Energy management system for boiler

Abstract

The invention relates to the technical field of boiler energy management, in particular to an energy management system for a boiler, and the system comprises a data collection module which collects fuel input, steam output, heat loss, combustion temperature, excess air coefficient, hearth temperature and flue gas temperature parameters according to the operation data of the boiler, and generates an operation parameter set. In the invention, through data analysis and a predictive maintenance strategy, the energy management efficiency of the boiler is optimized. Data collection can directly reflect the real-time state of boiler operation, key parameters are continuously monitored, energy use is optimized, and it is ensured that the fuel utilization rate is maximized. Deviation analysis and trend monitoring are implemented, so that the understanding of the operation condition of the boiler is enhanced, and the maintenance decision is more scientific and prospective. By means of historical data comparison, fault probability calculation and predictive maintenance, faults can be effectively prevented, so that accidental shutdown is reduced, and the service life of equipment is prolonged.

Description

Technical Field

[0001] The invention relates to the technical field of boiler energy management, and in particular to an energy management system for a boiler. Background Art

[0002] The boiler energy management technology area focuses on the development and implementation of systems and strategies to improve the energy efficiency and economic benefits of boiler operations. This includes the use of monitoring and control technologies to optimize combustion efficiency, reduce energy waste, lower emissions, and maintain long-term operational performance of the equipment. Through data analysis and real-time feedback, these management systems are able to identify energy consumption patterns and automatically adjust operating parameters to ensure that energy consumption is minimized while meeting the thermal energy needs of industrial and commercial facilities.

[0003] However, although existing technologies include energy monitoring and control, they lack real-time and dynamic maintenance decision support. Maintenance behaviors are often slow to respond and difficult to adapt to sudden changes, which not only increases operational risks but may also lead to unnecessary energy loss and equipment damage. For example, untimely fault handling and preventive measures may lead to reduced equipment efficiency and increased failure rates, thus affecting overall energy management and economic benefits. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an energy management system for a boiler.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: The energy management system for the boiler comprises:

[0006] The data acquisition module collects the fuel input, steam output, heat loss, combustion temperature, excess air coefficient, furnace temperature and flue gas temperature parameters according to the boiler operation data to generate an operation parameter set;

[0007] The state analysis module performs data cleaning and normalization according to the operating parameter set, calculates the deviation by subtracting the standard value from the current fuel input, analyzes the trend of combustion temperature over time, compares the furnace temperature with the standard operating range, determines the operating state, and obtains the boiler health index;

[0008] A risk prediction module, based on the boiler health index, retrieves historical fault records whose health index matches the current value, counts the number of faults that occur under similar conditions, and calculates the fault probability by dividing the number of faults by the total number of instances to obtain a fault risk value;

[0009] A maintenance decision module compares the fault risk value with a preset risk threshold value, and if the value exceeds the threshold value, arranges maintenance actions by setting a maintenance date and required tasks to generate a maintenance plan;

[0010] The real-time adjustment module monitors the real-time changes of the operating parameters according to the maintenance plan, recalculates the boiler health index to update the data, recalculates the fault risk value using the updated boiler health index, and adjusts the maintenance time by rescheduling.

[0011] As a further embodiment of the present invention, the operating parameter set includes fuel input, steam output, heat loss, combustion temperature, excess air coefficient, furnace temperature and flue gas temperature, and the maintenance plan includes a set maintenance date, tasks to be performed and scheduled maintenance actions.

[0012] As a further solution of the present invention, the data acquisition module includes:

[0013] The energy parameter acquisition submodule reads the fuel input through sensors, records the steam output, measures the heat loss, collects and organizes the data, and generates an energy parameter set based on the boiler's operating data;

[0014] The temperature parameter acquisition submodule uses a temperature sensor to obtain the combustion temperature, collect the excess air coefficient, measure the furnace temperature and flue gas temperature, summarize the obtained data, and generate a temperature parameter set based on the boiler's operating data;

[0015] The parameter set construction submodule, based on the energy parameter set and the temperature parameter set, aligns the data of the two parameter sets by timestamp, merges the corresponding data items, forms a complete data set, and generates an operation parameter set.

[0016] As a further solution of the present invention, the state analysis module includes:

[0017] A data cleaning submodule, based on the operating parameter set, checks missing values ​​and outliers in the data, fills in missing values, removes outliers, and normalizes each parameter to obtain a cleaned parameter set;

[0018] The deviation analysis submodule calculates the deviation by subtracting the standard value from the current fuel input based on the cleaned parameter set, calculates the deviation value of each parameter, analyzes the change of combustion temperature over time, draws a temperature change curve, and obtains a parameter deviation set;

[0019] The state assessment submodule compares the furnace temperature with the standard operating range based on the parameter deviation set, determines whether the temperature exceeds the normal range, determines the operating state, and obtains the boiler health index.

[0020] As a further solution of the present invention, the risk prediction module includes:

[0021] A matching record retrieval submodule, based on the boiler health index, retrieves fault records whose health index is close to the current value in the historical database, screens historical data that meets the conditions, and obtains a matching fault record set;

[0022] A fault probability calculation submodule, based on the matching fault record set, counts the number of faults that occur under the same conditions, calculates the total number of instances, divides the number of faults by the total number of instances, calculates the probability of the fault occurring, and obtains a fault probability value;

[0023] The risk value acquisition submodule determines the risk level of the current operating state based on the fault probability value, quantifies the fault risk, and obtains the fault risk value.

[0024] As a further solution of the present invention, the maintenance decision module includes:

[0025] The risk comparison submodule compares the fault risk value with a preset risk threshold value based on the fault risk value, determines whether the fault risk value exceeds the threshold value, and obtains a maintenance judgment result;

[0026] A maintenance scheduling submodule, based on the maintenance judgment result, if maintenance is required, determines the maintenance date, lists the required maintenance tasks, schedules maintenance actions, and obtains a maintenance schedule;

[0027] The plan generation submodule is configured to generate a maintenance plan based on the maintenance schedule, integrate the maintenance date and task information, and compile the maintenance plan.

[0028] As a further solution of the present invention, the real-time adjustment module includes:

[0029] A parameter monitoring submodule, based on the maintenance plan, continuously monitors the real-time changes of the operating parameters, collects the current fuel input and temperature parameters, updates the data records, and obtains the real-time operating parameters;

[0030] An index update submodule, based on the real-time operating parameters, recalculates the boiler health index, updates the health index value, reflects the current operating status, and obtains an updated boiler health index;

[0031] The plan adjustment submodule recalculates the fault risk value based on the updated boiler health index, determines whether the risk level has changed, adjusts the maintenance time if necessary, and obtains an adjusted maintenance plan.

[0032] As a further solution of the present invention, the quantified failure risk uses the following formula:

[0033]

[0034] Calculate the fault risk value R, where P is the fault probability value, S is the fault severity, C is the maintenance cost, D is the current key parameter value, D0 is the standard parameter value, and ∈ is a small positive number to prevent division by zero.

[0035] Compared with the prior art, the advantages and positive effects of the present invention are:

[0036] In the present invention, the energy management efficiency of the boiler is optimized through data analysis and predictive maintenance strategies. Data collection can directly reflect the real-time status of boiler operation and continuously monitor key parameters, so that energy use is optimized and fuel utilization is maximized. The implementation of deviation analysis and trend monitoring enhances the understanding of boiler operation status and makes maintenance decisions more scientific and forward-looking. By comparing historical data and calculating the probability of failure, predictive maintenance can effectively prevent failures from occurring, thereby reducing unexpected downtime and extending the service life of equipment. This proactive fault prediction and response mechanism improves the reliability of operation while reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0039] See also Figure 1 The present invention provides a technical solution: an energy management system for a boiler comprises:

[0040] The data acquisition module collects the fuel input, steam output, heat loss, combustion temperature, excess air coefficient, furnace temperature and flue gas temperature parameters according to the boiler operation data to generate an operation parameter set;

[0041] The status analysis module performs data cleaning and normalization based on the operating parameter set, calculates the deviation by subtracting the standard value from the current fuel input, analyzes the trend of combustion temperature over time, compares the furnace temperature with the standard operating range, determines the operating status, and obtains the boiler health index;

[0042] The risk prediction module, based on the boiler health index, retrieves historical fault records that match the health index with the current value, counts the number of faults that occur under the same conditions, and calculates the fault probability by dividing the number of faults by the total number of instances to obtain the fault risk value;

[0043] The maintenance decision module compares the failure risk value with the preset risk threshold. If the value exceeds the threshold, the maintenance action is scheduled by setting the maintenance date and required tasks to generate a maintenance plan.

[0044] The real-time adjustment module monitors the real-time changes of the operating parameters based on the maintenance plan, recalculates the boiler health index to update the data, recalculates the fault risk value using the updated boiler health index, and adjusts the maintenance time by rescheduling.

[0045] The operating parameter set includes fuel input, steam output, heat loss, combustion temperature, excess air coefficient, furnace temperature and flue gas temperature, and the maintenance plan includes the set maintenance date, tasks to be performed and scheduled maintenance actions.

[0046] In this embodiment, the data acquisition module includes:

[0047] The energy parameter acquisition submodule reads the fuel input through sensors, records the steam output, measures the heat loss, collects and organizes the data, and generates an energy parameter set based on the boiler's operating data;

[0048] The temperature parameter acquisition submodule uses a temperature sensor to obtain the combustion temperature, collect the excess air coefficient, measure the furnace temperature and flue gas temperature, summarize the obtained data, and generate a temperature parameter set based on the boiler's operating data;

[0049] The parameter set construction submodule, based on the energy parameter set and the temperature parameter set, aligns the data of the two parameter sets by timestamp, merges the corresponding data items, forms a complete data set, and generates an operating parameter set.

[0050] During the energy parameter acquisition process, the fuel input of the boiler system is monitored in real time by the sensor. The sensor obtains the specific data of the fuel input by accurately measuring the fuel flow rate. The data output by the sensor is then corrected and sorted by the data processing unit to ensure the accuracy and reliability of the data. The steam output read by the sensor is monitored by similar sensing technology to monitor the steam flow rate and pressure, and then the total steam output is calculated. The measurement of heat loss involves the analysis of thermal energy transmission, including but not limited to the determination of heat transfer coefficient and the evaluation of heat exchange efficiency. These data are summarized and optimized through statistical analysis methods to form an energy parameter set. The goal of this process is to provide the necessary parameters for boiler energy efficiency analysis through precise monitoring and data processing.

[0051] The temperature parameter acquisition submodule relies on the basic data of boiler operation and uses temperature sensors to accurately monitor the combustion temperature. These temperature sensors are designed to work stably under extreme temperature conditions to ensure accurate data collection. The combustion temperature is obtained by placing the sensor in the key combustion area of ​​the boiler to directly measure the heat energy generated by the flame and combustion. The measurement of the excess air coefficient is determined by analyzing the proportion of gas components after combustion. This process requires the cooperation of chemical analysis instruments. The measurement of furnace temperature and flue gas temperature is also implemented by installing high-temperature sensors at the corresponding positions. The collected data is used to generate a temperature parameter set after verification. This detail ensures the validity of the data and the reliability of the operation. The final result is used to optimize the boiler operation efficiency and reduce energy consumption.

[0052] In the above content, the parameter set construction submodule aligns the data of the energy parameter set and the temperature parameter set by timestamp, according to the formula Merge the corresponding data items, P represents the operating parameter set, E t represents the energy parameter at time t, Θ t Represents the temperature parameter at time point t. This step is key to integrating the two types of monitoring data to form a data set containing comprehensive operating information, which is helpful for subsequent data analysis and system optimization.

[0053] Calculation process: Considering that the energy and temperature parameters at each time point t are measured independently, we construct a complete set of operating parameters by accumulating the parameter values ​​at each time point. Here, ∑ represents the summation operation, covering all monitored time points. If the energy parameter E measured at a specific time point t is t is 150 units of energy, the temperature parameter Θ t The temperature is 350 units, so the comprehensive parameter P at this time point is t will be 500. By processing the data of all time points in a similar manner, the final operating parameter set P is the sum of the data of all time points.

[0054] In this embodiment, the status analysis module includes:

[0055] The data cleaning submodule checks the missing values ​​and outliers in the data based on the running parameter set, fills in the missing values, removes outliers, and normalizes each parameter to obtain the cleaned parameter set;

[0056] The deviation analysis submodule calculates the deviation by subtracting the standard value from the current fuel input based on the cleaned parameter set, calculates the deviation value of each parameter, analyzes the change of combustion temperature over time, draws the temperature change curve, and obtains the parameter deviation set;

[0057] The status assessment submodule compares the furnace temperature with the standard operating range based on the parameter deviation set, determines whether the temperature exceeds the normal range, determines the operating status, and obtains the boiler health index.

[0058] Before the data cleaning submodule normalizes the data in the running parameter set, it is necessary to first check the missing values ​​and outliers in the data set. This check is performed through an automated script. The script runs to identify inconsistencies and logical errors in the data. These missing values ​​are filled by interpolation, and outliers are identified and eliminated by standard deviation analysis. For normalization, the maximum and minimum normalization method is used. This method adjusts the data value so that the parameter value falls within a preset range, such as between 0 and 1. The normalized data can eliminate the impact of dimensions and improve the efficiency and accuracy of the algorithm in processing data. The cleaned parameter set provides a reliable input source for data analysis and subsequent processing.

[0059] In the above content, the deviation analysis submodule calculates the deviation value of each parameter according to formula D p =X current -X std , where D p represents the deviation value of parameter p, X current is the current observation value, X std It is the standard value. The change of combustion temperature over time is analyzed, and the temperature change curve is plotted to obtain the parameter deviation set. Through deviation analysis, the possible deviation from the standard behavior in the system operation can be monitored, and the operation can be adjusted in time to ensure the system performance.

[0060] Calculation process: The current fuel input measured at a certain moment is x current =300 units, standard fuel input X std =280 units, then the deviation value D p =300-280=20 units. This calculation shows that the current fuel input is 20 units more than the standard. This deviation analysis helps operators identify and correct deviations and maintain system efficiency.

[0061] In the above content, the state assessment submodule compares the furnace temperature with the standard operating range according to the formula Determine whether the temperature exceeds the normal range. HI represents the boiler health index, T represents the actual measured furnace temperature, and T std The health index calculated by this formula reflects the operating status of the boiler. If the furnace temperature T is within the standard temperature T std The smaller the deviation is, the higher the boiler health index is, and vice versa.

[0062] Formula calculation: In the formula |TT std|Indicates the absolute deviation between the actual temperature and the standard temperature. This deviation is used to evaluate the abnormality of the temperature. The deviation is converted to the ratio of the standard value, multiplied by 100 to convert it into a percentage, so as to intuitively express the percentage of the deviation to the standard temperature. 100-this percentage gives the health index. Assuming the standard operating temperature T std is 1000℃, the actual operating temperature T is 1020℃, and the deviation calculation process is as follows:

[0063] |TT std |=|1020-1000|=20

[0064]

[0065] HI=100-(0.02×100)=98

[0066] The results show that the boiler is operating at close to the standard temperature and the health index is high, indicating that the boiler is in good health.

[0067] In this embodiment, the risk prediction module includes:

[0068] The matching record retrieval submodule retrieves the fault records whose health index is close to the current value in the historical database based on the boiler health index, and selects the historical data that meets the conditions to obtain the matching fault record set;

[0069] The fault probability calculation submodule counts the number of faults under the same conditions based on the matching fault record set, calculates the total number of instances, divides the number of faults by the total number of instances, calculates the probability of the fault, and obtains the fault probability value;

[0070] The risk value acquisition submodule determines the risk level of the current operating state based on the fault probability value, quantifies the fault risk, and obtains the fault risk value.

[0071] The matching record retrieval submodule operates in the process of executing based on the boiler health index. First, the historical database is queried. This database contains boiler operation data with various health indexes. The submodule uses an algorithm to accurately match the health index and retrieves records that are close to the current health index. In this process, the degree of closeness between the health index of each record and the current value is obtained by calculating the difference between the two. Records with smaller differences are considered to meet the conditions. After screening these records, a matching fault record set is formed. This set provides a basis for predicting possible problems with the current boiler, allowing the maintenance team to prepare in advance to prevent failures.

[0072] In the above content, the fault probability calculation submodule counts the number of faults under the same conditions, according to the formula Calculate the probability of failure, where P represents the probability of failure, n faults is the number of failures, N is the total number of instances, and the failure probability value is obtained.

[0073] Formula calculation: In the matched historical data set, there are 150 instances where the failure occurred 30 times. The probability of failure occurrence is calculated as This means that under similar operating conditions, there is a 20% probability of failure. This calculation helps to quantitatively analyze the possibility of failure and provides a scientific basis for formulating preventive measures and maintenance plans.

[0074] In this embodiment, the maintenance decision module includes:

[0075] The risk comparison submodule compares the fault risk value with the preset risk threshold, determines whether the fault risk value exceeds the threshold, and obtains the maintenance judgment result;

[0076] The maintenance scheduling submodule determines the maintenance date, lists the required maintenance tasks, schedules the maintenance actions, and obtains the maintenance schedule based on the maintenance judgment result if maintenance is required;

[0077] The plan generation submodule compiles and generates a maintenance plan based on the maintenance schedule, integrating the maintenance date and task information.

[0078] In the above content, the risk comparison submodule compares the fault risk value with the preset risk threshold, and calculates and determines whether the fault risk value exceeds the threshold according to the formula R=r-θ, where R represents the over-threshold result, r is the current fault risk value, and θ is the preset risk threshold. This step determines whether maintenance needs to be performed.

[0079] Formula calculation: For example, the current fault risk value is r = 0.55 (indicating a 55% fault probability), and the preset risk threshold is θ = 0.50. Therefore, the over-threshold value is calculated as R = 0.55-0.50 = 0.05. This result indicates that the current fault risk value slightly exceeds the preset threshold, triggering a maintenance judgment result, and recommending a maintenance inspection.

[0080] After receiving the maintenance judgment result, the maintenance scheduling submodule first determines the specific date of maintenance based on the judgment result. This date is arranged according to the equipment usage and the availability of the maintenance team. Then, the required maintenance tasks are listed. These tasks may include replacing worn parts, updating software, and performing system calibration. The determination of maintenance tasks is based on historical maintenance data and manufacturer's recommendations. The maintenance actions are arranged through a detailed operation process plan to ensure that all maintenance activities can be completed within the scheduled time, optimize the efficiency and effectiveness of the maintenance process, and provide continuous and stable operation of the system.

[0081] The plan generation submodule integrates maintenance dates and task information based on maintenance arrangements. This module uses professional maintenance management software to prepare maintenance plans. The maintenance plan includes specific operating steps, a list of required materials, and an estimated time. This plan ensures that maintenance activities can be carried out systematically to avoid any omissions. The generated maintenance plan is transmitted electronically to relevant departments and personnel to ensure that each responsible person is clear about his or her tasks and time nodes. This process helps the maintenance team to effectively allocate resources and manage time, and ultimately forms a detailed and practical maintenance operation guide.

[0082] In this embodiment, the real-time adjustment module includes:

[0083] The parameter monitoring submodule continuously monitors the real-time changes of operating parameters based on the maintenance plan, collects the current fuel input and temperature parameters, updates the data records, and obtains the real-time operating parameters;

[0084] The index update submodule recalculates the boiler health index based on the real-time operating parameters, updates the health index value, reflects the current operating status, and obtains an updated boiler health index;

[0085] The plan adjustment submodule recalculates the fault risk value based on the updated boiler health index, determines whether the risk level has changed, adjusts the maintenance time if necessary, and obtains the adjusted maintenance plan.

[0086] The parameter monitoring submodule plays a core role in the real-time adjustment module. This submodule is responsible for continuously monitoring the operating parameters of the boiler based on the maintenance plan, such as the current fuel input and temperature parameters. The real-time data of these parameters are collected through high-precision sensors. The collected data is immediately transmitted to the central monitoring system for recording and updating to ensure that all operating data is up to date, providing a basis for subsequent health index updates and maintenance decisions. The real-time update of data allows the system to respond quickly to any outstanding changes, thereby improving the response speed and accuracy of the entire system.

[0087] The index update submodule recalculates the boiler's health index based on the real-time operating parameters collected from the parameter monitoring submodule. This calculation involves comparing and analyzing the real-time parameters with the historical performance data, and calculating the health index through an algorithm. In this process, the update of the health index reflects the current operating status of the boiler. The real-time updated health index is an intuitive representation of the boiler's performance evaluation, allowing the operation and maintenance team to promptly understand the operating status of the equipment and make corresponding adjustments or maintenance decisions based on changes in the health index.

[0088] In this embodiment, the following formula is used to quantify the failure risk:

[0089]

[0090] Calculate the fault risk value R, where P is the fault probability value, S is the fault severity, C is the maintenance cost, D is the current key parameter value, D0 is the standard parameter value, and ∈ is a small positive number to prevent division by zero.

[0091] First, P represents the probability of a fault, obtained from the previous step, S represents the severity of the fault, determined based on historical data or preset standards, C is the maintenance cost, which is associated with the economic impact of fault handling, D is the currently measured key parameter value, such as temperature or pressure, and D0 is the standard value of the parameter. The parameter deviation is obtained by calculating |D-D0|, and ∈ is added to prevent the denominator from being zero.

[0092] Then, calculate the adjustment factor Finally, multiply P, S and the adjustment factor to obtain the failure risk value R.

[0093] The calculation process is as follows:

[0094] Calculate the failure probability value P:

[0095] By analyzing historical data, under conditions similar to the current boiler health index, there were 100 operating instances, of which 20 failures occurred, so:

[0096]

[0097] Determine the fault severity S:

[0098] According to the impact that the failure may cause, if the failure may cause production interruption, set the severity:

[0099] S=4

[0100] Get the maintenance cost C:

[0101] Maintenance costs consist of the following:

[0102] Replacement parts cost: 5000 yuan

[0103] Labor cost: 2000 yuan

[0104] Downtime loss: 3,000 yuan

[0105] The total maintenance cost is:

[0106] C = 5000 + 2000 + 3000 = 10000 yuan

[0107] Get the current key parameter value D and standard parameter value D0:

[0108] Current measured boiler pressure:

[0109] D=9.5MPa

[0110] Standard pressure value:

[0111] D0=10MPa

[0112] Calculate the absolute value of the parameter deviation and add ∈:

[0113] |D-D0|+∈=|9.5-10|+0.0001=0.5+0.0001=0.5001

[0114] Calculate the adjustment factor:

[0115]

[0116] Calculate the failure risk value R:

[0117]

[0118] R = 0.8 × 141.414 = 113.131

[0119] The calculation results show that the fault risk value R≈113.131. This value represents the comprehensive fault risk level of the current boiler. If the risk value R exceeds the preset risk threshold (such as 100), it means that the risk is high and maintenance measures need to be taken immediately; if the risk value is lower than the threshold, the risk is within a controllable range and the equipment operation status can continue to be monitored.

Claims

1. An energy management system for a boiler, characterized in that, The system comprises: The data acquisition module collects the fuel input, steam output, heat loss, combustion temperature, excess air coefficient, furnace temperature and flue gas temperature parameters according to the boiler operation data to generate an operation parameter set; The state analysis module performs data cleaning and normalization according to the operating parameter set, calculates the deviation by subtracting the standard value from the current fuel input, analyzes the trend of combustion temperature over time, compares the furnace temperature with the standard operating range, determines the operating state, and obtains the boiler health index; A risk prediction module, based on the boiler health index, retrieves historical fault records whose health index matches the current value, counts the number of faults that occur under similar conditions, and calculates the fault probability by dividing the number of faults by the total number of instances to obtain a fault risk value; A maintenance decision module compares the fault risk value with a preset risk threshold value, and if the value exceeds the threshold value, arranges maintenance actions by setting a maintenance date and required tasks to generate a maintenance plan; The real-time adjustment module monitors the real-time changes of the operating parameters according to the maintenance plan, recalculates the boiler health index to update the data, recalculates the fault risk value using the updated boiler health index, and adjusts the maintenance time by rescheduling.

2. The energy management system for a boiler according to claim 1, characterized in that: The operating parameter set includes fuel input, steam output, heat loss, combustion temperature, excess air coefficient, furnace temperature and flue gas temperature, and the maintenance plan includes a set maintenance date, tasks to be performed and scheduled maintenance actions.

3. The energy management system for a boiler according to claim 1, characterized in that: The data acquisition module comprises: The energy parameter acquisition submodule reads the fuel input through sensors, records the steam output, measures the heat loss, collects and organizes the data, and generates an energy parameter set based on the boiler's operating data; The temperature parameter acquisition submodule uses a temperature sensor to obtain the combustion temperature, collect the excess air coefficient, measure the furnace temperature and flue gas temperature, summarize the obtained data, and generate a temperature parameter set based on the boiler's operating data; The parameter set construction submodule, based on the energy parameter set and the temperature parameter set, aligns the data of the two parameter sets by timestamp, merges the corresponding data items, forms a complete data set, and generates an operation parameter set.

4. The energy management system for a boiler according to claim 1, characterized in that: The state analysis module comprises: A data cleaning submodule, based on the operating parameter set, checks missing values ​​and outliers in the data, fills in missing values, removes outliers, and normalizes each parameter to obtain a cleaned parameter set; The deviation analysis submodule calculates the deviation by subtracting the standard value from the current fuel input based on the cleaned parameter set, calculates the deviation value of each parameter, analyzes the change of combustion temperature over time, draws a temperature change curve, and obtains a parameter deviation set; The state assessment submodule compares the furnace temperature with the standard operating range based on the parameter deviation set, determines whether the temperature exceeds the normal range, determines the operating state, and obtains the boiler health index.

5. The energy management system for a boiler according to claim 1, characterized in that: The risk prediction module comprises: A matching record retrieval submodule, based on the boiler health index, retrieves fault records whose health index is close to the current value in the historical database, screens historical data that meets the conditions, and obtains a matching fault record set; A fault probability calculation submodule, based on the matching fault record set, counts the number of faults that occur under the same conditions, calculates the total number of instances, divides the number of faults by the total number of instances, calculates the probability of the fault occurring, and obtains a fault probability value; The risk value acquisition submodule determines the risk level of the current operating state based on the fault probability value, quantifies the fault risk, and obtains the fault risk value.

6. The energy management system for a boiler according to claim 1, characterized in that: The maintenance decision module includes: The risk comparison submodule compares the fault risk value with a preset risk threshold value based on the fault risk value, determines whether the fault risk value exceeds the threshold value, and obtains a maintenance judgment result; A maintenance scheduling submodule, based on the maintenance judgment result, if maintenance is required, determines the maintenance date, lists the required maintenance tasks, schedules maintenance actions, and obtains a maintenance schedule; The plan generation submodule is configured to generate a maintenance plan based on the maintenance schedule, integrate the maintenance date and task information, and compile the maintenance plan.

7. The energy management system for a boiler according to claim 1, characterized in that: The real-time adjustment module comprises: A parameter monitoring submodule, based on the maintenance plan, continuously monitors the real-time changes of the operating parameters, collects the current fuel input and temperature parameters, updates the data records, and obtains the real-time operating parameters; An index update submodule, based on the real-time operating parameters, recalculates the boiler health index, updates the health index value, reflects the current operating status, and obtains an updated boiler health index; The plan adjustment submodule recalculates the fault risk value based on the updated boiler health index, determines whether the risk level has changed, adjusts the maintenance time if necessary, and obtains an adjusted maintenance plan.

8. The energy management system for a boiler according to claim 5, characterized in that: The quantified failure risk uses the following formula: Calculate the fault risk value R, where P is the fault probability value, S is the fault severity, C is the maintenance cost, D is the current key parameter value, D0 is the standard parameter value, and ∈ is a small positive number to prevent division by zero.

Images