Regional energy efficiency comprehensive evaluation method based on big data

Abstract

The invention relates to the technical field of energy efficiency evaluation, in particular to a regional energy efficiency comprehensive evaluation method based on big data, and the method comprises the steps: S1, simulating heat supply to obtain to-be-analyzed data of each heat supply member in a heat supply region, the to-be-analyzed data comprises the area size corresponding to each heat supply piece, a plurality of distance temperatures of each heat supply piece, the initial environment temperature and the area complexity; s2, the energy efficiency evaluation value of each heat supply piece is determined, and the comprehensive evaluation value is determined; s3, whether the regional energy efficiency is qualified or not is determined based on the comprehensive evaluation value and the data fluctuation characterization value, when it is determined that the regional energy efficiency is abnormal, the energy efficiency category of each heat supply piece is divided based on the energy efficiency evaluation value, and the heat supply medium flow velocity is corrected based on the weak energy efficiency category aggregation value; according to the method, the regional energy efficiency is evaluated by comprehensively considering various factors such as the regional size, the plurality of distance temperatures, the initial environment temperature and the regional complexity corresponding to each heat supply piece, and the efficiency of evaluating the regional energy efficiency is improved while the energy efficiency of the heat supply region is comprehensively evaluated.

Description

Technical Field

[0001] This invention relates to the field of energy efficiency evaluation technology, and in particular to a regional energy efficiency comprehensive evaluation method based on big data. Background Technology

[0002] In today's society, energy issues have increasingly become a global focus. With the acceleration of urbanization and the improvement of people's living standards, the demand for heating is constantly growing, and the proportion of energy consumption in total energy consumption by heating systems is also increasing. Improving the energy efficiency of heating systems can not only reduce energy consumption and environmental impact, but also save heating costs for users, resulting in significant economic and social benefits.

[0003] Traditional methods for evaluating the energy efficiency of heating systems often consider only a single factor, such as the thermal efficiency of heating equipment or the flow rate of the heating medium, lacking a comprehensive consideration of the entire heating area. However, the actual situation within a heating area is very complex and influenced by multiple factors. Therefore, a more comprehensive and accurate regional energy efficiency evaluation method is needed to achieve refined management and optimized operation of the heating system.

[0004] Chinese Patent Application Publication No. CN115422741A discloses an energy-saving evaluation method for an integrated grid-source heating system. The method includes: obtaining the first basic heat parameters of the first heat exchange system; obtaining the heat exchange efficiency analysis results of the first heat exchange system based on the first basic heat parameters and a pre-constructed model for heat exchange efficiency analysis; obtaining the nth basic heat parameters of the nth heat exchange system; obtaining the heat exchange efficiency analysis results of the nth heat exchange system based on the nth basic heat parameters and a pre-constructed model for heat exchange efficiency analysis; and obtaining the ratio of heat user gain to heat source steam loss based on the heat exchange efficiency analysis results of all heat exchange systems, and using this ratio as the overall grid heat exchange efficiency. Therefore, the above technical solution has the following problems: it does not consider the comprehensive factors of the area size, distance temperature, initial ambient temperature, and area complexity of each heating component for area energy efficiency evaluation, which affects both the accuracy and efficiency of the area energy efficiency evaluation. Summary of the Invention

[0005] To address this, the present invention provides a comprehensive regional energy efficiency evaluation method based on big data, which overcomes the problem in the existing technology that does not take into account factors such as the regional size, distance temperature, initial ambient temperature and regional complexity of each heating component when evaluating regional energy efficiency, thus affecting both the accuracy and efficiency of the evaluation of regional energy efficiency.

[0006] To achieve the above objectives, this invention provides a regional energy efficiency comprehensive evaluation method based on big data, comprising: Simulate heating to obtain the data to be analyzed for each heating component in the heating area, including the area size corresponding to each heating component, the temperature at several distances of each heating component, the initial ambient temperature, and the area complexity. The energy efficiency evaluation value of each heating component is determined based on the data to be analyzed, and the comprehensive evaluation value is determined based on each energy efficiency evaluation value. The determination of whether a region's energy efficiency is up to standard is based on a comprehensive evaluation value and data fluctuation characterization value, including: When regional energy efficiency anomalies are identified, the energy efficiency categories of each heating component are classified based on the energy efficiency evaluation value, and the flow rate of the heating medium is corrected based on the cluster value of the weak energy efficiency category. After completing the correction for the flow velocity of the heating medium, determine whether to re-determine the heating path based on the superimposed comparison coefficient based on the change coefficient for the weak energy efficiency area. After completing the correction of the heating path, the flow rate of the heating medium is corrected based on the medium attenuation coefficient, and the comprehensive evaluation value is re-acquired. If the re-acquired comprehensive evaluation value is still less than or equal to the preset comprehensive evaluation value, an alarm message for replacing the heating component due to abnormal energy efficiency is issued. Alternatively, when the energy efficiency of a region is determined to be up to standard, a notification of compliance can be issued, and the operating parameters and comprehensive evaluation values ​​of the heating components can be output.

[0007] Furthermore, the process of determining the energy efficiency evaluation value of each heating component based on the data to be analyzed includes: The heat dissipation impact value is obtained by comparing the area size of a single heating element with the maximum area size of all heating elements. The temperature value is obtained by calculating the ratio of the average temperature of a single heating element at each measurement distance to the preset standard temperature. Solve for the temperature difference between the minimum temperature at each measurement distance of a single heating element and the initial ambient temperature, and calculate the ratio of the temperature difference to the minimum temperature at each measurement distance to obtain the temperature increase value; The ratio of the total volume of all obstacles within the area of ​​a single heating component to the preset calibration volume is calculated to obtain the complexity reduction value; By assigning corresponding coefficients to the temperature value and the temperature increase value respectively, and summing them, the temperature effect value is obtained. The impact value is obtained by summing the corresponding coefficients assigned to the heat dissipation impact value and the complexity reduction value; The energy efficiency evaluation value of a single heating element is obtained by multiplying the temperature effect value and the influence effect value. The comprehensive evaluation value is determined based on each energy efficiency evaluation value, including calculating the average value of each energy efficiency evaluation value to obtain the comprehensive evaluation value.

[0008] Furthermore, the process of determining whether regional energy efficiency is qualified based on comprehensive evaluation value and data fluctuation characterization value includes determining whether regional energy efficiency is qualified based on data fluctuation characterization value when the comprehensive evaluation value is less than or equal to the preset comprehensive evaluation value; When the data fluctuation characterization value is less than or equal to the preset fluctuation value, regional energy efficiency anomaly is determined, the energy efficiency category of each heating component is divided based on the energy efficiency evaluation value, and the flow rate of the heating medium is corrected based on the cluster value of the weak energy efficiency category. When the data fluctuation characterization value is greater than the preset fluctuation value, the data processing is identified as abnormal, and the screening criteria for removing abnormal data are redefined based on the data fluctuation characterization value.

[0009] Furthermore, the process of obtaining data fluctuation characterization values ​​includes: Calculate the average value of a single distance data set after outlier filtering to obtain the average temperature of the single array. Multiple simulated heating sessions were conducted to obtain the average temperature of the corresponding distance data set in several simulated heating sessions. Solve for the variance of each temperature mean to obtain the single set of distance fluctuations; The mean value of the distance fluctuation in each group is calculated to obtain the data fluctuation characterization value.

[0010] Furthermore, the process of classifying the energy efficiency categories of each heating component based on its energy efficiency evaluation value includes identifying heating components with energy efficiency evaluation values ​​less than or equal to a preset comprehensive evaluation value as having weak energy efficiency.

[0011] Furthermore, the process of correcting the heating medium flow rate based on the cluster value of the weak energy efficiency category includes: Obtain the interval distance between adjacent low-efficiency heating components; The ratio of the average distance of each interval to the preset total pipe length is used to obtain the cluster value of the weak energy efficiency category; The increase in heating medium flow rate is positively correlated with the cluster value of the weak energy efficiency category.

[0012] Furthermore, the process of redetermining the screening criteria for outlier data based on data fluctuation characteristics includes: The increase in the screening weight used to remove outliers is positively correlated with the data fluctuation characterization value.

[0013] Furthermore, after correcting for the flow rate of the heating medium, the process of determining whether to re-determine the heating path based on the superimposed comparison coefficient, based on the change coefficient for the low-efficiency area, includes: The average value of the energy efficiency evaluation value of each category of low-energy-efficiency heating component is calculated to obtain the change coefficient of low-energy-efficiency area; When the change coefficient in a low-energy-efficiency area is less than or equal to the preset comprehensive evaluation value, the heating path is re-determined based on the superimposed comparison coefficient. When the change coefficient of a low-energy-efficiency area exceeds the preset comprehensive evaluation value, the area's energy efficiency is determined to be qualified, a qualification notification is issued, and the operating parameters and comprehensive evaluation value of the heating components are output.

[0014] Furthermore, the process of correcting the flow rate of the heating medium based on the medium attenuation coefficient includes: The heating medium is transported into the detection pipe, and the length of the detection pipe through which the heating medium flows from the initial temperature to the preset attenuation temperature is obtained, thus obtaining the medium attenuation coefficient. The increase in the flow velocity of the heating medium is negatively correlated with the medium attenuation coefficient.

[0015] Furthermore, when the comprehensive evaluation value is greater than the preset comprehensive evaluation value, the region's energy efficiency is determined to be qualified, a qualification notification is issued, and the operating parameters and comprehensive evaluation value of the heating components are output.

[0016] Compared with existing technologies, the beneficial effects of this invention lie in determining the energy efficiency evaluation value of each heating element, which quantifies the energy efficiency performance of a single heating element within its corresponding heating area. The invention reflects the heating efficiency, heat dissipation characteristics, and environmental complexity of the heating element by combining temperature effect values ​​and influence effect values. The temperature effect value, obtained by weighted summation of the temperature magnitude and the temperature increase value, represents the degree to which the heating element meets the temperature standard and the efficiency of temperature increase at the measurement distance. The temperature magnitude value is the ratio of the average value to the standard temperature, emphasizing the closeness of the actual temperature to the ideal temperature; the temperature increase value is the ratio of the temperature difference to the minimum temperature among all distances, emphasizing the magnitude of the temperature rise. Combining these two values ​​identifies whether the heating element raises the temperature rapidly and effectively. The influence effect value, obtained by weighted summation of the heat dissipation influence value and the reduction complexity value, represents the impact of the size of the area where the heating element is located and the influence of obstacles on the heating effect. The heat dissipation influence value is the ratio of the area size to the maximum size, reflecting the size of the heat dissipation area; the reduction complexity value is the ratio of the obstacle volume to the calibrated volume, reflecting the obstruction of heat conduction by the area complexity. The energy efficiency evaluation value is the product of the temperature effect value and the influence effect value, enhancing sensitivity. This means that when either the temperature effect or the influence effect is low, the energy efficiency evaluation value will significantly decrease. A higher energy efficiency evaluation value indicates better energy efficiency of the heating component under a given environment, achieving the expected temperature while being less affected by regional characteristics. The regional energy efficiency is determined based on the comprehensive evaluation value and data fluctuation characterization value. The comprehensive evaluation value reflects the average energy efficiency level of all heating components in the entire heating area, reflecting the overall heating effect of the area. When the comprehensive evaluation value is greater than the preset value, the overall energy efficiency is good and can be directly judged as qualified; if it is less than or equal to the preset value, it indicates that there may be problems with the overall energy efficiency, requiring further analysis based on the data fluctuation characterization value for accurate judgment. A rapid screening is first performed using the comprehensive evaluation value, and further analysis is then conducted for cases with potential problems. Multiple factors are considered in evaluating regional energy efficiency, including the area size corresponding to each heating component, several distance temperatures, initial ambient temperature, and regional complexity. This comprehensive evaluation of the heating area's energy efficiency improves the efficiency of regional energy efficiency assessment.

[0017] Furthermore, a data fluctuation characterization value is determined, which is used to measure the stability of heating data. By simulating heating multiple times, the average temperature of corresponding distance data groups in different simulations is obtained, and the variance of these averages is calculated, reflecting the fluctuation of temperature data across multiple simulations. Finally, the average of the distance fluctuations for each group is calculated, which more comprehensively reflects the degree of data fluctuation across the entire heating area. Based on the data fluctuation characterization value, the regional energy efficiency is determined to be up to standard. When the data fluctuation characterization value is less than or equal to the preset fluctuation value, the data is relatively stable, indicating an anomaly caused by energy efficiency issues in the heating system itself. When the data fluctuation characterization value is greater than the preset fluctuation value, the data fluctuation is too large, indicating an abnormal comprehensive evaluation value due to inadequate screening of abnormal data during data processing. Different solutions are adopted for different situations, avoiding blind adjustments to heating and improving the efficiency of regional energy efficiency evaluation.

[0018] Furthermore, when anomalies occur due to energy efficiency issues inherent in the heating system itself, the energy efficiency of each heating component is categorized based on its energy efficiency evaluation value. The flow rate of the heating medium is adjusted based on the cluster value of the weak energy efficiency category. The weak energy efficiency category consists of heating components whose energy efficiency evaluation value is less than or equal to a preset comprehensive evaluation value, representing heating components with poor heating performance. The interval distance is the pipe length through which the heating medium flows between adjacent weak energy efficiency category heating components, reflecting the pipe distance between them. The weak energy efficiency category cluster value is the ratio of the average of each interval distance to the preset total pipe length, reflecting the degree of dispersion in the distribution of weak energy efficiency heating components. The cluster value reflects the distribution of weak energy efficiency heating components; the larger the cluster value, the more dispersed the pipe distance distribution of weak energy efficiency heating components, indicating that some areas without heat radiation superposition cannot be heated in a timely manner. The heat radiation superposition area is the area where multiple heating components jointly heat the same area. By increasing the flow rate of the heating medium to accelerate heat transfer, and adjusting the flow rate of the heating medium according to the cluster value of the weak energy efficiency category, the heating situation in weak energy efficiency areas is improved in a targeted manner, thereby increasing the efficiency of regional energy efficiency evaluation.

[0019] Furthermore, the decision to re-determine the heating path based on the superimposed comparison coefficient is made based on the change coefficient for low-energy-efficiency areas. The change coefficient reflects the overall energy efficiency of low-energy-efficiency areas. When the change coefficient is less than or equal to the preset comprehensive evaluation value, it indicates that the problem in the low-energy-efficiency area is still quite serious, and the heating path needs to be replanned to improve the heating effect. The superimposed comparison coefficient is the ratio of the number of heating components in a relatively enclosed area to the volume of the relatively enclosed area, reflecting the distribution density of the heating components. The heating path is selected by comparing the superimposed comparison coefficients of different alternative heating components. The heating path is re-determined based on the change coefficient and the superimposed comparison coefficient to optimize the layout of the heating system, making the heat more evenly distributed throughout the area, improving heating efficiency, and thus improving the evaluation efficiency of regional energy efficiency.

[0020] Furthermore, the flow rate of the heating medium is corrected based on the medium attenuation coefficient. The medium attenuation coefficient is the length of the detection pipe through which the heating medium flows as its temperature drops from its initial temperature to a preset attenuation temperature, reflecting the energy loss of the heating medium during transmission. A smaller medium attenuation coefficient indicates greater energy loss, requiring a larger increase in the flow rate of the heating medium to accelerate heat transfer. Correcting the flow rate of the heating medium based on the medium attenuation coefficient allows for adjustments based on the actual energy loss of the heating medium, improving the energy utilization efficiency of the heating system and thus enhancing the efficiency of regional energy efficiency evaluation. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the steps of the regional energy efficiency comprehensive evaluation method based on big data, as described in an embodiment of the present invention. Figure 2 This is a logic diagram for determining whether a region's energy efficiency is qualified based on a comprehensive evaluation value and a data fluctuation characterization value, according to an embodiment of the present invention. Figure 3 This is a logic diagram for classifying the energy efficiency categories of various heating components based on energy efficiency evaluation values, according to an embodiment of the present invention. Figure 4 This is a logic diagram for determining whether to re-determine the heating path based on the superimposed comparison coefficient, according to an embodiment of the present invention, based on the change coefficient of the weak energy efficiency area. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate the direction or positional relationship, are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0025] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0026] Please see Figure 1 The diagram shows a flowchart of the regional energy efficiency comprehensive evaluation method based on big data according to an embodiment of the present invention; the regional energy efficiency comprehensive evaluation method based on big data according to an embodiment of the present invention includes: S1, simulate heating to obtain the data to be analyzed for each heating component in the heating area. The data to be analyzed includes the area size corresponding to each heating component, the temperature at several distances of each heating component, the initial ambient temperature, and the area complexity. S2, determine the energy efficiency evaluation value of each heating component based on the data to be analyzed, and determine the comprehensive evaluation value based on each energy efficiency evaluation value; S3, determining whether a region's energy efficiency is up to standard based on comprehensive evaluation values ​​and data fluctuation characterization values, including: When regional energy efficiency anomalies are identified, the energy efficiency categories of each heating component are classified based on the energy efficiency evaluation value, and the flow rate of the heating medium is corrected based on the cluster value of the weak energy efficiency category. After completing the correction for the flow velocity of the heating medium, determine whether to re-determine the heating path based on the superimposed comparison coefficient based on the change coefficient for the weak energy efficiency area. After completing the correction of the heating path, the flow rate of the heating medium is corrected based on the medium attenuation coefficient, and the comprehensive evaluation value is re-acquired. If the re-acquired comprehensive evaluation value is still less than or equal to the preset comprehensive evaluation value, an alarm message for replacing the heating component due to abnormal energy efficiency is issued. Alternatively, when the energy efficiency of a region is determined to be up to standard, a notification of compliance can be issued, and the operating parameters and comprehensive evaluation values ​​of the heating components can be output.

[0027] Specifically, there are no restrictions on the specific method of simulating heating. Simulation can be performed using a heating simulation model or computer numerical simulation. The method of establishing the heating simulation model is also unrestricted; it can be built using historical heating data and machine learning algorithms. Historical heating data is collected, including the size, shape, material properties, operating parameters of the heating components, regional temperature distribution, and environmental conditions. Material properties include the heating medium. The historical heating data is used to train a neural network model. During the heating simulation, different operating parameters of the heating components and environmental conditions are input to predict the corresponding data to be analyzed for each heating component. This is existing technology and will not be elaborated further.

[0028] Specifically, environmental conditions include initial ambient temperature, heating area size, total volume of obstacles within the heating area, and density, specific heat capacity, and thermal conductivity of building materials.

[0029] Specifically, the regional temperature distribution refers to the temperature at several distances from the heating element; the operating parameters of the heating element include heating power, heating medium flow rate, and the heating path through which the heating medium flows.

[0030] Specifically, the method of computer numerical simulation is not limited. Finite element analysis software can be used to create a three-dimensional geometric model of the heating area and heating components, setting the dimensions and shape of each part, and defining the density, specific heat capacity, and thermal conductivity of the heating medium and building materials. Boundary conditions are set, and the operating parameters and initial ambient temperature of the heating components are set. Numerical solutions are then performed to obtain the data to be analyzed for each heating component. This is existing technology and will not be elaborated further.

[0031] The S1 also includes preprocessing the data to be analyzed to screen out abnormal data. In a single distance data group, after screening outliers, the average value of the remaining distance temperatures in the group is calculated, and the average value is used to fill in the empty values ​​after screening.

[0032] Specifically, the preprocessing of the data to be analyzed includes: The predicted temperatures at different distances are grouped according to different distances to obtain several groups of distance data. For a single distance data set, calculate the mean (mu) and standard deviation (sigma) of the temperature at each distance in the data set; Data points in a single distance data set that are not within the interval [mu-C×sigma, mu+C×sigma] are identified as outliers and filtered out, with C being the filtering weight set to 2.

[0033] Specifically, the process of determining the energy efficiency evaluation value of each heating component based on the data to be analyzed includes: The heat dissipation impact value is obtained by taking the ratio of the area size of a single heating element to the maximum value of the area size of all heating elements. The temperature value is obtained by calculating the ratio of the average temperature of a single heating element at each measurement distance to the preset standard temperature. Solve for the temperature difference between the minimum temperature at each measurement distance of a single heating element and the initial ambient temperature, and calculate the ratio of the temperature difference to the minimum temperature at each measurement distance to obtain the temperature increase value; The ratio of the total volume of all obstacles within the area of ​​a single heating component to the preset calibration volume is calculated to obtain the complexity reduction value; By assigning corresponding coefficients to the temperature value and the temperature increase value respectively, and summing them, the temperature effect value is obtained. The impact value is obtained by summing the corresponding coefficients assigned to the heat dissipation impact value and the complexity reduction value; The energy efficiency evaluation value of a single heating element is obtained by multiplying the temperature effect value and the influence effect value. The comprehensive evaluation value is determined based on each energy efficiency evaluation value, including calculating the average value of each energy efficiency evaluation value to obtain the comprehensive evaluation value.

[0034] Specifically, the assignment method is multiplication, with the coefficients for both the temperature value and the temperature increase value being 0.5, to comprehensively consider the effect of the actual temperature and the temperature increase; the coefficients for both the heat dissipation impact value and the reduction complexity value are 0.5, to comprehensively consider the impact of the heating volume and obstacles in the heating area on the actual heating.

[0035] Specifically, the preset standard temperature is selected within the range [18, 22], in °C. Those skilled in the art can determine the preset standard temperature themselves, based on the indoor heating comfort standard ASHRAE 55-2020, or through historical heating data statistics or industry standards. It is understood that the preset standard temperature should be a standard for measuring the expected heating effect. In this embodiment, preferably, the preset standard temperature is 20. The preset calibration volume is selected within the range [9, 12], in m³. Those skilled in the art can select the preset calibration volume themselves, based on the obstacle volume statistics of typical residential or commercial rooms, or through on-site measurement or a building model database. It is understood that it should reflect the complexity of the actual heating area. In this embodiment, preferably, the preset calibration volume is 10.

[0036] Specifically, the initial ambient temperature used for simulation is greater than zero.

[0037] Specifically, the area size corresponding to a heating element is the volume within the heating area where the heating element is located. It can be understood that if multiple heating elements are included in a single relatively enclosed area, then the area size of a single heating element is the ratio of the total volume of the relatively enclosed area to the total number of built-in heating elements. A relatively enclosed area can be a single room.

[0038] Specifically, the selection distances corresponding to the various temperatures of the heating element are not limited and can be determined by equal intervals. The maximum distance selected among the various temperatures can be the farthest distance from the heating element within the corresponding area size or the maximum effective heat radiation distance of a single heating room determined by the experiment. The maximum effective heat radiation distance is the shortest distance between the point with a temperature of 18°C ​​detected under the preset standard ambient temperature and the heating element.

[0039] Please see Figure 2As shown, this is a logic diagram illustrating how the present invention determines whether a region's energy efficiency is qualified based on a comprehensive evaluation value and a data fluctuation characterization value. The process of determining whether a region's energy efficiency is qualified based on a comprehensive evaluation value and a data fluctuation characterization value includes: When the comprehensive evaluation value is greater than the preset comprehensive evaluation value, the region's energy efficiency is determined to be qualified, a qualification notification is issued, and the operating parameters and comprehensive evaluation value of the heating components are output. When the comprehensive evaluation value is less than or equal to the preset comprehensive evaluation value, the regional energy efficiency is determined based on the data fluctuation characterization value.

[0040] Specifically, the preset comprehensive evaluation value is selected within the range [0.81, 0.87]. Those skilled in the art can determine the preset comprehensive evaluation value themselves. It can be determined by combining historical data, industry standards, experimental verification, and actual application scenarios. A large amount of historical heating data can be collected, including the energy efficiency evaluation values ​​of each heating component that meets energy consumption standards under qualified energy efficiency conditions. The average value of these energy efficiency evaluation values ​​is calculated as the benchmark for the preset comprehensive evaluation value. Historical data should cover different initial ambient temperatures, area sizes, and operating flow rates. Those skilled in the art can determine the preset comprehensive evaluation value from historical data according to the application scenario. In this embodiment, preferably, the preset comprehensive evaluation value is 0.87.

[0041] Specifically, an energy efficiency evaluation value is determined for each heating element, quantifying its energy performance within its corresponding heating area. This is achieved by combining temperature effect and impact effect values ​​to reflect the heating efficiency, heat dissipation characteristics, and the influence of environmental complexity. The temperature effect value, obtained by weighted summation of the temperature magnitude and the temperature increase value, represents the degree to which the heating element meets temperature standards and the efficiency of temperature increase at the measurement distance. The temperature magnitude value is the ratio of the average temperature to the standard temperature, emphasizing the closeness of the actual temperature to the ideal temperature. The temperature increase value is the ratio of the temperature difference to the minimum temperature among all distances, emphasizing the magnitude of the temperature rise. Combining these two values ​​helps identify whether the heating element raises the temperature rapidly and effectively. The impact effect value, obtained by weighted summation of heat dissipation impact and complexity reduction value, represents the influence of the size of the area where the heating element is located and the impact of obstacles on the heating effect. The heat dissipation impact value is the ratio of the area size to the maximum size, reflecting the size of the heat dissipation area. The complexity reduction value is the ratio of the obstacle volume to the calibrated volume, reflecting the obstruction of heat conduction by the area's complexity. The energy efficiency evaluation value is the product of the temperature effect value and the influence effect value, enhancing sensitivity. This means that when either the temperature effect or the influence effect is low, the energy efficiency evaluation value will significantly decrease. A higher energy efficiency evaluation value indicates better energy efficiency of the heating component under a given environment, achieving the expected temperature while being less affected by regional characteristics. The regional energy efficiency is determined based on the comprehensive evaluation value and data fluctuation characterization value. The comprehensive evaluation value reflects the average energy efficiency level of all heating components in the entire heating area, reflecting the overall heating effect of the area. When the comprehensive evaluation value is greater than the preset value, the overall energy efficiency is good and can be directly judged as qualified; if it is less than or equal to the preset value, it indicates that there may be problems with the overall energy efficiency, requiring further analysis based on the data fluctuation characterization value for accurate judgment. A rapid screening is first performed using the comprehensive evaluation value, and further analysis is then conducted for cases with potential problems. Multiple factors are considered in evaluating regional energy efficiency, including the area size corresponding to each heating component, several distance temperatures, initial ambient temperature, and regional complexity. This comprehensive evaluation of the heating area's energy efficiency improves the efficiency of regional energy efficiency assessment.

[0042] Specifically, the process of obtaining data fluctuation characterization values ​​includes: Calculate the average value of a single distance data set after outlier filtering to obtain the average temperature of the single array. A single distance data set includes several temperature values ​​for each heating element at the same distance; Multiple simulated heating sessions were conducted to obtain the average temperature of the corresponding distance data set in several simulated heating sessions. Solve for the variance of each temperature mean to obtain the single set of distance fluctuations; The mean value of the distance fluctuation in each group is calculated to obtain the data fluctuation characterization value.

[0043] Specifically, determining whether a region's energy efficiency meets standards based on data fluctuation characteristics includes: If the data fluctuation characterization value is less than or equal to the preset fluctuation value, the regional energy efficiency is determined to be abnormal. The energy efficiency categories of each heating component are divided based on the energy efficiency evaluation value, and the flow rate of the heating medium is corrected based on the cluster value of the weak energy efficiency category. If the data fluctuation characterization value is greater than the preset fluctuation value, the data processing is determined to be abnormal, and the screening criteria for removing abnormal data are re-determined based on the data fluctuation characterization value.

[0044] Specifically, the preset fluctuation value is selected within the range [0.12, 0.17], with the unit being °C². Those skilled in the art can select and determine the preset fluctuation value themselves. Multiple simulations can be performed on different heating component operating parameters and environmental conditions to obtain several simulation data. The preset fluctuation value is determined based on the data fluctuation characterization value distribution of the simulation data. It can be understood that it can be used to classify whether there are abnormal fluctuations after multiple simulations of the data to be analyzed. In this embodiment, preferably, the preset fluctuation value is 0.12.

[0045] Specifically, a data fluctuation characterization value is determined, which measures the stability of heating data. By simulating heating multiple times, the average temperature of corresponding distance data groups in different simulations is obtained, and the variance of these averages is calculated, reflecting the temperature data fluctuation across multiple simulations. Finally, the average of the distance fluctuations for each group is calculated, providing a more comprehensive picture of the overall data fluctuation across the heating area. Based on the data fluctuation characterization value, the regional energy efficiency is determined. When the data fluctuation characterization value is less than or equal to a preset fluctuation value, the data is relatively stable, indicating an anomaly caused by energy efficiency issues in the heating system itself. When the data fluctuation characterization value is greater than the preset fluctuation value, the data fluctuation is too large, indicating an abnormal comprehensive evaluation value due to inadequate screening of abnormal data during data processing. Different solutions are adopted for different situations, avoiding blind adjustments to heating and improving the efficiency of regional energy efficiency evaluation.

[0046] Please see Figure 3 As shown, this is a logic diagram for classifying the energy efficiency categories of each heating component based on energy efficiency evaluation values ​​according to an embodiment of the present invention. The process of classifying the energy efficiency categories of each heating component based on energy efficiency evaluation values ​​and correcting the flow rate of the heating medium based on the cluster value of weak energy efficiency categories includes: Heating components with energy efficiency ratings less than or equal to the preset comprehensive rating are classified as low-energy-efficiency components; heating components with energy efficiency ratings greater than the preset comprehensive rating are classified as high-energy-efficiency components. Obtain the interval distance between adjacent low-efficiency heating components; The ratio of the average distance of each interval to the preset total pipe length is used to obtain the cluster value of the weak energy efficiency category; The increase in heating medium flow rate is positively correlated with the cluster value of the weak energy efficiency category.

[0047] Specifically, the two heating elements through which the heating medium flows in sequence are the heating elements with the shortest interval. The interval between the two heating elements is the length of the pipe through which the heating medium flows between the two heating elements. The heating element with the shortest interval to a single low-efficiency category heating element is the adjacent low-efficiency category heating element.

[0048] Specifically, the preset total pipe length is the total length of the pipes flowing between each heating component.

[0049] In this embodiment, optionally, The cluster value of the weak energy efficiency category is compared with the first cluster value and the second cluster value; If the cluster value of the weak energy efficiency category is less than or equal to the first cluster value, the flow rate of the heating medium will be corrected to 1.11 times the initial flow rate. If the cluster value of the weak energy efficiency category is less than or equal to the second cluster value and greater than the first cluster value, the flow rate of the heating medium will be corrected to 1.17 times the initial flow rate. If the cluster value of the weak energy efficiency category is greater than the second cluster value, the flow rate of the heating medium will be corrected to 1.22 times the initial flow rate. The first cluster value is 0.12, and the second cluster value is 0.28.

[0050] Specifically, when anomalies occur due to energy efficiency issues inherent in the heating system itself, the energy efficiency of each heating component is categorized based on its energy efficiency evaluation value. The flow rate of the heating medium is adjusted based on the cluster value of the weak energy efficiency category. The weak energy efficiency category consists of heating components whose energy efficiency evaluation value is less than or equal to a preset comprehensive evaluation value, representing heating components with poor heating performance. The interval distance is the pipe length through which the heating medium flows between adjacent weak energy efficiency category heating components, reflecting the pipe distance between these components. The weak energy efficiency category cluster value is the ratio of the average of all interval distances to the preset total pipe length, reflecting the degree of dispersion in the distribution of weak energy efficiency heating components. The cluster value reflects the distribution of weak energy efficiency heating components; the larger the cluster value, the more dispersed the pipe distance distribution of weak energy efficiency heating components, indicating that some areas without heat radiation superposition cannot be heated in a timely manner. Heat radiation superposition areas are those where multiple heating components jointly heat the same area. By increasing the flow rate of the heating medium to accelerate heat transfer, and adjusting the flow rate of the heating medium according to the cluster value of the weak energy efficiency category, the heating situation in weak energy efficiency areas is improved in a targeted manner, thereby increasing the efficiency of regional energy efficiency evaluation.

[0051] Specifically, the process of redetermining the screening criteria for outlier data based on data fluctuation characteristics includes: The increase in the screening weight used to remove outliers is positively correlated with the data fluctuation characterization value.

[0052] In this embodiment, optionally, The data fluctuation representation is compared with the first preset fluctuation comparison value and the second preset fluctuation comparison value; If the data fluctuation is less than or equal to the first preset fluctuation comparison value, the screening weight used to screen out abnormal data will be adjusted to 1.07 times the initial screening weight. If the data fluctuation is less than or equal to the second preset fluctuation comparison value and greater than the first preset fluctuation comparison value, the screening weight used to screen out abnormal data will be adjusted to 1.13 times the initial screening weight. If the data fluctuation is less than or equal to the first preset fluctuation comparison value, the screening weight used to screen out abnormal data will be adjusted to 1.21 times the initial screening weight. The first preset fluctuation comparison value is 0.25, and the second preset fluctuation comparison value is 0.35, with the unit being °C².

[0053] Specifically, redefining the screening criteria makes data processing more accurate, thereby improving the accuracy of subsequent energy efficiency assessments. If the overall evaluation value is still unsatisfactory after redefinition, further adjustments can be made to the heating system, avoiding erroneous judgments and unnecessary adjustments due to inaccurate data.

[0054] Specifically, after adjusting the screening weights, the regional energy efficiency is determined based on the redefined comprehensive evaluation value. If the redefined comprehensive evaluation value is still less than or equal to the preset comprehensive evaluation value, the regional energy efficiency is determined to be abnormal. The energy efficiency categories of each heating component are divided based on the energy efficiency evaluation value, and the flow rate of the heating medium is corrected based on the cluster value of the weak energy efficiency category.

[0055] Please see Figure 4 As shown, this is a logic diagram illustrating whether to re-determine the heating path based on the superimposed comparison coefficient, according to an embodiment of the present invention, based on the change coefficient for low-energy-efficiency areas. After correcting the flow velocity of the heating medium, the process of determining whether to re-determine the heating path based on the change coefficient for low-energy-efficiency areas includes: The average value of the energy efficiency evaluation value of each category of low-energy-efficiency heating component is calculated to obtain the change coefficient of low-energy-efficiency area; When the change coefficient in a low-energy-efficiency area is less than or equal to the preset comprehensive evaluation value, the heating path is re-determined based on the superimposed comparison coefficient. When the change coefficient of a low-energy-efficiency area exceeds the preset comprehensive evaluation value, the area's energy efficiency is determined to be qualified, a qualification notification is issued, and the operating parameters and comprehensive evaluation value of the heating components are output.

[0056] Specifically, the process of redetermining the heating path based on the superimposed comparison coefficients includes: The two alternative heating elements that are closest to the starting heating element in terms of spatial distance are identified, and neither of the alternative heating elements has been planned. Calculate the superposition comparison coefficient of the two candidate heating rooms, and determine the candidate heating room with the smaller superposition comparison coefficient as the heating element adjacent to the starting heating room; The alternative heating components for each heating component are determined sequentially to complete the determination of the adjacent heating components for each heating component.

[0057] Spatial distance is the straight-line distance between a single heating element and the heating element.

[0058] The superposition comparison coefficient is the ratio of the number of heating components in the corresponding relatively enclosed area to the volume of the relatively enclosed area.

[0059] Specifically, the decision to re-determine the heating path based on the superimposed comparison coefficient is made based on the change coefficient for low-energy-efficiency areas. The change coefficient reflects the overall energy efficiency of low-energy-efficiency areas. When the change coefficient is less than or equal to the preset comprehensive evaluation value, it indicates that the problem in the low-energy-efficiency area remains serious, requiring a redesign of the heating path to improve heating performance. The superimposed comparison coefficient is the ratio of the number of heating components within a relatively enclosed area to the volume of that area, reflecting the distribution density of the heating components. By comparing the superimposed comparison coefficients of different alternative heating components, a heating path is selected. The heating path is re-determined based on the change coefficient and the superimposed comparison coefficient to optimize the layout of the heating system, ensuring a more even distribution of heat throughout the area, improving heating efficiency, and thus enhancing the efficiency of the regional energy efficiency evaluation.

[0060] Specifically, the process of correcting the flow rate of the heating medium based on the medium attenuation coefficient includes: The heating medium is transported into the detection pipe, and the length of the detection pipe through which the heating medium flows from the initial temperature to the preset attenuation temperature is obtained, thus obtaining the medium attenuation coefficient. The increase in the flow velocity of the heating medium is negatively correlated with the medium attenuation coefficient.

[0061] In this embodiment, optionally, The dielectric attenuation coefficient is compared with the first preset attenuation comparison value and the second preset attenuation comparison value; If the medium attenuation coefficient is less than or equal to the first preset attenuation ratio, the flow rate of the heating medium will be adjusted to 1.31 times the current flow rate. If the medium attenuation coefficient is less than or equal to the second preset attenuation ratio and greater than the first preset attenuation ratio, the flow rate of the heating medium will be adjusted to 1.25 times the current flow rate. If the medium attenuation coefficient is greater than the second preset attenuation ratio, the flow rate of the heating medium will be adjusted to 1.17 times the current flow rate. The first preset attenuation comparison value is 10m, and the second preset attenuation comparison value is 15m.

[0062] Specifically, the flow rate of the heating medium is corrected based on the medium attenuation coefficient. The medium attenuation coefficient is the length of the detection pipe through which the heating medium flows as its temperature drops from its initial temperature to a preset attenuation temperature, reflecting the energy loss of the heating medium during transmission. A smaller medium attenuation coefficient indicates greater energy loss, requiring a larger increase in the flow rate of the heating medium to accelerate heat transfer. Correcting the flow rate of the heating medium based on the medium attenuation coefficient allows for adjustments based on the actual energy loss of the heating medium, improving the energy utilization efficiency of the heating system and thus enhancing the efficiency of regional energy efficiency evaluation.

[0063] Specifically, by comprehensively considering multiple factors, the energy efficiency of the heating area is fully and accurately assessed and adjusted to effectively improve heating efficiency, reduce energy consumption, and achieve intelligent management of heating.

[0064] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A comprehensive regional energy efficiency evaluation method based on big data, characterized in that, include: Simulate heating to obtain the data to be analyzed for each heating component in the heating area, including the area size corresponding to each heating component, the temperature at several distances of each heating component, the initial ambient temperature, and the area complexity. The energy efficiency evaluation value of each heating component is determined based on the data to be analyzed, and the comprehensive evaluation value is determined based on each energy efficiency evaluation value. The determination of whether a region's energy efficiency is up to standard is based on a comprehensive evaluation value and data fluctuation characterization value, including: When regional energy efficiency anomalies are identified, the energy efficiency categories of each heating component are classified based on the energy efficiency evaluation value, and the flow rate of the heating medium is corrected based on the cluster value of the weak energy efficiency category. After completing the correction for the flow velocity of the heating medium, determine whether to re-determine the heating path based on the superimposed comparison coefficient based on the change coefficient for the weak energy efficiency area. After completing the correction of the heating path, the flow rate of the heating medium is corrected based on the medium attenuation coefficient, and the comprehensive evaluation value is re-acquired. If the re-acquired comprehensive evaluation value is still less than or equal to the preset comprehensive evaluation value, an alarm message for replacing the heating component due to abnormal energy efficiency is issued. Alternatively, when the energy efficiency of a region is determined to be up to standard, a notification of compliance can be issued, and the operating parameters and comprehensive evaluation values ​​of the heating components can be output.

2. The regional energy efficiency comprehensive evaluation method based on big data according to claim 1, characterized in that, The process of determining the energy efficiency evaluation value of each heating component based on the data to be analyzed includes: The heat dissipation impact value is obtained by comparing the area size of a single heating element with the maximum area size of all heating elements. The temperature value is obtained by calculating the ratio of the average temperature of a single heating element at each measurement distance to the preset standard temperature. Solve for the temperature difference between the minimum temperature at each measurement distance of a single heating element and the initial ambient temperature, and calculate the ratio of the temperature difference to the minimum temperature at each measurement distance to obtain the temperature increase value; The ratio of the total volume of all obstacles within the area of ​​a single heating component to the preset calibration volume is calculated to obtain the complexity reduction value; By assigning corresponding coefficients to the temperature value and the temperature increase value respectively, and summing them, the temperature effect value is obtained. The impact value is obtained by summing the corresponding coefficients assigned to the heat dissipation impact value and the complexity reduction value; The energy efficiency evaluation value of a single heating element is obtained by multiplying the temperature effect value and the influence effect value. The comprehensive evaluation value is determined based on each energy efficiency evaluation value, including calculating the average value of each energy efficiency evaluation value to obtain the comprehensive evaluation value.

3. The regional energy efficiency comprehensive evaluation method based on big data according to claim 2, characterized in that, The process of determining whether a region’s energy efficiency is qualified based on the comprehensive evaluation value and the data fluctuation characterization value includes determining whether a region’s energy efficiency is qualified based on the data fluctuation characterization value when the comprehensive evaluation value is less than or equal to the preset comprehensive evaluation value. When the data fluctuation characterization value is less than or equal to the preset fluctuation value, regional energy efficiency anomaly is determined, the energy efficiency category of each heating component is divided based on the energy efficiency evaluation value, and the flow rate of the heating medium is corrected based on the cluster value of the weak energy efficiency category. When the data fluctuation characterization value is greater than the preset fluctuation value, the data processing is identified as abnormal, and the screening criteria for removing abnormal data are redefined based on the data fluctuation characterization value.

4. The regional energy efficiency comprehensive evaluation method based on big data according to claim 3, characterized in that, The process of obtaining data fluctuation characterization values ​​includes: Calculate the average value of a single distance data set after outlier filtering to obtain the average temperature of the single array. Multiple simulated heating sessions were conducted to obtain the average temperature of the corresponding distance data set in several simulated heating sessions. Solve for the variance of each temperature mean to obtain the single set of distance fluctuations; The mean value of the distance fluctuation in each group is calculated to obtain the data fluctuation characterization value.

5. The regional energy efficiency comprehensive evaluation method based on big data according to claim 4, characterized in that, The process of classifying the energy efficiency categories of each heating component based on its energy efficiency evaluation value includes identifying heating components with energy efficiency evaluation values ​​less than or equal to a preset comprehensive evaluation value as weak energy efficiency categories.

6. The regional energy efficiency comprehensive evaluation method based on big data according to claim 5, characterized in that, The process of correcting the flow rate of the heating medium based on the cluster value of the weak energy efficiency category includes: Obtain the interval distance between adjacent low-efficiency heating components; The ratio of the average distance of each interval to the preset total pipe length is used to obtain the cluster value of the weak energy efficiency category; The increase in heating medium flow rate is positively correlated with the cluster value of the weak energy efficiency category.

7. The regional energy efficiency comprehensive evaluation method based on big data according to claim 6, characterized in that, The process of redetermining the screening criteria for outlier data based on data fluctuation characteristics includes: The increase in the screening weight used to remove outliers is positively correlated with the data fluctuation characterization value.

8. The regional energy efficiency comprehensive evaluation method based on big data according to claim 7, characterized in that, After correcting for the flow velocity of the heating medium, the process of determining whether to re-determine the heating path based on the superimposed comparison coefficient, based on the change coefficient for the low-efficiency area, includes: The average value of the energy efficiency evaluation value of each category of low-energy-efficiency heating component is calculated to obtain the change coefficient of low-energy-efficiency area; When the change coefficient in a low-energy-efficiency area is less than or equal to the preset comprehensive evaluation value, the heating path is re-determined based on the superimposed comparison coefficient. When the change coefficient of a low-energy-efficiency area exceeds the preset comprehensive evaluation value, the area's energy efficiency is determined to be qualified, a qualification notification is issued, and the operating parameters and comprehensive evaluation value of the heating components are output.

9. The regional energy efficiency comprehensive evaluation method based on big data according to claim 8, characterized in that, The process of correcting the flow rate of the heating medium based on the medium attenuation coefficient includes: The heating medium is transported into the detection pipe, and the length of the detection pipe through which the heating medium flows from the initial temperature to the preset attenuation temperature is obtained, thus obtaining the medium attenuation coefficient. The increase in the flow velocity of the heating medium is negatively correlated with the medium attenuation coefficient.

10. The regional energy efficiency comprehensive evaluation method based on big data according to claim 3, characterized in that, When the comprehensive evaluation value is greater than the preset comprehensive evaluation value, the region's energy efficiency is determined to be qualified, a qualification notification is issued, and the operating parameters and comprehensive evaluation value of the heating components are output.

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