Method for establishing smart heating data resource pool

Abstract

The present invention relates to the field of data pool technology, specifically a method for establishing a smart heating data resource pool, comprising the following steps: based on the target heating data acquisition environment, monitoring external environmental factor data, adjusting the sampling frequency of the heating data acquisition equipment, matching environmental changes, and obtaining data collection frequency adjustment results. In the present invention, by dynamically monitoring and adjusting the data acquisition frequency, the real-time responsiveness and acquisition efficiency of the data are improved; by directly monitoring external environmental factors, it is possible to flexibly adapt to environmental changes, ensure the accuracy and timeliness of data acquisition, and improve the speed of data access and storage efficiency based on the data migration strategy of the access frequency; by dynamically adjusting the storage capacity, it effectively copes with the challenges brought by data volume fluctuations; real-time monitoring and anomaly detection of data streams enhance reliability and security; and by instantly identifying potential data anomalies and faults, it reduces the complexity and cost of maintenance.

Description

Technical Field

[0001] The present invention relates to the field of data pool technology, and in particular to a method for establishing a smart heating data resource pool. Background Art

[0002] The field of data pooling encompasses a range of technologies and methodologies for designing and managing data resources to support data-driven decision-making and intelligent automated control systems. This area focuses on data integration, storage, management, and optimization to improve data accessibility and system efficiency. By establishing an effective data pool, data from multiple sources can be centrally processed and analyzed, supporting complex analytical applications and increasing the practical value of data.

[0003] The patent application title, "A Method for Establishing a Smart Heating Data Resource Pool," refers to a method for designing and implementing centralized data management and optimized access for heating systems. The patent addresses technical matters such as the integration, management, and application of data resources, specifically the creation of a unified data access layer to support performance monitoring and management decisions for heating systems. By integrating data distributed across various systems and devices to establish a centralized data pool, this method allows system administrators and automated tools to more effectively access and utilize data, thereby optimizing the operating efficiency and energy utilization of heating systems.

[0004] The established methods lack sufficient flexibility and adaptability in data processing and management to cope with rapidly changing environmental conditions. For example, fixed data collection strategies cannot reflect environmental changes in a timely manner, resulting in limited timeliness and accuracy of data collection. In addition, traditional methods fail to efficiently utilize storage resources, allocating storage space to frequently accessed data and infrequently accessed data, which leads to resource waste and access delays. The monitoring and processing of data anomalies relies on manual intervention and lacks effective automated tools, which increases operation and maintenance costs, affects the ability to respond quickly, and makes it difficult to meet the needs of real-time and efficient heating management. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a method for establishing a smart heating data resource pool.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solution: a method for establishing a smart heating data resource pool, comprising the following steps:

[0007] S1: Based on the target heating data collection environment, monitor the external environmental factor data, adjust the sampling frequency of the heating data collection equipment to match the environmental changes, and obtain the data collection frequency adjustment result;

[0008] S2: Based on the data collection frequency adjustment result, collect the temperature, flow rate, and pressure data of the heating equipment, align the timestamps of the collected data, and store the converted data in a temporary buffer to obtain a standardized data set;

[0009] S3: Based on the standardized data set, perform attribute analysis on the data, identify the data type, and transfer each type of data to a corresponding classification storage queue to obtain a classified data set;

[0010] S4: Based on the classified data set, data is migrated according to the access frequency of the data, and high-frequency access data is migrated to a high-speed storage medium, and low-frequency access data is migrated to a stable storage medium to obtain optimized storage information;

[0011] S5: Based on the optimized storage information, evaluate the data volume change trend, determine whether storage space needs to be expanded, dynamically adjust the data storage capacity to match real-time data demand, and obtain a capacity dynamic adjustment result;

[0012] S6: Based on the dynamic capacity adjustment result, analyze abnormal patterns in the data, identify potential data anomalies and failures, and automatically record events and notify management personnel of the anomaly when an anomaly is detected, thereby obtaining anomaly detection information.

[0013] As a further solution of the present invention, the data collection frequency adjustment result includes an updated data sampling interval, an adjusted data collection schedule and a collection equipment configuration adapted to environmental changes; the standardized data set includes data entries in a unified time format, unified data type identifiers and time-synchronized data records; the classified data set includes a temperature monitoring data queue, a flow monitoring data queue and a pressure monitoring data queue; the optimized storage information includes a data list of a high-speed storage area, a data index of a stable storage area and data access frequency analysis information; the dynamic capacity adjustment result includes a storage space expansion record, a resource optimization log and real-time monitoring information of storage capacity; the anomaly detection information includes the timestamp of the abnormal data, abnormal pattern information and abnormal notification information.

[0014] As a further solution of the present invention, based on the target heating data collection environment, the external environmental factor data is monitored, the sampling frequency of the heating data collection device is adjusted to match the environmental changes, and the steps of obtaining the data collection frequency adjustment result are specifically as follows:

[0015] S101: Based on the target heating data collection environment, extract temperature changes, seasonal transitions, and user demand peak information of the target area through multi-source data collection to obtain environmental monitoring data information;

[0016] S102: Analyzing data change patterns based on the environmental monitoring data information, automatically adjusting the sampling frequency of the data acquisition device according to the change patterns, optimizing the real-time performance and accuracy of data acquisition, and obtaining an adjusted sampling frequency;

[0017] S103: Based on the adjusted sampling frequency, the data collection schedule is updated to optimize the synchronization of collection activities with actual environmental requirements, and a data collection frequency adjustment result is obtained.

[0018] As a further solution of the present invention, the formula for adjusting the sampling frequency of the data acquisition device is:

[0019]

[0020] Where f represents the adjusted sampling frequency, w1 represents the weight of the temperature data, T represents the temperature reduction value of the target area, w2 represents the weight of seasonal change, S represents the seasonal impact index, w3 represents the weight of the user demand peak, U represents the user demand peak information, and N represents the number of sampled data points.

[0021] As a further solution of the present invention, based on the data collection frequency adjustment result, the temperature, flow rate, and pressure data of the heating equipment are collected, the collected data are timestamp aligned, and the converted data are stored in a temporary buffer area. The specific steps of obtaining a standardized data set are as follows:

[0022] S201: Based on the data collection frequency adjustment result, the temperature, flow rate, pressure of the heating equipment and the temperature, wind speed and weather conditions of the external environment are synchronously collected through the heating data collection device to obtain a heating status data set;

[0023] S202: Based on the heating status dataset, align the data in the dataset with timestamps to optimize the time consistency of the data entries, and convert the data into a unified format using a formatting tool to obtain a time-aligned dataset;

[0024] S203: Based on the time-aligned data set, the data is transferred to a temporary buffer area, and the data receiving buffer area setting is adjusted to optimize the continuity of the data stream and reduce the transmission error to obtain a standardized data set.

[0025] As a further solution of the present invention, based on the standardized data set, the data attributes are analyzed to identify the data type, and each type of data is transferred to a corresponding classified storage queue to obtain the classified data set. Specifically, the steps are as follows:

[0026] S301: Based on the standardized data set, perform attribute analysis on the data, identify the type of differentiated data, mark the data points of temperature, flow rate and pressure, and obtain a labeled attribute data set;

[0027] S302: Based on the labeled attribute data set, the data is divided into categories and corresponding data queues are set. Each queue corresponds to a data type. Each type of data is directed to a preset storage queue through data division to obtain a queue classification data set;

[0028] S303: Based on the queue classification data set, adjust the storage configuration and access strategy of each queue, set a corresponding access path for each type of data, optimize data processing speed and access response, and obtain a classification data set.

[0029] As a further solution of the present invention, based on the classified data set, data migration is performed according to the access frequency of the data, high-frequency access data is migrated to a high-speed storage medium, and low-frequency access data is migrated to a stable storage medium, and the steps of obtaining optimized storage information are specifically as follows:

[0030] S401: Based on the classified data set, log analysis is performed on each type of data, the number of accesses to each type of data is counted, the data access frequency is evaluated, and the access frequency is compared with a preset access threshold to identify high-frequency access data and low-frequency access data to obtain an access frequency analysis result;

[0031] S402: Based on the access frequency analysis result, data is sorted for migration, high-frequency access data is directed to high-speed storage media, and redundant backup is created for the data to optimize data security and obtain a high-speed storage configuration;

[0032] S403: Based on the access frequency analysis result and the high-speed storage configuration, the low-frequency access data is migrated to a stable storage medium to optimize the stability and security of data storage and obtain optimized storage information.

[0033] As a further solution of the present invention, the formula for evaluating data access frequency is:

[0034]

[0035] Among them, F i represents the access frequency index of the i-th type of data, W1, W2, W3 are weight coefficients, N i represents the number of accesses to the i-th type of data in the current window, D i Represents the time since the last access to the i-th category data, T i represents the total access time of the i-th type of data during the observation period, H i represents the duration of the peak access period of the i-th type of data during the observation period, N total Represents the total number of accesses for all data types.

[0036] As a further solution of the present invention, based on the optimized storage information, the data volume change trend is evaluated, whether the storage space needs to be expanded, and the data storage capacity is dynamically adjusted to match the real-time data demand. The steps of obtaining the dynamic capacity adjustment result are specifically as follows:

[0037] S501: Based on the optimized storage information, extract data storage records over a period of time, monitor storage space usage, evaluate the increase and decrease trend of storage demand, and obtain storage trend analysis results;

[0038] S502: Based on the storage trend analysis result, evaluate the storage demand in the future period, compare the current storage capacity with the predicted data growth, determine whether storage expansion is needed, and obtain storage expansion demand information;

[0039] S503: Based on the storage expansion demand information, dynamically adjust the storage space configuration, increase storage resources for data with a growth trend, and optimize the storage allocation of low-demand data to obtain a capacity dynamic adjustment result.

[0040] As a further solution of the present invention, based on the dynamic capacity adjustment result, abnormal patterns in the data are analyzed to identify potential data anomalies and faults. When an anomaly is detected, the event is automatically recorded and an abnormality notification is issued to the management personnel. The steps of obtaining the abnormality detection information are specifically as follows:

[0041] S601: Based on the dynamic capacity adjustment result, track the data flow of the heating equipment in real time, record the data transmission and processing activities, including the size of the data flow and the number of data packets, to obtain real-time data flow monitoring information;

[0042] S602: Analyze the fluctuations and peaks of the data stream based on the real-time data stream monitoring information, identify abnormal patterns of the data stream by comparing them with normal data patterns, and obtain abnormal data identification results;

[0043] S603: Based on the abnormal data identification result, when data abnormality is detected, the event time and corresponding information are recorded immediately, and a notification is sent to the management personnel to obtain abnormality detection information.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by dynamically monitoring and adjusting the data collection frequency, the real-time responsiveness and collection efficiency of data are improved. By directly monitoring external environmental factors, including temperature changes and seasonal changes, it can flexibly adapt to environmental changes to ensure the accuracy and timeliness of data collection. By establishing classified storage queues and data migration strategies based on access frequency, the speed of data access and storage efficiency are improved. By dynamically adjusting the storage capacity, the challenges brought by data volume fluctuations are effectively addressed, ensuring the optimal allocation of storage resources. Real-time monitoring and anomaly detection of data streams enhance reliability and security. By instantly identifying potential data anomalies and faults, the complexity and cost of maintenance are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0048] Figure 2 This is a schematic diagram of the refinement of S1 of the present invention;

[0049] Figure 3 This is a schematic diagram of the refinement of S2 of the present invention;

[0050] Figure 4 This is a schematic diagram of the refinement of S3 of the present invention;

[0051] Figure 5 This is a schematic diagram of the refinement of S4 of the present invention;

[0052] Figure 6 This is a schematic diagram of the refinement of S5 of the present invention;

[0053] Figure 7 This is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0056] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0057] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0058] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0059] See also Figure 1 The present invention provides a technical solution: a method for establishing a smart heating data resource pool, comprising the following steps:

[0060] S1: Based on the target heating data collection environment, monitor external environmental factor data, extract temperature changes, seasonal transitions, and user demand peak information in the target area, adjust the sampling frequency of the heating data collection equipment, and update the data collection schedule to match the environmental changes to obtain the data collection frequency adjustment results;

[0061] S2: Based on the data collection frequency adjustment results, the heating data acquisition equipment is used to collect temperature, flow, and pressure data of the heating equipment, as well as temperature, wind speed, and weather condition data of the external environment. The collected data are timestamp-aligned, the data format is standardized, and the converted data is stored in a temporary buffer area. The data receiving buffer area is adjusted to optimize data continuity to obtain a standardized data set.

[0062] S3: Based on the standardized data set, perform attribute analysis on the data to identify data types, including temperature, flow, and pressure. Each type of data is transferred to the corresponding classified storage queue to obtain a classified data set.

[0063] S4: Based on the classified data set and the data access records, the access frequency of each type of data is counted. Data is migrated based on the data access frequency. High-frequency access data is migrated to high-speed storage media and corresponding redundant backup is performed. Low-frequency access data is moved to stable storage media to obtain optimized storage information.

[0064] S5: Based on the optimized storage information, the system analyzes the changes in data storage volume over a period of time, evaluates the data volume change trend, and determines whether storage space expansion is needed based on the data volume change trend. The system dynamically adjusts the data storage capacity to match the real-time data demand, and obtains the dynamic capacity adjustment result.

[0065] S6: Based on the results of dynamic capacity adjustment, the data flow of the heating system is monitored in real time. Through data comparison and analysis, abnormal patterns in the data are analyzed to identify potential data anomalies and faults. When an anomaly is detected, the event is automatically recorded and the management personnel are notified of the anomaly to obtain anomaly detection information.

[0066] The results of data collection frequency adjustment include the updated data sampling interval, the adjusted data collection schedule and the collection equipment configuration adapted to environmental changes. The standardized data set includes data entries in a unified time format, unified data type identifiers and time-synchronized data records. The classified data set includes temperature monitoring data queues, flow monitoring data queues and pressure monitoring data queues. The optimized storage information includes data lists in high-speed storage areas, data indexes in stable storage areas and data access frequency analysis information. The results of dynamic capacity adjustment include storage space expansion records, resource optimization logs and real-time monitoring information of storage capacity. The anomaly detection information includes the timestamp of abnormal data, abnormal pattern information and abnormal notification information.

[0067] See also Figure 2 Based on the target heating data collection environment, the external environmental factor data is monitored, the temperature change, seasonal transition and user demand peak information of the target area are extracted, the sampling frequency of the heating data collection equipment is adjusted, and the data collection schedule is updated to match the environmental changes. The specific steps to obtain the data collection frequency adjustment result are as follows:

[0068] S101: Based on the target heating data collection environment, extract temperature changes, seasonal transitions, and user demand peak information of the target area through multi-source data collection to obtain environmental monitoring data information;

[0069] Based on the target heating data collection environment, attention is paid to the type and format of environmental monitoring data. Data is obtained through a variety of sensors, such as temperature sensors to collect temperature changes in the target area, record seasonal changes in the environment, and use demand monitoring to count user demand peaks. Sensor data is sent to the data processing center through a wireless network. The data types include real-time temperature readings, timestamps of seasonal changes, and numerical data of user demand peaks. Each type of data is collected through a specific sensor within a specific time interval, such as temperature once every ten minutes, seasonal changes once a day, and user demand peaks once an hour. Data is transmitted through encrypted encryption to ensure information security. After arriving at the data processing center, it is uniformly managed and scheduled through the data interface. The entire data collection process must ensure the authenticity and timeliness of the data to ensure the accuracy and practicality of subsequent data analysis and obtain environmental monitoring data information.

[0070] S102: Analyze the data change pattern based on the environmental monitoring data information, automatically adjust the sampling frequency of the data acquisition device according to the change pattern, optimize the real-time performance and accuracy of data acquisition, and obtain an adjusted sampling frequency;

[0071] The formula for adjusting the sampling frequency of the data acquisition device is:

[0072]

[0073] Where f represents the adjusted sampling frequency, w1 represents the weight of the temperature data, T represents the temperature reduction value of the target area, w2 represents the weight of seasonal change, S represents the seasonal impact index, w3 represents the weight of the user demand peak, U represents the user demand peak information, and N represents the number of sampled data points.

[0074] The formula is:

[0075]

[0076] Parameter details and how to obtain them:

[0077] Temperature data weight w1: The weight is used to adjust the impact of temperature changes on the sampling frequency. It is derived from historical data analysis and reflects the actual impact of temperature changes on heating system performance.

[0078] Temperature reduction value T of the target area: The actual temperature change data is obtained through real-time monitoring of the sensor. These data reflect the change of the ambient temperature in the target area over time.

[0079] Seasonal change weight w2: The seasonal change weight is derived based on statistical analysis of past seasonal data and seasonal demand changes, and is used to adjust the sampling strategy to adapt to seasonal changes.

[0080] Seasonal impact index S: Seasonal data are collected through the environmental monitoring system, including date, time, and season-related environmental variables such as sunshine duration and climate conditions.

[0081] The weight w3 of the user demand peak is obtained by analyzing the user's usage data in different time periods. This data comes from the user's historical usage records and prediction models.

[0082] User demand peak information U: User demand data is collected through the user interface or monitoring system, reflecting the changes in user demand for heating within a specific time.

[0083] Number of sampled data points N: The number of data points is the total amount of data actually collected, including the sum of the readings of all sensors within the set time.

[0084] Calculation example:

[0085] The given parameters are set as follows: w1 = 0.3, w2 = 0.4, w3 = 0.3, T = 15 degrees, S = 2 seasonal transitions, U = 50 user peak demands, and N = 100 data points.

[0086] Calculation process:

[0087]

[0088] The result f = 2.03 indicates an adjusted sampling frequency of 2.03 times per minute. This means that to accommodate current temperature fluctuations, seasonal changes, and peak user demand, the data acquisition device's sampling frequency needs to be set to approximately 2 times per minute. This frequency effectively captures critical data while avoiding data redundancy caused by oversampling.

[0089] S103: Based on the adjusted sampling frequency, the data collection schedule is updated to optimize the synchronization of collection activities with actual environmental requirements, and a data collection frequency adjustment result is obtained.

[0090] According to the adjusted sampling frequency, the data collection schedule is updated, which is automatically completed through the schedule management software. The software automatically updates the schedule based on the latest sampling frequency data. The update process includes reading the current sampling frequency, then checking the existing schedule, comparing the sampling frequency with the schedule, and adjusting the sampling time points in the schedule. The updated schedule is synchronized to all data collection devices through the network again. The data collection devices adjust their own sampling times according to the updated schedule, which ensures that the collection activities are synchronized with the actual environmental requirements. The optimized collection schedule is presented in the form of a digital table for easy viewing and management. The process increases the flexibility and response speed of data collection, while also reducing data collection errors caused by failure to update the schedule in a timely manner, and obtains the result of data collection frequency adjustment.

[0091] See also Figure 3 Based on the data collection frequency adjustment results, the heating data acquisition equipment is used to collect the temperature, flow, and pressure data of the heating equipment, as well as the temperature, wind speed, and weather condition data of the external environment. The collected data are timestamp-aligned, the data format is standardized, the converted data is stored in a temporary buffer area, and the data receiving buffer area is adjusted to optimize the continuity of the data. The specific steps to obtain a standardized data set are as follows:

[0092] S201: Based on the data collection frequency adjustment result, the temperature, flow rate, pressure of the heating equipment and the temperature, wind speed and weather conditions of the external environment are synchronously collected through the heating data collection device to obtain a heating status data set;

[0093] Based on the results of the data collection frequency adjustment, key information is synchronously collected through the heating data acquisition equipment, including the temperature, flow, pressure of the heating equipment and the temperature, wind speed and weather conditions of the external environment. Data collection is completed using a variety of sensors installed on site. Temperature sensors, flow meters, pressure gauges and environmental monitoring equipment such as anemometers and weather stations are configured in key locations. Real-time monitoring data is sent to the central monitoring platform through a wireless transmission module. Each data item is timestamped and geographically tagged to ensure accurate data tracking and the effectiveness of subsequent processing, ensuring the comprehensiveness and synchronization of the data and obtaining a heating status data set.

[0094] S202: Based on the heating status dataset, align the timestamps of the data in the dataset to optimize the time consistency of the data entries, and convert the data into a unified format using a formatting tool to obtain a time-aligned dataset;

[0095] Based on the heating status dataset, the data in the dataset is timestamp aligned. Using data integration tools, the timestamps of each data item are scanned, the time stamps of each data point are compared, and the time deviations of the data are automatically adjusted through algorithms to ensure the consistency of all data on the timeline. Subsequently, formatting tools are used to unify the data formats, such as unifying all date and time formats to the ISO 8601 standard and the number formats to floating-point representation. Formatting not only improves data processing efficiency but also facilitates subsequent data analysis and storage. During the processing process, each step is automatically executed by the software without manual intervention, ensuring processing speed and accuracy. The resulting time-aligned dataset provides a solid foundation for the next step of data analysis and application.

[0096] S203: Based on the time-aligned data set, the data is transferred to a temporary buffer area, and the data receiving buffer setting is adjusted to optimize the continuity of the data stream and reduce the transmission error to obtain a standardized data set.

[0097] The time-aligned data set is transferred to a temporary buffer area, which is specially designed to handle large amounts of data streams. The buffer size and response speed are dynamically adjusted based on the real-time monitoring results of the data stream to adapt to different data transmission requirements. For example, during data peak periods, the buffer capacity is automatically increased to reduce data loss and transmission errors. At the same time, the settings of the data receiving buffer are adjusted to optimize the continuity of the data stream and ensure the integrity and consistency of the data during transmission. The adjustment is automatically completed through network management tools and protocols. During the entire process, the data stream status is monitored in real time, and the parameters are adjusted in time to cope with different network environments and data loads to obtain a standardized data set.

[0098] See also Figure 4 Based on the standardized data set, the data attributes are analyzed to identify the data type, including temperature, flow and pressure, and each type of data is transferred to the corresponding classification storage queue. The specific steps to obtain the classified data set are:

[0099] S301: Based on the standardized data set, perform attribute analysis on the data, identify the type of differentiated data, mark the data points of temperature, flow rate and pressure, and obtain a labeled attribute data set;

[0100] Based on standardized data sets, each type of data is automatically classified and identified through the use of machine learning technology to ensure accurate data classification and efficient subsequent processing. During the labeling process, temperature data uses threshold detection technology to identify outliers, and flow and pressure data use pattern recognition algorithms to determine the normal range and deviation of the data. Through real-time data monitoring, the accuracy and practicality of data processing are ensured. The labeled data is organized into a format that is easy to analyze and store, providing a reliable foundation for data operations and obtaining a labeled attribute data set.

[0101] S302: Based on the labeled attribute data set, the data is divided into categories and corresponding data queues are set. Each queue corresponds to a data type. Through data division, each type of data is directed to a preset storage queue to obtain a queue classification data set;

[0102] Based on the labeled attribute data set, each type of data such as temperature, flow and pressure is assigned to a different queue. Each queue has preset storage and processing rules. Classification processing is achieved through automated data routing technology to ensure that each type of data can flow to the correct processing channel quickly and efficiently. Queue management relies on database technology, such as NoSQL or high-performance queue management software, to maintain data flow and security. By automatically monitoring data flow and processing status, queue attributes are dynamically adjusted according to data volume and processing requirements to obtain a queue classification data set.

[0103] S303: Based on the queue classification data set, adjust the storage configuration and access policy of each queue, set a corresponding access path for each type of data, optimize the data processing speed and access response, and obtain a classified data set.

[0104] Based on queue-based classified data sets, the most suitable access path and storage parameters are set for each type of data according to the data type and processing requirements, such as I / O performance optimization, data compression options and backup strategies. The access strategy ensures that data can be quickly accessed when needed while ensuring data security and integrity. The optimal settings are determined by simulating and testing the performance of different configurations. The performance test results help administrators make data-driven decisions to optimize data processing speed and access response. It can process large-scale data sets while maintaining high efficiency and high availability, thereby obtaining highly optimized and secure classified data sets.

[0105] See also Figure 5 Based on the classified data set and the data access records, the access frequency of each type of data is counted. Data migration is performed based on the data access frequency. High-frequency access data is migrated to high-speed storage media and corresponding redundant backup is performed. Low-frequency access data is transferred to stable storage media. The specific steps for optimizing storage information are as follows:

[0106] S401: Based on the classified data set, log analysis is performed on each type of data, the number of accesses to each type of data is counted, the data access frequency is evaluated, and the access frequency is compared with a preset access threshold to identify high-frequency access data and low-frequency access data to obtain access frequency analysis results;

[0107] The formula for evaluating data access frequency is:

[0108]

[0109] Among them, F i represents the access frequency index of the i-th type of data, W1, W2, W3 are weight coefficients, N i represents the number of accesses to the i-th type of data in the current window, D i Represents the time since the last access to the i-th category data, T i represents the total access time of the i-th type of data during the observation period, H i represents the duration of the peak access period of the i-th type of data during the observation period, N total Represents the total number of accesses for all data types.

[0110] The formula is:

[0111]

[0112] Parameter meaning and acquisition method:

[0113] N i This parameter represents the number of accesses to the i-th data type within a specific time window. This parameter is directly calculated by analyzing the access records in the system log files and can be automatically collected and aggregated by the log management system.

[0114] D i The time since the last access to the data of type i is used to calculate the time decay effect of access. This value can be obtained from the data management system, which records the timestamp of the last access of each data type.

[0115] T i The total access time of the i-th type of data during the observation period. This is obtained through log file analysis. The duration of each data access is recorded and accumulated to give the total access time.

[0116] H i The duration of the peak access period for the i-th category of data during the observation period. This is usually determined by analyzing the time distribution pattern of data access in the logs and finding the continuous time period with the highest access frequency.

[0117] N total The total number of visits across all data types, used to normalize the frequency index. This is calculated by summing the number of visits across all data types, ensuring that the frequency of visits for each type is calculated relative to the overall activity.

[0118] Calculation examples and process explanations

[0119] Set the following parameter values: N i =120 times, D i = 10 minutes, T i =500 minutes, H i =50 minutes, N total =1000 times, W1=0.5, W2=0.3, W3=0.2.

[0120] Calculate F i :

[0121]

[0122] Get, F i = 0.06295. This value represents the access frequency index of data type i, reflecting how frequently data type i is accessed relative to all other data types. This result can be used to further analyze data type usage patterns and optimize data storage and access strategies.

[0123] S402: Based on the access frequency analysis results, data is sorted for migration, and frequently accessed data is directed to high-speed storage media. Redundant backups are created for the data to optimize data security and obtain a high-speed storage configuration.

[0124] Based on the results of access frequency analysis, frequently accessed data is migrated to storage media for high-speed access, such as SSDs or more advanced cache devices. During the migration process, to ensure data security, redundant backups are created for each data item in different physical locations. The configuration is designed to optimize data read and write speeds and reduce data recovery time, ensuring system stability and responsiveness during peak data access periods. The data migration and backup process is controlled by automated data management tools, which preset data migration logic and backup rules to ensure the accuracy and timeliness of operations and achieve high-speed storage configuration.

[0125] S403: Based on the access frequency analysis results and the high-speed storage configuration, the low-frequency access data is migrated to a stable storage medium to optimize the stability and security of data storage and obtain optimized storage information.

[0126] Based on the results of access frequency analysis and high-speed storage configuration, low-frequency access data is migrated to stable storage media to optimize the long-term preservation and security of data. The data is automatically migrated to more stable storage media such as tape libraries or high-capacity hard drives. During the migration process, each data type is re-evaluated for its storage needs and security requirements to ensure that the integrity of the data is not affected during the migration process. At the same time, in order to increase the disaster resistance of the data, data backups will also be synchronized in multiple geographical locations. This method not only ensures the security and reliability of the data, but also optimizes the storage cost and convenience of data maintenance, and obtains optimized storage information.

[0127] See also Figure 6 Based on the optimized storage information, by analyzing the changes in data storage capacity over a period of time, the data volume change trend is evaluated. According to the data volume change trend, it is determined whether the storage space needs to be expanded. The data storage capacity is dynamically adjusted to match the real-time data demand. The specific steps to obtain the dynamic capacity adjustment result are as follows:

[0128] S501: Based on the optimized storage information, extract data storage records over a period of time, monitor storage space usage, evaluate the increase and decrease trend of storage demand, and obtain storage trend analysis results;

[0129] Based on optimized storage information, data storage records within a specific time period are extracted, and storage space utilization is monitored, including aggregating data storage logs from multiple storages and using data visualization tools to generate time series charts of usage. The charts show the usage trends of storage space, including peaks and troughs. By analyzing the trends, changes in storage demand, such as increasing or decreasing trends, can be evaluated. During the evaluation process, regression analysis and time series prediction models are used to predict future storage needs. This analysis helps understand the rate and pattern of data growth, provides a scientific basis for storage planning, and thus obtains accurate storage trend analysis results.

[0130] S502: Based on the storage trend analysis results, evaluate the storage demand in the future period, compare the current storage capacity with the predicted data growth, determine whether storage expansion is needed, and obtain storage expansion demand information;

[0131] Based on the results of storage trend analysis, the storage demand in the future is evaluated. Using data analysis models, the future data growth is predicted and compared with the current storage capacity to determine whether there is a need for storage expansion. During the analysis, the existing storage trend data is used as input, and exponential smoothing and seasonal adjustment techniques are used to predict future storage demand. If the forecast results show that data growth will exceed the existing storage capacity, it is automatically marked as requiring expansion. The data growth rate and possible data peaks during the period are recorded to ensure the accuracy and reliability of the forecast and obtain information on storage expansion needs.

[0132] S503: Based on the storage expansion demand information, dynamically adjust the storage space configuration, increase storage resources for data with a growth trend, and optimize the storage allocation of low-demand data to obtain a dynamic capacity adjustment result.

[0133] Based on storage expansion demand information, storage space configuration is dynamically adjusted. For data with predicted growth trends, additional storage resources are automatically allocated. At the same time, in order to optimize storage costs and efficiency, storage of low-demand data is reallocated. Storage configuration is dynamically adjusted according to the access frequency and size of the data. A tiered storage strategy is used to migrate infrequently accessed data to lower-cost storage media, while frequently accessed data is retained on fast-access media. This optimizes the efficiency of storage space utilization, reduces overall storage costs, and achieves dynamic capacity adjustment results to ensure the continuity and efficiency of data storage.

[0134] See also Figure 7 Based on the results of dynamic capacity adjustment, the data flow of the heating system is monitored in real time. Through data comparison and analysis, abnormal patterns in the data are analyzed to identify potential data anomalies and faults. When an anomaly is detected, the event is automatically recorded and the management personnel are notified of the anomaly. The specific steps for obtaining anomaly detection information are as follows:

[0135] S601: Based on the result of the dynamic capacity adjustment, the data flow of the heating equipment is tracked in real time, and the data transmission and processing activities, including the size of the data flow and the number of data packets, are recorded to obtain real-time data flow monitoring information;

[0136] Based on the results of dynamic capacity adjustment, the data flow of the heating equipment is tracked in real time, including data transmission and processing activities. The monitoring process involves multiple sensors and network interfaces. The transmitted data flow is recorded and analyzed in real time by the software. By capturing the amount of data generated every second and the flow of data packets, the data is summarized and displayed on the control panel, providing intuitive charts and indicators to show the real-time status of the data flow, including peak traffic and data packet frequency. Real-time monitoring helps the technical team quickly identify any potential data transmission problems or bottlenecks, ensure continuous operation and performance optimization, obtain real-time data flow monitoring information, and provide a basis for data management and maintenance decisions.

[0137] S602: Based on the real-time data flow monitoring information, analyze the fluctuations and peaks of the data flow, identify abnormal patterns of the data flow by comparing them with normal data patterns, and obtain abnormal data identification results;

[0138] By monitoring information through real-time data streams, analyzing fluctuations and peaks in data streams, and using data analysis tools to compare historical data patterns with current data streams to identify any abnormal patterns, the analysis process uses machine learning models to identify deviations in data patterns, such as abnormal traffic or unexpected increases in data packets. Anomaly detection is configured to automatically mark data stream dynamics that deviate significantly from preset thresholds, including sudden increases or decreases in traffic, abnormal changes in data packet sizes, etc. In this way, analysis tools can instantly discover and record abnormal states in data streams, thereby generating abnormal data identification results. The results are critical to maintaining network security and data integrity, providing management teams with immediate alerts and data support to take necessary response measures.

[0139] S603: Based on the abnormal data identification result, when data anomaly is detected, the event time and corresponding information are recorded immediately, and a notification is sent to the management personnel to obtain anomaly detection information.

[0140] Based on the results of abnormal data identification, the relevant event time and details are recorded immediately, and the notification process is automatically triggered. The notification is sent to management and technical support teams to ensure that abnormal events can be responded to and handled quickly. During the process, integrated communication tools such as email and SMS platforms are used to automatically send alerts. Each notification contains detailed information about the abnormality, such as the time of occurrence, affected devices, specific data flow abnormality parameters, etc. The timely transmission of information allows for rapid fault diagnosis and problem solving, thereby minimizing the potential impact caused by data anomalies and obtaining anomaly detection information.

[0141] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0142] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0143] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0144] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0145] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0146] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0147] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0148] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0149] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0150] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for establishing a smart heating data resource pool, characterized in that: The following steps are involved: S1: Based on the target heating data collection environment, monitor the external environmental factor data, adjust the sampling frequency of the heating data collection equipment to match the environmental changes, and obtain the data collection frequency adjustment result; S2: Based on the data collection frequency adjustment result, collect the temperature, flow rate, and pressure data of the heating equipment, align the timestamps of the collected data, and store the converted data in a temporary buffer to obtain a standardized data set; S3: Based on the standardized data set, perform attribute analysis on the data, identify the data type, and transfer each type of data to a corresponding classification storage queue to obtain a classified data set; S4: Based on the classified data set, data is migrated according to the access frequency of the data, and high-frequency access data is migrated to a high-speed storage medium, and low-frequency access data is migrated to a stable storage medium to obtain optimized storage information; S5: Based on the optimized storage information, evaluate the data volume change trend, determine whether storage space needs to be expanded, dynamically adjust the data storage capacity to match real-time data demand, and obtain a capacity dynamic adjustment result; S6: Based on the dynamic capacity adjustment result, analyze abnormal patterns in the data, identify potential data anomalies and failures, and automatically record events and notify management personnel of the anomaly when an anomaly is detected, thereby obtaining anomaly detection information.

2. The method for establishing a smart heating data resource pool according to claim 1, characterized in that: The data collection frequency adjustment result includes the updated data sampling interval, the adjusted data collection schedule and the collection equipment configuration adapted to environmental changes; the standardized data set includes data entries in a unified time format, unified data type identifiers and time-synchronized data records; the classified data set includes a temperature monitoring data queue, a flow monitoring data queue and a pressure monitoring data queue; the optimized storage information includes a data list of a high-speed storage area, a data index of a stable storage area and data access frequency analysis information; the dynamic capacity adjustment result includes a storage space expansion record, a resource optimization log and real-time monitoring information of storage capacity; the anomaly detection information includes the timestamp of the abnormal data, abnormal pattern information and abnormal notification information.

3. The method for establishing a smart heating data resource pool according to claim 1, characterized in that: Based on the target heating data collection environment, monitor the external environmental factor data, adjust the sampling frequency of the heating data collection equipment to match the environmental changes, and obtain the data collection frequency adjustment results in the following steps: S101: Based on the target heating data collection environment, extract temperature changes, seasonal transitions, and user demand peak information of the target area through multi-source data collection to obtain environmental monitoring data information; S102: Analyzing data change patterns based on the environmental monitoring data information, automatically adjusting the sampling frequency of the data acquisition device according to the change patterns, optimizing the real-time performance and accuracy of data acquisition, and obtaining an adjusted sampling frequency; S103: Based on the adjusted sampling frequency, the data collection schedule is updated to optimize the synchronization of collection activities with actual environmental requirements, and a data collection frequency adjustment result is obtained.

4. The method for establishing a smart heating data resource pool according to claim 3 is characterized in that: The formula for adjusting the sampling frequency of the data acquisition device is: Where f represents the adjusted sampling frequency, w1 represents the weight of the temperature data, T represents the temperature reduction value of the target area, w2 represents the weight of seasonal change, S represents the seasonal impact index, w3 represents the weight of the user demand peak, U represents the user demand peak information, and N represents the number of sampled data points.

5. The method for establishing a smart heating data resource pool according to claim 1, characterized in that: Based on the data collection frequency adjustment result, the temperature, flow rate, and pressure data of the heating equipment are collected, the collected data are timestamp aligned, and the converted data are stored in a temporary buffer area. The specific steps of obtaining a standardized data set are as follows: S201: Based on the data collection frequency adjustment result, the temperature, flow rate, pressure of the heating equipment and the temperature, wind speed and weather conditions of the external environment are synchronously collected through the heating data collection device to obtain a heating status data set; S202: Based on the heating status dataset, align the data in the dataset with timestamps to optimize the time consistency of the data entries, and convert the data into a unified format using a formatting tool to obtain a time-aligned dataset; S203: Based on the time-aligned data set, the data is transferred to a temporary buffer area, and the data receiving buffer area setting is adjusted to optimize the continuity of the data stream and reduce the transmission error to obtain a standardized data set.

6. The method for establishing a smart heating data resource pool according to claim 1, characterized in that: Based on the standardized data set, the data is attribute analyzed to identify the data type, and each type of data is transferred to the corresponding classification storage queue. The steps of obtaining the classified data set are as follows: S301: Based on the standardized data set, perform attribute analysis on the data, identify the type of differentiated data, mark the data points of temperature, flow rate and pressure, and obtain a labeled attribute data set; S302: Based on the labeled attribute data set, the data is divided into categories and corresponding data queues are set. Each queue corresponds to a data type. Each type of data is directed to a preset storage queue through data division to obtain a queue classification data set; S303: Based on the queue classification data set, adjust the storage configuration and access strategy of each queue, set a corresponding access path for each type of data, optimize data processing speed and access response, and obtain a classification data set.

7. The method for establishing a smart heating data resource pool according to claim 1, characterized in that: Based on the classified data set, data migration is performed according to the access frequency of the data, high-frequency access data is migrated to a high-speed storage medium, and low-frequency access data is migrated to a stable storage medium. The steps of obtaining optimized storage information are specifically as follows: S401: Based on the classified data set, log analysis is performed on each type of data, the number of accesses to each type of data is counted, the data access frequency is evaluated, and the access frequency is compared with a preset access threshold to identify high-frequency access data and low-frequency access data to obtain an access frequency analysis result; S402: Based on the access frequency analysis result, data is sorted for migration, high-frequency access data is directed to high-speed storage media, and redundant backup is created for the data to optimize data security and obtain a high-speed storage configuration; S403: Based on the access frequency analysis result and the high-speed storage configuration, the low-frequency access data is migrated to a stable storage medium to optimize the stability and security of data storage and obtain optimized storage information.

8. The method for establishing a smart heating data resource pool according to claim 7, characterized in that: The formula for evaluating data access frequency is: Among them, F i represents the access frequency index of the i-th type of data, W1, W2, W3 are weight coefficients, N i represents the number of accesses to the i-th type of data in the current window, D i Represents the time since the last access to the i-th category data, T i represents the total access time of the i-th type of data during the observation period, H i represents the duration of the peak access period of the i-th type of data during the observation period, N total Represents the total number of accesses for all data types.

9. The method for establishing a smart heating data resource pool according to claim 1, characterized in that: Based on the optimized storage information, the data volume change trend is evaluated, whether the storage space needs to be expanded is determined, and the data storage capacity is dynamically adjusted to match the real-time data demand. The steps of obtaining the capacity dynamic adjustment result are specifically as follows: S501: Based on the optimized storage information, extract data storage records over a period of time, monitor storage space usage, evaluate the increase and decrease trend of storage demand, and obtain storage trend analysis results; S502: Based on the storage trend analysis results, evaluate storage demand in the future period, compare the current storage capacity with the predicted data growth, determine whether storage expansion is needed, and obtain storage expansion demand information; S503: Based on the storage expansion demand information, dynamically adjust the storage space configuration, increase storage resources for data with a growth trend, and optimize the storage allocation of low-demand data to obtain a capacity dynamic adjustment result.

10. The method for establishing a smart heating data resource pool according to claim 1, characterized in that: Based on the dynamic capacity adjustment results, abnormal patterns in the data are analyzed to identify potential data anomalies and faults. When an anomaly is detected, the event is automatically recorded and an abnormality notification is issued to the management personnel. The specific steps for obtaining anomaly detection information are as follows: S601: Based on the dynamic capacity adjustment result, track the data flow of the heating equipment in real time, record the data transmission and processing activities, including the size of the data flow and the number of data packets, to obtain real-time data flow monitoring information; S602: Analyze the fluctuations and peaks of the data stream based on the real-time data stream monitoring information, identify abnormal patterns of the data stream by comparing them with normal data patterns, and obtain abnormal data identification results; S603: Based on the abnormal data identification result, when data abnormality is detected, the event time and corresponding information are recorded immediately, and a notification is sent to the management personnel to obtain abnormality detection information.

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