Complete-cycle technical service digital application system for industry secretary industry committee

Abstract

The invention relates to the technical field of data integration, and discloses a industry secretary industry committee full-cycle technical service digital application system, which comprises the following modules: a data interaction module, a data mapping and conversion technology module, a data processing module, a data processing module and a data processing module, the system comprises an intelligent processing module, an architecture management module, a data analysis module and a cross-platform development framework, wherein the intelligent processing module is used for processing data collected in real time at a high speed. Through the established data format conversion algorithm, the data source structure change can be automatically monitored in real time, the mapping rule can be dynamically generated, frequent manual intervention is not needed, the data processing efficiency is greatly improved, semantic analysis and deep learning technologies are used in the algorithm, the data meaning can be deeply understood, and the data processing efficiency is improved. Various heterogeneous data are converted into a unified or mutually compatible format, and seamless data connection and sharing among different systems are realized, so that the compatibility among the data is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of data integration, and particularly to a digital application system for the full-cycle technical services of property secretaries and owners' committees. Background Art

[0002] With the rapid development of information technology, digital platforms have become the core tools for the operation and management of various industries. Existing digital platforms are usually based on cloud computing, big data, and Internet of Things technologies, aiming to achieve efficient integration and processing of data.

[0003] In the prior art, the owners' committee is an important unit in community governance. However, due to relevant professional norms, the tenure of the owners' committee organization is generally 3 to 5 years, which is not conducive to its quickly adapting to and mastering the skills of fulfilling its duties in accordance with the norms.

[0004] Therefore, under the management of the existing full-cycle technical services of property secretaries and owners' committees, the frequent replacement of management personnel often leads to incompatibility of data formats and protocols between different systems, resulting in the phenomenon of data islands. In addition, the current data service platform has insufficient security and privacy protection capabilities and is difficult to cope with increasingly complex cyberattacks. At the same time, the complexity of the user interface and the cumbersome operation also reduce the user experience. In view of this, we propose a digital application system for the full-cycle technical services of property secretaries and owners' committees. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a digital application system for the full-cycle technical services of property secretaries and owners' committees, which solves the problem that under the management of the existing full-cycle technical services of property secretaries and owners' committees, the frequent replacement of management personnel often leads to incompatibility of data formats and protocols between different systems, resulting in the phenomenon of data islands.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A digital application system for the full-cycle technical services of property secretaries and owners' committees includes the following modules: Data Interaction Module: Design and construct a unified data interface system, adopt standardized data protocols to be compatible with common interfaces, and adapt to various relational databases and non-relational databases. With the help of data mapping and conversion technologies, complete the docking of different data formats between different systems; Data Processing Module: Based on a distributed computing framework, use in-memory computing and parallel processing technologies to process the real-time collected data at high speed; Use data cleaning algorithms to remove noise data, integrate multi-source data through data integration technologies, and adopt real-time data storage technologies to achieve fast reading and writing of data, and support real-time analysis and processing of massive data; Intelligent Processing Module: Integrate machine learning and deep learning algorithm libraries. Through learning historical data, construct an intelligent decision-making model. Use natural language processing technology to achieve automatic classification and understanding of text information such as owners' feedback and complaints. Use image recognition technology to analyze the image data of community facilities and equipment, and achieve automated facility inspection and fault warning; Architecture Management Module: Adopt the microservices architecture design concept, split the platform functions into multiple independent microservice units. Each unit communicates through lightweight communication protocols. Use container orchestration technology to achieve automated deployment, scaling, and fault recovery of services. Through the configuration center, achieve parameterized configuration of each microservice; Data Analysis Module: Integrate professional big data analysis tools, combine data mining algorithms to deeply analyze the massive historical data accumulated by the platform. Through constructing a data warehouse, use ETL technology to achieve data extraction, transformation, and loading, and support multi-dimensional data visualization display; Cross-platform Development Framework: Combine the responsive design concept to develop application programs that can run stably on different operating systems and devices: The cross-platform development framework combines responsive design to develop application programs that can run stably on different operating systems and devices.

[0007] Preferably, the data interaction module has an adaptive data format conversion function. Based on semantic analysis and deep learning models, it automatically identifies changes in the data source structure, dynamically adjusts data mapping rules, can complete data format conversion without manual intervention, and at the same time supports rapid adaptation to new data formats. At the same time, introduce a data traffic prediction mechanism, and allocate network bandwidth resources in advance according to historical data traffic and business peak and trough periods.

[0008] Preferably, the data processing module adopts a real-time data aggregation algorithm based on time windows, which can not only efficiently aggregate data at different time granularities, but also automatically select the optimal aggregation function according to data characteristics and business requirements. For example, it automatically selects the sum function when processing financial data and the status statistical function when processing equipment status data.

[0009] Preferably, the intelligent processing module uses transfer learning technology. By constructing a meta-learning model, it quickly identifies similar features in different community scenarios, automatically migrates and optimizes existing models. At the same time, it adopts generative adversarial network technology to expand data diversity.

[0010] Preferably, the architecture management module realizes fine-grained control of traffic between microservices and service governance through service mesh technology. In addition to traffic routing, load balancing, and fault injection testing, it also introduces a service reputation evaluation mechanism based on blockchain, and automatically generates a reputation score according to indicators such as the running stability and response time of microservices, as the basis for traffic allocation and service scheduling.

[0011] Preferably, the data analysis module introduces interactive data exploration technology. Through natural language query and in combination with knowledge graph technology, users can achieve in-depth data mining. In addition, the system automatically parses natural language into precise data query statements and uses semantic association analysis technology to recommend relevant data dimensions and analysis perspectives.

[0012] Preferably, the cross-platform support module is developed using native technology and is finely optimized for multiple operating systems to ensure that the application can be used on various devices. In addition, by introducing dynamic update technology, the application can achieve seamless online upgrades.

[0013] Preferably, the data interaction module supports blockchain technology to achieve distributed storage and non-tamperable verification of data. Using the consortium chain architecture and in combination with zero-knowledge proof technology, while ensuring data security, it realizes fine-grained control of data access permissions. Only authorized nodes can access specific data, and data access records cannot be tampered with.

[0014] Preferably, the data processing module has a data tracking function. Using distributed ledger technology to record the whole process of data from collection to processing, it not only records the data source, processing steps and operation entities, but also calculates data fingerprints for each processing link through the hash algorithm to ensure data integrity and traceability.

[0015] Preferably, the intelligent processing module automatically optimizes the business process execution strategy through reinforcement learning algorithms. Combining deep Q-network and policy gradient algorithms, it adjusts the strategy in real time according to the business execution results to explore the optimal business process path. At the same time, using hierarchical reinforcement learning technology, it decomposes complex business processes into multiple sub-tasks and optimizes the strategies separately to improve the efficiency and accuracy of automated processing.

[0016] The present invention provides a digital application system for the full-cycle technical services of the industry secretary and the industry committee, which has the following beneficial effects: 1. Through the established data format conversion algorithm, the present invention can automatically and real-time monitor the changes in the data source structure, dynamically generate mapping rules, without frequent manual intervention, greatly improving the data processing efficiency. In addition, semantic analysis and deep learning technologies are used in the algorithm to deeply understand the data meaning, convert various heterogeneous data into a unified or mutually compatible format, and achieve seamless docking and sharing of data between different systems, thereby enhancing the compatibility between data.

[0017] 2. The present invention establishes a real-time data aggregation algorithm based on time windows. This algorithm divides continuous real-time data streams according to time windows and can batch process the data within the windows. Compared with processing each piece of data in real time, it reduces the number of processing times, lowers the system's computational load and resource consumption, and at the same time can complete the aggregation calculation of data within a short time, providing real-time statistical information for the system.

[0018] 3. The present invention establishes a reinforcement learning algorithm that enables the system to automatically explore the optimal execution strategy in the business process. By combining the deep Q-network and the policy gradient algorithm, the system can continuously try different ways of activity planning, organization, and promotion, and adjust the strategy in real time according to feedback results such as the participation rate and satisfaction of the activity, and finally find the execution path that can achieve the best activity effect, avoiding the limitations and blindness that may exist in traditional manual decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the digital application system for the full-cycle technical service of the industry secretary industry committee; Figure 2 It is a flow chart of the data format conversion algorithm of the present invention; Figure 3 It is a schematic diagram of the real-time data aggregation algorithm based on time windows of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Embodiment: Please refer to the attached Figure 1 - attached Figure 3 , the embodiment of the present invention provides a digital application system for the full-cycle technical service of the industry secretary industry committee, including the following modules: Data interaction module: Design and construct a unified data interface system, adopt a standardized data protocol to be compatible with common interfaces, and adapt to various relational databases and non-relational databases. With the help of data mapping and conversion technologies, complete the docking of different data formats between different systems; Data processing module: Based on a distributed computing framework, use in-memory computing and parallel processing technologies to process the real-time collected data at high speed; use data cleaning algorithms to remove noise data, integrate multi-source data through data integration technologies, and adopt real-time data storage technologies to achieve fast reading and writing of data, and support real-time analysis and processing of massive data; Intelligent Processing Module: Integrates machine learning and deep learning algorithm libraries. Through learning historical data, constructs an intelligent decision-making model, and uses natural language processing technology to achieve automatic classification and understanding of text information such as owners' feedback and complaints. Utilizes image recognition technology to analyze image data of community facilities and equipment, realizing automated facility inspections and fault warnings; Architecture Management Module: Adopts the microservices architecture design concept, splits the platform functions into multiple independent microservice units, and each unit communicates through lightweight communication protocols. Utilizes container orchestration technology to achieve automated deployment, scaling, and fault recovery of services, and realizes parameterized configuration of each microservice through the configuration center; Data Analysis Module: Integrates professional big data analysis tools, combines data mining algorithms to deeply analyze the massive historical data accumulated by the platform. Through constructing a data warehouse, uses ETL technology to achieve data extraction, transformation, and loading, and supports multi-dimensional data visualization display; Cross-platform Development Framework, combined with the responsive design concept, develops application programs that can run stably on different operating systems and devices: The cross-platform development framework combines responsive design to develop application programs that can run stably on different operating systems and devices.

[0022] The data interaction module has an adaptive data format conversion function. Based on semantic analysis and deep learning models, it automatically identifies changes in the data source structure, dynamically adjusts data mapping rules, can complete data format conversion without manual intervention, and at the same time supports rapid adaptation to new data formats. At the same time, a data traffic prediction mechanism is introduced. According to historical data traffic and business peak and trough periods, network bandwidth resources are allocated in advance. For data information from different sources, the following algorithms are proposed for unified format processing: S101: Data Preprocessing Clean and normalize the original data. Cleaning mainly removes noise data. Assuming a data point in the original data set is , through the set cleaning rule function remove the noise point to obtain the cleaned data ; During normalization, for numerical data, map the data to the [0, 1] interval. Assuming the minimum value of the data is min and the maximum value is max, the normalization formula is:

[0023] S102: Semantic Analysis Semantic understanding is carried out using natural language processing technology and knowledge graphs. For text data, word embedding techniques (such as Word2Vec, GloVe) are used to convert the words in the text into vector representations. Taking the Skip-Gram model as an example, the goal is to predict the context words based on the center word. Assuming the size of the vocabulary is , the center word has a word vector of , and the context word has a word vector of , then the prediction probability is:

[0024] The model is trained by maximizing this probability to obtain the vector representation of each word, thereby capturing the semantic information of the words S103: Deep learning model construction If a recurrent neural network (RNN) is adopted, the calculation process of the basic unit of the RNN at time step is as follows:

[0025] Among them, is the hidden state at time step , is the input at time step , is the weight matrix from the hidden layer to the hidden layer, is the weight matrix from the input layer to the hidden layer, is the bias of the hidden layer, is the activation function (such as tanh or ReLU). Through such a cyclic structure, the RNN can process data with sequential characteristics and learn the semantic features in the data; S104: Dynamic mapping rule generation After the deep learning model learns the semantic features, it generates mapping rules through the function . Assuming the output of the deep learning model is the semantic feature vector , the mapping rule function generates mapping rule parameters according to , which can be expressed as:

[0026] These parameters define the mapping relationship from the source data to the target data; S105: Model training and optimization A loss function is used to measure the difference between the mapping rules generated by the model and the true mapping relationship, and the model parameters are adjusted through an optimization algorithm. Taking the mean squared error (MSE) loss function as an example, assume there are samples, the mapping result predicted by the model is , and the true mapping result is . Then the loss function is:

[0027] Optimization algorithms such as stochastic gradient descent (SGD) are adopted to update the model parameters according to the gradient of the loss function. The parameter update formula of SGD is

[0028] where are the parameters at the current moment, is the learning rate, and is the gradient of the loss function at the current parameters; S106: Format Conversion and Data Update According to the generated mapping rule parameters , the source data is subjected to format conversion to obtain the target data . Assuming the conversion function is , then:

[0029] Meanwhile, the structure change of the data source is monitored in real time. When a change is detected, data is recollected and preprocessed, semantic analysis and model training are carried out. Through continuous learning and updating, the model can adapt to the continuously changing data format, ensuring the accuracy and efficiency of data format conversion.

[0030] The data processing module adopts a real-time data aggregation algorithm based on time windows, which can not only efficiently aggregate data at different time granularities, but also automatically select the optimal aggregation function according to data characteristics and business requirements. For example, it automatically selects the sum function when processing financial data and the status statistical function when processing device status data, including the following steps: The continuous real-time data stream is divided according to the set time window, and data aggregation operations are performed within each time window. The time window can be of a fixed length (such as one window every 5 minutes) or sliding (for example, sliding 1 minute each time, with a window length of 5 minutes). At the same time, the algorithm can automatically select the most appropriate aggregation function to process the data within the window according to the data characteristics (such as whether the data type is numerical, boolean or others) and specific business requirements; (1) Definition of time window: First, it is necessary to determine the type of time window (fixed window or sliding window), length, and sliding step (for sliding window). For example, define a fixed time window with a length of 1 hour, that is, each window contains data within consecutive 1 hour starting from a certain moment. For a sliding window, assume the window length is 30 minutes and the sliding step is 10 minutes, then a new window containing 30 minutes of data will be generated every 10 minutes.

[0031] (2) Data collection and timestamp marking: Collect data in real-time and mark each piece of data with an accurate timestamp to record the specific time when the data is generated. For example, when monitoring the water and electricity consumption data of a community, each piece of data collected (such as the electricity consumption value at a certain moment) carries the corresponding time information.

[0032] (3) Data grouping: According to the defined time window, allocate the data with timestamps to the corresponding time windows. For example, if the current time window is [9:00 - 10:00], then the data generated during this time period will be divided into this window.

[0033] (4) Selection of aggregation functions: Automatically select appropriate aggregation functions according to the characteristics of the data and business requirements. Common aggregation functions include: Sum function: Used for numerical data to calculate the sum of all numerical values within the window. For example, calculate the total electricity consumption of a community within a certain hour.

[0034] Average value function: Also applicable to numerical data to calculate the average value of numerical values within the window. For example, calculate the average operating temperature of a device within a certain time period.

[0035] Count function: Count the data records within the window, which can be used to count the number of times an event occurs. For example, count the number of times the access control system of a community is swiped per hour.

[0036] Maximum / minimum value function: Find the maximum or minimum value of numerical data within the window. For example, determine the highest and lowest temperatures within a community in a certain day.

[0037] Status statistics function: For boolean or status data (such as the operating status of a device: normal / fault), count the number of times or proportion of a certain status. For example, count the proportion of the duration of the elevator fault status within every half hour.

[0038] (5) Aggregation calculation: Use the selected aggregation function to calculate the data within each time window to obtain the aggregation result. For example, in the aggregation of electricity consumption data with a 1-hour time window, use the sum function to calculate the total electricity consumption within that hour.

[0039] (6) Result Output and Storage: Output the aggregation result for subsequent data analysis, decision support, or display. Meanwhile, the result can be stored in a database or other storage media for querying and analyzing historical data; Fixed-Time Window Aggregation Formula Let the time window be and the data set be where represents a data record, represents the data corresponding timestamp. Assume the selected aggregation function is the sum function Then the aggregation result is:

[0040] If the average value function is selected, then the aggregation result is:

[0041] Sliding-Time Window Aggregation Formula where represents the number of data records that meet the time window conditions. Let the sliding window length be and the sliding step be The start time of the current window is and the end time is . The data set is also . Taking the sum function as an example, the aggregation result is:

[0042] After each window slide, the above aggregation calculation is re-performed according to the new time range.

[0043] The intelligent processing module uses transfer learning technology to quickly identify similar features of different cell scenarios by constructing a meta-learning model, automatically migrating and optimizing existing models. Meanwhile, it adopts generative adversarial network technology to expand data diversity.

[0044] The architecture management module realizes fine-grained control of traffic between microservices and service governance through service mesh technology. In addition to traffic routing, load balancing, and fault injection testing, it also introduces a service reputation evaluation mechanism based on blockchain, automatically generating reputation scores according to indicators such as the running stability and response time of microservices, which are used as the basis for traffic allocation and service scheduling.

[0045] The data analysis module introduces interactive data exploration technology. Through natural language query and combined with knowledge graph technology, users can achieve in-depth data mining. In addition, the system automatically parses natural language into precise data query statements and uses semantic association analysis technology to recommend relevant data dimensions and analysis perspectives.

[0046] The cross-platform support module is developed using native technology and is finely optimized for multiple operating systems to ensure that the application can be used on various devices. In addition, by introducing dynamic update technology, the application can achieve seamless online upgrades.

[0047] The data interaction module supports blockchain technology to achieve distributed storage and non-tamperable verification of data. Using the consortium blockchain architecture and combined with zero-knowledge proof technology, while ensuring data security, it realizes fine-grained control of data access permissions. Only authorized nodes can access specific data, and data access records cannot be tampered with.

[0048] The data processing module has a data tracking function. Using distributed ledger technology to record the whole process of data from collection to processing, it not only records the data source, processing steps, and operating entity, but also calculates data fingerprints for each processing link through the hash algorithm to ensure data integrity and traceability.

[0049] The intelligent processing module automatically optimizes the business process execution strategy through reinforcement learning algorithms. Combining deep Q-network and policy gradient algorithms, it adjusts the strategy in real-time according to the business execution results to explore the optimal business process path. At the same time, using hierarchical reinforcement learning technology, it decomposes complex business processes into multiple sub-tasks and optimizes the strategies separately to improve the efficiency and accuracy of automated processing, including the following steps: Combining deep neural network and Q-learning algorithm to solve the optimal strategy. Through the Q-value function to estimate the long-term value of taking action in state , where are network parameters; The update formula of the Q-value function is:

[0050] where, is the learning rate, is the target Q-value, is the state at time step , is the action taken at time step , is the gradient of the Q-value function with respect to the parameter ; For the policy Optimize it with the goal of maximizing the expected cumulative reward; The policy gradient formula is:

[0051] Among them, is the objective function (the expected cumulative reward), represents the expectation, is the state, is the action, is the probability of taking action in state , is the cumulative reward starting from time step , is the gradient with respect to parameter ; Decompose the complex business process into multiple subtasks, each subtask has an independent policy, and optimize the overall policy by optimizing the policies of the subtasks respectively Suppose the business process is decomposed into subtasks, and the policy of each subtask is , and the reward function of subtask is , then the objective function of subtask is:

[0052] Among them, is the parameter of the policy of subtask , and by optimizing each , the efficiency and accuracy of the overall business process can be improved.

[0053] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The digital application system for the full-cycle technical service of the industry secretary and industry committee is characterized by: Includes the following modules: Data interaction module: Design and build a unified data interface system, use standardized data protocols to be compatible with common interfaces, and adapt to various relational databases and non-relational databases. With the help of data mapping and conversion technology, complete the connection between different data formats and different systems; Data processing module: Based on the distributed computing framework, it uses memory computing and parallel processing technology to process the real-time collected data at high speed; it uses data cleaning algorithms to remove noise data, integrates multi-source data through data integration technology, and adopts real-time data storage technology to achieve fast data reading and writing, supporting real-time analysis and processing of massive data; Intelligent processing module: Integrates machine learning and deep learning algorithm libraries, builds intelligent decision-making models by learning historical data, uses natural language processing technology to automatically classify and understand text information such as owner feedback and complaints, and uses image recognition technology to analyze image data of community facilities and equipment to achieve automated facility inspections and fault warnings; Architecture management module: Adopting the microservice architecture design concept, the platform functions are divided into multiple independent microservice units. Each unit communicates through a lightweight communication protocol, and uses container orchestration technology to achieve automatic deployment, expansion and contraction, and fault recovery of services. The configuration center implements parameterized configuration of each microservice. Data analysis module: Integrate professional big data analysis tools and combine data mining algorithms to conduct in-depth analysis of the massive historical data accumulated by the platform. By building a data warehouse and using ETL technology to extract, transform and load data, it supports multi-dimensional data visualization. A cross-platform development framework, combined with responsive design concepts, develops applications that can run stably on different operating systems and devices: A cross-platform development framework, combined with responsive design, develops applications that can run stably on different operating systems and devices.

2. According to claim 1, a digital application system for the full-cycle technical service of the industry secretary and industry committee is characterized in that: The data interaction module has the function of adaptive data format conversion. Based on semantic analysis and deep learning models, it can automatically identify changes in data source structure and dynamically adjust data mapping rules. It can complete data format conversion without human intervention and support rapid adaptation to new data formats. At the same time, a data traffic prediction mechanism is introduced to allocate network bandwidth resources in advance based on historical data traffic and business peak and trough periods.

3. According to claim 1, the digital application system for the full-cycle technical service of the industry secretary and industry committee is characterized in that: The data processing module adopts a real-time data aggregation algorithm based on a time window, which can not only efficiently aggregate data at different time granularities, but also automatically select the optimal aggregation function according to data characteristics and business needs, such as automatically selecting summation when processing financial data and automatically selecting a status statistics function when processing equipment status data.

4. According to claim 1, the digital application system for the full-cycle technical service of the industry secretary and industry committee is characterized in that: The intelligent processing module uses transfer learning technology to build a meta-learning model to quickly identify similar features of different community scenarios, automatically migrate and optimize existing models, and at the same time, adopts adversarial generative network technology to expand data diversity.

5. According to claim 1, the digital application system for the full-cycle technical service of the industry secretary and industry committee is characterized in that: The architecture management module uses service mesh technology to achieve refined control and service governance of traffic between microservices. In addition to traffic routing, load balancing and fault injection testing, it also introduces a blockchain-based service reputation evaluation mechanism to automatically generate reputation scores based on indicators such as the operating stability and response time of microservices as the basis for traffic allocation and service scheduling.

6. The digital application system for the full-cycle technical service of the industry secretary and industry committee according to claim 1 is characterized in that: The data analysis module introduces interactive data exploration technology. Users can achieve in-depth data mining through natural language query combined with knowledge graph technology. In addition, the system automatically parses natural language into precise data query statements and uses semantic association analysis technology to recommend relevant data dimensions and analysis perspectives.

7. According to claim 1, the digital application system for the full-cycle technical service of the industry secretary and industry committee is characterized in that: The cross-platform support module is developed using native technology and is finely optimized for a variety of operating systems to ensure that the application can be used on all types of devices; in addition, by introducing dynamic update technology, the application can be seamlessly upgraded online.

8. According to claim 1, the digital application system for the full-cycle technical service of the industry secretary and industry committee is characterized in that: The data interaction module supports blockchain technology to achieve distributed storage and tamper-proof verification of data. It adopts a consortium chain architecture and combines zero-knowledge proof technology to achieve fine-grained control of data access rights while ensuring data security. Only authorized nodes can access specific data, and data access records cannot be tampered with.

9. The digital application system for the full-cycle technical service of the industry secretary and industry committee according to claim 1 is characterized in that: The data processing module has a data tracking function and uses distributed ledger technology to record the entire process from data collection to processing. It not only records the data source, processing steps and operating subjects, but also calculates data fingerprints for each processing link through a hash algorithm to ensure data integrity and traceability.

10. The digital application system for the full-cycle technical service of the industry secretary and industry committee according to claim 1 is characterized in that: The intelligent processing module automatically optimizes the business process execution strategy through the reinforcement learning algorithm, combines the deep Q network and the policy gradient algorithm, adjusts the strategy according to the real-time feedback of the business execution results, explores the optimal business process path, and uses the hierarchical reinforcement learning technology to decompose the complex business process into multiple subtasks, and optimizes the strategies separately to improve the efficiency and accuracy of automated processing.

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