Intelligent heat supply management platform

Abstract

The invention relates to the technical field of heat supply management, in particular to an intelligent heat supply management platform which comprises a demand prediction module, a strategy making module, a real-time adjusting module, a heat supply abnormity monitoring module, a heat supply efficiency evaluation module and a heat supply maintenance management module. According to the method, the heat consumption data and the external temperature data of the user are automatically analyzed, the platform is allowed to recognize the heat consumption mode, future demand changes are predicted, and real-time optimization of the heat supply adjustment direction is ensured, so that compared with a traditional method, the energy utilization efficiency is effectively improved, dynamic optimization of the heat supply amount and the heat supply time is achieved, and the energy utilization rate is increased. The reliability of the system is improved through real-time monitoring and automatic abnormality diagnosis, potential faults are responded quickly, energy waste is reduced, the operation cost is reduced, and the system can plan maintenance activities in advance, prevent sudden faults, prolong the service life of equipment and improve the quality of heat supply service by predicting future maintenance requirements.

Description

Technical Field

[0001] The present invention relates to the field of heat supply management technology, and in particular to a smart heat supply management platform. Background Art

[0002] Heating management technology involves the technologies and methods for the design, implementation, monitoring and optimization of heating systems. The core goal is to improve heating efficiency, ensure the reliable supply of thermal energy, and reduce energy consumption and operating costs. Technical means include but are not limited to automated control systems, heat metering, temperature control technology and energy data analysis. Modern heating management usually uses sensors and controllers to achieve dynamic adjustment of the heating network through real-time data collection and analysis. It can automatically adjust heating parameters according to the actual needs of the building and changes in the external environment, thereby optimizing energy efficiency and improving comfort.

[0003] Among them, the smart heating management platform is a system that integrates information technology and automatic control technology, aiming to improve the intelligence level and operational efficiency of heating management. The platform can adjust the operating status of heating equipment in real time by implementing intelligent algorithms and remote monitoring functions to respond to the heat needs of different users and external climate changes. Its main uses include reducing energy waste, improving the response speed and reliability of the heating system, and providing a user-friendly interface to monitor and manage heating services.

[0004] Existing technologies are not flexible in responding to changing environments or changes in demand. This rigid control often leads to overheating during non-peak hours or underheating during peak hours, and inefficient energy use. In addition, traditional technologies have limited functions in fault prevention and maintenance planning, and often need to respond after a fault occurs, resulting in heating interruptions and user inconvenience, increasing operating and maintenance costs, and lack of effective abnormal monitoring and pre-maintenance mechanisms, resulting in problems in the long-term stable operation of the heating system, especially in areas with high dependence on reliable heating. Summary of the invention

[0005] In order to solve the problem of inflexible response to changes in the environment or demand in the existing technology, this rigid control often leads to overheating during non-peak hours or insufficient heating during peak hours, and low energy efficiency. In addition, traditional technology has limited functions in fault prevention and maintenance planning, and often needs to respond after the fault occurs, resulting in heating interruption and user inconvenience, increasing operation and maintenance costs, and lack of effective abnormal monitoring and pre-maintenance mechanism, resulting in problems in the long-term stable operation of the heating system, especially in areas with high dependence on reliable heating. The embodiment of the present invention provides a smart heating management platform. The technical solution is as follows:

[0006] On the one hand, it provides a smart heating management platform, including:

[0007] The demand forecasting module automatically analyzes temperature fluctuations and time dependencies based on user heat usage data and external temperature data, identifies heat usage patterns associated with environmental changes, and predicts future peak demand based on heat usage patterns to obtain the direction of heat supply adjustment.

[0008] The strategy formulation module performs multiple heating configuration simulations based on the heating adjustment direction, adjusts the heating amount and heating time, evaluates energy efficiency and user response, determines the heating configuration with the best energy consumption and user satisfaction, and obtains heating configuration optimization information;

[0009] The real-time adjustment module adjusts the operating parameters of the heating equipment based on the heating configuration optimization information, monitors the ambient temperature and the actual heat consumption of the user in real time, dynamically adjusts the heating settings to match the current heating demand, and optimizes the operating efficiency of the heating equipment to obtain dynamic adjustment indicators;

[0010] The heating abnormality monitoring module continuously monitors the operating data of the heating system based on the dynamic adjustment index, analyzes the abnormal patterns in the data stream, identifies the abnormalities or deviations in operation, and automatically adjusts the heating parameters to obtain abnormal state diagnosis records;

[0011] The heating efficiency evaluation module analyzes the operating efficiency and energy efficiency of the heating equipment based on the abnormal state diagnosis record, calculates the performance index, and predicts the equipment maintenance demand in the future time period according to the calculation results to obtain the maintenance prediction index;

[0012] The heating maintenance management module executes maintenance tasks and resource allocation based on the maintenance prediction indicators, performs preventive maintenance on heating equipment, monitors the effectiveness of maintenance activities, and evaluates the performance and status of equipment after maintenance to obtain maintenance performance analysis results.

[0013] On the other hand, the heating adjustment direction includes peak demand response information, energy-saving priority, and user behavior patterns; the heating configuration optimization information specifically includes energy-saving configuration, response speed standard, and user satisfaction score; the dynamic adjustment indicators include temperature response efficiency, heat consumption matching, and equipment adjustment sensitivity; the abnormal status diagnosis record includes the identified abnormality category and occurrence frequency; the maintenance prediction indicators specifically include expected maintenance intervals, potential failure points, and performance attenuation prediction results; the maintenance performance analysis results include maintenance recovery efficiency, equipment post-maintenance status, and performance recovery ratio.

[0014] On the other hand, the demand forecasting module includes a data analysis submodule, a pattern recognition submodule, and a demand forecasting submodule;

[0015] The data analysis submodule removes inconsistent data and outliers based on user heat data and external temperature data, extracts key time series features using the sliding average method, and analyzes temperature change trends and data periodicity to obtain an overview of time series features;

[0016] The pattern recognition submodule distinguishes different heat usage patterns based on the time series feature overview, analyzes the correlation between heat usage patterns and environmental factors in combination with environmental change data, and extracts key change trends to obtain environmental heat usage correlation information;

[0017] The demand forecasting submodule predicts the heating demand during the future peak period based on the environmental heat use related information, and adjusts the demand forecasting process in combination with the historical data for the same period and seasonal changes to obtain the heating adjustment direction.

[0018] On the other hand, the strategy formulation module includes a heating configuration submodule, an efficiency evaluation submodule, and a strategy optimization submodule;

[0019] The heating configuration submodule simulates a variety of heating configurations based on the heating adjustment direction, adjusts the heating amount and heating time, and evaluates the energy efficiency and user response to obtain an overview of the adjusted configurations;

[0020] The performance evaluation submodule extracts the energy efficiency data of each heating configuration based on the adjusted configuration overview, compares the energy efficiency and user responsiveness of multiple configurations, calculates the cost-effectiveness ratio of each configuration, and obtains the configuration performance comparison analysis results;

[0021] The strategy optimization submodule evaluates the energy consumption and user satisfaction of the heating configuration based on the configuration performance comparison analysis results, and determines the optimal heating time and heating amount to obtain the heating configuration optimization information.

[0022] On the other hand, the real-time adjustment module includes a parameter adjustment submodule, a monitoring and analysis submodule, and a dynamic adjustment submodule;

[0023] The parameter adjustment submodule adjusts the operating parameters of the heating equipment, including thermal efficiency and heating frequency, based on the heating configuration optimization information, and adjusts the heating output according to the ambient temperature change and the user's heating mode, and generates a parameter adjustment record;

[0024] The monitoring and analysis submodule continuously monitors the ambient temperature and the user's heat consumption based on the parameter adjustment record, performs real-time data analysis, identifies the performance deviation of the heating system, and obtains the performance deviation analysis result;

[0025] The dynamic adjustment submodule dynamically adjusts the heating settings based on the performance deviation analysis results, including adjusting the heating time and output power, matching the real-time heating demand, optimizing the operating efficiency of the heating equipment, and obtaining dynamic adjustment indicators.

[0026] On the other hand, the heating abnormality monitoring module includes a data acquisition submodule and an abnormality diagnosis submodule;

[0027] The data acquisition submodule continuously collects the operation data of the heating system based on the dynamic adjustment index, including temperature, pressure and flow information, and verifies the integrity of the collected data to obtain a data monitoring set;

[0028] The abnormality diagnosis submodule performs data flow analysis based on the data monitoring set, identifies abnormalities or deviations in the heating system, identifies the conditions and patterns of abnormality occurrence, and automatically adjusts heating parameters to obtain abnormal status diagnosis records.

[0029] On the other hand, the data flow analysis is performed to identify anomalies or deviations in the heating system, according to the formula:

[0030]

[0031] Calculate the deviation of a data point from the mean, where AD represents the anomaly detection metric and x i represents the i-th data point, μ represents the mean of the i-th data point, and n is the total number of data points.

[0032] On the other hand, the heating efficiency evaluation module includes an efficiency data analysis submodule, a performance index calculation submodule, and a maintenance demand prediction submodule;

[0033] The efficiency data analysis submodule analyzes the operation data of the heating equipment based on the abnormal state diagnosis record, collects energy consumption data and operation time, and evaluates the energy efficiency of the heating equipment to obtain energy efficiency analysis information;

[0034] The performance index calculation submodule calculates the key performance indicators of the heating equipment operation based on the energy efficiency analysis information, including the efficiency ratio and the operating load rate, and quantitatively evaluates the performance of the heating equipment to obtain the performance evaluation indicators;

[0035] The maintenance demand prediction submodule predicts the maintenance cycle and maintenance demand in the future time period based on the performance evaluation index and the aging and performance decline trend of the heating equipment to obtain the maintenance prediction index.

[0036] On the other hand, the key performance indicators of the heating equipment operation are calculated according to the formula:

[0037]

[0038] and

[0039]

[0040] The efficiency ratio η and the operating load rate LR are obtained, where Eout is the output energy, E in is the input energy, L actual is the actual load, L max is the maximum load.

[0041] On the other hand, the heating maintenance management module includes a maintenance planning submodule, a maintenance execution submodule, and a performance evaluation submodule;

[0042] The maintenance planning submodule deploys human and material resources based on the maintenance prediction indicators, performs maintenance tasks and resource allocation, and sets maintenance time to obtain a maintenance planning overview;

[0043] The maintenance execution submodule performs preventive maintenance on the heating equipment based on the maintenance planning overview, including replacing consumables, adjusting mechanical parts and calibrating control settings, and monitors the maintenance process to obtain a maintenance execution record;

[0044] The performance evaluation submodule detects the operating status and performance indicators of the equipment after maintenance based on the maintenance execution record, and verifies whether the heating equipment has recovered to the target performance level to obtain the maintenance performance analysis result.

[0045] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0046] By automatically analyzing user heat usage data and external temperature data, the platform can identify heat usage patterns and predict future demand changes, ensuring real-time optimization of the heating adjustment direction. Compared with traditional methods, it effectively improves energy utilization efficiency and realizes dynamic optimization of heating amount and heating time. Real-time monitoring and automatic diagnosis of abnormalities improve system reliability, respond to potential faults agilely, reduce energy waste, and reduce operating costs. By predicting future maintenance needs, the system can plan maintenance activities in advance, prevent sudden failures, extend equipment life, and improve the quality of heating services. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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.

[0048] Figure 1 is a schematic diagram of a platform of the present invention;

[0049] Figure 2 It is a schematic diagram of the platform framework of the present invention;

[0050] Figure 3 It is a flow chart of the demand forecasting module of the present invention;

[0051] Figure 4 A flow chart of the strategy formulation module of the present invention;

[0052] Figure 5 This is a flow chart of the real-time adjustment module of the present invention;

[0053] Figure 6 This is a flow chart of the abnormal heating monitoring module of the present invention;

[0054] Figure 7 A flow chart of a heating efficiency evaluation module of the present invention;

[0055] Figure 8 This is a flow chart of the heating maintenance management module of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0057] 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 "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0058] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0059] 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.

[0060] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0061] The embodiment of the present invention provides a smart heating management platform, such as Figure 1 As shown, the platform includes:

[0062] The demand forecasting module automatically analyzes temperature fluctuations and time dependencies based on user heat usage data and external temperature data, identifies heat usage patterns associated with environmental changes, and predicts future peak demand based on heat usage patterns to obtain the direction of heat supply adjustment.

[0063] The strategy formulation module performs multiple heating configuration simulations based on the heating adjustment direction, adjusts the heating amount and heating time, evaluates energy efficiency and user response, and compares multiple heating configurations to determine the heating configuration with the best energy consumption and user satisfaction, and obtains heating configuration optimization information;

[0064] The real-time adjustment module adjusts the operating parameters of the heating equipment based on the heating configuration optimization information, monitors the ambient temperature and the actual heat consumption of users in real time, dynamically adjusts the heating settings to match the current heating demand, and optimizes the operating efficiency of the heating equipment to obtain dynamic adjustment indicators;

[0065] The heating abnormality monitoring module continuously monitors the operating data of the heating system based on dynamic adjustment indicators, analyzes abnormal patterns in the data stream, identifies abnormalities or deviations in operation, and automatically adjusts heating parameters to obtain abnormal status diagnosis records;

[0066] The heating efficiency evaluation module analyzes the operating efficiency and energy efficiency of the heating equipment based on the abnormal status diagnosis records, calculates the performance indicators, and predicts the equipment maintenance needs in the future time period based on the calculation results to obtain the maintenance prediction indicators;

[0067] The heating maintenance management module executes maintenance tasks and resource allocation based on maintenance prediction indicators, performs preventive maintenance on heating equipment, monitors the effectiveness of maintenance activities, and evaluates the performance and status of equipment after maintenance to obtain maintenance performance analysis results.

[0068] The heating adjustment direction includes peak demand response information, energy-saving priority, and user behavior patterns. The heating configuration optimization information includes energy-saving configuration, response speed standard, and user satisfaction score. Dynamic adjustment indicators include temperature response efficiency, heat consumption matching, and equipment adjustment sensitivity. Abnormal status diagnosis records include identified abnormality categories and occurrence frequency. Maintenance prediction indicators include expected maintenance intervals, potential failure points, and performance degradation prediction results. Maintenance performance analysis results include maintenance recovery efficiency, equipment post-maintenance status, and performance recovery ratio.

[0069] like Figure 2 and Figure 3 As shown, the demand forecasting module includes a data analysis submodule, a pattern recognition submodule, and a demand forecasting submodule;

[0070] The data analysis submodule removes inconsistent data and outliers based on user heat data and external temperature data, extracts key time series features using the sliding average method, and analyzes temperature change trends and data periodicity to obtain an overview of time series features;

[0071] The original data are aligned and integrated into a unified data set according to the timestamps. Null values, duplicate values ​​or abnormal data exceeding the preset range are screened and deleted. The data are segmented using the sliding window technology. The sliding average and sliding change range are calculated for each segment of data. The key features in the data are extracted, including the daily average heat consumption and the temperature change range. At the same time, statistical methods are used to detect and eliminate abnormal points. Then, through periodic analysis and extraction of repetitive change patterns in the data, the external temperature change characteristics are fitted into a continuous change trend. Combined with the heat consumption data, its seasonal characteristics, long-term trends and random fluctuation characteristics are decomposed according to daily and weekly cycles. Finally, the processed results are summarized as an overview of the key features of the time series.

[0072] The pattern recognition submodule distinguishes different heat usage patterns based on the overview of time series features, analyzes the correlation between heat usage patterns and environmental factors in combination with environmental change data, and extracts key change trends to obtain environmental heat usage correlation information;

[0073] The users' heat usage patterns are classified, and the data are divided into different categories according to the characteristics of daily heat peak, change amplitude and change trend of external temperature. The typical heat usage patterns are preliminarily divided by grouping method, and then the data is sorted and verified, and the differences between different heat usage patterns under specific temperature conditions are analyzed. At the same time, the main impact range of temperature change on heat usage pattern is extracted, and the influence of the change speed and range of external temperature on heat usage change in each pattern is analyzed. Further, the relationship between heat usage pattern and environmental factors is deeply analyzed by logical classification method or tree structure, and the key factors affecting pattern differences are summarized. Finally, the main correlation trend information between environmental factors and heat usage pattern is summarized.

[0074] The demand forecasting submodule predicts the heating demand during the future peak period based on the environmental heat use correlation information, and adjusts the demand forecasting process based on the historical data of the same period and seasonal changes to obtain the heating adjustment direction;

[0075] To predict future peak heating demand, historical heat consumption data and external temperature data are used as input, and a forecasting method is used to establish a model. The length of the time series data, the forecast step and the adjustment parameter range are set. The model is used to fit and verify the historical data for the same period. The key change characteristics extracted from environmental factors are input into the model to correct the forecast trend. Adjustments are made to the short-term and long-term forecast demands respectively. The growth or adjustment trend of heating demand is determined by comparing the peak values ​​and variation ranges of the forecast data with those of historical data. Combined with the seasonal changes in heating demand, the forecast results are finally corrected to determine the adjustment direction of future peak heating demand.

[0076] like Figure 2 and Figure 4 As shown, the strategy formulation module includes a heating configuration submodule, an efficiency evaluation submodule, and a strategy optimization submodule;

[0077] The heating configuration submodule simulates various heating configurations based on the heating adjustment direction, adjusts the heating amount and heating time, and evaluates the energy efficiency and user response to obtain an overview of the adjusted configuration;

[0078] The adjusted heating amount and heating time are combined multiple times to generate different heating plans, the heating supply distribution and time period in each heating plan are recorded, and the heating configuration is tested or simulated with field data to evaluate whether it meets the user's heating needs. The heating amount and thermal efficiency per unit time in each heating plan are calculated one by one, and the user's response data in different heating time periods are collected. This includes feedback data on heating comfort, timeliness of heating, etc. The correlation between user data and energy efficiency indicators is comprehensively analyzed, and data for each heating plan is sorted and screened. Plans with excessive heating supply or low user response are eliminated, ultimately forming an overview of the adjusted heating configuration.

[0079] The performance evaluation submodule extracts the energy efficiency data of each heating configuration based on the adjusted configuration overview, compares the energy efficiency and user responsiveness of multiple configurations, calculates the cost-effectiveness ratio of each configuration, and obtains the configuration performance comparison analysis results;

[0080] The energy efficiency data of each heating configuration is extracted, the energy consumption data of each configuration during the heating process and the corresponding user response indicators are recorded, the cost-benefit ratio of each configuration is calculated one by one, and the changing relationship between the heating energy efficiency and the user responsiveness is analyzed respectively. The energy consumption distribution of each configuration is divided into high-efficiency intervals and low-efficiency intervals according to the time interval, and the user response data is analyzed to see whether it corresponds to the heating changes in the time period. The corresponding cost-benefit ratio is calculated according to the heating amount, energy efficiency value and user feedback data in the time interval, and the configuration with the lowest cost-benefit ratio is excluded. At the same time, all the retained configuration performance data are compared, and the configuration performance comparison and analysis results are summarized and sorted out.

[0081] The strategy optimization submodule evaluates the energy consumption and user satisfaction of the heating configuration based on the configuration efficiency comparison analysis results, and determines the optimal heating time and heating amount to obtain the heating configuration optimization information;

[0082] The energy consumption and user satisfaction of the heating configuration are quantified respectively, the heating demand is divided by time period, the user satisfaction score and energy consumption data of each configuration in different time periods are matched and sorted, and the time periods and heating supply intervals with the lowest energy consumption and higher user satisfaction scores are analyzed. Based on the time distribution analysis of different configurations, the time periods and heating supply distribution rules of efficient configurations are extracted. Combined with the performance of similar configurations in historical data, the scheme with a balanced energy consumption and user satisfaction score is selected, the heating time and heating supply of the scheme are fine-tuned, and finally the optimal heating configuration optimization information is determined.

[0083] like Figure 2 and Figure 5 As shown, the real-time adjustment module includes a parameter adjustment submodule, a monitoring and analysis submodule, and a dynamic adjustment submodule;

[0084] The parameter adjustment submodule adjusts the operating parameters of the heating equipment, including thermal efficiency and heating frequency, based on the heating configuration optimization information, and adjusts the heating output according to the ambient temperature changes and the user's heating mode, and generates parameter adjustment records;

[0085] According to the heating time and heating amount recommended in the heating configuration optimization information, determine the adjustment target value, including the target range of equipment thermal efficiency and the specific value of heating frequency, read the current operating parameters in real time, set the adjustment interval time as a fixed period, gradually increase or decrease the equipment operating power in each period to achieve the target thermal efficiency, adjust the heating frequency by adjusting the control valve opening or water pump flow, dynamically adjust the heating output according to the ambient temperature change data, calculate the deviation between the ambient temperature and the heat demand in real time, and adjust the increase or decrease of the heating output according to the user's heating mode. At the same time, the target value, actual adjustment range and corresponding timestamp of each parameter adjustment are recorded in the system log, and finally a parameter adjustment record is generated.

[0086] The monitoring and analysis submodule continuously monitors the ambient temperature and user heat consumption based on parameter adjustment records, performs real-time data analysis, identifies performance deviations of the heating system, and obtains performance deviation analysis results;

[0087] Sensors are used to obtain real-time ambient temperature data and user heat consumption data, and the temperature change amplitude and heat consumption fluctuation range in the time series are recorded. By comparing the ambient temperature and user heat consumption data in continuous time periods, short-term fluctuation trends are extracted, and system performance deviations are evaluated, including insufficient heating power, excess heating, and mismatched user demand. The real-time data is further combined to analyze the power deviation value and heating frequency deviation value of each device in the system operation. The current performance deviation degree is calculated based on the gap between the target value set in the heating adjustment record and the actual operating value, and the performance deviation analysis results are compiled.

[0088] The dynamic adjustment submodule dynamically adjusts the heating settings based on the performance deviation analysis results, including adjusting the heating time and output power, matching the real-time heating demand, optimizing the operating efficiency of the heating equipment, and obtaining dynamic adjustment indicators;

[0089] The deviation values ​​of heating time and power in performance deviation analysis are extracted, and the time periods where the deviation values ​​exceed the set range are adjusted preferentially. The process of adjusting heating time includes the calculation of user demand in different time periods and the setting of power allocation ratio, and the actual demand is matched by controlling the start and stop time of the heating equipment. The process of adjusting output power sets the amplitude of power increase or decrease according to the user demand peak and the current equipment operating capacity. The key components in the equipment, such as the heater power and the flow rate of the circulating water pump, are adjusted in real time to change the heat output. Each adjustment operation records the corresponding time, equipment operating status and adjustment amplitude, and finally obtains the dynamic adjustment index.

[0090] like Figure 2 and Figure 6 As shown, the heating abnormality monitoring module includes a data acquisition submodule and an abnormality diagnosis submodule;

[0091] The data acquisition submodule continuously collects the operation data of the heating system based on the dynamic adjustment index, including temperature, pressure and flow information, and verifies the integrity of the collected data to obtain the data monitoring set;

[0092] Sensors are used to monitor key parameters in the heating system in real time, including temperature, pressure and flow information in the pipeline. Data is recorded as a continuous data stream based on timestamps, and integrity verification is performed on each data point, including checking whether the interval of data collection meets the preset sampling frequency standard and detecting whether there is missing data or abnormal data. Missing values ​​are supplemented by interpolation methods, and abnormal values ​​are eliminated by setting thresholds based on the distribution range of historical data. The verified data is then classified and sorted, and temperature, pressure and flow information are stored in independent database tables respectively. The data is grouped by time intervals, and key daily and hourly statistical values, such as maximum, minimum and average values, are extracted. The operating status of the heating system in a specific time period is recorded, and the sorted data is summarized as a data monitoring set.

[0093] The abnormality diagnosis submodule performs data flow analysis based on the data monitoring set, identifies abnormalities or deviations in the heating system, identifies the conditions and patterns of abnormality occurrence, and automatically adjusts the heating parameters to obtain abnormal status diagnosis records;

[0094] Perform dynamic trend analysis on the temperature, pressure and flow information in the data monitoring set in chronological order, calculate the change rate of each parameter and record the mutation point, identify abnormal points where the parameter changes exceed the threshold range by setting early warning thresholds, use the mutation points as the preliminary screening basis for abnormal events, and combine the operating mode of the corresponding conditions in the historical data to extract the time period when the abnormality occurred and the equipment operating status as the basis for diagnosis. Analyze the ambient temperature and user heat usage data before and after the mutation point to determine whether there is abnormal deviation caused by equipment failure or external factors. For confirmed abnormal conditions, extract the heating parameters in the system, automatically adjust the heating power and time period allocation according to the direction of the deviation, and save the record of the adjusted parameters together with the diagnostic conditions in the abnormal status diagnosis record.

[0095] Perform data flow analysis to identify anomalies or deviations in the heating system, according to the formula:

[0096]

[0097] Calculate the deviation of the data point from the mean, where AD represents the anomaly detection metric, which is the sum of the deviations of all data points in the data stream relative to their mean, and x i represents the ith data point, which can be temperature, pressure or other parameters for monitoring the performance of the heating system, μ represents the average value of the ith data point, and n is the total number of data points;

[0098] Data point x i is the temperature reading and has the following values: 70, 72, 68, 75, 71. First, calculate the average of the values ​​μ:

[0099]

[0100] Then calculate the deviation for each point and its sum:

[0101] AD=|70-71.2|+|72-71.2|+|68-71.2|+|75-71.2|+|71-71.2|;

[0102] AD=1.2+0.8+3.2+3.8+0.2;

[0103] AD = 9.2;

[0104] The results show that the sum of the deviations in the data set is 9.2, which represents the overall degree of deviation of the data during the monitoring period. Higher AD values ​​indicate anomalies or decreased equipment performance. The specific causes of the deviations can be further explored and the heating parameters can be adjusted to optimize system performance.

[0105] like Figure 2 and Figure 7 As shown, the heating efficiency evaluation module includes an efficiency data analysis submodule, a performance index calculation submodule, and a maintenance demand prediction submodule;

[0106] The efficiency data analysis submodule analyzes the operation data of the heating equipment based on the abnormal status diagnosis records, collects energy consumption data and operation time, and evaluates the energy efficiency of the heating equipment to obtain energy efficiency analysis information;

[0107] Extract key data from each operation of the heating equipment from the diagnostic records, including start time, stop time, average operating temperature, pressure and flow values, organize the data into an operation log in chronological order, and statistically analyze the energy consumption data in each time period, including calculating the energy consumption per unit time based on the equipment operating power and operating time, extracting the relationship between the equipment operating time and the heating amount, evaluating the average energy efficiency of the equipment through daily energy consumption data, and further analyzing the energy efficiency fluctuations and the stability of the operating mode in combination with the frequency of abnormal conditions occurring during equipment operation. Group and compare the energy efficiency data in different time periods, and finally organize the energy efficiency information of the heating equipment and summarize it into energy efficiency analysis information.

[0108] The performance index calculation submodule calculates the key performance indicators of the heating equipment operation, including efficiency ratio and operating load rate, based on the energy efficiency analysis information, and conducts quantitative evaluation of the heating equipment performance to obtain performance evaluation indicators;

[0109] The energy consumption, operating time and heating amount of the equipment in the energy efficiency analysis information are extracted, the efficiency ratio of the equipment is calculated according to the ratio of heating amount to energy consumption, the daily operating load data of the equipment is sorted out, and the changing trend of the equipment load rate over time is analyzed. At the same time, the average value and standard deviation of the equipment operating load rate are calculated by combining the operating time of the equipment and the completion rate of the heating task. The performance of the equipment under different operating modes is quantified by cross-analyzing the efficiency ratio and the operating load rate. The stability of the key performance parameters of the equipment is further evaluated based on the fluctuation of the efficiency ratio in the abnormal operation records, and finally the performance evaluation indicators of the heating equipment are obtained.

[0110] Calculate the key performance indicators of heating equipment operation according to the formula:

[0111]

[0112] and

[0113]

[0114] The efficiency ratio η and the operating load rate LR are obtained, where E out is the output energy, expressed in kilowatt-hours, which refers to the total amount of useful energy actually provided by the heating equipment within a certain period of time. in is the input energy, expressed in kilowatt-hours, which refers to the total amount of energy consumed by the heating equipment in the same period of time, L actual is the actual load,

[0115] In kilowatts, it represents the maximum load actually reached by the equipment during the measurement period, L max is the maximum load, expressed in kilowatts, which indicates the maximum workload that the equipment is designed to withstand;

[0116] The input energy E of the device in =1500 kWh, output energy E out =1350 kWh, actual load L actual =900 kW, maximum load L max =1200 kW, calculate efficiency ratio:

[0117]

[0118] It means that the energy efficiency of the equipment is 90%, that is, 0.9 kWh of useful energy is produced for every 1 kWh of energy consumed;

[0119] Calculate the operating load factor:

[0120]

[0121] Indicates that the equipment is operating at 75% of its maximum capacity;

[0122] The calculation results show that the heating equipment operates relatively efficiently, but its maximum operating capacity is not fully utilized. There is room to adjust the operating strategy to improve efficiency and reduce energy consumption.

[0123] The maintenance demand prediction submodule predicts the maintenance cycle and maintenance demand in the future time period based on the performance evaluation index and the aging and performance decline trend of the heating equipment, and obtains the maintenance prediction index;

[0124] Analyze the changing trends of the efficiency ratio and operating load rate of the equipment in the performance evaluation indicators, extract the time points of equipment aging and performance decline, and predict the performance decline in future operation by tracing the equipment performance decline rate in historical maintenance records and combining the evaluation information of the current operating status. Estimate the time period for the next maintenance of the equipment based on the decline rate of the performance evaluation indicators, take the key parameters that appear multiple times in the equipment operation data as the maintenance focus, adjust the maintenance cycle of the equipment, and manage the maintenance needs in a hierarchical manner in combination with the predicted operating load, and finally generate the maintenance prediction indicators for the heating equipment.

[0125] like Figure 2 and Figure 8 As shown, the heating maintenance management module includes a maintenance planning submodule, a maintenance execution submodule, and a performance evaluation submodule;

[0126] The maintenance planning submodule allocates human and material resources based on maintenance prediction indicators, performs maintenance tasks and resource allocation, and sets maintenance time to obtain an overview of the maintenance plan;

[0127] Based on the equipment maintenance cycle and maintenance requirements extracted from the forecast indicators, the equipment is prioritized according to the type of maintenance equipment and the urgency of the needs. Combined with the maintenance requirements of each equipment and the consumables required for maintenance, a material list is prepared and the inventory is checked. For materials with insufficient inventory, purchase applications are submitted. Maintenance personnel are assigned according to the number of equipment and the complexity of maintenance, and the person in charge and the content of each task are recorded. The specific execution time of the maintenance operation is determined, and the maintenance time is arranged according to the equipment operation plan to avoid high-load operation time periods. All personnel arrangements, material allocation and time planning records are organized into a maintenance task schedule, and finally an overview of the maintenance plan is formed.

[0128] The maintenance execution submodule performs preventive maintenance on heating equipment based on the maintenance planning overview, including replacing consumables, adjusting mechanical parts and calibrating control settings, and monitors the maintenance process to obtain maintenance execution records;

[0129] Check the equipment status and scheduled maintenance tasks recorded in the maintenance plan, and replace consumables in sequence, including replacing gaskets, filters and lubricants that are severely worn during equipment operation. Then adjust the mechanical parts, including checking and tightening loose connections, adjusting pump and valve openings, and correcting offset mechanical parts. After completion, calibrate the equipment's control system, including resetting the operating parameter range, correcting sensor reading deviations, and debugging the transmission status of control signals. During the execution of each task, record the actual operating steps and changes in the equipment's operating status. Use monitoring equipment to record parameters such as temperature, pressure and flow in real time. Finally, summarize the maintenance operation time, adjustment content and equipment status change information.

[0130] The performance evaluation submodule detects the operating status and performance indicators of the equipment after maintenance based on the maintenance execution records, and verifies whether the heating equipment has recovered to the target performance level, and obtains the maintenance performance analysis results;

[0131] Extract the adjustment parameters and replacement consumables information in the maintenance records, perform preliminary verification based on the operating status of the equipment after maintenance, start the equipment and record the temperature, pressure, flow and power consumption data during operation, compare and analyze with the performance data before maintenance, extract the operating efficiency, energy consumption change rate and operating stability indicators, and evaluate the recovery of the operating load and efficiency ratio in combination with the performance target value of the equipment. Perform in-depth analysis on the performance deviations after maintenance, detect whether there are still abnormal fluctuations or unstable operating parameters, organize all test data and calculate the recovery ratio of the equipment's operating status, and finally form the maintenance performance analysis results.

[0132] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0133] In the present invention, "at least one" means one or more, and "more than one" 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 be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0134] 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.

[0135] 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. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0136] Those skilled in the art can 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.

[0137] 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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 interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0139] 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.

[0140] 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 can be essentially or partly embodied in the form of a software product that contributes to the prior art. 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, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments 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.

[0141] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. Smart heating management platform, characterized by: The platform includes: The demand forecasting module automatically analyzes temperature fluctuations and time dependencies based on user heat usage data and external temperature data, identifies heat usage patterns associated with environmental changes, and predicts future peak demand based on heat usage patterns to obtain the direction of heat supply adjustment. The strategy formulation module performs multiple heating configuration simulations based on the heating adjustment direction, adjusts the heating amount and heating time, evaluates energy efficiency and user response, determines the heating configuration with the best energy consumption and user satisfaction, and obtains heating configuration optimization information; The real-time adjustment module adjusts the operating parameters of the heating equipment based on the heating configuration optimization information, monitors the ambient temperature and the actual heat consumption of the user in real time, dynamically adjusts the heating settings to match the current heating demand, and optimizes the operating efficiency of the heating equipment to obtain dynamic adjustment indicators; The heating abnormality monitoring module continuously monitors the operating data of the heating system based on the dynamic adjustment index, analyzes the abnormal patterns in the data stream, identifies the abnormalities or deviations in operation, and automatically adjusts the heating parameters to obtain abnormal state diagnosis records; The heating efficiency evaluation module analyzes the operating efficiency and energy efficiency of the heating equipment based on the abnormal state diagnosis record, calculates the performance index, and predicts the equipment maintenance demand in the future time period according to the calculation results to obtain the maintenance prediction index; The heating maintenance management module executes maintenance tasks and resource allocation based on the maintenance prediction indicators, performs preventive maintenance on heating equipment, monitors the effectiveness of maintenance activities, and evaluates the performance and status of equipment after maintenance to obtain maintenance performance analysis results.

2. The smart heating management platform according to claim 1 is characterized in that: The heating adjustment direction includes peak demand response information, energy-saving priority, and user behavior patterns; the heating configuration optimization information specifically includes energy-saving configuration, response speed standard, and user satisfaction score; the dynamic adjustment indicators include temperature response efficiency, heat consumption matching, and equipment adjustment sensitivity; the abnormal status diagnosis record includes the identified abnormality category and occurrence frequency; the maintenance prediction indicators specifically include expected maintenance intervals, potential failure points, and performance attenuation prediction results; the maintenance performance analysis results include maintenance recovery efficiency, equipment post-maintenance status, and performance recovery ratio.

3. The smart heating management platform according to claim 1 is characterized in that: The demand forecasting module comprises: The data analysis submodule removes inconsistent data and outliers based on user heat data and external temperature data, extracts key time series features using the sliding average method, and analyzes temperature change trends and data periodicity to obtain an overview of time series features; The pattern recognition submodule distinguishes different heat usage patterns based on the time series feature overview, analyzes the correlation between heat usage patterns and environmental factors in combination with environmental change data, and extracts key change trends to obtain environmental heat usage correlation information; The demand forecasting submodule predicts the heating demand during the future peak period based on the environmental heat use related information, and adjusts the demand forecasting process in combination with the historical data for the same period and seasonal changes to obtain the heating adjustment direction.

4. The smart heating management platform according to claim 1, characterized in that: The strategy formulation module includes: The heating configuration submodule simulates a variety of heating configurations based on the heating adjustment direction, adjusts the heating amount and heating time, and evaluates the energy efficiency and user response to obtain an overview of the adjusted configurations; The performance evaluation submodule extracts the energy efficiency data of each heating configuration based on the adjusted configuration overview, compares the energy efficiency and user responsiveness of multiple configurations, calculates the cost-effectiveness ratio of each configuration, and obtains the configuration performance comparison analysis results; The strategy optimization submodule evaluates the energy consumption and user satisfaction of the heating configuration based on the configuration performance comparison analysis results, and determines the optimal heating time and heating amount to obtain the heating configuration optimization information.

5. The smart heating management platform according to claim 1 is characterized in that: The real-time adjustment module comprises: The parameter adjustment submodule adjusts the operating parameters of the heating equipment, including thermal efficiency and heating frequency, based on the heating configuration optimization information, and adjusts the heating output according to the ambient temperature change and the user's heating mode, and generates a parameter adjustment record; The monitoring and analysis submodule continuously monitors the ambient temperature and the user's heat consumption based on the parameter adjustment record, performs real-time data analysis, identifies the performance deviation of the heating system, and obtains the performance deviation analysis result; The dynamic adjustment submodule dynamically adjusts the heating settings based on the performance deviation analysis results, including adjusting the heating time and output power, matching the real-time heating demand, optimizing the operating efficiency of the heating equipment, and obtaining dynamic adjustment indicators.

6. The smart heating management platform according to claim 1, characterized in that: The heating abnormality monitoring module comprises: The data acquisition submodule continuously collects the operation data of the heating system based on the dynamic adjustment index, including temperature, pressure and flow information, and verifies the integrity of the collected data to obtain a data monitoring set; The abnormality diagnosis submodule performs data flow analysis based on the data monitoring set, identifies abnormalities or deviations in the heating system, identifies the conditions and patterns of abnormality occurrence, and automatically adjusts heating parameters to obtain abnormal status diagnosis records.

7. The smart heating management platform according to claim 6, characterized in that: The data flow analysis is performed to identify anomalies or deviations in the heating system according to the formula: Calculate the deviation of a data point from the mean, where AD represents the anomaly detection metric and x i represents the i-th data point, μ represents the mean of the i-th data point, and n is the total number of data points.

8. The smart heating management platform according to claim 1, characterized in that: The heating efficiency evaluation module comprises: The efficiency data analysis submodule analyzes the operation data of the heating equipment based on the abnormal state diagnosis record, collects energy consumption data and operation time, and evaluates the energy efficiency of the heating equipment to obtain energy efficiency analysis information; The performance index calculation submodule calculates the key performance indicators of the heating equipment operation based on the energy efficiency analysis information, including the efficiency ratio and the operating load rate, and quantitatively evaluates the performance of the heating equipment to obtain the performance evaluation indicators; The maintenance demand prediction submodule predicts the maintenance cycle and maintenance demand in the future time period based on the performance evaluation index and the aging and performance decline trend of the heating equipment to obtain the maintenance prediction index.

9. The smart heating management platform according to claim 8, characterized in that: The key performance indicators of the heating equipment operation are calculated according to the formula: and The efficiency ratio η and the operating load rate LR are obtained, where E out is the output energy, E in is the input energy, L actual is the actual load, L max is the maximum load.

10. The smart heating management platform according to claim 1, characterized in that: The heating maintenance management module includes: The maintenance planning submodule deploys human and material resources based on the maintenance prediction indicators, performs maintenance tasks and resource allocation, and sets maintenance time to obtain a maintenance planning overview; The maintenance execution submodule performs preventive maintenance on the heating equipment based on the maintenance planning overview, including replacing consumables, adjusting mechanical parts and calibrating control settings, and monitors the maintenance process to obtain a maintenance execution record; The performance evaluation submodule detects the operating status and performance indicators of the equipment after maintenance based on the maintenance execution record, and verifies whether the heating equipment has recovered to the target performance level to obtain the maintenance performance analysis result.

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