Heat supply terminal data analysis management system

Abstract

The invention relates to the technical field of heat supply data management, in particular to a heat supply terminal data analysis and management system, which comprises a data acquisition module for periodically acquiring heat supply environment data and heat supply equipment data of a target area; the data analysis module is used for determining heat supply environment characteristics and heat supply equipment characteristics in a future preset time period and determining heat supply demand characteristics of the target area; the data judgment module is used for judging whether equipment parameter adjustment is triggered or not based on the heat supply demand characteristics, and determining an adjustment mode of the equipment parameters based on a comparison result of the heat supply demand characteristics and preset demand characteristics; and the feature updating module is used for determining the heat supply trend index of the target area based on the judgment result of the data judgment module, and updating the preset demand feature based on the heat supply trend index. The heat supply effect and the heat supply efficiency can be improved.

Description

Technical Field

[0001] This invention relates to the field of heating data management technology, and in particular to a heating terminal data analysis and management system. Background Technology

[0002] In traditional heating systems, the operation of heating equipment relies primarily on human experience and fixed operating parameters. Heating stations typically adjust boiler combustion intensity, circulating water pump frequency, and other heating equipment parameters manually based on weather forecasts and past operating experience. Monitoring of the heating environment also relies mainly on manual inspections and a limited number of fixed sensors, resulting in incomplete and untimely data acquisition.

[0003] With technological advancements, some heating systems have begun to incorporate automatic control and basic data analysis functions. For example, some systems can automatically adjust the water supply temperature based on changes in outdoor temperature; however, these adjustments are often based on a single environmental factor, lacking a comprehensive analysis of the heating environment and equipment operating status. Furthermore, existing systems have limited ability to predict and respond to heating demand, failing to make refined parameter adjustments based on users' actual needs and the actual operating conditions of the equipment, resulting in low heating effectiveness and efficiency.

[0004] Chinese Patent Application Publication No. CN117346214A discloses a smart heating control method, device, equipment, and computer storage medium. The method includes acquiring historical heating data; acquiring a first current time; acquiring multiple first heating data for the same period based on the historical heating data and the first current time; calculating an average value based on the multiple first heating data and assigning the average value to the planned heating capacity; and controlling heating based on the planned heating capacity.

[0005] The existing technology has the following problems: it only determines the planned heating capacity based on historical heating data to control heating, lacks a comprehensive analysis of the heating environment and heating equipment, and cannot make fine-grained parameter adjustments according to the actual needs of users and the actual operating conditions of the equipment, resulting in low heating effect and heating efficiency. Summary of the Invention

[0006] To address this, the present invention provides a heating terminal data analysis and management system to overcome the problems in the prior art, which lack comprehensive analysis of the heating environment and heating equipment, and cannot make fine-grained parameter adjustments based on the actual needs of users and the actual operating conditions of the equipment, resulting in low heating effect and efficiency.

[0007] To achieve the above objectives, the present invention provides a heating terminal data analysis and management system, comprising: The data acquisition module is used to periodically acquire heating environment data and heating equipment data of the target area; The data analysis module, which is connected to the data acquisition module, is used to determine the heating environment characteristics in a future preset time period based on the heating environment data in the target time period, and to determine the heating equipment characteristics in the future preset time period based on the heating equipment data in the target time period, and to determine the heating demand characteristics of the target area based on the heating environment characteristics in the future preset time period and the heating equipment characteristics. A data determination module, connected to both the data acquisition module and the data analysis module, is used to determine whether to trigger equipment parameter adjustments based on the heating demand characteristics, and to determine the equipment parameter adjustment method based on a comparison between the heating demand characteristics and preset demand characteristics, including a first adjustment method and a second adjustment method. Under the first adjustment method, the operating status of the heating equipment is determined based on the current heating equipment data, and the equipment parameters are adjusted based on the operating status. Under the second adjustment method, the peak value of the heating parameters of the heating equipment is determined based on the type of heating demand, and the equipment parameters are adjusted based on the peak value of the heating parameters. The feature update module, which is connected to the data determination module, is used to determine the heating trend index of the target area based on the determination result of the data determination module, and update the preset demand features based on the heating trend index.

[0008] Furthermore, the data analysis module includes: The heating environment analysis submodule is connected to the data acquisition module. It is used to construct a heating environment characteristic model of the target area based on the acquired heating environment data, and to determine the heating environment characteristics in a future preset time period based on the heating environment data and the heating environment characteristic model in the target time period.

[0009] Furthermore, the data analysis module includes: The heating equipment analysis submodule is connected to the data acquisition module. It is used to construct a heating equipment feature model of the target area based on the acquired heating equipment data, and to determine the heating equipment features in a future preset time period based on the heating equipment data and the heating equipment feature model in the target time period.

[0010] Furthermore, the data analysis module includes: The heating demand analysis submodule is connected to the heating environment analysis submodule and the heating equipment analysis submodule, respectively, to determine the heating demand type of the target area based on the correlation between the heating environment characteristics and the heating equipment characteristics within a future preset time period, and to determine the heating demand characteristics of the target area based on the heating demand type.

[0011] Furthermore, the data determination module includes: The data determination submodule, which is connected to the heating demand analysis submodule, is used to determine whether to trigger equipment parameter adjustment based on the comparison results between the heating demand characteristics and the preset demand characteristics.

[0012] Furthermore, the data determination module includes: The adjustment method determination submodule is connected to the heating demand analysis submodule and the data determination submodule, respectively, and is used to determine the adjustment method of equipment parameters based on the comparison result of heating demand characteristics and preset demand characteristics according to the first determination result. The first determination result is the determination by the data determination submodule to trigger device parameter adjustment.

[0013] Furthermore, the adjustment method determination submodule determines the demand characteristic value based on the comparison result between the heating demand characteristics and the preset demand characteristics; If the required characteristic value is less than the preset characteristic value, then the adjustment method of the equipment parameters is determined to be the first adjustment method; If the required characteristic value is greater than or equal to the preset characteristic value, then the adjustment method for the equipment parameters is determined to be the second adjustment method.

[0014] Furthermore, the data determination module includes: The first adjustment submodule is connected to the adjustment method determination submodule and the data acquisition module, respectively, and is used to determine the equipment operating status of the heating equipment based on the current heating equipment data, and to determine the first equipment adjustment coefficient based on the equipment operating status, and to adjust the equipment parameters based on the first equipment adjustment parameter, when the equipment parameter adjustment method is the first adjustment method.

[0015] Furthermore, the data determination module includes: The second adjustment submodule is connected to the adjustment method determination submodule and the heating demand analysis submodule, respectively. It is used to determine the peak value of the heating parameters of the heating equipment based on the heating demand type when the equipment parameter adjustment method is the second adjustment method, determine the second equipment adjustment parameters based on the comparison result of the peak value of the heating parameters and the characteristic value of the heating parameters, and adjust the equipment parameters based on the second equipment adjustment coefficient.

[0016] Furthermore, the feature update module includes: The indicator determination submodule is connected to the data determination submodule and the data acquisition module respectively, and is used to determine the heating trend indicator of the target area based on the second determination result and the acquired heating environment data and heating equipment data. An update submodule, which is connected to the indicator determination submodule and the heating demand analysis submodule respectively, is used to determine the update adjustment coefficient based on the comparison results of the heating trend indicator and the heating demand characteristics, and update the preset demand characteristics based on the update adjustment coefficient. The second determination result is that the data determination submodule determines that the device parameter adjustment is not triggered.

[0017] Compared with existing technologies, the advantages of this invention lie in that it periodically acquires heating environment data and heating equipment data for the target area through a data acquisition module. This enables real-time and accurate monitoring of the operating status of heating equipment and changes in the external environment, providing comprehensive basic data support for subsequent analysis and decision-making. By setting up a data analysis module to determine the characteristics of the heating environment and heating equipment within a preset time period, it allows for in-depth analysis of the heating environment and equipment conditions in the target area. This comprehensive analysis determines the heating demand characteristics of the target area. Different heating demand characteristics correspond to different heating equipment parameters, effectively improving heating effect and efficiency. Furthermore, by setting up a data judgment module, it determines whether to trigger equipment parameter adjustments based on heating demand characteristics and determines the specific adjustment method. This achieves intelligent decision-making and automated control of heating equipment operation. By providing two adjustment methods for different heating demand characteristics, it dynamically adjusts equipment parameters, flexibly responding to changes in heating demand and improving heating effect and efficiency. By setting a feature update module to determine the heating trend indicators of the target area and updating the preset demand characteristics accordingly, the heating demand can be dynamically adapted to the changing trend of heating demand, further improving the flexibility of determining the adjustment method of equipment parameters, thereby improving the heating effect and heating efficiency.

[0018] Furthermore, the data analysis module of this invention constructs a heating environment characteristic model of the target area by setting up a heating environment analysis sub-module, which can accurately determine the heating environment characteristics within a preset time period in the future, thereby comprehensively reflecting the overall characteristics of the heating environment in the target area, flexibly adapting to the dynamic changes in the heating environment, and providing a basis for subsequent heating demand analysis and optimization of heating resource allocation.

[0019] Furthermore, the data analysis module of this invention, by setting a characteristic model of the heating equipment in the target area of ​​the heating equipment analysis submodule, can accurately determine the characteristics of the heating equipment in the future preset time period, comprehensively reflect the overall performance of the heating equipment in the target area, and dynamically adapt to changes in the heating equipment, providing a basis for subsequent heating demand analysis and optimization of heating resource allocation.

[0020] Furthermore, the data analysis module of this invention determines the type of heating demand by setting up a heating demand analysis sub-module based on the correlation between heating environment characteristics and heating equipment characteristics. This enables a comprehensive analysis of the heating environment and heating equipment, thereby determining the heating demand characteristics of the target area. While ensuring heating efficiency, it can make personalized adjustments according to user needs, thereby improving heating effect and heating service quality.

[0021] Furthermore, the data determination module of this invention, by setting up a data determination sub-module to compare heating demand characteristics with preset demand characteristics, can accurately determine whether equipment parameters need to be adjusted. The preset demand characteristics reflect the critical heating demand under the corresponding heating environment and heating equipment data. By responding to changes in heating demand in a timely manner, accurate comparison and determination are achieved. Equipment parameter adjustments are only triggered when heating demand characteristics change significantly, avoiding unnecessary frequent adjustments, ensuring the stability of equipment operation, and further improving the heating effect.

[0022] Furthermore, the data determination module of the present invention, by setting an adjustment method determination sub-module, can determine the specific adjustment method based on the comparison results after the data determination sub-module determines that the equipment parameter adjustment is triggered. This enables an efficient and flexible response to changes in heating demand, ensuring that the operation of the heating equipment matches the actual heating demand, improving the heating effect and efficiency, and avoiding overheating or underheating.

[0023] Furthermore, the data determination module of the present invention, by setting a first adjustment submodule, indicates that the change in heating demand is not significant when the adjustment method of the equipment parameters is determined to be the first adjustment method. Based on the current heating equipment data, the operating status of the heating equipment can be accurately evaluated, thereby determining the first equipment adjustment coefficient. This enables the rapid and accurate optimization of the equipment's operating parameters, allowing the equipment to recover to its optimal operating state in a short period of time, thereby improving heating efficiency and heating effect.

[0024] Furthermore, the data determination module of this invention, by setting a second adjustment submodule, indicates that the heating demand changes significantly. Based on the characteristics of the heating demand, it determines the peak value of the heating parameters, which can accurately match the actual heating demand of the target area. By comparing the peak value of the heating parameters with the characteristic value of the heating parameters, it can ensure that the output of the heating equipment is highly consistent with the current heating demand, avoiding insufficient or excessive heating. The second equipment adjustment parameters determined according to the comparison results can accurately optimize the operating status of the heating equipment, thereby effectively improving heating efficiency and heating effect.

[0025] Furthermore, the feature update module of this invention, through the setting of an index determination submodule, can accurately determine the heating trend index of the target area after the data judgment submodule determines that no equipment parameter adjustment will be triggered. Through continuous monitoring and analysis of the heating trend index, abnormal fluctuations in heating demand can be warned in advance. By setting an update submodule, a reasonable update adjustment coefficient can be determined based on the comparison results of the heating trend index and heating demand characteristics. By continuously updating the preset demand characteristics, the heating system maintains good adaptability and flexibility in the face of complex and ever-changing heating environments, further optimizing the adjustment method of the operating parameters of the heating equipment and improving heating effect and efficiency. Attached Figure Description

[0026] Figure 1 This is a structural block diagram of the heating terminal data analysis and management system according to an embodiment of the present invention; Figure 2 This is a structural block diagram of the data analysis module in an embodiment of the present invention; Figure 3 This is a structural block diagram of the data determination module in an embodiment of the present invention; Figure 4 This is a structural block diagram of the feature update module in an embodiment of the present invention; Figure 5 This is a logic diagram for determining the adjustment method of equipment parameters in an embodiment of the present invention. Detailed Implementation

[0027] 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.

[0028] 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.

[0029] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying 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.

[0030] 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.

[0031] Please see Figures 1-5 As shown, Figure 1 This is a structural block diagram of the heating terminal data analysis and management system according to an embodiment of the present invention; Figure 2 This is a structural block diagram of the data analysis module in an embodiment of the present invention; Figure 3 This is a structural block diagram of the data determination module in an embodiment of the present invention; Figure 4 This is a structural block diagram of the feature update module in an embodiment of the present invention; Figure 5 This invention provides a logic diagram for determining the adjustment method of equipment parameters in an embodiment of the invention; the invention also provides a heating terminal data analysis and management system, including: The data acquisition module is used to periodically acquire heating environment data and heating equipment data of the target area; In practice, heating environment data refers to data corresponding to heating environment parameters, such as temperature, humidity, wind speed, and solar radiation intensity. Heating equipment data refers to data corresponding to heating equipment operating parameters, such as operating time, equipment temperature, and energy efficiency ratio. It is understood that there are no restrictions on the specific methods and equipment used to obtain heating environment data and heating equipment data for the target area. For example, corresponding sensors or data acquisition devices can be installed in the target area and on the heating equipment.

[0032] The data analysis module, which is connected to the data acquisition module, is used to determine the heating environment characteristics in a future preset time period based on the heating environment data in the target time period, and to determine the heating equipment characteristics in the future preset time period based on the heating equipment data in the target time period, and to determine the heating demand characteristics of the target area based on the heating environment characteristics in the future preset time period and the heating equipment characteristics. Specifically, the data analysis module includes: The heating environment analysis submodule is connected to the data acquisition module. It is used to construct a heating environment characteristic model of the target area based on the acquired heating environment data, and to determine the heating environment characteristics in a future preset time period based on the heating environment data and the heating environment characteristic model in the target time period.

[0033] In practice, those skilled in the art will know that any predictive model in the prior art that can analyze the characteristics of the heating environment within a preset time period falls within the protection scope of this invention. In actual application, data preprocessing can be performed based on the acquired heating environment data, including data cleaning, normalization, and time series alignment, to obtain environmental training data. The initial model can then be trained based on the environmental training data to construct a heating environment characteristic model.

[0034] Understandably, the heating environment data for the target time period is preprocessed to obtain the target heating environment data. This target heating environment data is then input into the heating environment characteristic model of the target area to obtain the heating environment characteristics for the future preset time period output by the heating environment characteristic model. The heating environment characteristics of the target area are used to reflect the adverse external environmental characteristics that the heating equipment in the target area needs to cope with during operation, such as cooling and strong winds.

[0035] It is understandable that implementers can set a target time period and a preset time period based on the actual situation. The end point of the target time period is the current time, and the start point of the preset time period is the current time. Preferably, the duration of the preset time period is shorter than the duration of the target time period. The target time period includes at least the current data collection time point, which can acquire current heating environment data and current heating equipment data.

[0036] The data analysis module of this invention constructs a heating environment characteristic model of the target area by setting up a heating environment analysis sub-module. This model can accurately determine the heating environment characteristics within a preset time period, thereby comprehensively reflecting the overall characteristics of the heating environment in the target area and flexibly adapting to dynamic changes in the heating environment. This provides a basis for subsequent heating demand analysis and optimization of heating resource allocation.

[0037] Specifically, the data analysis module includes: The heating equipment analysis submodule is connected to the data acquisition module. It is used to construct a heating equipment feature model of the target area based on the acquired heating equipment data, and to determine the heating equipment features in a future preset time period based on the heating equipment data and the heating equipment feature model in the target time period.

[0038] In practice, those skilled in the art will know that any predictive model in the prior art that can analyze the characteristics of heating equipment falls within the protection scope of this invention. In practical applications, data preprocessing can be performed based on the acquired heating equipment data, including data cleaning, normalization, and time series alignment, to obtain equipment training data. The initial model can then be trained based on the equipment training data to construct a heating equipment characteristic model.

[0039] Understandably, data preprocessing is performed on heating equipment data within the target time period to obtain target heating equipment data. This target heating equipment data is then input into the heating equipment characteristic model of the target area to obtain the heating equipment characteristics of the target area output by the model. The heating equipment characteristics of the target area are used to reflect the adverse internal equipment characteristics that the heating equipment in the target area needs to address during operation, such as excessively high equipment temperature, excessively long operating time, and equipment stability.

[0040] The data analysis module of this invention can accurately determine the characteristics of heating equipment in a future preset time period by setting a characteristic model of heating equipment in the target area of ​​the heating equipment analysis submodule. It can comprehensively reflect the overall performance of heating equipment in the target area and dynamically adapt to changes in heating equipment, providing a basis for subsequent heating demand analysis and optimization of heating resource allocation.

[0041] Specifically, the data analysis module includes: The heating demand analysis submodule is connected to the heating environment analysis submodule and the heating equipment analysis submodule, respectively, to determine the heating demand type of the target area based on the correlation between the heating environment characteristics and the heating equipment characteristics within a future preset time period, and to determine the heating demand characteristics of the target area based on the heating demand type.

[0042] In implementation, the correlation between heating environment characteristics and heating equipment characteristics can be strong, weak, or no. A strong correlation indicates that heating environment characteristics have a significant impact on heating equipment characteristics; a weak correlation indicates that heating environment characteristics have a minor impact; and no correlation indicates that heating environment characteristics have virtually no impact on heating equipment characteristics. A correlation model between heating environment characteristics, heating equipment characteristics, and heating demand can be constructed based on historical data. Methods such as multiple linear regression and neural networks can be used. Heating demand types include basic demand, comfort demand, and peak demand. For basic demand, the external environment that heating equipment in the target area needs to cope with during operation is relatively mild (e.g., temperature 10℃~18℃, humidity 40%). For heating systems with humidity levels between 0°C and 60%, the internal equipment characteristics are relatively excellent (equipment operating time less than 30 minutes, equipment operating temperature 10°C to 30°C, etc.). The heating environment characteristics have virtually no impact on the heating equipment characteristics. The main requirement for heating is to maintain the indoor temperature within a relatively comfortable range. In this case, there is no correlation between the heating environment characteristics and the heating equipment characteristics. For comfort-oriented systems, the external environment that the heating equipment needs to cope with during operation in the target area is generally moderate (e.g., temperature 0°C to 10°C, humidity 60%). For peak demand areas, where the external environment is relatively harsh (e.g., temperature below 0℃, humidity above 70%), and the internal equipment characteristics are generally moderate (running time 30-60 minutes, equipment operating temperature 30℃-50℃), the heating environment characteristics affect the characteristics of the heating equipment, and the requirements for heating comfort are relatively high. In this case, the correlation between the characteristics of the heating environment and the characteristics of the heating equipment is weak. For peak demand areas, where the external environment that the heating equipment needs to cope with during operation is relatively harsh (e.g., temperature below 0℃, humidity above 70%), and the internal equipment characteristics are generally moderate (running time exceeding 60 minutes, equipment operating temperature exceeding 50℃), the heating environment characteristics have a significant impact on the characteristics of the heating equipment. The demand for heating is very urgent, requiring the heating equipment to provide sufficient heat to resist the severe cold. In this case, the correlation between the characteristics of the heating environment and the characteristics of the heating equipment is strong.

[0043] It is understandable that each type of heating demand has corresponding heating demand characteristics. Practitioners can set up corresponding heating demand comparison tables based on the actual situation. Heating demand characteristics include heating load, heating time, heating stability, etc.

[0044] The data analysis module of this invention determines the type of heating demand by setting up a heating demand analysis sub-module based on the correlation between the characteristics of the heating environment and the characteristics of the heating equipment. It can achieve a comprehensive analysis of the heating environment and heating equipment, thereby determining the heating demand characteristics of the target area. While ensuring heating efficiency, it can make personalized adjustments according to the needs of users, thereby improving the heating effect and the quality of heating services.

[0045] A data determination module, connected to both the data acquisition module and the data analysis module, is used to determine whether to trigger equipment parameter adjustments based on the heating demand characteristics, and to determine the equipment parameter adjustment method based on a comparison between the heating demand characteristics and preset demand characteristics, including a first adjustment method and a second adjustment method. Under the first adjustment method, the operating status of the heating equipment is determined based on the current heating equipment data, and the equipment parameters are adjusted based on the operating status. Under the second adjustment method, the peak value of the heating parameters of the heating equipment is determined based on the type of heating demand, and the equipment parameters are adjusted based on the peak value of the heating parameters. Specifically, the data determination module includes: The data determination submodule, which is connected to the heating demand analysis submodule, is used to determine whether to trigger equipment parameter adjustment based on the comparison results between the heating demand characteristics and the preset demand characteristics.

[0046] In implementation, based on the heating demand characteristics Y1, Y2, ..., Y... j , ..., Y m With the preset requirement characteristics E1, E2, ..., E j , ..., E m Determine the first matching degree P1; where j = 1, 2, ..., m, P1 = (∑ m j=1 Y j ×E j ) / (sqrt(∑ m j=1 (Y j ) 2 )×sqrt(∑ m j=1 (E j ) 2 ), sqrt() is a preset square root determination function, m is the number of heating demand characteristics, Y j Let E be the j-th heating demand characteristic value. j For the j-th preset requirement feature value, if the first matching degree is less than the first preset matching degree, it is determined that the device parameter adjustment is triggered; if the first matching degree is greater than or equal to the first preset matching degree, it is determined that the device parameter adjustment is not triggered.

[0047] It is understandable that the implementers can set the first preset matching degree based on the actual situation. Preferably, the first preset matching degree is set to a value range of 0.6 to 0.7. The implementers can set the preset demand characteristics based on the actual situation or the average value of heating demand characteristics that have passed the qualification test in historical data.

[0048] The data determination module of this invention compares heating demand characteristics with preset demand characteristics by setting a data determination sub-module. This allows for accurate determination of whether equipment parameters need to be adjusted. The preset demand characteristics reflect the critical heating demand under the corresponding heating environment and heating equipment data. By responding promptly to changes in heating demand, accurate comparison and determination are achieved. Equipment parameter adjustments are only triggered when heating demand characteristics change significantly, avoiding unnecessary frequent adjustments, ensuring the stability of equipment operation, and further improving the heating effect.

[0049] Specifically, the data determination module includes: The adjustment method determination submodule is connected to the heating demand analysis submodule and the data determination submodule, respectively, and is used to determine the adjustment method of equipment parameters based on the comparison result of heating demand characteristics and preset demand characteristics according to the first determination result. The first determination result is the determination by the data determination submodule to trigger device parameter adjustment.

[0050] The data determination module of this invention determines the adjustment method by setting an adjustment method sub-module. After the data determination sub-module determines that the equipment parameter adjustment is triggered, it determines the specific adjustment method based on the comparison results. This enables an efficient and flexible response to changes in heating demand, ensuring that the operation of the heating equipment matches the actual heating demand, improving the heating effect and efficiency, and avoiding overheating or underheating.

[0051] Specifically, the adjustment method determination submodule determines the demand characteristic value based on the comparison result between the heating demand characteristics and the preset demand characteristics; If the required characteristic value is less than the preset characteristic value, then the adjustment method of the equipment parameters is determined to be the first adjustment method; If the required characteristic value is greater than or equal to the preset characteristic value, then the adjustment method for the equipment parameters is determined to be the second adjustment method.

[0052] In implementation, the demand characteristic value is determined based on the ratio of the preset matching degree to the first matching degree. If the demand characteristic value is less than the preset characteristic value, it indicates that the heating demand characteristics are relatively close to the preset demand characteristics and the heating demand does not change much. In this case, the equipment parameter adjustment method is determined to be the first adjustment method. If the demand characteristic value is greater than or equal to the preset characteristic value, it indicates that the heating demand characteristics are significantly different from the preset demand characteristics and the heating demand does not change much. In this case, the equipment parameter adjustment method is determined to be the second adjustment method.

[0053] Specifically, the data determination module includes: The first adjustment submodule is connected to the adjustment method determination submodule and the data acquisition module, respectively, and is used to determine the equipment operating status of the heating equipment based on the current heating equipment data, and to determine the first equipment adjustment coefficient based on the equipment operating status, and to adjust the equipment parameters based on the first equipment adjustment parameter, when the equipment parameter adjustment method is the first adjustment method.

[0054] During implementation, the equipment operating status includes normal and abnormal states, based on the current heating equipment data H1, H2, ..., H... i H n With preset heating equipment data G1, G2, ..., G i , ..., G n Determine the second matching degree P2, where i = 1, 2, ..., n, P2 = (∑ n i=1 H i ×G i ) / (sqrt(∑ n i=1 (H i ) 2 )×sqrt(∑ n i=1 (G i ) 2 In this context, n represents the number of parameters for the current heating equipment. If the second matching degree P2 is greater than the second preset matching degree Q, the corresponding equipment operating state is normal, and the first equipment adjustment coefficient W1 = (P2 - Q) / Q. If the second matching degree is less than or equal to the second preset matching degree, the corresponding equipment operating state is abnormal, and the first equipment adjustment coefficient W1 = (Q - P2) / Q. It can be understood that under the first adjustment method, the target equipment parameters are determined based on the product of the first equipment adjustment parameters and the original equipment parameters. The implementer can set the second preset matching degree based on the actual situation. Preferably, the second preset matching degree is set to a range of 0.6 to 0.7. The original equipment parameters include heating temperature, water supply pressure, hot water flow rate, and operating current.

[0055] The data determination module of this invention, by setting a first adjustment submodule, indicates that the change in heating demand is not significant when the equipment parameter adjustment method is determined to be the first adjustment method. Based on the current heating equipment data, the module can accurately assess the equipment operating status and determine the first equipment adjustment coefficient. This enables the module to quickly and accurately optimize the equipment operating parameters, allowing the equipment to recover to its optimal operating state in a short time, thereby improving heating efficiency and heating effect.

[0056] Specifically, the data determination module includes: The second adjustment submodule is connected to the adjustment method determination submodule and the heating demand analysis submodule, respectively. It is used to determine the peak value of the heating parameters of the heating equipment based on the heating demand type when the equipment parameter adjustment method is the second adjustment method, determine the second equipment adjustment parameters based on the comparison result of the peak value of the heating parameters and the characteristic value of the heating parameters, and adjust the equipment parameters based on the second equipment adjustment coefficient.

[0057] In implementation, each heating demand type has a corresponding peak value for equipment parameters. This peak value represents the maximum selectable value for the heating equipment parameters corresponding to that heating demand type. In practice, this peak value can be determined based on the equipment parameter variation curve during equipment operation. The ratio of the peak heating parameter to the characteristic value of the heating parameter can be used as the second equipment adjustment parameter. Under this second adjustment method, the target equipment parameter is determined by multiplying the second adjustment parameter by the original equipment parameter. Implementers can set the characteristic value of the heating parameter based on the actual situation or the maximum value of the equipment parameter in historical data.

[0058] The data determination module of this invention, by setting a second adjustment submodule, indicates that the heating demand changes significantly. Based on the characteristics of the heating demand, it determines the peak value of the heating parameters, which can accurately match the actual heating demand of the target area. By comparing the peak value of the heating parameters with the characteristic value of the heating parameters, it can ensure that the output of the heating equipment is highly consistent with the current heating demand, avoiding insufficient or excessive heating. The second equipment adjustment parameters determined according to the comparison results can accurately optimize the operating status of the heating equipment, thereby effectively improving heating efficiency and heating effect.

[0059] The feature update module, which is connected to the data determination module, is used to determine the heating trend index of the target area based on the determination result of the data determination module, and update the preset demand features based on the heating trend index.

[0060] Specifically, the feature update module includes: The indicator determination submodule is connected to the data determination submodule and the data acquisition module respectively, and is used to determine the heating trend indicator of the target area based on the second determination result and the acquired heating environment data and heating equipment data. In implementation, if the data determination submodule determines that no equipment parameter adjustment is triggered, it indicates that the current equipment parameters can meet the heating demand. A dataset can be constructed based on the acquired heating environment data and heating equipment data. The initial neural network model is then trained on this dataset to obtain the target neural network model. This target neural network model can comprehensively analyze the heating environment trend and the heating equipment operation trend, thereby outputting a heating trend index. The heating trend index uses the maximum and minimum predicted feature values ​​corresponding to the heating demand characteristics as its upper and lower limits.

[0061] An update submodule, which is connected to the indicator determination submodule and the heating demand analysis submodule, is used to determine the update adjustment coefficient based on the comparison results of the heating trend indicator and the heating demand characteristics, and to update the preset demand characteristics based on the update adjustment coefficient.

[0062] In implementation, the update adjustment coefficient K = (∑ m j=1 (Y j -R min ) / (R max -R min ))) / m, where R min R is the minimum predictive feature value. max To obtain the maximum predicted feature value, the product of the update adjustment coefficient and the preset demand feature value is determined as the updated preset demand feature value.

[0063] The second determination result is that the data determination submodule determines that the device parameter adjustment is not triggered.

[0064] The feature update module of this invention, through the setting of an index determination submodule, can accurately determine the heating trend index of the target area after the data judgment submodule determines that no equipment parameter adjustment will be triggered. Continuous monitoring and analysis of the heating trend index can provide early warning of abnormal fluctuations in heating demand. By setting an update submodule, a reasonable update adjustment coefficient can be determined based on the comparison results of the heating trend index and heating demand characteristics. By continuously updating the preset demand characteristics, the heating system maintains good adaptability and flexibility in the face of complex and ever-changing heating environments, further optimizing the adjustment methods of heating equipment operating parameters and improving heating effect and efficiency.

[0065] Specifically, this invention uses a data acquisition module to periodically acquire heating environment and heating equipment data for a target area. This enables real-time and accurate monitoring of the operating status of heating equipment and changes in the external environment, providing comprehensive basic data support for subsequent analysis and decision-making. A data analysis module determines the characteristics of the heating environment and heating equipment within a preset time period, allowing for in-depth analysis of the target area's heating environment and equipment status. This comprehensive analysis determines the heating demand characteristics of the target area; different heating demand characteristics correspond to different heating equipment parameters, effectively improving heating effect and efficiency. A data judgment module determines whether to trigger equipment parameter adjustments based on heating demand characteristics and identifies the specific adjustment method. This achieves intelligent decision-making and automated control of heating equipment operation. By providing two adjustment methods for different heating demand characteristics, the device parameters can be dynamically adjusted to flexibly respond to changes in heating demand, improving heating effect and efficiency. A feature update module determines the heating trend indicators of the target area and updates the preset demand characteristics accordingly. This dynamically adapts to changing heating demand trends, further improving the flexibility of determining equipment parameter adjustment methods, thereby enhancing heating effect and efficiency.

[0066] 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.

Claims

1. A data analysis and management system for heating terminals, characterized in that, include: The data acquisition module is used to periodically acquire heating environment data and heating equipment data of the target area; The data analysis module, which is connected to the data acquisition module, is used to determine the heating environment characteristics in a future preset time period based on the heating environment data in the target time period, and to determine the heating equipment characteristics in the future preset time period based on the heating equipment data in the target time period, and to determine the heating demand characteristics of the target area based on the heating environment characteristics in the future preset time period and the heating equipment characteristics. A data determination module, connected to both the data acquisition module and the data analysis module, is used to determine whether to trigger equipment parameter adjustments based on the heating demand characteristics, and to determine the equipment parameter adjustment method based on a comparison between the heating demand characteristics and preset demand characteristics, including a first adjustment method and a second adjustment method. Under the first adjustment method, the operating status of the heating equipment is determined based on the current heating equipment data, and the equipment parameters are adjusted based on the operating status. Under the second adjustment method, the peak value of the heating parameters of the heating equipment is determined based on the type of heating demand, and the equipment parameters are adjusted based on the peak value of the heating parameters. The feature update module, which is connected to the data determination module, is used to determine the heating trend index of the target area based on the determination result of the data determination module, and update the preset demand features based on the heating trend index.

2. The heating terminal data analysis and management system according to claim 1, characterized in that, The data analysis module includes: The heating environment analysis submodule is connected to the data acquisition module. It is used to construct a heating environment characteristic model of the target area based on the acquired heating environment data, and to determine the heating environment characteristics in a future preset time period based on the heating environment data and the heating environment characteristic model in the target time period.

3. The heating terminal data analysis and management system according to claim 2, characterized in that, The data analysis module includes: The heating equipment analysis submodule is connected to the data acquisition module. It is used to construct a heating equipment feature model of the target area based on the acquired heating equipment data, and to determine the heating equipment features in a future preset time period based on the heating equipment data and the heating equipment feature model in the target time period.

4. The heating terminal data analysis and management system according to claim 3, characterized in that, The data analysis module includes: The heating demand analysis submodule is connected to the heating environment analysis submodule and the heating equipment analysis submodule, respectively, to determine the heating demand type of the target area based on the correlation between the heating environment characteristics and the heating equipment characteristics within a future preset time period, and to determine the heating demand characteristics of the target area based on the heating demand type.

5. The heating terminal data analysis and management system according to claim 4, characterized in that, The data determination module includes: The data determination submodule, which is connected to the heating demand analysis submodule, is used to determine whether to trigger equipment parameter adjustment based on the comparison results between the heating demand characteristics and the preset demand characteristics.

6. The heating terminal data analysis and management system according to claim 5, characterized in that, The data determination module includes: The adjustment method determination submodule is connected to the heating demand analysis submodule and the data determination submodule, respectively, and is used to determine the adjustment method of equipment parameters based on the comparison result of heating demand characteristics and preset demand characteristics according to the first determination result. The first determination result is the determination by the data determination submodule to trigger device parameter adjustment.

7. The heating terminal data analysis and management system according to claim 6, characterized in that, The adjustment method determination submodule determines the demand characteristic value based on the comparison result between the heating demand characteristics and the preset demand characteristics; If the required characteristic value is less than the preset characteristic value, then the adjustment method of the equipment parameters is determined to be the first adjustment method; If the required characteristic value is greater than or equal to the preset characteristic value, then the adjustment method for the equipment parameters is determined to be the second adjustment method.

8. The heating terminal data analysis and management system according to claim 7, characterized in that, The data determination module includes: The first adjustment submodule is connected to the adjustment method determination submodule and the data acquisition module, respectively, and is used to determine the equipment operating status of the heating equipment based on the current heating equipment data, and to determine the first equipment adjustment coefficient based on the equipment operating status, and to adjust the equipment parameters based on the first equipment adjustment parameter, when the equipment parameter adjustment method is the first adjustment method.

9. The heating terminal data analysis and management system according to claim 8, characterized in that, The data determination module includes: The second adjustment submodule is connected to the adjustment method determination submodule and the heating demand analysis submodule, respectively. It is used to determine the peak value of the heating parameters of the heating equipment based on the heating demand type when the equipment parameter adjustment method is the second adjustment method, determine the second equipment adjustment parameters based on the comparison result of the peak value of the heating parameters and the characteristic value of the heating parameters, and adjust the equipment parameters based on the second equipment adjustment coefficient.

10. The heating terminal data analysis and management system according to claim 9, characterized in that, The feature update module includes: The indicator determination submodule is connected to the data determination submodule and the data acquisition module respectively, and is used to determine the heating trend indicator of the target area based on the second determination result and the acquired heating environment data and heating equipment data. An update submodule, which is connected to the indicator determination submodule and the heating demand analysis submodule respectively, is used to determine the update adjustment coefficient based on the comparison results of the heating trend indicator and the heating demand characteristics, and update the preset demand characteristics based on the update adjustment coefficient. The second determination result is that the data determination submodule determines that the device parameter adjustment is not triggered.

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