Energy-saving heat dissipation method and system of intelligent snow melting heat dissipation belt

Abstract

The invention relates to the technical field of snow-melting heat dissipation belts, and discloses an energy-saving heat dissipation method and system for an intelligent snow-melting heat dissipation belt, and the method comprises the steps: collecting a plurality of snow-melting temperatures based on historical collection moments, and constructing a plurality of snow-melting temperature change groups; analyzing each snow melting temperature change group, and calculating a snow melting temperature index of the intelligent snow melting heat dissipation belt; historical snow melting energy consumption corresponding to each historical collection moment is obtained, all the historical snow melting energy consumption is analyzed, and a snow melting energy consumption index of the intelligent snow melting heat dissipation belt is calculated based on an analysis result; according to the energy-saving heat dissipation method, the energy-saving regulation and control gain index of the intelligent snow melting heat dissipation belt is determined based on the snow melting temperature index and the snow melting energy consumption index, energy-saving heat dissipation control is conducted on the intelligent snow melting heat dissipation belt according to the energy-saving regulation and control gain index, the snow melting temperature and energy consumption rule can be dynamically analyzed based on historical data, and the power regulation and control strategy is optimized. The working efficiency of the intelligent snow melting heat dissipation belt is improved, energy consumption is reduced, and meanwhile the stable snow melting effect is ensured.

Description

Technical Field

[0001] This invention relates to the field of snow melting heat dissipation technology, and more specifically, to an energy-saving heat dissipation method and system for an intelligent snow melting heat dissipation belt. Background Technology

[0002] With frequent snowfalls in winter, snow accumulation and icing on roads, bridges, rooftops, and other areas pose serious challenges to traffic safety and building maintenance. Traditional snow melting methods mainly rely on mechanical snow removal or chemical de-icing agents, but these methods suffer from low efficiency, significant environmental pollution, and corrosion of infrastructure. In recent years, intelligent snow melting heat dissipation strips have gradually been applied as a highly efficient and environmentally friendly snow melting technology. They convert electrical energy into heat energy through electrothermal conversion, achieving rapid melting of snow.

[0003] However, existing intelligent snow melting heat dissipation belts suffer from high energy consumption and inaccurate temperature control during operation. Due to the dynamic changes in ambient temperature, snow thickness, humidity, and other factors, the traditional constant power output mode is difficult to adapt to actual needs, easily leading to energy waste or poor snow melting effect. Moreover, most of them only rely on simple control based on real-time temperature, failing to comprehensively consider the temperature change trend and energy consumption characteristics during the snow melting process, resulting in limited energy-saving effect. Summary of the Invention

[0004] This invention provides an energy-saving heat dissipation method and system for an intelligent snow melting heat dissipation strip. This invention can dynamically analyze the snow melting temperature and energy consumption patterns based on historical data, and optimize the power control strategy accordingly to improve the working efficiency of the intelligent snow melting heat dissipation strip, reduce energy consumption, and ensure a stable snow melting effect.

[0005] To achieve the above objectives, the present invention provides an energy-saving heat dissipation method for an intelligent snow melting heat dissipation strip, comprising: Multiple historical data collection times are pre-set, multiple snow melting temperatures are collected based on these historical data collection times, and multiple snow melting temperature change groups are constructed based on all the snow melting temperatures. Each snow melting temperature change group was analyzed, and the snow melting temperature index of the intelligent snow melting heat dissipation belt was calculated based on the analysis results. The historical snow melting energy consumption corresponding to each historical collection moment is obtained, all historical snow melting energy consumption is analyzed, and the snow melting energy consumption index of the intelligent snow melting heat dissipation belt is calculated based on the analysis results. The energy-saving control gain index of the intelligent snow melting heat dissipation belt is determined based on the snow melting temperature index and the snow melting energy consumption index, and the energy-saving heat dissipation control of the intelligent snow melting heat dissipation belt is performed according to the energy-saving control gain index.

[0006] Furthermore, before constructing the snowmelt temperature variation group based on all snowmelt temperatures, the following is also included: The process involves iterating through and preprocessing each historical data collection moment and the corresponding snow melting temperature. The preprocessing includes deleting duplicate snow melting temperatures and incorrect snow melting temperatures. A snowmelt temperature variation group is constructed based on the preprocessed historical acquisition times and the corresponding snowmelt temperature at each historical acquisition time.

[0007] Furthermore, when collecting multiple snowmelt temperatures based on historical data collection times and constructing snowmelt temperature change groups based on all snowmelt temperatures, this includes: Curve fitting was performed on all snow melting temperatures to obtain the snow melting temperature fitting curve; Analyze the snow melting temperature on the snow melting temperature fitting curve to determine the temperature extreme values ​​of the snow melting temperature fitting curve; Calculate the temperature extreme value difference between any two adjacent temperature extreme values, wherein the temperature extreme value difference is the absolute value of the difference between two adjacent temperature extreme values; Determine the historical acquisition time corresponding to each two adjacent temperature extremes, and determine the historical acquisition time interval corresponding to two historical acquisition times. The ratio of the extreme temperature difference to the corresponding historical data collection time interval is used as the snow melting temperature fluctuation coefficient. All identical snowmelt temperature fluctuation coefficients are grouped into the snowmelt temperature variation group.

[0008] Furthermore, when analyzing each group of snow melting temperature changes and calculating the snow melting temperature index of the intelligent snow melting heat dissipation belt based on the analysis results, the following is included: Count the number of the first snowmelt temperature change group in the snowmelt temperature change group; Extract a snow melting temperature fluctuation coefficient from each of the snow melting temperature variation groups, and calculate the sum of the first snow melting temperature fluctuation coefficients; A preset snow melting temperature fluctuation coefficient is set in advance. All snow melting temperature change groups with a value less than the preset snow melting temperature fluctuation coefficient are removed. The number of second snow melting temperature change groups of the remaining snow melting temperature change groups is counted. Extract a snow melting temperature fluctuation coefficient from each of the remaining snow melting temperature variation groups, and calculate the second snow melting temperature fluctuation coefficient and its value. The snow melting temperature index of the intelligent snow melting heat dissipation belt is calculated based on the number of the first snow melting temperature change groups, the number of the second snow melting temperature change groups, the sum of the first snow melting temperature fluctuation coefficient and the sum of the second snow melting temperature fluctuation coefficient.

[0009] Furthermore, when calculating the snow melting temperature index of the intelligent snow melting heat dissipation belt based on the number of the first snow melting temperature change groups, the number of the second snow melting temperature change groups, the sum of the first snow melting temperature fluctuation coefficients, and the sum of the second snow melting temperature fluctuation coefficients, the calculation includes: The snow melting temperature index of the intelligent snow melting heat dissipation belt is calculated according to the following formula: ; Where s is the snow melting temperature index of the intelligent snow melting heat dissipation belt, q1 is the number of the first snow melting temperature change group, q2 is the number of the second snow melting temperature change group, w1 is the sum of the first snow melting temperature fluctuation coefficients, and w2 is the sum of the second snow melting temperature fluctuation coefficients.

[0010] Furthermore, in analyzing all historical snowmelt energy consumption, including: Determine the historical expected snowmelt energy consumption corresponding to each historical data collection moment; Based on the historical expected snow melting energy consumption, all historical snow melting energy consumption is analyzed. When the historical snow melting energy consumption is greater than the historical expected snow melting energy consumption, an excess snow melting energy consumption identifier is generated for the corresponding historical snow melting energy consumption. When the historical snow melting energy consumption is equal to the historical expected snow melting energy consumption, an equivalent snow melting energy consumption identifier is generated for the corresponding historical snow melting energy consumption. When the historical snow melting energy consumption is less than the historical expected snow melting energy consumption, an insufficient snow melting energy consumption flag is generated for the corresponding historical snow melting energy consumption. Determine the difference between the historical snow melting energy consumption corresponding to the excess snow melting energy consumption indicator and the historical expected snow melting energy consumption; Determine the difference between the historical snow melting energy consumption corresponding to the under-received snow melting energy consumption marker and the historical expected snow melting energy consumption.

[0011] Furthermore, when calculating the snow melting energy consumption index of the intelligent snow melting heat dissipation belt based on the analysis results, the following steps are included: Randomly extract one excess snow melting energy consumption difference from all excess snow melting energy consumption differences, and randomly extract one insufficient snow melting energy consumption difference from all insufficient snow melting energy consumption differences to obtain a set of snow melting energy consumption differences; Determine multiple sets of snow melting energy consumption difference values, calculate the first snow melting energy consumption difference sum value corresponding to each set of snow melting energy consumption difference values, and extract the maximum first snow melting energy consumption difference sum value; Determine the maximum excess snow melting energy consumption difference from all excess snow melting energy consumption differences, and determine the minimum under-snow melting energy consumption difference from all under-snow melting energy consumption differences; Determine the second snow melting energy consumption difference and the sum of the maximum excess snow melting energy consumption difference and the minimum under-snow melting energy consumption difference; The ratio of the second sum of snow melting energy consumption differences to the sum of the maximum first sum of snow melting energy consumption differences is used as the snow melting energy consumption index of the intelligent snow melting heat dissipation belt.

[0012] Further, when determining the energy-saving control gain index of the intelligent snow-melting heat dissipation belt based on the snow-melting temperature index and the snow-melting energy consumption index, and when performing energy-saving heat dissipation control on the intelligent snow-melting heat dissipation belt according to the energy-saving control gain index, the method includes: The energy-saving regulation gain index of the intelligent snow melting heat dissipation belt is obtained by weighted summation of the snow melting temperature index and the snow melting energy consumption index. Obtain the real-time snow melting temperature and corresponding expected power output of the intelligent snow melting heat dissipation belt; Pre-set the first preset energy-saving control gain index and the second preset energy-saving control gain index; The first preset power adjustment factor, the second preset power adjustment factor, and the third preset power adjustment factor are preset. When the energy-saving regulation gain index is less than the first preset energy-saving regulation gain index, the first product value of the first preset power adjustment factor and the expected power output is calculated, and the energy-saving heat dissipation control of the intelligent snow melting heat dissipation belt is performed based on the first product value. When the energy-saving regulation gain index is greater than or equal to the first preset energy-saving regulation gain index and less than the second preset energy-saving regulation gain index, the second product value of the second preset power adjustment factor and the expected power output is calculated, and the energy-saving heat dissipation control of the intelligent snow melting heat dissipation belt is performed based on the second product value. When the energy-saving regulation gain index is greater than or equal to the second preset energy-saving regulation gain index, the third product value of the third preset power adjustment factor and the expected power output is calculated, and the intelligent snow melting heat dissipation belt is controlled for energy saving based on the third product value.

[0013] To achieve the above objectives, the present invention also provides an energy-saving heat dissipation system for an intelligent snow melting heat dissipation strip, comprising: The temperature acquisition module is used to pre-set multiple historical acquisition times, acquire multiple snow melting temperatures based on the historical acquisition times, and construct multiple snow melting temperature change groups based on all the snow melting temperatures. The first calculation module is used to analyze each group of snow melting temperature changes and calculate the snow melting temperature index of the intelligent snow melting heat dissipation belt based on the analysis results. The second calculation module is used to obtain the historical snow melting energy consumption corresponding to each historical collection time, analyze all the historical snow melting energy consumption, and calculate the snow melting energy consumption index of the intelligent snow melting heat dissipation belt based on the analysis results. An energy-saving heat dissipation module is used to determine the energy-saving control gain index of the intelligent snow melting heat dissipation belt based on the snow melting temperature index and the snow melting energy consumption index, and to perform energy-saving heat dissipation control on the intelligent snow melting heat dissipation belt according to the energy-saving control gain index.

[0014] Furthermore, it also includes: The preprocessing module is used to traverse and preprocess each historical data acquisition time and the corresponding snow melting temperature. The preprocessing includes deleting duplicate snow melting temperatures and erroneous snow melting temperatures. The module is used to construct snow melting temperature variation groups based on the preprocessed historical acquisition times and the corresponding snow melting temperature at each historical acquisition time.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention collects multiple snow melting temperatures at historical data points to construct multiple snow melting temperature change groups. It analyzes each snow melting temperature change group to calculate the snow melting temperature index of the intelligent snow melting heat dissipation belt. It obtains the historical snow melting energy consumption corresponding to each historical data collection point, analyzes all historical snow melting energy consumption, and calculates the snow melting energy consumption index of the intelligent snow melting heat dissipation belt based on the analysis results. Based on the snow melting temperature index and the snow melting energy consumption index, it determines the energy-saving control gain index of the intelligent snow melting heat dissipation belt and performs energy-saving heat dissipation control based on the energy-saving control gain index. This energy-saving heat dissipation method, which dynamically analyzes the snow melting temperature and energy consumption patterns based on historical data and optimizes the power control strategy, improves the working efficiency of the intelligent snow melting heat dissipation belt, reduces energy consumption, and ensures stable snow melting effects. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating an energy-saving heat dissipation method for an intelligent snow melting heat dissipation strip according to an embodiment of the present invention is shown. Figure 2 A schematic diagram of an energy-saving heat dissipation system for an intelligent snow melting heat dissipation strip according to an embodiment of the present invention is shown. Detailed Implementation

[0017] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0018] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly 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 between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses an energy-saving heat dissipation method for an intelligent snow melting heat dissipation strip, comprising: S110: Pre-set multiple historical data collection times, collect multiple snow melting temperatures based on the historical data collection times, and construct multiple snow melting temperature change groups based on all the snow melting temperatures; In this embodiment, the number of historical data collection times is preferably 20, which means collecting the snow melting temperature over the past 20 times. The historical data collection times can be set according to actual needs, such as the 6th hour, the 12th hour, the 18th hour, etc.

[0023] In some embodiments of this application, before constructing a set of snowmelt temperature variations based on all snowmelt temperatures, the method further includes: The process involves iterating through and preprocessing each historical data collection moment and the corresponding snow melting temperature. The preprocessing includes deleting duplicate snow melting temperatures and incorrect snow melting temperatures. A snowmelt temperature variation group is constructed based on the preprocessed historical acquisition times and the corresponding snowmelt temperature at each historical acquisition time.

[0024] In this embodiment, each historical data collection moment corresponds to a snow melting temperature. If two temperatures are present, there may be duplicate or incorrect snow melting temperatures. If the temperature is significantly lower or higher than the normal range, it may be an incorrect snow melting temperature. Therefore, the snow melting temperatures corresponding to each historical data collection moment are iterated through, and duplicate and incorrect snow melting temperatures are deleted.

[0025] The beneficial effects of the above technical solution are: the present invention traverses and preprocesses each historical acquisition time and the corresponding snow melting temperature, which can ensure the accuracy of the analysis of snow melting temperature changes.

[0026] In some embodiments of this application, when collecting multiple snowmelt temperatures based on historical acquisition times and constructing a snowmelt temperature change group based on all snowmelt temperatures, the method includes: Curve fitting was performed on all snow melting temperatures to obtain the snow melting temperature fitting curve; Analyze the snow melting temperature on the snow melting temperature fitting curve to determine the temperature extreme values ​​of the snow melting temperature fitting curve; Calculate the temperature extreme value difference between any two adjacent temperature extreme values, wherein the temperature extreme value difference is the absolute value of the difference between two adjacent temperature extreme values; Determine the historical acquisition time corresponding to each two adjacent temperature extremes, and determine the historical acquisition time interval corresponding to two historical acquisition times. The ratio of the extreme temperature difference to the corresponding historical data collection time interval is used as the snow melting temperature fluctuation coefficient. All identical snowmelt temperature fluctuation coefficients are grouped into the snowmelt temperature variation group.

[0027] In this embodiment, the curve fitting method is relatively mature, so it will not be described again here.

[0028] In this embodiment, it should be noted that the temperature extreme value is not the maximum or minimum temperature value. The extreme value refers to the peak or valley value reached within a local range in the sequence.

[0029] In this embodiment, each temperature extreme value corresponds to a historical acquisition time. Therefore, the historical acquisition time interval between two temperature extreme values ​​can be determined.

[0030] The beneficial effects of the above technical solution are as follows: This invention uses the ratio of extreme temperature differences to the corresponding historical data collection time intervals as the snow melting temperature fluctuation coefficient, thereby realizing targeted analysis of snow melting temperature changes and temporal changes. All identical snow melting temperature fluctuation coefficients are divided into snow melting temperature change groups, laying the foundation for the calculation of the snow melting temperature index and ensuring the accuracy of energy-saving heat dissipation of the intelligent snow melting heat dissipation belt.

[0031] S120: Analyze each snow melting temperature change group and calculate the snow melting temperature index of the intelligent snow melting heat dissipation belt based on the analysis results; In some embodiments of this application, when analyzing each group of snow melting temperature changes and calculating the snow melting temperature index of the intelligent snow melting heat dissipation belt based on the analysis results, the following steps are included: Count the number of the first snowmelt temperature change group in the snowmelt temperature change group; Extract a snow melting temperature fluctuation coefficient from each of the snow melting temperature variation groups, and calculate the sum of the first snow melting temperature fluctuation coefficients; A preset snow melting temperature fluctuation coefficient is set in advance. All snow melting temperature change groups with a value less than the preset snow melting temperature fluctuation coefficient are removed. The number of second snow melting temperature change groups of the remaining snow melting temperature change groups is counted. Extract a snow melting temperature fluctuation coefficient from each of the remaining snow melting temperature variation groups, and calculate the second snow melting temperature fluctuation coefficient and its value. The snow melting temperature index of the intelligent snow melting heat dissipation belt is calculated based on the number of the first snow melting temperature change groups, the number of the second snow melting temperature change groups, the sum of the first snow melting temperature fluctuation coefficient and the sum of the second snow melting temperature fluctuation coefficient.

[0032] In this embodiment, the preset snow melting temperature fluctuation coefficient is preferably 0.5, but it can be adjusted adaptively according to the actual situation.

[0033] The beneficial effects of the above technical solution are as follows: The present invention calculates the snow melting temperature index of the intelligent snow melting heat dissipation belt based on the number of the first snow melting temperature change group, the number of the second snow melting temperature change group, the sum of the first snow melting temperature fluctuation coefficient and the sum of the second snow melting temperature fluctuation coefficient, which ensures the calculation accuracy and efficiency of the snow melting temperature index. The snow melting temperature index can characterize the change law of snow melting temperature, thereby optimizing the heating strategy, reducing equipment wear, extending service life and reducing maintenance costs.

[0034] In some embodiments of this application, when calculating the snow melting temperature index of the intelligent snow melting heat dissipation belt based on the number of the first snow melting temperature change groups, the number of the second snow melting temperature change groups, the sum of the first snow melting temperature fluctuation coefficients, and the sum of the second snow melting temperature fluctuation coefficients, the following steps are included: The snow melting temperature index of the intelligent snow melting heat dissipation belt is calculated according to the following formula: ; Where s is the snow melting temperature index of the intelligent snow melting heat dissipation belt, q1 is the number of the first snow melting temperature change group, q2 is the number of the second snow melting temperature change group, w1 is the sum of the first snow melting temperature fluctuation coefficients, and w2 is the sum of the second snow melting temperature fluctuation coefficients.

[0035] S130: Obtain the historical snow melting energy consumption corresponding to each historical collection moment, analyze all historical snow melting energy consumption, and calculate the snow melting energy consumption index of the intelligent snow melting heat dissipation belt based on the analysis results; In some embodiments of this application, the analysis of all historical snowmelt energy consumption includes: Determine the historical expected snowmelt energy consumption corresponding to each historical data collection moment; Based on the historical expected snow melting energy consumption, all historical snow melting energy consumption is analyzed. When the historical snow melting energy consumption is greater than the historical expected snow melting energy consumption, an excess snow melting energy consumption identifier is generated for the corresponding historical snow melting energy consumption. When the historical snow melting energy consumption is equal to the historical expected snow melting energy consumption, an equivalent snow melting energy consumption identifier is generated for the corresponding historical snow melting energy consumption. When the historical snow melting energy consumption is less than the historical expected snow melting energy consumption, an insufficient snow melting energy consumption flag is generated for the corresponding historical snow melting energy consumption. Determine the difference between the historical snow melting energy consumption corresponding to the excess snow melting energy consumption indicator and the historical expected snow melting energy consumption; Determine the difference between the historical snow melting energy consumption corresponding to the under-received snow melting energy consumption marker and the historical expected snow melting energy consumption.

[0036] In this embodiment, historical expected snowmelt energy consumption is a projected power value, which is the power value required for snowmelt in a historical period predicted or estimated based on historical meteorological data (such as temperature, precipitation, sunshine, etc.) and snowmelt models. Historical snowmelt energy consumption is an actual value, which is the power value actually observed during the snowmelt process.

[0037] In this embodiment, the difference in energy consumption for excess snow melting and the difference in energy consumption for insufficient snow melting are both absolute values ​​of the difference.

[0038] The beneficial effects of the above technical solution are: the present invention realizes intelligent analysis of historical snow melting energy consumption and achieves refined classification of historical snow melting energy consumption.

[0039] In some embodiments of this application, the calculation of the snow melting energy consumption index of the intelligent snow melting heat dissipation belt based on the analysis results includes: Randomly extract one excess snow melting energy consumption difference from all excess snow melting energy consumption differences, and randomly extract one insufficient snow melting energy consumption difference from all insufficient snow melting energy consumption differences to obtain a set of snow melting energy consumption differences; Determine multiple sets of snow melting energy consumption difference values, calculate the first snow melting energy consumption difference sum value corresponding to each set of snow melting energy consumption difference values, and extract the maximum first snow melting energy consumption difference sum value; Determine the maximum excess snow melting energy consumption difference from all excess snow melting energy consumption differences, and determine the minimum under-snow melting energy consumption difference from all under-snow melting energy consumption differences; Determine the second snow melting energy consumption difference and the sum of the maximum excess snow melting energy consumption difference and the minimum under-snow melting energy consumption difference; The ratio of the second sum of snow melting energy consumption differences to the sum of the maximum first sum of snow melting energy consumption differences is used as the snow melting energy consumption index of the intelligent snow melting heat dissipation belt.

[0040] In this embodiment, each time, one excess snow melting energy consumption difference is randomly extracted from the excess snow melting energy consumption difference, and one insufficient snow melting energy consumption difference is randomly extracted from all insufficient snow melting energy consumption differences, thus obtaining a set of snow melting energy consumption differences. Repeating the above steps can yield multiple sets of snow melting energy consumption differences. If there are unmatched excess snow melting energy consumption differences in the excess snow melting energy consumption difference set, they can be deleted. If there are unmatched insufficient snow melting energy consumption differences in the insufficient snow melting energy consumption difference set, they can be deleted.

[0041] The beneficial effects of the above technical solution are as follows: The present invention uses the ratio of the second snow melting energy consumption difference to the maximum first snow melting energy consumption difference as the snow melting energy consumption index of the intelligent snow melting heat dissipation belt. The snow melting energy consumption index characterizes the snow melting power change law generated by the intelligent snow melting heat dissipation belt when it melts snow, thereby providing reliable data support for precise energy-saving heat dissipation.

[0042] S140: Determine the energy-saving control gain index of the intelligent snow melting heat dissipation belt based on the snow melting temperature index and the snow melting energy consumption index, and perform energy-saving heat dissipation control on the intelligent snow melting heat dissipation belt according to the energy-saving control gain index.

[0043] In some embodiments of this application, when determining the energy-saving control gain index of the intelligent snow-melting heat dissipation strip based on the snow-melting temperature index and the snow-melting energy consumption index, and when performing energy-saving heat dissipation control on the intelligent snow-melting heat dissipation strip according to the energy-saving control gain index, the method includes: The energy-saving regulation gain index of the intelligent snow melting heat dissipation belt is obtained by weighted summation of the snow melting temperature index and the snow melting energy consumption index. Obtain the real-time snow melting temperature and corresponding expected power output of the intelligent snow melting heat dissipation belt; Pre-set the first preset energy-saving control gain index and the second preset energy-saving control gain index; The first preset power adjustment factor, the second preset power adjustment factor, and the third preset power adjustment factor are preset. When the energy-saving regulation gain index is less than the first preset energy-saving regulation gain index, the first product value of the first preset power adjustment factor and the expected power output is calculated, and the energy-saving heat dissipation control of the intelligent snow melting heat dissipation belt is performed based on the first product value. When the energy-saving regulation gain index is greater than or equal to the first preset energy-saving regulation gain index and less than the second preset energy-saving regulation gain index, the second product value of the second preset power adjustment factor and the expected power output is calculated, and the energy-saving heat dissipation control of the intelligent snow melting heat dissipation belt is performed based on the second product value. When the energy-saving regulation gain index is greater than or equal to the second preset energy-saving regulation gain index, the third product value of the third preset power adjustment factor and the expected power output is calculated, and the intelligent snow melting heat dissipation belt is controlled for energy saving based on the third product value.

[0044] In this embodiment, the snow melting temperature index and the snow melting energy consumption index are weighted. The weighting methods include subjective weighting and objective weighting, which will not be described in detail here. The preferred weight of the snow melting temperature index is 0.35, and the preferred weight of the snow melting energy consumption index is 0.65.

[0045] In this embodiment, the expected power output is set based on the real-time snow melting temperature and is an expected value.

[0046] In this embodiment, the first preset energy-saving control gain index is preferably 3, and the second preset energy-saving control gain index is preferably 6. The specific values ​​can be adjusted according to actual needs.

[0047] In this embodiment, the first preset power adjustment factor is preferably 0.85, the second preset power adjustment factor is preferably 1.05, and the third preset power adjustment factor is preferably 1.25. The specific values ​​can be adjusted according to actual needs.

[0048] In this embodiment, if the expected power output is 20 watts and the first preset power adjustment factor is selected, the first product value is 20 * 0.85, which is 17 watts. 17 watts is used as the snow melting power of the intelligent snow melting heat sink. If the second preset power adjustment factor is selected, the second product value is 20 * 1.05, which is 21 watts. 21 watts is used as the snow melting power of the intelligent snow melting heat sink. If the third preset power adjustment factor is selected, the second product value is 20 * 1.25, which is 25 watts. 25 watts is used as the snow melting power of the intelligent snow melting heat sink. This is just an example for ease of understanding and is not intended to be a specific limitation.

[0049] The beneficial effects of the above technical solution are: the present invention selects the corresponding preset power adjustment factor based on the energy-saving control gain index, the first preset energy-saving control gain index and the second preset energy-saving control gain index, thereby realizing the adjustment of the expected power output, which can not only ensure the snow melting accuracy, but also reduce the snow melting energy consumption of the intelligent snow melting heat dissipation belt.

[0050] To further illustrate the technical concept of this invention, the technical solution of this invention will now be described in conjunction with specific application scenarios.

[0051] Correspondingly, such as Figure 2 As shown, this application also provides an energy-saving heat dissipation system for an intelligent snow melting heat dissipation strip, comprising: The temperature acquisition module is used to pre-set multiple historical acquisition times, acquire multiple snow melting temperatures based on the historical acquisition times, and construct multiple snow melting temperature change groups based on all the snow melting temperatures. The first calculation module is used to analyze each group of snow melting temperature changes and calculate the snow melting temperature index of the intelligent snow melting heat dissipation belt based on the analysis results. The second calculation module is used to obtain the historical snow melting energy consumption corresponding to each historical collection time, analyze all the historical snow melting energy consumption, and calculate the snow melting energy consumption index of the intelligent snow melting heat dissipation belt based on the analysis results. An energy-saving heat dissipation module is used to determine the energy-saving control gain index of the intelligent snow melting heat dissipation belt based on the snow melting temperature index and the snow melting energy consumption index, and to perform energy-saving heat dissipation control on the intelligent snow melting heat dissipation belt according to the energy-saving control gain index.

[0052] In some embodiments of this application, it also includes: The preprocessing module is used to traverse and preprocess each historical data acquisition time and the corresponding snow melting temperature. The preprocessing includes deleting duplicate snow melting temperatures and erroneous snow melting temperatures. The module is used to construct snow melting temperature variation groups based on the preprocessed historical acquisition times and the corresponding snow melting temperature at each historical acquisition time.

[0053] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0054] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0055] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An energy-saving heat dissipation method for an intelligent snow melting heat dissipation strip, characterized in that, include: Multiple historical data collection times are pre-set, multiple snow melting temperatures are collected based on these historical data collection times, and multiple snow melting temperature change groups are constructed based on all the snow melting temperatures. Each snow melting temperature change group was analyzed, and the snow melting temperature index of the intelligent snow melting heat dissipation belt was calculated based on the analysis results. The historical snow melting energy consumption corresponding to each historical collection moment is obtained, all historical snow melting energy consumption is analyzed, and the snow melting energy consumption index of the intelligent snow melting heat dissipation belt is calculated based on the analysis results. The energy-saving control gain index of the intelligent snow melting heat dissipation belt is determined based on the snow melting temperature index and the snow melting energy consumption index, and the energy-saving heat dissipation control of the intelligent snow melting heat dissipation belt is performed according to the energy-saving control gain index.

2. The energy-saving heat dissipation method of the intelligent snow melting heat dissipation belt according to claim 1, characterized in that, Before constructing the snowmelt temperature variation group based on all snowmelt temperatures, the following is also included: The process involves iterating through and preprocessing each historical data collection moment and the corresponding snow melting temperature. The preprocessing includes deleting duplicate snow melting temperatures and incorrect snow melting temperatures. A snowmelt temperature variation group is constructed based on the preprocessed historical acquisition times and the corresponding snowmelt temperature at each historical acquisition time.

3. The energy-saving heat dissipation method of the intelligent snow melting heat dissipation belt according to claim 1, characterized in that, When collecting multiple snowmelt temperatures based on historical data collection times and constructing a snowmelt temperature variation group based on all snowmelt temperatures, the following is included: Curve fitting was performed on all snow melting temperatures to obtain the snow melting temperature fitting curve; Analyze the snow melting temperature on the snow melting temperature fitting curve to determine the temperature extreme values ​​of the snow melting temperature fitting curve; Calculate the temperature extreme value difference between any two adjacent temperature extreme values, wherein the temperature extreme value difference is the absolute value of the difference between two adjacent temperature extreme values; Determine the historical acquisition time corresponding to each two adjacent temperature extremes, and determine the historical acquisition time interval corresponding to two historical acquisition times. The ratio of the extreme temperature difference to the corresponding historical data collection time interval is used as the snow melting temperature fluctuation coefficient. All identical snowmelt temperature fluctuation coefficients are grouped into the snowmelt temperature variation group.

4. The energy-saving heat dissipation method of the intelligent snow melting heat dissipation belt according to claim 1, characterized in that, When analyzing each group of snow melting temperature changes and calculating the snow melting temperature index of the intelligent snow melting heat dissipation belt based on the analysis results, the following is included: Count the number of the first snowmelt temperature change group in the snowmelt temperature change group; Extract a snow melting temperature fluctuation coefficient from each of the snow melting temperature variation groups, and calculate the sum of the first snow melting temperature fluctuation coefficients; A preset snow melting temperature fluctuation coefficient is set in advance. All snow melting temperature change groups with a value less than the preset snow melting temperature fluctuation coefficient are removed. The number of second snow melting temperature change groups of the remaining snow melting temperature change groups is counted. Extract a snow melting temperature fluctuation coefficient from each of the remaining snow melting temperature variation groups, and calculate the second snow melting temperature fluctuation coefficient and its value. The snow melting temperature index of the intelligent snow melting heat dissipation belt is calculated based on the number of the first snow melting temperature change groups, the number of the second snow melting temperature change groups, the sum of the first snow melting temperature fluctuation coefficient and the sum of the second snow melting temperature fluctuation coefficient.

5. The energy-saving heat dissipation method of the intelligent snow melting heat dissipation belt according to claim 4, characterized in that, When calculating the snow melting temperature index of the intelligent snow melting heat dissipation belt based on the number of the first snow melting temperature change groups, the number of the second snow melting temperature change groups, the sum of the first snow melting temperature fluctuation coefficients, and the sum of the second snow melting temperature fluctuation coefficients, the following steps are included: The snow melting temperature index of the intelligent snow melting heat dissipation belt is calculated according to the following formula: ； Where s is the snow melting temperature index of the intelligent snow melting heat dissipation belt, q1 is the number of the first snow melting temperature change group, q2 is the number of the second snow melting temperature change group, w1 is the sum of the first snow melting temperature fluctuation coefficients, and w2 is the sum of the second snow melting temperature fluctuation coefficients.

6. The energy-saving heat dissipation method of the intelligent snow melting heat dissipation belt according to claim 1, characterized in that, When analyzing all historical snowmelt energy consumption, the following was included: Determine the historical expected snowmelt energy consumption corresponding to each historical data collection moment; Based on the historical expected snow melting energy consumption, all historical snow melting energy consumption is analyzed. When the historical snow melting energy consumption is greater than the historical expected snow melting energy consumption, an excess snow melting energy consumption identifier is generated for the corresponding historical snow melting energy consumption. When the historical snow melting energy consumption is equal to the historical expected snow melting energy consumption, an equivalent snow melting energy consumption identifier is generated for the corresponding historical snow melting energy consumption. When the historical snow melting energy consumption is less than the historical expected snow melting energy consumption, an insufficient snow melting energy consumption flag is generated for the corresponding historical snow melting energy consumption. Determine the difference between the historical snow melting energy consumption corresponding to the excess snow melting energy consumption indicator and the historical expected snow melting energy consumption; Determine the difference between the historical snow melting energy consumption corresponding to the under-received snow melting energy consumption marker and the historical expected snow melting energy consumption.

7. The energy-saving heat dissipation method of the intelligent snow melting heat dissipation belt according to claim 6, characterized in that, When calculating the snow melting energy consumption index of the intelligent snow melting heat dissipation belt based on the analysis results, the following is included: Randomly extract one excess snow melting energy consumption difference from all excess snow melting energy consumption differences, and randomly extract one insufficient snow melting energy consumption difference from all insufficient snow melting energy consumption differences to obtain a set of snow melting energy consumption differences; Determine multiple sets of snow melting energy consumption difference values, calculate the first snow melting energy consumption difference sum value corresponding to each set of snow melting energy consumption difference values, and extract the maximum first snow melting energy consumption difference sum value; Determine the maximum excess snow melting energy consumption difference from all excess snow melting energy consumption differences, and determine the minimum under-snow melting energy consumption difference from all under-snow melting energy consumption differences; Determine the second snow melting energy consumption difference and the sum of the maximum excess snow melting energy consumption difference and the minimum under-snow melting energy consumption difference; The ratio of the second sum of snow melting energy consumption differences to the sum of the maximum first sum of snow melting energy consumption differences is used as the snow melting energy consumption index of the intelligent snow melting heat dissipation belt.

8. The energy-saving heat dissipation method of the intelligent snow melting heat dissipation belt according to claim 1, characterized in that, When determining the energy-saving control gain index of the intelligent snow-melting heat dissipation belt based on the snow-melting temperature index and the snow-melting energy consumption index, and when performing energy-saving heat dissipation control on the intelligent snow-melting heat dissipation belt according to the energy-saving control gain index, the process includes: The energy-saving regulation gain index of the intelligent snow melting heat dissipation belt is obtained by weighted summation of the snow melting temperature index and the snow melting energy consumption index. Obtain the real-time snow melting temperature and corresponding expected power output of the intelligent snow melting heat dissipation belt; Pre-set the first preset energy-saving control gain index and the second preset energy-saving control gain index; The first preset power adjustment factor, the second preset power adjustment factor, and the third preset power adjustment factor are preset. When the energy-saving regulation gain index is less than the first preset energy-saving regulation gain index, the first product value of the first preset power adjustment factor and the expected power output is calculated, and the energy-saving heat dissipation control of the intelligent snow melting heat dissipation belt is performed based on the first product value. When the energy-saving regulation gain index is greater than or equal to the first preset energy-saving regulation gain index and less than the second preset energy-saving regulation gain index, the second product value of the second preset power adjustment factor and the expected power output is calculated, and the energy-saving heat dissipation control of the intelligent snow melting heat dissipation belt is performed based on the second product value. When the energy-saving regulation gain index is greater than or equal to the second preset energy-saving regulation gain index, the third product value of the third preset power adjustment factor and the expected power output is calculated, and the intelligent snow melting heat dissipation belt is controlled for energy saving based on the third product value.

9. An energy-saving heat dissipation system for an intelligent snow melting heat dissipation strip, applied to the energy-saving heat dissipation method of the intelligent snow melting heat dissipation strip as described in any one of claims 1-8, characterized in that, include: The temperature acquisition module is used to pre-set multiple historical acquisition times, acquire multiple snow melting temperatures based on the historical acquisition times, and construct multiple snow melting temperature change groups based on all the snow melting temperatures. The first calculation module is used to analyze each group of snow melting temperature changes and calculate the snow melting temperature index of the intelligent snow melting heat dissipation belt based on the analysis results. The second calculation module is used to obtain the historical snow melting energy consumption corresponding to each historical collection time, analyze all the historical snow melting energy consumption, and calculate the snow melting energy consumption index of the intelligent snow melting heat dissipation belt based on the analysis results. An energy-saving heat dissipation module is used to determine the energy-saving control gain index of the intelligent snow melting heat dissipation belt based on the snow melting temperature index and the snow melting energy consumption index, and to perform energy-saving heat dissipation control on the intelligent snow melting heat dissipation belt according to the energy-saving control gain index.

10. The energy-saving heat dissipation system of the intelligent snow melting heat dissipation belt according to claim 9, characterized in that, Also includes: The preprocessing module is used to traverse and preprocess each historical data acquisition time and the corresponding snow melting temperature. The preprocessing includes deleting duplicate snow melting temperatures and erroneous snow melting temperatures. The module is used to construct snow melting temperature variation groups based on the preprocessed historical acquisition times and the corresponding snow melting temperature at each historical acquisition time.

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