Think tank data management system based on training guarantee

Abstract

The invention relates to the field of training guarantee data management, in particular to a training guarantee-based think tank data management system, which comprises a data acquisition and integration module, a data management and analysis module, an intelligent application and service module, a resource optimization and regulation module and a data interaction center, according to the think tank data management system based on training guarantee, the static data acquisition unit is in butt joint with a multi-system interface and supports batch import, and the dynamic data acquisition unit is connected to an Internet of Things sensor and wearable equipment, so that acquisition of total factor data of personnel, equipment, sites, materials and the like is realized, the problem of limited acquisition range of an existing system is solved, and the data acquisition efficiency is improved. Meanwhile, resource waste points and shortage risk points are accurately identified, optimal allocation of resources is achieved, the resource utilization rate is maximized, the guarantee cost is minimized, and the problems that an existing system is unreasonable in resource allocation and serious in waste are solved.

Description

Technical Field

[0001] This invention relates to the field of training support data management, and more specifically to a think tank data management system based on training support. Background Technology

[0002] Currently, in the field of training support, data management systems have been gradually applied to assist in the management of the training process. Existing systems typically include basic data collection and storage functions, such as collecting data on trainees, basic equipment ledgers, and site usage records through manual input or simple interface connections, and using a single relational database for data storage. In terms of data application, some systems can perform simple input and query of training plans and basic statistics on equipment inventory. A few systems are attempting to introduce simple statistical analysis methods, such as calculating basic indicators like training achievement rate and equipment integrity rate using Excel spreadsheets, providing preliminary data support for training support. Meanwhile, with the development of IoT technology, some training sites have begun to deploy a small number of sensors, such as site access control devices and equipment power status monitoring devices, to assist in acquiring dynamic data during the training process. However, the scope of data collection is relatively limited, mostly focusing on single elements (such as personnel attendance and equipment power status).

[0003] Although existing training support data management systems have basic data management capabilities, they still have many shortcomings in practical applications:

[0004] Existing systems often limit their analytical functions to basic statistics, such as simple quantitative statistics and percentage calculations, lacking in-depth data mining and intelligent analysis. For example, they cannot predict equipment failure rates or material consumption trends based on historical data, and it is difficult to accurately identify bottlenecks in training support (such as the root cause of low site utilization). Furthermore, the analysis results are mostly presented in tabular form, with low visualization and an inability to intuitively present data patterns, making it difficult for decision-makers to quickly obtain effective information.

[0005] The existing system cannot generate personalized training plans based on individual differences among trainees (such as physical fitness level and injury status). The training plans are mostly general templates with poor adaptability. Resource scheduling relies on human experience, which can easily lead to problems such as site conflicts and unreasonable equipment allocation (such as high-value equipment being used for low-priority training). It also cannot be dynamically adjusted according to real-time needs. The system lacks training effect prediction functions, making it impossible to predict the achievement of training goals in advance. Furthermore, it has not built an intelligent question-and-answer mechanism for human-machine collaboration, and the answers to training support-related questions rely on manual consultation, which is inefficient.

[0006] Existing systems cannot accurately allocate resources based on historical data and real-time needs. Resource allocation often adopts "average allocation" or "experience allocation" methods, which can easily lead to resource waste (such as idle equipment occupying storage costs) or resource shortages (such as insufficient training space during peak training periods). There is a lack of real-time resource status monitoring and dynamic adjustment mechanisms. When the resource status is abnormal (such as sudden equipment failure or insufficient material inventory), alarms cannot be triggered in time and adjustment plans cannot be automatically generated. It is necessary to rely on manual discovery and handling, resulting in a high risk of training support interruption and affecting training progress.

[0007] The existing system suffers from poor data flow between its functional modules (such as data acquisition, analysis, and application). Acquired data cannot be synchronized to the analysis module in real time, and analysis results cannot be promptly fed back to the application module. Furthermore, each module operates independently, lacking a collaborative mechanism. For example, adjustments to the training plan cannot be automatically synchronized to the resource scheduling module, leading to a mismatch between resource allocation and the training plan. Equipment malfunction information cannot be promptly linked to training plan adjustments, easily resulting in situations where training cannot proceed normally due to equipment problems. This prevents the formation of a closed data loop of "acquisition-analysis-application-control-feedback." Summary of the Invention

[0008] In order to overcome the above-mentioned technical problems, the purpose of this invention is to provide a think tank data management system based on training assurance to solve the defects of the prior art mentioned in the background.

[0009] The objective of this invention can be achieved through the following technical solutions:

[0010] A think tank data management system based on training assurance includes: a data acquisition and integration module, a data management and analysis module, an intelligent application and service module, a resource optimization and control module, and a data interaction hub;

[0011] The data acquisition and integration module is used to collect static and dynamic data related to training support throughout the entire lifecycle and all elements, and transmits the collected data to the data interaction center.

[0012] The data management and analysis module obtains data from the data interaction center, cleans, labels, and standardizes the data, and then performs trend analysis, performance evaluation, and bottleneck diagnosis through a preset intelligent algorithm to generate analysis results and feed them back to the data interaction center.

[0013] The intelligent application and service module retrieves the analysis results from the data interaction center to realize personalized training plan formulation, intelligent resource scheduling, training effect prediction and human-computer collaborative intelligent question and answer functions, and sends the feedback data generated during the application back to the data interaction center.

[0014] The resource optimization and control module, based on historical data and real-time demand data in the data interaction center, accurately configures and dynamically schedules training support resources such as training venues, equipment, energy, and coaches, and simultaneously synchronizes the resource scheduling results to the data interaction center, forming a closed-loop data flow.

[0015] As a further aspect of the present invention: the data acquisition and integration module includes a static data acquisition unit and a dynamic data acquisition unit;

[0016] The static data acquisition unit connects to the personnel management system and equipment ledger system through API interface to synchronize basic personnel information, equipment model and specification information, and site basic parameter information on a regular basis. It also provides manual entry forms on the web and mobile terminals and supports batch import of initial material inventory data in Excel format.

[0017] The dynamic data acquisition unit connects to IoT sensors deployed on the training ground, wearable devices worn by trainees, and training recording terminals via the MQTT protocol. It collects data in real time on the site utilization rate, equipment operating parameters, personnel physiological indicators, and training project execution progress. The acquisition frequency is adjusted from once per minute to once per 10 minutes according to the training scenario requirements.

[0018] As a further aspect of the present invention: the data management and analysis module includes a data preprocessing unit, a tag generation unit, and an intelligent analysis unit;

[0019] The data preprocessing unit employs a rule-based cleaning algorithm to fill in missing data with the mean or mark it as pending verification. For abnormal data exceeding preset thresholds, including but not limited to personnel heart rate > 200 beats / minute and equipment temperature > 80℃, alarms are triggered and the data is isolated. Simultaneously, the date format is standardized to "YYYY-MM-DD" and the equipment lifespan unit is standardized to "hours" according to a preset data dictionary. The tag generation unit automatically generates tags such as "high-frequency fault equipment" and "personnel who meet physical fitness standards" based on machine learning clustering algorithms. It also supports administrators to customize tags such as "key protection sites" and "emergency supplies" and establishes the association between tags and multi-dimensional data.

[0020] The intelligent analysis unit uses the ARIMA model to predict the time series of equipment failure rate and material consumption trends, employs the analytic hierarchy process to construct a weighted scoring system for training efficiency and resource efficiency, and uses the isolated forest algorithm combined with knowledge graphs to achieve anomaly detection and root cause analysis of training support bottlenecks.

[0021] As a further aspect of the present invention: the intelligent application and service module includes a training plan generation unit, a resource scheduling unit, an effect prediction unit, and an intelligent question answering unit;

[0022] The training plan generation unit is based on personnel profile data, including integrated physical fitness, historical performance, and injury records. It combines a rule engine (such as avoiding high-intensity jumping training for injured personnel) and a collaborative filtering recommendation algorithm to automatically generate weekly / monthly training plans. Coaches can drag and drop to adjust the duration and order of training items on the web. The system records the adjustment traces in real time and associates them with subsequent training performance changes.

[0023] The resource scheduling unit is based on a genetic algorithm, combined with training priority rules (assessment training takes precedence over regular training) and resource constraints (site capacity, equipment quantity, personnel time conflicts), and outputs a resource allocation table. When the scheduling demand exceeds the resource capacity, it automatically recommends alternative resource solutions of the same type.

[0024] The effect prediction unit uses the Gradient Boosting Tree (GBDT) model. It takes the current training plan and real-time status data of the personnel as input, and predicts the performance improvement and the probability of achieving the goal in the next 1-3 months. The prediction results are displayed in the form of a line graph.

[0025] The intelligent question-answering unit is based on a training-supported knowledge base and the BERT-QA natural language processing model. It supports conversational queries (such as "check tomorrow's site usage schedule" or "how to reduce the failure rate of a certain type of equipment"). Complex questions are automatically routed to the corresponding administrator for processing, and the system automatically updates the knowledge base after the administrator replies.

[0026] As a further aspect of the present invention: the resource optimization and control module includes a resource analysis unit, an optimization configuration unit, and a real-time control unit;

[0027] The resource analysis unit uses an association rule mining algorithm to analyze resource usage data from the past 1-3 years to identify resource waste points (such as idle equipment with a weekly usage time of less than 5 hours) and shortage risk points (such as insufficient drinking water reserves during high-temperature seasons).

[0028] The optimization configuration unit adopts a linear programming model with the goal of "maximizing resource utilization and minimizing support costs" to solve the optimal allocation scheme of training venues (matching capacity according to training type), material inventory (setting safety stock based on consumption trends), and coaches (matching training needs according to their areas of expertise).

[0029] The real-time control unit deploys a resource status monitoring dashboard to display data on site occupancy, equipment operating status, and material inventory in real time. When the data exceeds a preset threshold (e.g., site occupancy > 90% or material inventory below 20% of safety stock), a dual alarm is triggered via SMS and system pop-up. After the administrator confirms the control command, the system automatically synchronizes the adjustment plan to the mobile APPs of trainees, equipment administrators, and site dispatchers.

[0030] As a further aspect of this invention: the data interaction hub adopts a hybrid storage architecture combining relational databases, time-series databases, and unstructured databases; wherein, the MySQL relational database is used to store structured data such as basic personnel information and equipment ledgers, and is partitioned by department / equipment type; the InfluxDB time-series database is used to store dynamic time-series data such as site utilization rate and personnel physiological indicators, and is partitioned by hour / day; the MongoDB unstructured database is used to store training videos, equipment failure images, and training plan PDF files, and supports retrieval by file type and upload time; the data interaction hub deploys the Flink real-time computing framework to achieve millisecond-level synchronization and flow of data between modules, while using SSL / TLS encryption protocols to ensure data transmission security, and setting data access permissions based on a role-based access control model, so that different roles (trainees, coaches, administrators) can only view / operate data within their corresponding permission scope.

[0031] As a further aspect of the present invention: the IoT sensors include an infrared counting sensor deployed at the site entrance for counting the real-time number of people on site; a vibration sensor installed on key equipment components for monitoring the vibration frequency of the equipment operation; and a temperature and humidity sensor installed in the material warehouse for monitoring the storage environment. The data collected by the sensors is preprocessed (noise data filtered) by the edge gateway and then uploaded to the dynamic data acquisition unit via a 5G / wired network. The wearable device includes a smart bracelet and a smart training suit. The smart bracelet collects the person's heart rate, number of steps, and location information. The smart training suit collects muscle electrical signals and respiratory rate data through fabric sensors. The device and the system use the Bluetooth Low Energy (BLE) protocol for data transmission, and the battery life is not less than 72 hours.

[0032] As a further aspect of the present invention: the personnel profile data also includes training preference data, including but not limited to training items they are good at and their preference for training time, and environmental adaptation data, including but not limited to training performance in high temperature and low temperature environments. When generating a training plan, the training plan generation unit uses the training preference data and environmental adaptation data as weighting factors to prioritize training items that personnel are good at and avoid extreme environmental periods (such as the high temperature period from 12:00 to 14:00 in summer).

[0033] As a further aspect of the present invention, the resource optimization and regulation module also includes a regulation effect evaluation unit. The regulation effect evaluation unit compares the resource utilization rate (e.g., percentage increase in site utilization rate), training achievement rate (e.g., percentage of trainees whose performance improved), and guarantee cost (e.g., amount of reduction in equipment maintenance costs) before and after regulation, and generates a regulation effect score (0-100 points) using the fuzzy comprehensive evaluation method. The score result serves as the basis for subsequent optimization configuration model parameter adjustments.

[0034] The beneficial effects of this invention are:

[0035] This invention utilizes a static data acquisition unit that interfaces with multiple systems and supports batch import, while a dynamic data acquisition unit connects to IoT sensors and wearable devices to collect data on all elements, including personnel, equipment, sites, and materials, thus solving the problem of limited data collection scope in existing systems. Simultaneously, the data preprocessing unit employs a rule engine to automatically clean and unify data formats, and the tag generation unit automatically generates multi-dimensional tags, significantly reducing manual operations, lowering the probability of data duplication, missing data, and anomalies, and improving data quality. The data interaction hub adopts a hybrid storage architecture and a real-time computing framework to achieve real-time data synchronization and flow, ensuring data timeliness and providing a reliable data foundation for subsequent analysis and applications.

[0036] The intelligent analysis unit of this invention introduces multiple intelligent algorithms such as the ARIMA time series forecasting model, the analytic hierarchy process, and the isolated forest algorithm. It can not only accurately predict equipment failure rates and material consumption trends, but also construct a multi-dimensional performance evaluation system, accurately identify training and support bottlenecks and analyze their root causes, thus solving the problem of insufficient analysis depth in existing systems. At the same time, the analysis results are displayed through a visual dashboard (line graph, heat map, radar chart), which intuitively presents data patterns, making it easier for decision-makers to quickly obtain effective information and improve decision-making efficiency.

[0037] The training plan generation unit of this invention generates personalized training plans based on personnel profiling and intelligent algorithms, and supports flexible adjustments, solving the problem of strong universality but poor adaptability of training plans in existing systems; the resource scheduling unit, based on genetic algorithms and priority rules, realizes intelligent resource scheduling and conflict early warning, avoiding unreasonable resource allocation; the effect prediction unit can predict the achievement of training objectives in advance, providing a basis for training adjustments; the intelligent question answering unit, based on a natural language processing model, realizes rapid question answering and automatic knowledge base updates, improving service efficiency and meeting the intelligent needs of modern training support.

[0038] The resource analysis unit of this invention accurately identifies resource waste points and shortage risk points through association rule mining; the optimization configuration unit, based on a linear programming model, achieves optimal resource allocation, maximizing resource utilization and minimizing support costs, solving the problems of unreasonable resource allocation and serious waste in existing systems; the real-time control unit deploys a monitoring dashboard and a dual alarm mechanism, which can promptly detect abnormal resource states and automatically generate control plans. After administrator confirmation, the plans can be quickly synchronized to relevant personnel, realizing dynamic resource control, significantly reducing the risk of training support interruption, and ensuring smooth training.

[0039] This invention enables real-time data flow between modules through a data interaction hub. Data after training plan adjustments is automatically synchronized to the resource scheduling module, and equipment fault information is linked to the training plan adjustment module in real time, forming a data closed loop of "collection-analysis-application-control-feedback". At the same time, the modules can work together based on the data interaction hub. For example, training plan prediction triggers consumable demand prediction and equipment health check, and overtraining risk warning triggers medical resource preparation prompts. This solves the problem of independent modules and lack of linkage in existing systems, and improves the overall coordination and support efficiency of the system. Attached Figure Description

[0040] The invention will now be further described with reference to the accompanying drawings.

[0041] Figure 1 This is a schematic diagram of a think tank data management system based on training assurance, as described in this invention. Detailed Implementation

[0042] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1:

[0044] Please see Figure 1 As shown, this embodiment is a think tank data management system based on training assurance, including: a data acquisition and integration module, a data management and analysis module, an intelligent application and service module, a resource optimization and control module, and a data interaction hub;

[0045] The data acquisition and integration module is used to collect static and dynamic data related to training support throughout the entire lifecycle and all elements, and transmits the collected data to the data interaction center.

[0046] The data management and analysis module obtains data from the data interaction center, cleans, labels, and standardizes the data, and then performs trend analysis, performance evaluation, and bottleneck diagnosis through a preset intelligent algorithm to generate analysis results and feed them back to the data interaction center.

[0047] The intelligent application and service module retrieves the analysis results from the data interaction center to realize personalized training plan formulation, intelligent resource scheduling, training effect prediction and human-computer collaborative intelligent question and answer functions, and sends the feedback data generated during the application back to the data interaction center.

[0048] The resource optimization and control module, based on historical data and real-time demand data in the data interaction center, accurately configures and dynamically schedules training support resources such as training venues, equipment, energy, and coaches, and simultaneously synchronizes the resource scheduling results to the data interaction center, forming a closed-loop data flow.

[0049] The data acquisition and integration module includes a static data acquisition unit and a dynamic data acquisition unit;

[0050] The static data acquisition unit connects to the personnel management system and equipment ledger system through API interface to synchronize basic personnel information, equipment model and specification information, and site basic parameter information on a regular basis. It also provides manual entry forms on the web and mobile terminals and supports batch import of initial material inventory data in Excel format.

[0051] The dynamic data acquisition unit connects to IoT sensors deployed on the training ground, wearable devices worn by trainees, and training recording terminals via the MQTT protocol. It collects data in real time on the site utilization rate, equipment operating parameters, personnel physiological indicators, and training project execution progress. The acquisition frequency is adjusted from once per minute to once per 10 minutes according to the training scenario requirements.

[0052] The data management and analysis module includes a data preprocessing unit, a tag generation unit, and an intelligent analysis unit;

[0053] The data preprocessing unit employs a rule-based cleaning algorithm to fill in missing data with the mean or mark it as pending verification. For abnormal data exceeding preset thresholds, including but not limited to personnel heart rate > 200 beats / minute and equipment temperature > 80℃, alarms are triggered and the data is isolated. Simultaneously, the date format is standardized to "YYYY-MM-DD" and the equipment lifespan unit is standardized to "hours" according to a preset data dictionary. The tag generation unit automatically generates tags such as "high-frequency fault equipment" and "personnel who meet physical fitness standards" based on machine learning clustering algorithms. It also supports administrators to customize tags such as "key protection sites" and "emergency supplies" and establishes the association between tags and multi-dimensional data.

[0054] The intelligent analysis unit uses the ARIMA model to predict the time series of equipment failure rate and material consumption trends, employs the analytic hierarchy process to construct a weighted scoring system for training efficiency and resource efficiency, and uses the isolated forest algorithm combined with knowledge graphs to achieve anomaly detection and root cause analysis of training support bottlenecks.

[0055] The intelligent application and service module includes a training plan generation unit, a resource scheduling unit, an effect prediction unit, and an intelligent question answering unit.

[0056] The training plan generation unit is based on personnel profile data, including integrated physical fitness, historical performance, and injury records. It combines a rule engine (such as avoiding high-intensity jumping training for injured personnel) and a collaborative filtering recommendation algorithm to automatically generate weekly / monthly training plans. Coaches can drag and drop to adjust the duration and order of training items on the web. The system records the adjustment traces in real time and associates them with subsequent training performance changes.

[0057] The resource scheduling unit is based on a genetic algorithm, combined with training priority rules (assessment training takes precedence over regular training) and resource constraints (site capacity, equipment quantity, personnel time conflicts), and outputs a resource allocation table. When the scheduling demand exceeds the resource capacity, it automatically recommends alternative resource solutions of the same type.

[0058] The effect prediction unit uses the Gradient Boosting Tree (GBDT) model. It takes the current training plan and real-time status data of the personnel as input, and predicts the performance improvement and the probability of achieving the goal in the next 1-3 months. The prediction results are displayed in the form of a line graph.

[0059] The intelligent question-answering unit is based on a training-supported knowledge base and the BERT-QA natural language processing model. It supports conversational queries (such as "check tomorrow's site usage schedule" or "how to reduce the failure rate of a certain type of equipment"). Complex questions are automatically routed to the corresponding administrator for processing, and the system automatically updates the knowledge base after the administrator replies.

[0060] The resource optimization and control module includes a resource analysis unit, an optimization configuration unit, and a real-time control unit.

[0061] The resource analysis unit uses an association rule mining algorithm to analyze resource usage data from the past 1-3 years to identify resource waste points (such as idle equipment with a weekly usage time of less than 5 hours) and shortage risk points (such as insufficient drinking water reserves during high-temperature seasons).

[0062] The optimization configuration unit adopts a linear programming model with the goal of "maximizing resource utilization and minimizing support costs" to solve the optimal allocation scheme of training venues (matching capacity according to training type), material inventory (setting safety stock based on consumption trends), and coaches (matching training needs according to their areas of expertise).

[0063] The real-time control unit deploys a resource status monitoring dashboard to display data on site occupancy, equipment operating status, and material inventory in real time. When the data exceeds a preset threshold (e.g., site occupancy > 90% or material inventory below 20% of safety stock), a dual alarm is triggered via SMS and system pop-up. After the administrator confirms the control command, the system automatically synchronizes the adjustment plan to the mobile APPs of trainees, equipment administrators, and site dispatchers.

[0064] The data interaction hub adopts a hybrid storage architecture combining relational databases, time-series databases, and unstructured databases. Specifically, the MySQL relational database stores structured data such as basic personnel information and equipment ledgers, partitioned by department / equipment type; the InfluxDB time-series database stores dynamic time-series data such as site utilization and personnel physiological indicators, partitioned by hour / day; and the MongoDB unstructured database stores training videos, equipment malfunction images, and training plan PDF files, supporting retrieval by file type and upload time. The data interaction hub deploys the Flink real-time computing framework to achieve millisecond-level synchronization and flow of data between modules. SSL / TLS encryption protocols ensure data transmission security, and a role-based access control model sets data access permissions, allowing different roles (trainees, instructors, administrators) to view / operate data only within their corresponding permission scope.

[0065] The IoT sensors include an infrared counting sensor deployed at the site entrance to count the number of people in real time; vibration sensors installed on key equipment components to monitor the vibration frequency of the equipment; and temperature and humidity sensors installed in the material warehouse to monitor the storage environment. The data collected by the sensors is preprocessed (noise filtering) by the edge gateway and then uploaded to the dynamic data acquisition unit via 5G / wired network. The wearable devices include a smart bracelet and smart training clothing. The smart bracelet collects the person's heart rate, number of steps, and location information. The smart training clothing collects muscle electrical signals and respiratory rate data through fabric sensors. Data transmission between the devices and the system is performed using the Bluetooth Low Energy (BLE) protocol, and the battery life is no less than 72 hours.

[0066] The personnel profile data also includes training preference data, including but not limited to training items they are good at and their preference for training time, and environmental adaptation data, including but not limited to training performance in high-temperature and low-temperature environments. When generating a training plan, the training preference data and environmental adaptation data are used as weighting factors to prioritize training items that personnel are good at and avoid extreme environmental periods (such as the high-temperature period from 12:00 to 14:00 in summer).

[0067] The resource optimization and regulation module also includes a regulation effect evaluation unit. The regulation effect evaluation unit compares the resource utilization rate (such as the percentage increase in site utilization rate), training achievement rate (such as the percentage of trainees whose performance improved), and support cost (such as the amount of reduction in equipment maintenance costs) before and after regulation, and generates a regulation effect score (0-100 points) using the fuzzy comprehensive evaluation method. The score result serves as the basis for subsequent optimization configuration model parameter adjustments.

[0068] Example 2:

[0069] The specific processing steps of a think tank data management system based on training assurance are as follows:

[0070] S1. Initialization Configuration:

[0071] S11. The administrator logs into the system and sets core rules, including training task priority (e.g., assessment > drill > daily), abnormal data threshold (e.g., alarm when heart rate exceeds 200 beats / minute), and data collection frequency (e.g., 1 time / minute for drills, 1 time / 5 minutes for daily).

[0072] S12. Import basic data: Upload static data such as personnel information, equipment ledger, and site parameters, and initialize the training support knowledge base (such as equipment manuals and first aid guidelines);

[0073] S13. Assign role permissions: Assign corresponding operation permissions to trainees, coaches, equipment administrators, etc. (e.g., trainees can only view personal data, and coaches can create training plans).

[0074] S2, Full-Element Data Collection:

[0075] S21. Automatic data collection: IoT sensors (number of people on site, equipment vibration) and wearable devices (heart rate, movement trajectory) upload dynamic data in real time, and the system automatically synchronizes the static update data of the personnel and equipment systems.

[0076] S22. Manual Supplement: Temporary data (such as newly added emergency supplies or temporary sites) is uploaded by the corresponding personnel (supply administrators, site dispatchers) by filling out a form on their mobile devices, and the system automatically verifies and stores it.

[0077] Step S3: Automatic Data Processing and Analysis

[0078] S31. Data cleaning: The system removes duplicate data, completes missing data, marks abnormal data (such as heart rate exceeding the threshold) and issues an alarm in real time;

[0079] S32. Intelligent Analysis: Automatically generate data tags (such as "high-frequency equipment failure" and "personnel with excellent physical fitness"), predict equipment failure and material consumption trends, evaluate training effectiveness (pass rate and resource utilization rate), and diagnose support bottlenecks (such as the reasons for low site utilization).

[0080] S4, Smart Application Deployment (Multi-role On-Demand Operation)

[0081] S41. Develop a training plan: The coach selects the training group, and the system automatically generates a plan based on the personnel profile (physical fitness, injuries). The coach can make minor adjustments and confirm the plan, which is then synchronized to the training personnel.

[0082] S42. Resource scheduling: The system automatically allocates venues, equipment, and coaches according to the training plan. When there are conflicts, it recommends alternative solutions, and locks resources after administrator confirmation.

[0083] S43. Effect Prediction and Q&A: The system predicts the probability of achieving the training objective, supports conversational queries (such as "check tomorrow's venue"), and automatically forwards complex questions to the administrator for answers.

[0084] S5. Dynamic resource control (system alarm + manual confirmation)

[0085] S51. Real-time monitoring: Administrators can view site occupancy, equipment status, and material inventory through the dashboard. Data is refreshed every 30 seconds.

[0086] S51. Abnormal Control: When data exceeds the threshold (e.g., venue is full, supplies are insufficient), the system sends an SMS and pop-up alarm, and the administrator issues adjustment instructions (e.g., adjust venue, replenish supplies), which are then synchronized to relevant personnel.

[0087] S6. Monthly maintenance report generated: including data such as training compliance rate and resource utilization rate, and collection of user suggestions (such as optimizing fault warning).

[0088] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0089] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A think tank data management system based on training assurance, characterized in that, include: The system includes a data acquisition and integration module, a data management and analysis module, an intelligent application and service module, a resource optimization and control module, and a data interaction hub. The data acquisition and integration module is used to collect static and dynamic data related to training support throughout the entire lifecycle and all elements, and transmits the collected data to the data interaction center. The data management and analysis module obtains data from the data interaction center, cleans, labels, and standardizes the data, and then performs trend analysis, performance evaluation, and bottleneck diagnosis through a preset intelligent algorithm to generate analysis results and feed them back to the data interaction center. The intelligent application and service module retrieves the analysis results from the data interaction center to realize personalized training plan formulation, intelligent resource scheduling, training effect prediction and human-computer collaborative intelligent question and answer functions, and sends the feedback data generated during the application back to the data interaction center. The resource optimization and control module, based on historical data and real-time demand data in the data interaction center, accurately configures and dynamically schedules training support resources such as training venues, equipment, energy, and coaches, and simultaneously synchronizes the resource scheduling results to the data interaction center, forming a closed-loop data flow.

2. The think tank data management system based on training assurance according to claim 1, characterized in that, The data acquisition and integration module includes a static data acquisition unit and a dynamic data acquisition unit; The static data acquisition unit connects to the personnel management system and equipment ledger system through API interface to synchronize basic personnel information, equipment model and specification information, and site basic parameter information on a regular basis. It also provides manual entry forms on the web and mobile terminals and supports batch import of initial material inventory data in Excel format. The dynamic data acquisition unit connects to IoT sensors deployed on the training ground, wearable devices worn by trainees, and training recording terminals via the MQTT protocol. It collects data in real time on the site utilization rate, equipment operating parameters, personnel physiological indicators, and training project execution progress. The acquisition frequency is adjusted from once per minute to once per 10 minutes according to the training scenario requirements.

3. The think tank data management system based on training assurance according to claim 1, characterized in that, The data management and analysis module includes a data preprocessing unit, a tag generation unit, and an intelligent analysis unit; The data preprocessing unit employs a rule-based cleaning algorithm to fill in missing data with the mean or mark it as pending verification. For abnormal data exceeding preset thresholds, including but not limited to personnel heart rate > 200 beats / minute and equipment temperature > 80℃, an alarm is triggered and the data is isolated. Simultaneously, the date format is standardized to "YYYY-MM-DD" and the equipment lifespan unit is standardized to "hours" according to a preset data dictionary. The tag generation unit automatically generates tags such as "high-frequency fault equipment" and "personnel who meet physical fitness standards" based on machine learning clustering algorithms. It also supports administrators to customize tags such as "key protection sites" and "emergency supplies" and establishes the association between tags and multi-dimensional data. The intelligent analysis unit uses the ARIMA model to predict the time series of equipment failure rate and material consumption trends, employs the analytic hierarchy process to construct a weighted scoring system for training efficiency and resource efficiency, and uses the isolated forest algorithm combined with knowledge graphs to achieve anomaly detection and root cause analysis of training support bottlenecks.

4. The think tank data management system based on training assurance according to claim 1, characterized in that, The intelligent application and service module includes a training plan generation unit, a resource scheduling unit, an effect prediction unit, and an intelligent question answering unit. The training plan generation unit is based on personnel profile data, including physical fitness, historical performance, and injury records. It combines a rule engine and collaborative filtering recommendation algorithm to automatically generate weekly / monthly training plans. Coaches can drag and drop to adjust the duration and order of training items on the web. The system records the adjustment traces in real time and associates them with subsequent training performance changes. The resource scheduling unit is based on a genetic algorithm, combined with training priority rules and resource constraints, to output a resource allocation table. When the scheduling demand exceeds the resource capacity, it automatically recommends alternative resource solutions of the same type. The effect prediction unit uses the Gradient Boosting Tree (GBDT) model. It takes the current training plan and real-time status data of the personnel as input, and predicts the performance improvement and the probability of achieving the goal in the next 1-3 months. The prediction results are displayed in the form of a line graph. The intelligent question-answering unit is based on a training-guaranteed knowledge base and the BERT-QA natural language processing model. It supports conversational queries, and complex questions are automatically routed to the corresponding administrators for processing. After the administrators reply, the system automatically updates the knowledge base.

5. A think tank data management system based on training assurance according to claim 1, characterized in that, The resource optimization and control module includes a resource analysis unit, an optimization configuration unit, and a real-time control unit. The resource analysis unit uses an association rule mining algorithm to analyze resource usage data from the past 1-3 years to identify resource waste points and shortage risk points. The optimization configuration unit adopts a linear programming model with the goal of "maximizing resource utilization and minimizing support costs" to solve the optimal allocation scheme of training venues (matching capacity according to training type), material inventory, and coaches. The real-time control unit deploys a resource status monitoring dashboard to display data on site occupancy, equipment operating status, and material inventory in real time. When the data exceeds a preset threshold, a dual alarm is triggered via SMS and system pop-up. After the administrator confirms the control command, the system automatically synchronizes the adjustment plan to the mobile apps of trainees, equipment administrators, and site dispatchers.

6. The think tank data management system based on training assurance according to claim 1, characterized in that, The data interaction hub adopts a hybrid storage architecture combining relational databases, time-series databases, and unstructured databases. The MySQL relational database stores structured data such as basic personnel information and equipment ledgers, partitioned by department / equipment type. The InfluxDB time-series database stores dynamic time-series data such as site utilization and personnel physiological indicators, partitioned by hour / day. The MongoDB unstructured database stores training videos, equipment failure images, and training plan PDF files, supporting retrieval by file type and upload time. The data interaction hub deploys the Flink real-time computing framework to achieve millisecond-level synchronization and flow of data between modules. SSL / TLS encryption protocols ensure data transmission security, and a role-based access control model sets data access permissions, allowing different roles to view / operate data only within their corresponding permission scope.

7. A think tank data management system based on training assurance according to claim 2, characterized in that, The IoT sensors include an infrared counting sensor deployed at the site entrance to count the number of people in real time, vibration sensors installed on key equipment components to monitor the vibration frequency of the equipment, and temperature and humidity sensors installed in the material warehouse to monitor the storage environment. The data collected by the sensors is preprocessed by the edge gateway and then uploaded to the dynamic data acquisition unit via 5G / wired network. The wearable devices include a smart bracelet and a smart training suit. The smart bracelet collects the person's heart rate, number of steps, and location information. The smart training suit collects muscle electrical signals and respiratory rate data through fabric sensors. The devices and the system use Bluetooth Low Energy (BLE) protocol for data transmission, and the battery life is no less than 72 hours.

8. A think tank data management system based on training assurance according to claim 4, characterized in that, The personnel profile data also includes training preference data, including but not limited to training items they are good at and their preference for training time, and environmental adaptation data, including but not limited to training performance in high-temperature and low-temperature environments. When generating a training plan, the training preference data and environmental adaptation data are used as weighting factors to prioritize training items that personnel are good at and avoid extreme environmental periods.

9. A think tank data management system based on training assurance according to claim 5, characterized in that, The resource optimization and regulation module also includes a regulation effect evaluation unit. The regulation effect evaluation unit compares the resource utilization rate, training achievement rate and guarantee cost data before and after regulation, and generates a regulation effect score using the fuzzy comprehensive evaluation method. The score result serves as the basis for subsequent optimization configuration model parameter adjustment.

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