as to improve the pertinence and accuracy of perception.

[0021] Furthermore, the data storage unit in the data monitoring module has data backup and recovery functions to prevent data loss and damage and ensure data security and integrity.

[0022] Furthermore, the task management module can prioritize tasks according to their urgency and importance, giving priority to important and urgent tasks to ensure the efficient implementation of social governance work.

[0023] Furthermore, the data analysis unit of the data processing and analysis center is capable of performing real-time analysis and processing of real-time data, achieving rapid response and decision support to social governance issues, with the analysis time delay not exceeding 5 minutes.

[0024] Furthermore, the charts and curves in the data display and warning module can be customized and displayed according to user needs, including but not limited to the display of data analysis results for different time periods, different regions, and different themes, to meet the personalized viewing needs of different users.

[0025] Furthermore, the SMS push management module can perform intelligent push according to the user's behavior data and preferences, thereby improving the accuracy and effectiveness of the push, and the push accuracy rate is not less than 90%.

[0026] Furthermore, the robot management module has remote monitoring and management functions, and the administrator can monitor and operate the robot in real time through a remote terminal, including but not limited to the robot's start, stop, parameter setting, fault diagnosis and other functions, so as to improve the convenience and efficiency of management.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] First, in terms of grassroots governance, the system accurately manages user group data through the group management module, and can promptly identify user identities and group member information within the grassroots scope of communities and neighborhoods, providing an accurate data basis for grassroots governance. At the same time, the perception configuration module can quickly configure the data perception rules of the intelligent robot to ensure efficient perception of the demands of grassroots mobile terminal group feedback, greatly improving the speed of community staff to discover residents' demands and the efficiency of handling them, solving the problems of untimely discovery and inefficient handling in the past.

[0029] Secondly, the data monitoring module can monitor data in real time, and the intelligent robot can quickly collect and transmit group messages, providing a timely source of information for grassroots governance. The task management module reasonably allocates tasks to ensure that robot resources are effectively scheduled and optimized, thereby improving the efficiency of grassroots governance. The data processing and analysis center uses advanced natural language processing technology and statistical analysis methods to build an intention recognition model, which can accurately understand residents' demands and provide a scientific decision-making basis for grassroots governance.

[0030] Furthermore, the data display and early warning module displays the analysis results in the form of intuitive charts and curves, which makes it easy for staff to quickly understand the grassroots governance situation. When prohibited speech occurs or alarm rules are triggered, SMS notifications are automatically sent to alert information, which enhances the security and stability of grassroots governance. The SMS push management module pushes messages accurately according to the personalized needs of users, improving the pertinence and effectiveness of information transmission. The robot management module maintains and upgrades the functions of the robots to ensure that the system always maintains a good operating state.

[0031] Finally, the system innovatively extends grassroots governance to residents' daily mobile phones, forming a new grassroots governance model with mobile phone groups as the hub and AI intelligent perception robots participating in real-time online. It achieves rapid response to residents' demands, pushes the acceptance status and processing progress of demands, and truly achieves "small matters do not leave the grid, big matters do not leave the community, and difficult matters do not leave the street", which effectively improves the level of social governance modernization. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic block diagram of the cross-platform perception robot system for social governance based on a vertical large model proposed by the present invention;

[0033] Figure 2 This is a schematic block diagram of the application method of the cross-platform perception robot system for social governance based on the vertical big model proposed in the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0036] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0037] Reference Figure 1-2 : A cross-platform perception robot system for social governance based on a vertical large model, including:

[0038] The group management module is used to manage user group data, identify user identities and group members in the group, and use the JSON format to customize various group information, including performing information entropy analysis on the historical group data sequence set and the historical business group use sub-data sequence set, respectively, to obtain the historical group information entropy set and the historical business use information entropy set, and to centrally analyze the historical group information entropy set and the historical business use information entropy set to obtain M centralized information entropy sets, and import the obtained M centralized information entropy sets into the module as the basic group information set, and finally obtain the group data information;

[0039] The perception configuration module is used to configure the rules for intelligent robots to perceive data. The rule configuration includes robot keyword detection configuration, question reply rule configuration, and information release rule configuration. The rule configuration is executed in parallel through programs. For data services in the field of social governance, Q data perception rules are configured for N robot groups. For the obtained data perception rule configuration, the rule verification algorithm is applied to check the perception rules for errors. The specific verification algorithm is as follows:

[0040] in , m is the length of regular sequence A, n is the length of regular sequence B, It represents the distance or difference measurement between the i-th element in rule sequence A and the j-th element in rule sequence B (the appropriate measurement method is defined according to the specific characteristics of the rule elements). When the similarity score calculation result is equal to 1, it means that the data perception rule verification has passed;

[0041] The data monitoring module is used to monitor the generated data, including a data collection unit, a data transmission unit, and a data storage unit. An intelligent robot is introduced into the group. When a user sends a message to the group, the intelligent robot collects the message in the group in JSON format, and transmits the collected data to the control center through the data transmission unit by wired or wireless means. After receiving the transmitted data, the control center stores the data in a centralized manner to facilitate the next step of data processing and analysis. The data collection unit is a robot that automatically collects the data sent by the group users in real time based on the API interface, and parses the acquired data. The data transmission unit uploads the data parsed by the robot to the control center by wired or wireless means.

[0042] The task management module is used to manage and coordinate various tasks performed by the robot. According to the nature of the task, the robot's capabilities and the current workload, the task is assigned to the most suitable robot to achieve effective scheduling and optimization of robot resources. Control instructions can be sent to the robot as needed to ensure that the task is carried out in a predetermined manner and sequence, including task delivery and reception (tasks are delivered to the robot through the system interface or API interface, and the robot task management module is responsible for receiving and parsing task instructions to ensure the accuracy and completeness of task information), task monitoring (real-time monitoring of task progress and status during task execution, timely alarm and taking measures when abnormal situations or task failures occur), and task statistics (collecting task execution results after task completion and feeding them back to the user, generating statistical data and reports on task execution);

[0043] The data processing and analysis center includes a data storage unit, a data analysis unit and a model building unit. The data storage unit uses a large-capacity hard disk or solid-state hard disk to store the data uploaded by the data transmission unit to ensure that the data will not be lost. The data analysis unit uses natural language processing technology and statistical analysis methods to analyze and process the collected data in real time. The model building unit builds an intention recognition model based on machine learning algorithms and finite element analysis methods, and uses support vector machines (SVMs) and artificial neural network (ANN) machine learning algorithms to learn and train data information related to the field of social governance, establish a mapping relationship between intentions and entities, and combine finite element analysis methods to model and analyze user messages, optimize model parameters, and improve the accuracy and reliability of the model. The prediction accuracy of the model is not less than 90% on the test set. When determining the artificial neural network (ANN) machine learning algorithm, ,in is the input value, are their respective weights, and the final result y is the sum of their multiplications. The activation function is only related to the input value and the weight. When the input function remains unchanged and the output is equal to the activation function, the neuron is said to be linear, and the output function is an improved S-type function: , where x is the input variable, k, m, n are constant variables, and e is the base of the natural logarithm. For large positive variables, the value of the function gradually approaches 1, and for large negative variables, the value of the function gradually approaches 0. At zero, the value of the function is 0.5, giving a smooth transition between high and low neuron outputs;

[0044] The data display and warning module is used to display the results of data processing and analysis in the form of intuitive charts and curves, including user message growth curves and user consultation problem distribution cloud maps. When prohibited speech is detected or the preset alarm rules are triggered, SMS notifications are automatically sent to warn the user. The alarm rules are set and adjusted according to the actual operation situation. It supports multi-user access at the same time. Users can view the robot's operation status and alarm information in real time through a web browser or mobile terminal. Different users have different operating permissions. Administrator users have advanced permissions such as system settings, display settings, and user permission settings. Ordinary users only have the permission to view data and receive alarm information.

[0045] The SMS push management module is used to formulate a time node sequence set according to the user's personalized push requirements to identify the push plan of the target data service, obtain the user's personalized push plan set, and manage the SMS push of the target data service based on the user's personalized push plan;

[0046] The robot management module is used to maintain and upgrade the robot functions, send control instructions to the robot, and realize the background management function of the robot.

[0047] In the present invention, the group management module plays a vital role. It uses JSON format and customized group information to manage user group data comprehensively and accurately. Among them, the group information covers multiple aspects, including not only intuitive group names, which clearly identify the unique identity of each group; but also the definition of group types, so that groups of different natures can be distinguished, which is convenient for adopting specific management strategies for different types of groups. The group member list records the member information in each group in detail, ensures the accurate grasp of the participants, and provides a basis for subsequent communication and management. In addition, the group permission setting is a key link. By reasonably setting the permissions of different members in the group, the activities within the group can be effectively regulated to ensure the security and orderly exchange of information. In short, the comprehensive use of these detailed information enables the group management module to manage group data comprehensively and accurately, and provide solid data support for the efficient operation of the cross-platform perception robot system of social governance in grassroots governance.

[0048] The perception configuration module of the present invention is highly flexible and targeted. Among them, the keyword detection configuration can be customized according to different fields and scenarios of social governance. In terms of safety management, keywords such as "fire hazards" and "public security issues" can be set to timely perceive and deal with safety-related issues; in the field of environmental sanitation, keywords such as "garbage accumulation" and "sewage discharge" can be covered to help improve the supervision of environmental quality; and in terms of public order, the setting of keywords such as "illegal parking" and "occupying the road for business" can effectively strengthen the maintenance of public order. Through such customized settings, the perception configuration module can more accurately capture key information in different fields and scenarios, greatly improve the pertinence and accuracy of perception, and provide more accurate information support for social governance.

[0049] The data monitoring module plays a key role in data protection in the present invention. The data storage unit therein has powerful data backup and recovery functions. In daily operation, the data backup function will regularly back up the collected data to prevent data loss due to various unexpected situations. Whether it is hardware failure, software error or human error, it will not easily cause permanent loss of data. When the data is lost or damaged, the data recovery function can be quickly started, and the system can be restored to a normal state according to the backup data to ensure the security and integrity of the data. This function provides reliable data protection for the stable operation of the cross-platform perception robot system for social governance, ensuring that accurate data support can be provided for social governance work under any circumstances.

[0050] The task management module of the present invention has a powerful task scheduling capability. It can prioritize tasks according to their urgency and importance. For those important and urgent tasks, such as emergencies involving public safety, major livelihood issues, etc., the task management module will place them in a priority position, quickly allocate resources, and ensure that these tasks can be processed in a timely manner. Such a priority sorting mechanism can effectively ensure the efficient implementation of social governance work and avoid delays and adverse consequences caused by improper task processing order. At the same time, the task management module will also monitor the progress and status of tasks in real time, discover problems in a timely manner and take corresponding treatment measures to ensure that each task can be successfully completed.

[0051] The data processing and analysis center of the present invention has efficient data analysis capabilities. The data analysis unit therein can analyze and process real-time data in real time, which is crucial for rapid response and decision-making support for social governance issues. When faced with complex and changeable social governance issues, the data analysis unit can quickly extract key information, conduct in-depth analysis, and provide a scientific basis for decision-making. Moreover, the present invention clearly stipulates that the analysis time delay does not exceed 5 minutes, which means that data analysis can be completed in a very short time, providing timely information support for social governance work. This efficient data analysis capability can greatly improve the efficiency and quality of social governance and provide strong technical support for solving various social problems.

[0052] The data display and early warning module is highly flexible and practical in the present invention. The charts and curves therein can be customized and displayed according to user needs. Users can analyze and display data according to different time periods, such as viewing the changing trend of social governance issues within a specific time period; they can also display according to different regions and compare the differences in social governance in different regions; they can also display for different topics to gain an in-depth understanding of social governance issues under specific topics. This custom setting and display function can meet the personalized viewing needs of different users, provide users with a more intuitive and clear data display, and help users better understand and grasp the social governance situation, so as to make more scientific and reasonable decisions.

[0053] The SMS push management module of the present invention has the advantage of intelligent push. It can push accurately according to the user's behavioral data and preferences. By analyzing the user's historical behavioral data, the user's areas of concern and demand tendencies are understood, so as to push relevant information in a targeted manner. For example, if the user often pays attention to environmental sanitation issues, the SMS push management module will give priority to pushing information related to environmental sanitation. This intelligent push method greatly improves the accuracy and effectiveness of the push, ensuring that users can obtain the information they care about in a timely manner. At the same time, the present invention clearly stipulates that the push accuracy rate is not less than 90%, which means that in most cases, users can receive accurate information that meets their needs, providing a strong guarantee for the information transmission of social governance work.

[0054] The robot management module in the present invention provides great convenience for the management of the system. It has remote monitoring and management functions, and the administrator can monitor and operate the robot in real time through the remote terminal. Whether it is the start, stop, parameter setting, fault diagnosis and other functions of the robot, they can be easily completed remotely. This function greatly improves the convenience and efficiency of management, allowing administrators to manage robots anytime and anywhere and deal with various problems in a timely manner. Especially in the face of emergencies or the need to urgently adjust the robot parameters, the remote monitoring and management functions can respond quickly to ensure the stable operation of the cross-platform perception robot system for social governance.

[0055] In the present invention, if it involves the collection of personal privacy data, such as facial and mobile phone usage information, the individual's permission will be obtained in advance, including but not limited to verbal reminders, posting posters, mobile phone reminders, etc.; if it involves conflicts with laws and regulations, it will be produced or used within the scope permitted by laws and regulations.

[0056] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A cross-platform perception robot system for social governance based on a vertical large model, characterized by: include: The group management module manages user group data and identifies the identities of users and group members in the group; The perception configuration module configures the rules for intelligent robots to perceive data. The rule configuration is executed in parallel through programs. For N robot groups of data services in the field of social governance, Q data perception rules are configured, and the rule verification algorithm is applied to check the perception rules for errors; A data monitoring module monitors the generated data, including a data acquisition unit, a data transmission unit, and a data storage unit; Task management module, which manages and coordinates the tasks performed by the robots, assigns tasks to the robots, and sends control instructions; The data processing and analysis center includes a data storage unit, a data analysis unit, and a model building unit. The data analysis unit uses natural language processing technology and statistical analysis methods to perform real-time analysis and processing on the collected data. The model building unit builds an intent recognition model based on machine learning algorithms and finite element analysis methods. It uses support vector machines (SVMs) and artificial neural network (ANN) machine learning algorithms to learn and train data information and establish a mapping relationship between intent and entities. The data display and early warning module displays the results of data processing and analysis in the form of charts and curves; The SMS push management module formulates a time node sequence set according to the user's personalized push requirements to identify the push plan of the target data service and manage the SMS push of the target data service; The robot management module manages the maintenance and upgrade of robot functions and sends control instructions to the robot; The keyword detection configuration in the perception configuration module is customized according to different fields and scenarios of social governance, including keywords in security management, environmental sanitation, and public order, to improve the pertinence and accuracy of perception; The specific formula design of the rule verification algorithm is as follows: in , m is the length of regular sequence A, n is the length of regular sequence B, It represents the distance or difference measure between the i-th element in rule sequence A and the j-th element in rule sequence B. When the similarity score calculation result is equal to 1, it means that the data-aware rule verification has passed.

2. The cross-platform perception robot system for social governance based on vertical big model according to claim 1 is characterized in that: The group management module uses the JSON format and customized multiple group information to perform information entropy analysis on the historical group data sequence set and the historical business group usage sub-data sequence set, respectively, to obtain a historical group information entropy set and a historical business usage information entropy set, and performs centralized analysis on the historical group information entropy set and the historical business usage information entropy set to obtain M centralized information entropy sets, and imports the obtained M centralized information entropy sets into the module as basic group information sets, and finally obtains group data information; JSON format and customized group information include group name, group type, group member list, and group permission setting information, which are used to accurately manage group data.

3. The social governance cross-platform perception robot system based on vertical big model according to claim 1 is characterized in that: The data monitoring module introduces an intelligent robot into the group. When a user sends a message to the group, the intelligent robot collects the messages in the group in JSON format, and transmits the collected data to the control center through a data transmission unit in a wired or wireless manner. After receiving the transmitted data, the control center stores the data in a centralized manner. The data acquisition unit in the data monitoring module is a robot that automatically collects data sent by group users in real time based on the API interface, and parses the acquired data. The data transmission unit uploads the data parsed by the robot to the control center by wired or wireless means. The data storage unit in the data monitoring module has data backup and recovery functions to prevent data loss and damage and is used for data security and integrity.

4. The social governance cross-platform perception robot system based on vertical big model according to claim 1 is characterized in that: The control instructions sent by the task management module include task issuance and reception, task monitoring, and task statistics; The task management module can prioritize tasks according to their urgency and importance, give priority to important and urgent tasks, and ensure the efficient implementation of social governance work.

5. The cross-platform perception robot system for social governance based on vertical big model according to claim 1 is characterized in that: The data analysis unit of the data processing and analysis center can analyze and process real-time data in real time, realize rapid response and decision support to social governance issues, and the analysis time delay shall not exceed 5 minutes; the data storage unit adopts a large-capacity hard disk or solid-state hard disk; When determining the artificial neural network ANN machine learning algorithm, the formula is used ,in is the input value, are their respective weights, and the final result y is the sum of their multiplications. The activation function is only related to the input value and the weight. When the input function remains unchanged and the output is equal to the activation function, the neuron is said to be linear, and the output function is an improved S-type function: , where x is the input variable, k, m, n are constant variables, and e is the base of the natural logarithm.

6. The social governance cross-platform perception robot system based on vertical big model according to claim 1 is characterized in that: The data display and warning module includes a user message growth curve and a user consultation problem distribution cloud map. When prohibited speech is detected or a preset alarm rule is triggered, an automatic SMS notification of the warning information is sent. The alarm rules are set and adjusted according to the actual operation situation. It supports simultaneous access by multiple users. Users can view the robot operation status and alarm information in real time through a web browser or mobile terminal. Different users have different operation permissions. Among them, administrator users have advanced permissions such as system settings, display settings, and user permission settings. Ordinary users only have the permission to view data and receive alarm information. Among them, charts and curves are customized and displayed according to user needs, including the display of data analysis results of different time periods, different regions, and different topics, to meet the personalized viewing needs of different users.

7. The social governance cross-platform perception robot system based on vertical big model according to claim 1 is characterized in that: The SMS push management module can perform intelligent push according to the user's behavior data and preferences, and the push accuracy rate is not less than 90%.

8. The cross-platform perception robot system for social governance based on vertical big model according to claim 1 is characterized in that: The robot management module has remote monitoring and management functions. The administrator can monitor and operate the robot in real time through a remote terminal, including starting, stopping, parameter setting, and fault diagnosis of the robot, so as to improve the convenience and efficiency of management.

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