Intelligent prediction method and system based on thermal working area and maintenance range

Abstract

The invention provides an intelligent prediction method and system based on a thermal working area and a maintenance range. The method comprises the following steps: S1, acquiring point location data of a thermal working area in real time, and uploading the point location data to a server through a data transmission protocol; s2, storing point location data by using a time sequence database, and performing data management based on a timestamp, a tag field and a measurement value; s3, performing incremental training on historical data and real-time data based on a machine learning model, and dynamically predicting a maintenance loss risk prediction result; s4, dynamically loading the thermodynamic point location data according to the zoom level of the user interface map; s5, generating a resource allocation strategy based on a prediction result, and dynamically scheduling maintenance resources according to risk levels; the invention has the following advantages; 1, the real-time performance of data acquisition and processing is enhanced; 2, the intelligence and accuracy of the prediction algorithm are improved; 3, high efficiency of thermodynamic point location data display; and 4, the expansibility and the performance of database design are optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent operation and maintenance and prediction analysis, and more specifically, relates to an intelligent prediction method and system based on thermal working areas and maintenance ranges. Background Art

[0002] In modern energy management and infrastructure maintenance, monitoring of thermal work areas and management of facility maintenance are crucial. Traditional maintenance management methods rely on periodic inspections and manual judgment, which presents the following problems:

[0003] 1. Insufficient data acquisition: The lack of real-time thermal distribution data makes it difficult to accurately locate high-risk areas.

[0004] 2. Limited predictive capabilities: Assessments of the extent of disrepair are primarily based on historical data and static models, making it impossible to dynamically adjust prediction strategies.

[0005] 3. Serious waste of resources: Maintenance plans are difficult to match with actual needs, resulting in inefficient allocation of human and material resources.

[0006] To improve facility maintenance efficiency and reliability, intelligent predictive technology has become a research hotspot. By integrating technologies such as artificial intelligence and big data analysis, accurate predictions of thermal zones and areas of maintenance disrepair can be achieved, thereby optimizing management decisions and resource allocation.

[0007] Existing technologies include: 1. A thermal monitoring and management system that uses thermal imaging technology or temperature sensor layout to monitor regional thermal distribution and generate thermal distribution maps. However, these systems have limitations: they lack intelligent prediction capabilities and cannot assess the scope of future disrepair risks; 2. A facility maintenance optimization system based on big data analysis that uses historical data to model the failure modes and maintenance cycles of facilities and optimize maintenance plans. These systems also have limitations: they have a low reliance on real-time data and cannot dynamically respond to changes in thermal areas and the expansion of disrepair areas.

[0008] The existing technology has the following problems:

[0009] 1. Challenges posed by growing data volumes in storage and processing: As the volume of data reported by operators in real time increases, higher requirements may be placed on database storage capacity, query efficiency, and system performance, necessitating the design of more efficient storage structures and expansion mechanisms.

[0010] 2. Optimizing algorithm interface query efficiency: The rapid growth of data volume may lead to reduced efficiency of algorithm interfaces when executing complex queries and calculations, affecting system response speed. Algorithm implementation and query logic need to be optimized to ensure real-time performance and accuracy under large-scale data.

[0011] 3. Bottlenecks in thermal point data visualization performance: As thermal point data increases, existing display methods may face bottlenecks in rendering speed and user experience. Further optimization of data screening, hierarchical loading, and visualization is needed to ensure smooth and accurate display effects. Summary of the Invention

[0012] Therefore, in order to solve the above technical problems, the present invention proposes an intelligent prediction method and system based on thermal working areas and maintenance scopes, which specifically include the following steps; S1, real-time collection of point data of thermal working areas, and uploading to the server through a data transmission protocol; S2, using a time series database to store the point data, and performing data management based on timestamps, label fields and measurement values, and performing batch writing, data partitioning, and hot and cold data separation storage and compression archiving; S3, incremental training of historical data and real-time data based on a machine learning model, and dynamic prediction of maintenance disrepair risk prediction results; S4, dynamic loading of thermal point data according to the zoom level of the user interface map, loading spatial aggregation data at a high zoom level, loading original point data at a low zoom level, and using WebGL technology to achieve GPU accelerated rendering; S5, generating a resource allocation strategy based on the prediction results, dynamically scheduling maintenance resources according to the risk level, and feeding back scheduling instructions through a visualization platform; the advantages of the present invention are: 1. Enhanced real-time performance of data acquisition and processing; 2. Improved intelligence and accuracy of the prediction algorithm; 3. Efficiency of thermal point data display; 4. Scalability and performance optimization of database design; 5. Refinement and efficiency of resource management; 6. Modularity and ease of use of system architecture.

[0013] An intelligent prediction method based on thermal working area and maintenance range includes the following steps:

[0014] S1, collects point data of the thermal working area in real time and uploads it to the server through the data transmission protocol;

[0015] S2, using a time series database to store the point data, and performing data management based on timestamps, tag fields, and measurement values, and performing batch writing, data partitioning, and separate storage and compression archiving of hot and cold data;

[0016] S3, based on machine learning models, conducts incremental training on historical data and real-time data to dynamically predict maintenance and disrepair risk prediction results;

[0017] S4 dynamically loads thermal point data according to the zoom level of the user interface map, loads spatially aggregated data at high zoom levels, and loads raw point data at low zoom levels, and uses WebGL technology to achieve GPU accelerated rendering;

[0018] S5 generates a resource allocation strategy based on the prediction results, dynamically schedules and maintains resources according to risk levels, and feeds back scheduling instructions through a visualization platform.

[0019] Furthermore, in S1, the point data includes point ID, area ID, longitude and latitude information, status parameters and timestamp.

[0020] Furthermore, in S1, the data transmission protocol transmits the point data to the server through the API interface, and the data transmission protocol adopts HTTP / 2, and performs integrity verification and abnormal data filtering on the uploaded point data through the data verification module. The abnormal data filtering includes automatic elimination of geographic location offsets exceeding a preset threshold, duplicate reported points and invalid timestamps.

[0021] Furthermore, in S2, the time series database is OpenTSDB, InfluxDB or TimescaleDB, and is configured with a label index and a time partitioning strategy. Indexes are established for high-frequency query fields to improve query efficiency. The label index establishes a composite index for the region ID, device type and status label, supporting multi-dimensional joint queries. The time partitioning strategy divides the data storage interval by day or week, and automatically archives expired data in combination with a rolling window mechanism.

[0022] Furthermore, in S2, the hot and cold data separation strategy stores frequently accessed hot data in SSD devices, regularly archives infrequent cold data to low-cost storage media, and automatically migrates it to AWS S3 or HDFS when the data access frequency falls below a set threshold (such as no access within 30 days).

[0023] Furthermore, in S3, the machine learning model includes regression analysis, time series prediction or neural network, which only trains new data through an incremental update mechanism, and dynamically corrects the prediction results by combining historical trends and real-time changes. The incremental update mechanism uses an online learning algorithm to train the new data stream in real time, and regularly integrates historical data sets for model recalibration.

[0024] Furthermore, in S3, the prediction results are fed back to the management personnel in real time through the visualization platform, and the utilization efficiency of maintenance resources is optimized in combination with the work order allocation algorithm. The work order allocation algorithm generates the optimal work order sequence based on the predicted risk value, maintenance personnel skill matching and equipment availability, and optimizes the scheduling path through the genetic algorithm.

[0025] Furthermore, the specific method of the incremental training in S3 is:

[0026] S3.1, filter the newly added point data by time window and calculate the feature difference with the historical data;

[0027] S3.2, triggers local model training only for incremental data with a difference higher than the threshold, and updates the weights of the neural network output layer;

[0028] S3.3, retrain the model on the entire data set once a week to ensure consistent prediction accuracy.

[0029] Furthermore, in step S4, the spatial aggregation adopts a quadtree algorithm, and the aggregation rule is:

[0030] S4.1 Set the aggregation granularity threshold according to the map zoom level. When the point density exceeds the threshold, it is merged into an aggregated heat point according to the geographic grid;

[0031] S4.2 The numerical weight of the aggregated thermal point is generated by weighted calculation of the original point state parameters;

[0032] S4.3 uses TopoJSON format to compress and transmit data, reducing network bandwidth usage.

[0033] Furthermore, in S5, the resource allocation strategy includes dividing maintenance priorities based on risk levels, and feeding back scheduling results through a real-time monitoring module to support manual intervention and dynamic adjustment. The risk level division is based on a comprehensive calculation of priority scores based on the predicted probability of disrepair (e.g., >80% is high risk), the fluctuation range of heat demand, and the historical failure frequency.

[0034] An intelligent prediction system based on thermal working area and maintenance scope, including:

[0035] Data acquisition module: configured to collect and upload thermal point data in real time via mobile terminals, supporting data integrity verification and anomaly filtering;

[0036] Time series database module: uses InfluxDB to store data, integrates tag indexing, time partitioning, and hot and cold separation strategies, and supports batch writing and asynchronous compression;

[0037] Intelligent prediction engine: Built-in machine learning model for dynamic analysis of historical and real-time data, output of maintenance and disrepair risk prediction results, and support for incremental updates;

[0038] Visualization platform: Based on the WebGL rendering engine, it realizes hierarchical loading and spatial aggregation display of thermal points, and provides an interactive interface for resource scheduling;

[0039] Resource allocation module: Generates maintenance work orders based on prediction results and dynamically allocates human, equipment and material resources according to risk levels.

[0040] Furthermore, the data acquisition module interacts with the intelligent prediction engine through a RESTful API, and a token bucket current limiting mechanism is used for data upload to ensure transmission stability in high-concurrency scenarios.

[0041] Furthermore, it also includes a data security sub-module, which is used to: implement AES-256 encryption on point data in transmission; isolate data access by role permissions in the time series database and record operation logs; and desensitize sensitive information in the prediction results.

[0042] Beneficial effects of the invention: The present invention proposes an intelligent prediction method and system based on thermal working areas and maintenance scopes, which specifically include the following steps: S1, real-time collection of point data of thermal working areas, and uploading to a server through a data transmission protocol; S2, using a time series database to store the point data, and performing data management based on timestamps, tag fields, and measurement values, and performing batch writing, data partitioning, and separate storage and compression archiving of hot and cold data; S3, performing incremental training on historical data and real-time data based on a machine learning model, and dynamically predicting maintenance disrepair risk prediction results; S4, dynamically loading thermal point data according to the zoom level of the user interface map, and loading spatial aggregation data at a high zoom level, Load the original point data at a low zoom level and use WebGL technology to achieve GPU accelerated rendering; S5, generate a resource allocation strategy based on the prediction results, dynamically schedule maintenance resources according to the risk level, and feedback the scheduling instructions through the visualization platform; the advantages of the present invention are: 1. Enhanced real-time performance of data acquisition and processing: through the real-time reporting of the operator's point information combined with the time series database, the real-time performance of data acquisition is ensured, and the overall response speed of the system is improved through efficient data preprocessing and batch writing mechanisms; 2. Improved intelligence and accuracy of the prediction algorithm: An intelligent prediction algorithm based on multidimensional data analysis is introduced, combining real-time data with historical trends to dynamically evaluate the scope of maintenance disrepair and future risks. The model supports incremental updates to ensure that the prediction results are more accurate and real-time; 3. Efficiency of thermal point data display: The display performance of thermal points is optimized using technologies such as hierarchical loading, spatial aggregation and GPU acceleration. Real-time rendering and dynamic filtering significantly enhance user experience and system scalability. 4. Database design scalability and performance optimization: Using a time-series database as the storage core, combined with strategies such as data partitioning, hot and cold data separation, and compressed storage, significantly improves the system's storage efficiency and query performance under massive data conditions. 5. Refined and efficient resource management: Incorporating intelligent prediction results, optimized resource allocation strategies are provided for different thermal work areas and risk levels, enabling refined management of maintenance resources and improving resource utilization efficiency. 6. Modular and usable system architecture: Using a modular design, each part of the system (data collection, storage, prediction, and display) is independent of each other, facilitating expansion and maintenance. It also supports integration with other platforms and systems, enhancing applicability and usability. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1This is a flow chart of an intelligent prediction method and system based on thermal working area and maintenance range of the present invention.

[0044] Figure 2 This is a flow chart of an intelligent prediction method and system based on thermal working area and maintenance range of the present invention.

[0045] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0046] The following examples are described to assist in understanding the present application, and the examples are not and should not be interpreted in any way as limiting the scope of protection of the present application.

[0047] In the following description, those skilled in the art will recognize that throughout this discussion, components may be described as separate functional units (which may include sub-units), but those skilled in the art will recognize that various components or portions thereof may be divided into separate components or may be integrated together (including within a single system or component).

[0048] At the same time, the connections between components or systems are not intended to be limited to direct connections. Instead, data between these components may be modified, reformatted, or otherwise changed by intermediate components. In addition, additional or fewer connections may be used. It should also be noted that the terms "coupled," "connected," or "input" should be understood to include direct connections, indirect connections through one or more intermediate devices, and wireless connections. Example 1:

[0049] like Figure 1 The figure shows a flow chart of an intelligent prediction method and system based on thermal working area and maintenance range of the present invention.

[0050] like Figure 2 The figure shows a flow chart of an intelligent prediction method and system based on thermal working area and maintenance range of the present invention.

[0051] An intelligent prediction method based on thermal working area and maintenance range includes the following steps:

[0052] S1, collects point data of the thermal working area in real time and uploads it to the server through the data transmission protocol;

[0053] S2, using a time series database to store the point data, and performing data management based on timestamps, tag fields, and measurement values, and performing batch writing, data partitioning, and separate storage and compression archiving of hot and cold data;

[0054] S3, based on machine learning models, conducts incremental training on historical data and real-time data to dynamically predict maintenance and disrepair risk prediction results;

[0055] S4 dynamically loads thermal point data according to the zoom level of the user interface map, loads spatially aggregated data at high zoom levels, and loads raw point data at low zoom levels, and uses WebGL technology to achieve GPU accelerated rendering;

[0056] S5 generates a resource allocation strategy based on the prediction results, dynamically schedules and maintains resources according to risk levels, and feeds back scheduling instructions through a visualization platform.

[0057] In S1, the point data includes point ID, area ID, longitude and latitude information, status parameters and timestamp.

[0058] In S1, the data transmission protocol transmits the point data to the server through the API interface, and the data transmission protocol adopts HTTP / 2, and the uploaded point data is integrity checked and abnormal data filtered through the data verification module. The abnormal data filtering includes automatic elimination of geographic location offsets exceeding a preset threshold, duplicate reported points, and invalid timestamps.

[0059] In S2, the time series database is OpenTSDB, InfluxDB or TimescaleDB, and the tag index and time partitioning strategy are configured to index high-frequency query fields to improve query efficiency. The tag index establishes a composite index for the region ID, device type and status tag, supporting multi-dimensional joint queries. The time partitioning strategy divides the data storage interval by day or week, and combines the rolling window mechanism to automatically archive expired data.

[0060] In S2, the hot and cold data separation strategy stores frequently accessed hot data on SSD devices, regularly archives infrequent cold data to low-cost storage media, and automatically migrates it to AWS S3 or HDFS when the data access frequency falls below a set threshold (such as no access within 30 days).

[0061] In S3, the machine learning model includes regression analysis, time series prediction or neural network, which only trains new data through an incremental update mechanism, and dynamically corrects the prediction results by combining historical trends and real-time changes. The incremental update mechanism uses an online learning algorithm to train the new data stream in real time and regularly integrates historical data sets for model recalibration.

[0062] In S3, the prediction results are fed back to management personnel in real time through a visualization platform, and the efficiency of maintenance resource utilization is optimized in combination with a work order allocation algorithm. The work order allocation algorithm generates an optimal work order sequence based on the predicted risk value, maintenance personnel skill matching, and equipment availability, and optimizes the scheduling path through a genetic algorithm.

[0063] The specific method of incremental training described in S3 is:

[0064] S3.1, filter the newly added point data by time window and calculate the feature difference with the historical data;

[0065] S3.2, triggers local model training only for incremental data with a difference higher than the threshold, and updates the weights of the neural network output layer;

[0066] S3.3, retrain the model on the entire data set once a week to ensure consistent prediction accuracy.

[0067] In step S4, the spatial aggregation adopts the quadtree algorithm, and the aggregation rule is:

[0068] S4.1 Set the aggregation granularity threshold according to the map zoom level. When the point density exceeds the threshold, it is merged into an aggregated heat point according to the geographic grid;

[0069] S4.2 The numerical weight of the aggregated thermal point is generated by weighted calculation of the original point state parameters;

[0070] S4.3 uses TopoJSON format to compress and transmit data, reducing network bandwidth usage.

[0071] In S5, the resource allocation strategy includes dividing maintenance priorities based on risk levels, and feedback of scheduling results through a real-time monitoring module, supporting manual intervention and dynamic adjustment. The risk level division is based on a comprehensive calculation of priority scores based on the predicted probability of disrepair (e.g., >80% is high risk), the fluctuation range of heat demand, and the historical failure frequency.

[0072] An intelligent prediction system based on thermal working area and maintenance scope, including:

[0073] Data acquisition module: configured to collect and upload thermal point data in real time via mobile terminals, supporting data integrity verification and anomaly filtering;

[0074] Time series database module: uses InfluxDB to store data, integrates tag indexing, time partitioning, and hot and cold separation strategies, and supports batch writing and asynchronous compression;

[0075] Intelligent prediction engine: Built-in machine learning model for dynamic analysis of historical and real-time data, output of maintenance and disrepair risk prediction results, and support for incremental updates;

[0076] Visualization platform: Based on the WebGL rendering engine, it realizes hierarchical loading and spatial aggregation display of thermal points, and provides an interactive interface for resource scheduling;

[0077] Resource allocation module: Generates maintenance work orders based on prediction results and dynamically allocates human, equipment and material resources according to risk levels.

[0078] The data acquisition module interacts with the intelligent prediction engine through a RESTful API, and data upload adopts a token bucket current limiting mechanism to ensure transmission stability in high concurrency scenarios.

[0079] It also includes a data security sub-module, which is used to: implement AES-256 encryption on point data in transmission; isolate data access by role permissions in the time series database and record operation logs; and desensitize sensitive information in prediction results.

[0080] Beneficial effects of the invention: The present invention proposes an intelligent prediction method and system based on thermal working areas and maintenance scopes, which specifically include the following steps: S1, real-time collection of point data of thermal working areas, and uploading to a server through a data transmission protocol; S2, using a time series database to store the point data, and performing data management based on timestamps, tag fields, and measurement values, and performing batch writing, data partitioning, and separate storage and compression archiving of hot and cold data; S3, performing incremental training on historical data and real-time data based on a machine learning model, and dynamically predicting maintenance disrepair risk prediction results; S4, dynamically loading thermal point data according to the zoom level of the user interface map, and loading spatial aggregation data at a high zoom level, Load the original point data at a low zoom level and use WebGL technology to achieve GPU accelerated rendering; S5, generate a resource allocation strategy based on the prediction results, dynamically schedule maintenance resources according to the risk level, and feedback the scheduling instructions through the visualization platform; the advantages of the present invention are: 1. Enhanced real-time performance of data acquisition and processing: through the real-time reporting of the operator's point information combined with the time series database, the real-time performance of data acquisition is ensured, and the overall response speed of the system is improved through efficient data preprocessing and batch writing mechanisms; 2. Improved intelligence and accuracy of the prediction algorithm: An intelligent prediction algorithm based on multidimensional data analysis is introduced, combining real-time data with historical trends to dynamically evaluate the scope of maintenance disrepair and future risks. The model supports incremental updates to ensure that the prediction results are more accurate and real-time; 3. Efficiency of thermal point data display: The display performance of thermal points is optimized using technologies such as hierarchical loading, spatial aggregation and GPU acceleration. Real-time rendering and dynamic filtering significantly enhance user experience and system scalability. 4. Database design scalability and performance optimization: Using a time-series database as the storage core, combined with strategies such as data partitioning, hot and cold data separation, and compressed storage, significantly improves the system's storage efficiency and query performance under massive data conditions. 5. Refined and efficient resource management: Incorporating intelligent prediction results, optimized resource allocation strategies are provided for different thermal work areas and risk levels, enabling refined management of maintenance resources and improving resource utilization efficiency. 6. Modular and usable system architecture: Using a modular design, each part of the system (data collection, storage, prediction, and display) is independent of each other, facilitating expansion and maintenance. It also supports integration with other platforms and systems, enhancing applicability and usability.

[0081] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. An intelligent prediction method based on thermal working area and maintenance range, characterized by: The following steps are included: S1, collects point data of the thermal working area in real time and uploads it to the server through the data transmission protocol; S2, using a time series database to store the point data, and performing data management based on timestamps, tag fields, and measurement values, and performing batch writing, data partitioning, and separate storage and compression archiving of hot and cold data; S3, based on machine learning models, conducts incremental training on historical data and real-time data to dynamically predict maintenance and disrepair risk prediction results; S4 dynamically loads thermal point data according to the zoom level of the user interface map, loads spatially aggregated data at high zoom levels, and loads raw point data at low zoom levels, and uses WebGL technology to achieve GPU accelerated rendering; S5 generates a resource allocation strategy based on the prediction results, dynamically schedules and maintains resources according to risk levels, and feeds back scheduling instructions through a visualization platform.

2. The intelligent prediction method based on thermal working area and maintenance range according to claim 1 is characterized by: In S2, the time series database is OpenTSDB, InfluxDB or TimescaleDB, and the tag index and time partitioning strategy are configured to index high-frequency query fields to improve query efficiency. The tag index establishes a composite index for the region ID, device type and status tag, supporting multi-dimensional joint queries. The time partitioning strategy divides the data storage interval by day or week, and combines the rolling window mechanism to automatically archive expired data.

3. The intelligent prediction method based on thermal working area and maintenance range according to claim 2 is characterized by: In S2, the hot and cold data separation strategy stores frequently accessed hot data on SSD devices, regularly archives infrequent cold data to low-cost storage media, and automatically migrates it to AWS S3 or HDFS when the data access frequency falls below a set threshold (such as no access within 30 days).

4. The intelligent prediction method based on thermal working area and maintenance range according to claim 1 is characterized by: In S3, the machine learning model includes regression analysis, time series prediction or neural network, which only trains new data through an incremental update mechanism, and dynamically corrects the prediction results by combining historical trends and real-time changes. The incremental update mechanism uses an online learning algorithm to train the new data stream in real time and regularly integrates historical data sets for model recalibration.

5. The intelligent prediction method based on thermal working area and maintenance range according to claim 4 is characterized by: In S3, the prediction results are fed back to management personnel in real time through a visualization platform, and the efficiency of maintenance resource utilization is optimized in combination with a work order allocation algorithm. The work order allocation algorithm generates an optimal work order sequence based on the predicted risk value, maintenance personnel skill matching, and equipment availability, and optimizes the scheduling path through a genetic algorithm.

6. The intelligent prediction method based on thermal working area and maintenance range according to claim 5 is characterized by: The specific method of incremental training described in S3 is: S3.1, filter the newly added point data by time window and calculate the feature difference with the historical data; S3.2, triggers local model training only for incremental data with a difference higher than the threshold, and updates the weights of the neural network output layer; S3.3, retrain the model on the entire data set once a week to ensure consistent prediction accuracy.

7. The intelligent prediction method based on thermal working area and maintenance range according to claim 1 is characterized by: In step S4, the spatial aggregation adopts the quadtree algorithm, and the aggregation rule is: S4.1 Set the aggregation granularity threshold according to the map zoom level. When the point density exceeds the threshold, it is merged into an aggregated heat point according to the geographic grid; S4.2 The numerical weight of the aggregated thermal point is generated by weighted calculation of the original point state parameters; S4.3 uses TopoJSON format to compress and transmit data, reducing network bandwidth usage.

8. The intelligent prediction method based on thermal working area and maintenance range according to claim 1 is characterized by: In S5, the resource allocation strategy includes dividing maintenance priorities based on risk levels, and feedback of scheduling results through a real-time monitoring module, supporting manual intervention and dynamic adjustment. The risk level division is based on a comprehensive calculation of priority scores based on the predicted probability of disrepair (e.g., >80% is high risk), the fluctuation range of heat demand, and the historical failure frequency.

9. An intelligent prediction system based on thermal working area and maintenance range, characterized by: include: Data acquisition module: configured to collect and upload thermal point data in real time via mobile terminals, supporting data integrity verification and anomaly filtering; Time series database module: uses InfluxDB to store data, integrates tag indexing, time partitioning, and hot and cold separation strategies, and supports batch writing and asynchronous compression; Intelligent prediction engine: Built-in machine learning model for dynamic analysis of historical and real-time data, output of maintenance and disrepair risk prediction results, and support for incremental updates; Visualization platform: Based on the WebGL rendering engine, it realizes hierarchical loading and spatial aggregation display of thermal points, and provides an interactive interface for resource scheduling; Resource allocation module: Generates maintenance work orders based on prediction results and dynamically allocates human, equipment and material resources according to risk levels.

10. The intelligent prediction system based on thermal working area and maintenance range according to claim 9 is characterized in that: It also includes a data security sub-module, which is used to: implement AES-256 encryption on point data in transmission; isolate data access by role permissions in the time series database and record operation logs; and desensitize sensitive information in prediction results.

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