Intelligent rural user and object intelligent management method based on deep learning technology

Abstract

The invention relates to an intelligent rural user and object intelligent management method based on a deep learning technology, and the method comprises the steps: a data collection layer collects the big data (agricultural production, environment monitoring, safety management, social public service, asset data and rural traffic) of intelligent rural users and objects in different dimensions, and calls a big language model through a deployed model API; and performing feature recognition on the data features of each dimension, outputting an intelligent management strategy of the user or the object of the corresponding dimension, and finally enabling a management end to receive and respond to the intelligent management strategy of the user or the object. The intelligent management and service process can be carried out on all aspects of the country, the intelligent management efficiency of the development content of all the countries is effectively considered, intelligent data collection and analysis are adopted, the model can be used for intelligently matching the country management strategy of the corresponding dimension, intelligent management is achieved, the country treatment cost and time are saved, and the management efficiency of the country is improved. Therefore, the rural career is developed in a digital form, and the transformation development of the smart rural is promoted.

Description

Technical Field

[0001] The present disclosure relates to the field of smart management technology, and in particular to a method, system and electronic device for intelligent management of smart village users and objects based on deep learning technology. Background Art

[0002] Smart rural management refers to the process of using modern information technology, especially the Internet of Things, big data, cloud computing, artificial intelligence, etc., to intelligently manage and serve various aspects of rural resources, environment, economy, society, etc. It aims to improve the efficiency of rural governance, promote agricultural modernization, improve the quality of life of rural residents, and achieve sustainable development.

[0003] Existing smart village management solutions are generally only aimed at personnel or asset management, especially the behavior management of rural users. For example, CN117542096A discloses a face recognition system and method based on AI technology, which effectively improves the efficiency and accuracy of security implementation by realizing accurate face recognition and behavior analysis. This system mainly uses incremental convolutional neural networks and sparse autoencoders. The entire system is stable, fast and accurate, and can be widely used in the fields of security monitoring in smart communities and smart villages.

[0004] However, smart rural management usually includes the following rural development contents: 1. Smart agriculture: Through sensors, drones, satellite remote sensing and other technologies, intelligent management of crop planting, growth monitoring, pest and disease control, precision fertilization and irrigation can be achieved.

[0005] 2. Smart environment: Use IoT technology to monitor rural environmental quality, including air quality, water quality, soil conditions, etc., to promptly detect and address environmental problems.

[0006] 3. Smart public services: Provide public services such as education, medical care, and culture through information technology to improve the convenience and satisfaction of rural residents.

[0007] 4. Smart Transportation: Establish an intelligent transportation management system, optimize rural road networks, and improve transportation efficiency and safety.

[0008] 5. Smart governance: Use big data analysis and cloud computing platforms to improve the decision-making support capabilities of rural governance and achieve transparent and efficient government management.

[0009] 6. Smart security: Deploy security facilities such as video surveillance and alarm systems to ensure rural safety and prevent and reduce crime.

[0010] Therefore, it is impossible to comprehensively monitor the contents of rural development by only managing personnel or assets, and it is impossible to effectively consider the intelligent management efficiency of various rural development contents. The existing rural development contents are mainly implemented by village cadres, etc., and in the face of the smart rural cause of information and digital development, this way of manually supervising rural development seems to be technologically backward. Summary of the invention

[0011] In order to solve the above problems, the present application proposes a method, system and electronic device for intelligent management of smart village users and objects based on deep learning technology.

[0012] On the one hand, this application proposes a method for intelligent management of smart village users and objects based on deep learning technology, including the following steps: S1, the data collection layer collects big data of smart village users and objects in different dimensions; S2. The data processing center performs feature engineering on the big data of the smart village users and objects, obtains data features of corresponding dimensions, and calls the big language model through the deployed model API to perform feature recognition on the data features, and outputs the intelligent management strategy for users or objects of corresponding dimensions; S3. The data processing center sends the intelligent management strategy of the user or object in the corresponding dimension to the management end through the 5G network; S4, the management end receives and responds to the intelligent management strategy of the user or object, and feeds back the response result to the data processing center in real time; S5. The data processing center determines whether the intelligent management strategy of the user or object in the corresponding dimension is implemented in place based on the response result feedback.

[0013] As an optional implementation scheme of the present application, optionally, the big data of smart village users and objects includes data of the following dimensions: Agricultural production, environmental monitoring, safety management, social public services, asset data and rural transportation.

[0014] As an optional implementation scheme of the present application, optionally, S1, the data collection layer collects big data of smart village users and objects in different dimensions, including: Perform network configuration through the management terminal to establish a data communication link between each collection device in the data collection layer and the smart village gateway; Configure the data collection frequency for the smart village gateway, so that the smart village gateway regularly activates the collection equipment in the data collection layer to collect data of corresponding dimensions, and obtain big data of smart village users or objects of corresponding dimensions; Uploading the collected big data of the smart village users or objects of corresponding dimensions to the smart database through the smart village gateway; The smart database stores the currently collected big data of the smart village users or objects in the corresponding dimensions in a database matching the big data according to the dimensions of the big data of the smart village users or objects; The following dimensions are set in the wisdom database: Agricultural production database, environmental monitoring database, safety management database, social public service database, asset data database and rural transportation database.

[0015] As an optional implementation scheme of the present application, optionally, an APP of a corresponding dimension is deployed on the management terminal, including: Agriculture APP, environment APP, security APP, social APP, asset APP and transportation APP are used by administrators to manage corresponding agricultural production data, environmental monitoring data, safety management data, social public service data, asset data and rural transportation data respectively; When performing network configuration through the management terminal, the administrator selects the APP of the corresponding dimension according to the needs, and establishes the corresponding data communication link between the collection device of the dimension and the smart village gateway.

[0016] As an optional implementation scheme of the present application, optionally, in step S2, the data processing center performs feature engineering on the big data of the smart village users and objects to obtain data features of corresponding dimensions, including: The smart database monitors the big data of the newly emerged smart village users and objects, and synchronizes them to the data processing center as target data; The data processing center performs feature extraction on the target data to extract a number of data features under corresponding dimensions; According to the dimensional attribute of the target data, a target feature that best matches the dimensional attribute of the target data is selected from the extracted data features as the data feature of the target data.

[0017] As an optional implementation scheme of the present application, optionally, in step S2, the large language model is called through the deployed model API to perform feature recognition on the data features and output the intelligent management strategy of the user or object of the corresponding dimension, including: The data processing center calls the corresponding large language model from the model library through the deployed model API; Importing the data features of the target data into the large language model, performing feature recognition on the data features through the large language model, and outputting the intelligent management strategy matching the features based on the association between the data features of each dimension pre-trained and learned by the model and the corresponding intelligent management strategy; The data processing center records the intelligent management strategy and binds it with the target data.

[0018] On the other hand, the present application proposes a smart village user and object intelligent management system based on deep learning technology, which is used to implement a smart village user and object intelligent management method based on deep learning technology, including: The data collection layer is used to collect big data of smart village users and objects in different dimensions; The smart village gateway is used to activate the data collection layer to collect data according to the configuration of the management end, and upload the collected big data of the smart village users or objects of the corresponding dimensions to the smart database; A smart database, for storing the currently collected big data of the smart village users or objects in the corresponding dimensions in a database that matches the big data according to the dimensions of the big data of the smart village users or objects; and for monitoring the newly appeared big data of the smart village users and objects, and synchronizing them as target data to the data processing center; The data processing center is used to perform feature engineering on the big data of the smart village users and objects, obtain data features of corresponding dimensions, and call the big language model through the deployed model API to perform feature recognition on the data features, and output the intelligent management strategy of the users or objects of the corresponding dimensions; and the data processing center sends the intelligent management strategy of the users or objects of the corresponding dimensions to the management end through the 5G network; A management terminal is used to receive and respond to the intelligent management strategy of the user or object, and feed back the response result to the data processing center in real time; The data processing center is further used to determine whether the intelligent management strategy of the user or object in the corresponding dimension is implemented in place according to the response result feedback; The management terminal is connected to the data processing center via 5G network communication; The data collection layer is connected to the smart database through the smart village gateway; The management terminal is connected to the smart village gateway via Internet of Things communication; The data processing center is connected to the wisdom database through an ODBC database interface.

[0019] In another aspect, the present application further provides an electronic device, comprising: processor; a memory for storing processor-executable instructions; Among them, the processor is configured to implement the smart village user and object intelligent management method based on deep learning technology when executing the executable instructions.

[0020] Technical effects of the present invention: This application collects big data of smart rural users and objects of different dimensions (agricultural production, environmental monitoring, safety management, social public services, asset data and rural transportation) through the data collection layer, and calls the large language model through the deployed model API to perform feature recognition on the data features of each dimension, output the intelligent management strategy of the user or object of the corresponding dimension, and finally let the management end receive and respond to the intelligent management strategy of the user or object. It is a process that can intelligently manage and serve various aspects of rural resources, environment, economy, society, etc. It aims to improve the efficiency of rural governance and promote agricultural modernization. The present invention can comprehensively monitor the content of rural development, effectively take into account the intelligent management efficiency of each rural development content, and adopt the intelligent data collection and analysis of the present invention. It can use the model to intelligently match the rural management strategy of the corresponding dimension, realize intelligent management, save rural governance costs and time, improve the efficiency of smart rural management, and enable rural undertakings to develop in a digital form, and promote the transformation and development of smart villages.

[0021] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0023] Figure 1 It is shown as a schematic diagram of the implementation process of the present invention; Figure 2 A schematic diagram of the model architecture of a large language model of the present invention is shown; Figure 3 Shown is a schematic diagram of the system composition structure of the present invention; Figure 4 A schematic diagram of a management terminal communication mode supporting 5G and IoT communications is shown in the present invention; Figure 5 It is a schematic diagram showing the application of the electronic device of the present invention. DETAILED DESCRIPTION

[0024] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0025] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0026] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present disclosure. Example 1

[0027] like Figure 1 As shown, on the one hand, the present application proposes a smart village user and object intelligent management method based on deep learning technology, including the following steps: S1, the data collection layer collects big data of smart village users and objects in different dimensions; S2. The data processing center performs feature engineering on the big data of the smart village users and objects, obtains data features of corresponding dimensions, and calls the big language model through the deployed model API to perform feature recognition on the data features, and outputs the intelligent management strategy for users or objects of corresponding dimensions; S3. The data processing center sends the intelligent management strategy of the user or object in the corresponding dimension to the management end through the 5G network; S4, the management end receives and responds to the intelligent management strategy of the user or object, and feeds back the response result to the data processing center in real time; S5. The data processing center determines whether the intelligent management strategy of the user or object in the corresponding dimension is implemented in place based on the response result feedback.

[0028] The main technical ideas of the present invention are: The data collection layer collects big data of smart rural users and objects in different dimensions (agricultural production, environmental monitoring, safety management, social public services, asset data and rural transportation), and calls the large language model through the deployed model API to perform feature recognition on the data features of each dimension, and output the intelligent management strategy for users or objects in the corresponding dimension. Finally, the management end receives and responds to the intelligent management strategy of the user or object.

[0029] The following will describe in conjunction with the application system of the present invention.

[0030] Combined with Figure 3 As shown in the system architecture diagram, smart villages involve data management of various people and objects.

[0031] As an optional implementation scheme of the present application, optionally, the big data of smart village users and objects includes data of the following dimensions: Agricultural production, environmental monitoring, safety management, social public services, asset data and rural transportation.

[0032] Specifically, agricultural production data may include crop planting information, soil quality data, irrigation system data, crop growth conditions, yield data, agricultural machinery usage, etc.

[0033] Environmental monitoring data may include air quality index, water quality monitoring data, soil pollution conditions, noise levels, and meteorological information (such as temperature, humidity, wind speed, etc.).

[0034] Security management data may include village security incident records, fire monitoring data, burglar alarm system information, public security camera footage, emergency response records, etc.

[0035] Social public service data may include education service information, medical and health service data, cultural activity records, community service facility usage, residents’ health records, etc.

[0036] Asset data may include land use rights information, public facility asset lists, agricultural equipment asset records, infrastructure maintenance records, financial revenue and expenditure information, etc.

[0037] Rural transportation data may include road condition information, traffic flow statistics, traffic accident records, public transportation operation conditions, logistics distribution information, etc.

[0038] The data of each dimension can be collected through various collection devices deployed in the data collection layer at the corresponding location points in the smart village, uploaded to the system through the smart village gateway, and stored in the corresponding database of the system.

[0039] The data collection layer mainly uses sensors, cameras, drones and other equipment to collect data on rural environment, crop growth, weather, etc. The data types of the above different dimensions can be collected by the corresponding collection devices in the data collection layer. The specific data collection method and equipment are not limited in this embodiment. The data can be collected according to the data requirements of each dimension. The data format can be uniformly required.

[0040] On the management side, the network configuration and sampling configuration of the smart rural gateway (gateway) can be performed. The administrator can manage the gateway through the administrator. It can perform data sampling of the corresponding dimension and activate the collection device for collecting data of the corresponding dimension through the gateway. For example, if the administrator needs to understand the rural traffic data, he can use the traffic APP to send the camera activation command of the corresponding traffic point to the gateway, collect the camera image of the corresponding traffic point and upload it to the database through the gateway. The various APPs on the management side can be downloaded and installed by the administrator. The administrator can perform network activation configuration of the corresponding device according to the needs.

[0041] Administrators can also log in to the data processing center to view data of different dimensions, allowing the data processing center to dispatch data of corresponding dimensions from the smart database and feed it back to the management end to achieve data sharing.

[0042] like Figure 4 As shown, the management terminal of the present invention, such as a mobile terminal or a PDA terminal, can support 5G communication and Internet of Things communication.

[0043] The data processing center performs feature engineering on the big data of the smart village users and objects, obtains data features of corresponding dimensions, and calls the large language model through the deployed model API to perform feature recognition on the data features, and outputs intelligent management strategies for users or objects of corresponding dimensions.

[0044] When building an unsupervised learning model, you first need to collect and organize data features of different dimensions. These features may include but are not limited to user behavior data, transaction records, text content, image information, etc. Next, select the appropriate unsupervised learning algorithm based on the characteristics of the data, such as cluster analysis, principal component analysis (PCA), association rule learning, etc.

[0045] After selecting the algorithm, the data needs to be preprocessed, including data cleaning, standardization, normalization and other steps to ensure data quality and improve the performance of the model. Then, the selected algorithm is applied to analyze the data to discover patterns and structures in the data.

[0046] Based on the analysis results, corresponding management strategies can be formulated. For example, in the scenario of customer segmentation, customers can be divided into different groups through clustering algorithms, and then personalized marketing strategies can be formulated based on the characteristics of each group. In the scenario of anomaly detection, corresponding risk control measures can be formulated by identifying abnormal patterns in the data.

[0047] Finally, the model needs to be evaluated and optimized to ensure the effectiveness of the management strategy. This may include adjusting model parameters, using different algorithm combinations, or introducing new data features. Through iterative optimization, the accuracy of the model and the adaptability of the management strategy can be continuously improved.

[0048] Combined with Figure 2 As shown, the present invention can directly call a large language model, such as the Wenyan Yixin large language model (also called the "Wenxin large model") built based on the Transformer structure. By using pre-training technology, unsupervised learning is performed on a large amount of text data (corresponding to the dimensional data and the data set constructed by the corresponding intelligent management strategy), thereby obtaining rich language knowledge and context understanding capabilities. This enables a more accurate understanding of the user's questions and needs, and provides more appropriate answers and solutions.

[0049] The Transformer is a deep learning model that processes sequential data, such as text, by using a self-attention mechanism and is able to take into account long-range dependencies in the input data when modeling.

[0050] In the model architecture, the encoder and decoder parts of Transformer have been optimized and expanded to handle the complex characteristics of Chinese and other languages. At the same time, Baidu's powerful technology and resources in the field of deep learning, including large-scale training data and advanced algorithms, are also used to continuously improve my performance and capabilities.

[0051] After model training, it can learn to identify the smart management strategies corresponding to data features of different dimensions. Therefore, the model can be used to intelligently predict rural governance strategies (smart management strategies) for data features of different dimensions, thus realizing smart rural management.

[0052] As an optional implementation scheme of the present application, optionally, S1, the data collection layer collects big data of smart village users and objects in different dimensions, including: Perform network configuration through the management terminal to establish a data communication link between each collection device in the data collection layer and the smart village gateway; Configure the data collection frequency for the smart village gateway, so that the smart village gateway regularly activates the collection equipment in the data collection layer to collect data of corresponding dimensions, and obtain big data of smart village users or objects of corresponding dimensions; Uploading the collected big data of the smart village users or objects of corresponding dimensions to the smart database through the smart village gateway; The smart database stores the currently collected big data of the smart village users or objects in the corresponding dimensions in a database matching the big data according to the dimensions of the big data of the smart village users or objects; The following dimensions are set in the wisdom database: Agricultural production database, environmental monitoring database, safety management database, social public service database, asset data database and rural transportation database.

[0053] The management end can configure the sampling of the gateway, so that the gateway controls the corresponding data collection equipment to collect data of the corresponding dimensions. After the network manager collects the big data of the corresponding dimensions according to the configured data collection frequency, it is uploaded to the smart database through the gateway. The smart database can identify the dimensions of the currently collected big data and store it in the database of the corresponding dimensions. The smart database has a sub-library of the corresponding dimension, so you only need to let the smart database listen to the new big data and store it in the corresponding sub-library according to the dimension of the newly sampled big data. In this example, the smart database can use the database of the HDFS distributed file system, use the name node to identify the dimension attributes of the currently monitored big data, and store it in the nodo node of the corresponding attribute.

[0054] As an optional implementation scheme of the present application, optionally, an APP of a corresponding dimension is deployed on the management terminal, including: Agriculture APP, environment APP, security APP, social APP, asset APP and transportation APP are used by administrators to manage corresponding agricultural production data, environmental monitoring data, safety management data, social public service data, asset data and rural transportation data respectively; When performing network configuration through the management terminal, the administrator selects the APP of the corresponding dimension according to the needs, and establishes the corresponding data communication link between the collection device of the dimension and the smart village gateway.

[0055] Each APP can be downloaded and installed by the administrator.

[0056] As an optional implementation scheme of the present application, optionally, in step S2, the data processing center performs feature engineering on the big data of the smart village users and objects to obtain data features of corresponding dimensions, including: The smart database monitors the big data of the newly emerged smart village users and objects, and synchronizes them to the data processing center as target data; The data processing center performs feature extraction on the target data to extract a number of data features under corresponding dimensions; According to the dimensional attribute of the target data, a target feature that best matches the dimensional attribute of the target data is selected from the extracted data features as the data feature of the target data.

[0057] The smart database is configured with a corresponding listening port, and a code-designed listening program is used to monitor whether new big data appears. If new big data is monitored, the smart database will synchronize the newly monitored big data to the data processing center after storage. The data processing center will perform feature engineering on it and extract the data features under the corresponding dimensions. In order for the model to quickly identify and output the corresponding smart management strategy, the data processing center needs to select the target features that match the dimensional data of the current new big data from the identified data features according to the dimensional attributes. For example, for environmental big data, water quality characteristic values ​​and PM2.5 characteristic values ​​are selected as the target features that best match the environmental attributes, and these target features are input into the model. The model matches the strategy to a corresponding degree according to the data characteristics of the environment.

[0058] The smart management strategies corresponding to the data feature values ​​at different specific levels are constructed in advance by the administrator.

[0059] Typically, feature engineering includes the following steps: 1. Data cleaning: remove duplicate data, handle missing values, outliers and noise.

[0060] 2. Data conversion: Normalize or standardize the data to make it comparable.

[0061] 3. Feature extraction: extract useful information from the raw data, such as statistical features (mean, variance, skewness, kurtosis, etc.) and frequency features (frequency, frequency, etc.).

[0062] 4. Feature construction: Generate new features by combining and transforming existing features, such as polynomial features, interactive features, etc.

[0063] 5. Feature selection: Select the most representative and discriminative features from a large number of features, such as model-based feature selection, filtering-based feature selection, etc.

[0064] 6. Feature encoding: Convert non-numeric features into numerical features, such as One-Hot Encoding, Label Encoding, etc.

[0065] 7. Feature dimensionality reduction: Reduce feature dimensions through methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce computational complexity and overfitting risks.

[0066] 8. Feature crossover: Cross-combine different features to generate new features to capture the interactions between features.

[0067] 9. Time series features: For time series data, extract time features, such as sliding window statistical features, time period features, etc.

[0068] 10. Text features: For text data, extract text features, such as Bag of Words, TF-IDF, Word2Vec, etc.

[0069] In this embodiment, for data of different dimensions, it is necessary to select a suitable feature engineering mode for processing.

[0070] As an optional implementation scheme of the present application, optionally, in step S2, the large language model is called through the deployed model API to perform feature recognition on the data features and output the intelligent management strategy of the user or object of the corresponding dimension, including: The data processing center calls the corresponding large language model from the model library through the deployed model API; Importing the data features of the target data into the large language model, performing feature recognition on the data features through the large language model, and outputting the intelligent management strategy matching the features based on the association between the data features of each dimension pre-trained and learned by the model and the corresponding intelligent management strategy; The data processing center records the intelligent management strategy and binds it with the target data.

[0071] Because in the early training process of the model, the data features of different degrees under different dimensions and the corresponding intelligent (smart) management strategies have been learned accordingly, so the model can identify the characteristic values ​​corresponding to the indicators at different levels and the corresponding governance strategies. When the large model is subsequently called to identify and output the corresponding strategies, the corresponding strategy matching and output can be performed through the identified characteristic values ​​(or text features). The data processing center will then send the strategy output by the model to the management end, allowing the on-site administrator to execute the strategy. After the administrator executes it, the execution result will be fed back to the data processing center through the management end. The data processing center determines whether the behavior data of people or things under this dimension has been executed. If the data processing center receives the sampled data under this dimension again, it means that the execution has not been completed, which needs to be executed again according to the above steps.

[0072] Therefore, this application can be used to intelligently manage and serve various aspects of rural resources, environment, economy, society, etc. It aims to improve the efficiency of rural governance and promote agricultural modernization. The present invention can comprehensively monitor the content of rural development, effectively consider the efficiency of intelligent management of various rural development contents, and adopt the intelligent data collection and analysis of the present invention. It can use the model to intelligently match the rural management strategy of the corresponding dimension, realize intelligent management, save rural governance costs and time, improve the efficiency of smart rural management, enable rural undertakings to develop in a digital form, and promote the transformation and development of smart villages.

[0073] Obviously, those skilled in the art should understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Those skilled in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviated as: HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory. Example 2

[0074] like Figure 3 As shown, based on the implementation principle of Example 1, on the other hand, the present application proposes a smart village user and object intelligent management system based on deep learning technology, which is used to implement a smart village user and object intelligent management method based on deep learning technology in Example 1, including: The data collection layer is used to collect big data of smart village users and objects in different dimensions; The smart village gateway is used to activate the data collection layer to collect data according to the configuration of the management end, and upload the collected big data of the smart village users or objects of the corresponding dimensions to the smart database; A smart database, for storing the currently collected big data of the smart village users or objects in the corresponding dimensions in a database that matches the big data according to the dimensions of the big data of the smart village users or objects; and for monitoring the newly appeared big data of the smart village users and objects, and synchronizing them as target data to the data processing center; The data processing center is used to perform feature engineering on the big data of the smart village users and objects, obtain data features of corresponding dimensions, and call the big language model through the deployed model API to perform feature recognition on the data features, and output the intelligent management strategy of the users or objects of the corresponding dimensions; and the data processing center sends the intelligent management strategy of the users or objects of the corresponding dimensions to the management end through the 5G network; A management terminal is used to receive and respond to the intelligent management strategy of the user or object, and feed back the response result to the data processing center in real time; The data processing center is further used to determine whether the intelligent management strategy of the user or object in the corresponding dimension is implemented in place according to the response result feedback; The management terminal is connected to the data processing center via 5G network communication; The data collection layer is connected to the smart database through the smart village gateway; The management terminal is connected to the smart village gateway via Internet of Things communication; The data processing center is connected to the wisdom database through an ODBC database interface.

[0075] Please understand this in conjunction with Example 1.

[0076] The modules or steps of the present invention described above can be implemented by a general-purpose computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software. Example 3

[0077] like Figure 5 As shown, further, in another aspect, the present application also proposes an electronic device, including: processor; a memory for storing processor-executable instructions; Among them, the processor is configured to implement a smart village user and object intelligent management method based on deep learning technology as described in Example 2 when executing the executable instructions.

[0078] The electronic device of the embodiment of the present disclosure includes a processor and a memory for storing instructions executable by the processor. The processor is configured to implement the smart management method of smart village users and objects based on deep learning technology described in the above embodiment 2 when executing the executable instructions.

[0079] Here, it should be noted that the number of processors can be one or more. At the same time, the electronic device of the embodiment of the present disclosure may also include an input system and an output system. Among them, the processor, memory, input system and output system may be connected through a bus or in other ways, which are not specifically limited here.

[0080] As a computer-readable storage medium, the memory can be used to store software programs, computer executable programs and various modules, such as: the program or module corresponding to the smart management method of smart village users and objects based on deep learning technology in the embodiment of the present disclosure. The processor executes various functional applications and data processing of the electronic device by running the software programs or modules stored in the memory.

[0081] The input system can be used to receive input numbers or signals. The signal can be a key signal related to user settings and function control of the device / terminal / server. The output system can include display devices such as display screens.

[0082] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for intelligent management of smart village users and objects based on deep learning technology, characterized in that: The steps include: S1, the data collection layer collects big data of smart village users and objects in different dimensions; S2. The data processing center performs feature engineering on the big data of the smart village users and objects, obtains data features of corresponding dimensions, and calls the big language model through the deployed model API to perform feature recognition on the data features, and outputs the intelligent management strategy for users or objects of corresponding dimensions; S3. The data processing center sends the intelligent management strategy of the user or object in the corresponding dimension to the management end through the 5G network; S4, the management end receives and responds to the intelligent management strategy of the user or object, and feeds back the response result to the data processing center in real time; S5. The data processing center determines whether the intelligent management strategy of the user or object in the corresponding dimension is implemented in place based on the response result feedback.

2. According to claim 1, a method for intelligent management of smart village users and objects based on deep learning technology is characterized in that: The big data of smart village users and objects includes data in the following dimensions: Agricultural production, environmental monitoring, safety management, social public services, asset data and rural transportation.

3. According to claim 1, a method for intelligent management of smart village users and objects based on deep learning technology is characterized in that: S1. The data collection layer collects big data of smart village users and objects in different dimensions, including: Perform network configuration through the management terminal to establish a data communication link between each collection device in the data collection layer and the smart village gateway; Configure the data collection frequency for the smart village gateway, so that the smart village gateway regularly activates the collection equipment in the data collection layer to collect data of corresponding dimensions, and obtain big data of smart village users or objects of corresponding dimensions; Uploading the collected big data of the smart village users or objects of corresponding dimensions to the smart database through the smart village gateway; The smart database stores the currently collected big data of the smart village users or objects in the corresponding dimensions in a database matching the big data according to the dimensions of the big data of the smart village users or objects; The following dimensions are set in the wisdom database: Agricultural production database, environmental monitoring database, safety management database, social public service database, asset data database and rural transportation database.

4. According to claim 3, a method for intelligent management of smart village users and objects based on deep learning technology is characterized in that: The management terminal is deployed with APPs of corresponding dimensions, including: Agriculture APP, environment APP, security APP, social APP, asset APP and transportation APP are used by administrators to manage corresponding agricultural production data, environmental monitoring data, safety management data, social public service data, asset data and rural transportation data respectively; When performing network configuration through the management terminal, the administrator selects the APP of the corresponding dimension according to the needs, and establishes the corresponding data communication link between the collection device of the dimension and the smart village gateway.

5. According to claim 3, a method for intelligent management of smart village users and objects based on deep learning technology is characterized in that: In step S2, the data processing center performs feature engineering on the big data of the smart village users and objects to obtain data features of corresponding dimensions, including: The smart database monitors the big data of the newly emerged smart village users and objects, and synchronizes them to the data processing center as target data; The data processing center performs feature extraction on the target data to extract a number of data features under corresponding dimensions; According to the dimensional attribute of the target data, a target feature that best matches the dimensional attribute of the target data is selected from the extracted data features as the data feature of the target data.

6. According to claim 5, a method for intelligent management of smart village users and objects based on deep learning technology is characterized in that: In step S2, the large language model is called through the deployed model API to perform feature recognition on the data features and output the intelligent management strategy of the user or object of the corresponding dimension, including: The data processing center calls the corresponding large language model from the model library through the deployed model API; Importing the data features of the target data into the large language model, performing feature recognition on the data features through the large language model, and outputting the intelligent management strategy matching the features based on the association between the data features of each dimension pre-trained and learned by the model and the corresponding intelligent management strategy; The data processing center records the intelligent management strategy and binds it with the target data.

7. A smart village user and object intelligent management system based on deep learning technology, used to implement a smart village user and object intelligent management method based on deep learning technology according to any one of claims 1-6, characterized in that: include: The data collection layer is used to collect big data of smart village users and objects in different dimensions; The smart village gateway is used to activate the data collection layer to collect data according to the configuration of the management end, and upload the collected big data of the smart village users or objects of the corresponding dimensions to the smart database; A smart database is used to store the currently collected big data of the smart village users or objects in the corresponding dimensions in a database that matches the big data according to the dimensions of the big data of the smart village users or objects; and to monitor the newly appeared big data of the smart village users and objects and synchronize them as target data to the data processing center; The data processing center is used to perform feature engineering on the big data of the smart village users and objects, obtain data features of corresponding dimensions, and call the big language model through the deployed model API to perform feature recognition on the data features, and output the intelligent management strategy of the users or objects of the corresponding dimensions; and the data processing center sends the intelligent management strategy of the users or objects of the corresponding dimensions to the management end through the 5G network; A management terminal is used to receive and respond to the intelligent management strategy of the user or object, and feed back the response result to the data processing center in real time; The data processing center is further used to determine whether the intelligent management strategy of the user or object in the corresponding dimension is implemented in place according to the response result feedback; The management terminal is connected to the data processing center via 5G network communication; The data collection layer is connected to the smart database through the smart village gateway; The management terminal is connected to the smart village gateway via Internet of Things communication; The data processing center is connected to the wisdom database through an ODBC database interface.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Among them, the processor is configured to implement a smart village user and object intelligent management method based on deep learning technology as described in any one of claims 1-6 when executing the executable instructions.

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