阅读说明：本技术 一种基于大数据分析集中供热控制方法 (Central heating control method based on big data analysis ) 是由 李生 白新奎 李恭斌 孙玉成 邓秦生 白旭 尚海军 李万军 尹军波 杜攀 杜志君 于 2021-09-08 设计创作，主要内容包括：本发明属于集中供热控制技术领域,尤其为一种基于大数据分析集中供热控制方法,包括以下步骤：S1、获取集中供热管网历史数据,学习并训练,基于大数据分析建立参数集数据库；S2、运用大数据分析结果,实施当前集中供热最优控制方式。本发明基于大数据分析提供集中供热控制方式,充分运用大数据分析结果,实施当前集中供热最优控制方式,能够在限制供热成本的情况下,根据最优配比数据集,通过提高管网供水温度的方式增加总供热量,或配合进行相应供水流量调整的方式,能够在较大程度上降低管网热损失,为提高集中供热效率、改善供热管网节能运行和降低集中供热管网能耗具有现实的节能环保意义。(The invention belongs to the technical field of centralized heating control, in particular to a centralized heating control method based on big data analysis, which comprises the following steps: s1, acquiring historical data of the centralized heating pipe network, learning and training, and establishing a parameter set database based on big data analysis; and S2, implementing the current central heating optimal control mode by using the big data analysis result. The invention provides a centralized heating control mode based on big data analysis, fully utilizes big data analysis results, implements the current optimal control mode of centralized heating, can increase the total heating load by increasing the water supply temperature of the pipe network according to the optimal proportioning data set under the condition of limiting the heating cost, or cooperates with the mode of adjusting the corresponding water supply flow, can reduce the heat loss of the pipe network to a greater extent, and has practical energy-saving and environment-friendly significance for improving the efficiency of centralized heating, improving the energy-saving operation of the heating pipe network and reducing the energy consumption of the centralized heating pipe network.)

1. A central heating control method based on big data analysis is characterized in that: the method comprises the following steps:

s1, acquiring historical data of the centralized heating pipe network, learning and training, and establishing a parameter set database based on big data analysis;

and S2, implementing the current central heating optimal control mode by using the big data analysis result.

2. A big data analysis based central heating control method according to claim 1, characterized in that: the parameter set database is established by the following steps:

s1, classifying the historical data of the massive centralized heat supply pipe network by using a naive Bayes algorithm, and performing distributed learning on the historical data of the massive centralized heat supply pipe network in a training stage, namely acquiring the opening data of a valve group of the centralized heat supply pipe network, the water supply flow of the pipe network, the water supply temperature data of the pipe network and the external environment temperature data;

and S2, calculating the heat loss rate of the pipe network, and establishing a parameter set database matched with the optimal pipe network water supply temperature under the conditions of different heat supply pipe network valve group openness, pipe network water supply flow and external environment temperature data based on big data analysis according to the calculation result.

3. A big data analysis based central heating control method according to claim 1, characterized in that: the historical data of the heat supply pipe network comprises pertinence test data of the pipe network, namely under the condition of the same external environment temperature and the same heat insulation structure, a valve group and a circulating pump are not regulated, constant flow operation is kept, the same measuring point on the pipe network is tested for multiple times, and test heat loss rate average value data is obtained.

4. A big data analysis based central heating control method according to claim 1, characterized in that: the steps of the current central heating optimal control method are as follows:

s1, determining the current external environment temperature, the pipe network water supply temperature and the pipe network water supply flow, and extracting a pipe network water supply temperature and pipe network water supply flow data set which is optimally matched with the current external environment temperature data in the parameter set database;

s2, determining a water supply temperature and water supply flow adjustment scheme required by a pipe network according to the optimal proportioning data set;

and S3, adjusting the water supply temperature and the water supply flow of the pipe network to the current optimal ratio based on the big data analysis result.

5. A big data analysis based central heating control method according to claim 1, characterized in that: the steps of the current central heating optimal control method are as follows:

s1, determining the current external environment temperature, the pipe network water supply temperature and the pipe network water supply flow, and extracting a pipe network water supply temperature data set optimally matched with the current external environment temperature and pipe network water supply flow data in the parameter set database;

s2, determining a water supply temperature adjusting scheme required by the pipe network according to the optimal proportioning data set;

and S3, adjusting the water supply temperature of the pipe network to the current optimal ratio based on the big data analysis result.

6. A big data analysis based central heating control method according to claim 1, characterized in that: the implementation of the current central heating optimal control mode also comprises a method for controlling the thickness of the heat preservation layer, and the reasonable thickness of the heat preservation layer is determined according to the economic condition, and the method comprises the following steps:

s1, acquiring historical data of heat loss of the centralized heat supply pipe network under different heat preservation layer thicknesses and different pipe network water supply temperatures, and establishing an optimal heat preservation layer thickness data set according to big data analysis;

and S2, adjusting the thickness of the current heat preservation layer on the pipe network according to the water supply temperature of the pipe network.

7. A big data analysis based central heating control method according to claim 1, characterized in that: the pipe network water supply temperature adjusting mode is realized through an electric heating layer which is preset between a pipeline and a heat preservation layer and used for heating the pipeline, the pipe network water supply temperature is adjusted through the electric heating pipeline, and the water supply temperature is monitored in real time by using a temperature sensor when the water supply temperature is adjusted.

8. A big data analysis based central heating control method according to claim 1, characterized in that: the pipe network water supply temperature adjusting mode is realized by serially connecting heat exchangers on pipelines, the pipe network water supply temperature is adjusted in a heat exchange mode, and the water supply temperature is monitored in real time by using a temperature sensor when the water supply temperature is adjusted.

Technical Field

The invention relates to the technical field of centralized heating control, in particular to a centralized heating control method based on big data analysis.

Background

The central heating is a way of supplying steam and hot water generated by a central heat source to heat required for production, heating and life of a city (town) or a part of a region through a pipe network. Central heating is one of the infrastructures of modern cities and is also an important facility of urban utilities. The central heating can not only provide stable and reliable high-grade heat source for cities, improve people's life, but also save energy, reduce urban pollution, be beneficial to city beautification and effectively utilize the effective space of the cities. Therefore, the centralized heating has obvious economic and social benefits.

The heat loss of the centralized heat supply pipe network is a key problem of influencing heat supply quality and causing energy waste. For a long time, the problems of low operation efficiency, large energy consumption and high heating cost of a centralized heating system are not effectively solved, and the heat loss of a centralized heating pipe network is one of the main influencing factors causing the problems. The centralized heating control method has a good effect of coping with the heat loss of the pipe network, and has practical energy-saving and environment-friendly significance for improving the centralized heating efficiency, improving the energy-saving operation of the heat supply pipe network and reducing the energy consumption of the centralized heat supply pipe network.

Disclosure of Invention

Technical problem to be solved

Aiming at the defects of the prior art, the invention provides a centralized heating control method based on big data analysis, and solves the problems of low operation efficiency, high energy consumption and high heating cost of a centralized heating system.

The second technical proposal.

The invention specifically adopts the following technical scheme for realizing the purpose:

a central heating control method based on big data analysis comprises the following steps:

s1, acquiring historical data of the centralized heating pipe network, learning and training, and establishing a parameter set database based on big data analysis;

and S2, implementing the current central heating optimal control mode by using the big data analysis result.

Further, the parameter set database establishing step is as follows:

s1, classifying the historical data of the massive centralized heat supply pipe network by using a naive Bayes algorithm, and performing distributed learning on the historical data of the massive centralized heat supply pipe network in a training stage, namely acquiring the opening data of a valve group of the centralized heat supply pipe network, the water supply flow of the pipe network, the water supply temperature data of the pipe network and the external environment temperature data;

and S2, calculating the heat loss rate of the pipe network, and establishing a parameter set database matched with the optimal pipe network water supply temperature under the conditions of different heat supply pipe network valve group openness, pipe network water supply flow and external environment temperature data based on big data analysis according to the calculation result.

Further, the historical data of the heat supply pipe network comprises targeted test data of the pipe network, namely under the condition of the same external environment temperature and the same heat insulation structure, the valve group and the circulating pump are not regulated, the constant flow operation is kept, the same measuring point on the pipe network is tested for multiple times, and the average data of the heat loss rate is measured.

Further, the implementation of the current central heating optimal control mode comprises the following steps:

s1, determining the current external environment temperature, the pipe network water supply temperature and the pipe network water supply flow, and extracting a pipe network water supply temperature and pipe network water supply flow data set which is optimally matched with the current external environment temperature data in the parameter set database;

s2, determining a water supply temperature and water supply flow adjustment scheme required by a pipe network according to the optimal proportioning data set;

and S3, adjusting the water supply temperature and the water supply flow of the pipe network to the current optimal ratio based on the big data analysis result.

Further, the implementation of the current central heating optimal control mode comprises the following steps:

s1, determining the current external environment temperature, the pipe network water supply temperature and the pipe network water supply flow, and extracting a pipe network water supply temperature data set optimally matched with the current external environment temperature and pipe network water supply flow data in the parameter set database;

s2, determining a water supply temperature adjusting scheme required by the pipe network according to the optimal proportioning data set;

and S3, adjusting the water supply temperature of the pipe network to the current optimal ratio based on the big data analysis result.

Further, the implementation of the current optimal control mode for central heating further comprises a method for controlling the thickness of the insulating layer, and the reasonable thickness of the insulating layer is determined according to economic conditions, and the method comprises the following steps:

s1, acquiring historical data of heat loss of the centralized heat supply pipe network under different heat preservation layer thicknesses and different pipe network water supply temperatures, and establishing an optimal heat preservation layer thickness data set according to big data analysis;

and S2, adjusting the thickness of the current heat preservation layer on the pipe network according to the water supply temperature of the pipe network.

Further, pipe network water supply temperature adjustment mode is realized through the electric heating layer that is used for the pipeline heating who predetermines between pipeline and heat preservation, adjusts pipe network water supply temperature through electric heating pipeline's mode, utilizes temperature sensor real-time supervision water supply temperature when adjustment water supply temperature.

Furthermore, the pipe network water supply temperature adjusting mode is realized through a heat exchanger connected in series on a pipeline, the pipe network water supply temperature is adjusted through a heat exchange mode, and the water supply temperature is monitored in real time by using a temperature sensor when the water supply temperature is adjusted.

(III) advantageous effects

Compared with the prior art, the invention provides a centralized heating control method based on big data analysis, which has the following beneficial effects:

1. according to the centralized heating control method, a centralized heating control mode is provided based on big data analysis, historical data of a centralized heating pipe network are obtained, the centralized heating control method is learned and trained, a parameter set database is established based on big data analysis, a big data analysis result is fully utilized, the current optimal control mode of centralized heating is implemented, the total heating quantity can be increased by improving the water supply temperature of the pipe network according to an optimal matching data set under the condition of limiting the heating cost, or the corresponding water supply flow adjustment mode is matched, the heat loss of the pipe network can be reduced to a large extent, and the energy-saving and environment-friendly significance is realized for improving the centralized heating efficiency, improving the energy-saving operation of the heating pipe network and reducing the energy consumption of the centralized heating pipe network.

Drawings

FIG. 1 is a schematic diagram of the steps of a big data analysis-based centralized heating control method according to the present invention;

FIG. 2 is a diagram illustrating the steps of establishing a database of parameter sets according to the present invention;

FIG. 3 is a schematic diagram illustrating one of the steps of the current optimal control method for central heating according to the present invention;

fig. 4 is a second step diagram of the present optimal control method for central heating according to the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Examples

As shown in fig. 1 to 4, a central heating control method based on big data analysis according to an embodiment of the present invention includes the following steps:

s1, acquiring historical data of the centralized heating pipe network, learning and training, and establishing a parameter set database based on big data analysis;

specifically, as shown in fig. 2, the parameter set database is established as follows:

1) classifying the historical data of the massive centralized heat supply pipe network by using a naive Bayes algorithm, and performing distributed learning on the historical data of the massive centralized heat supply pipe network in a training stage to obtain the opening data of a valve group of the centralized heat supply pipe network, the water supply flow of the pipe network, the water supply temperature data of the pipe network and the external environment temperature data;

2) and calculating the heat loss rate of the pipe network, and establishing a parameter set database matched with the optimal pipe network water supply temperature under the conditions of different heat supply pipe network valve group openness, pipe network water supply flow and external environment temperature data based on big data analysis according to the calculation result.

S2, applying the big data analysis result to implement the current central heating optimal control mode;

specifically, in one embodiment, as shown in fig. 3, the steps of implementing the current central heating optimal control mode are as follows:

1) determining the current external environment temperature, the pipe network water supply temperature and the pipe network water supply flow, and extracting a pipe network water supply temperature and pipe network water supply flow data set which is optimally matched with the current external environment temperature data in the parameter set database;

2) determining a water supply temperature and water supply flow adjusting scheme required by a pipe network according to the optimal proportioning data set;

3) and adjusting the water supply temperature and the water supply flow of the pipe network to the current optimal ratio based on the big data analysis result.

The mode is that the external environment temperature is certain, the control mode of adjusting the pipe network water supply temperature and the pipe network water supply flow is selected to be at the current environment temperature, the current pipe network water supply temperature is collected through a temperature sensor, the current pipe network water supply flow is detected through a flowmeter, according to a big data analysis result, the most suitable current adjustment scheme is extracted, namely, the heating medium is heated, meanwhile, a valve is adjusted, a corresponding flow control means is implemented, in the set scheme, after the water supply flow is adjusted, the water supply temperature is increased to the optimal water supply temperature matched with the adjusted pipe network water supply flow and the external environment temperature, under the water supply temperature, the heat loss of a pipe network can be guaranteed to be minimum, meanwhile, the heat supply cost can be controlled, the adjustment mode is more flexible, and more options can be achieved.

In another embodiment, as shown in fig. 4, the current central heating optimal control mode is implemented by the following steps:

1) determining the current external environment temperature, the pipe network water supply temperature and the pipe network water supply flow, and extracting a pipe network water supply temperature data set optimally matched with the current external environment temperature and pipe network water supply flow data in the parameter set database;

2) determining a water supply temperature adjusting scheme required by the pipe network according to the optimal proportioning data set;

3) and adjusting the pipe network water supply temperature to the current optimal ratio based on the big data analysis result.

The above-mentioned mode is that external environment temperature is certain, under the unchangeable circumstances of pipe network water supply temperature and pipe network water supply flow, only carry out the control mode that adjusts to pipe network water supply temperature, select to be in under current ambient temperature, gather current pipe network water supply temperature through temperature sensor, detect current pipe network water supply flow through the flowmeter, according to big data analysis result, extract the most suitable current adjustment scheme, heat the heat medium promptly, promote the water supply temperature to with current pipe network water supply flow and the best water supply temperature of external environment temperature assorted, under this water supply temperature, can guarantee the heat loss minimum of pipe network, can control the heat supply cost simultaneously, this kind of adjustment mode is more simple and convenient.

In some embodiments, the historical data of the heat supply pipe network comprises targeted test data of the pipe network, that is, under the condition of the same external environment temperature and the same heat preservation structure, the valve group and the circulating pump are not regulated, the constant flow operation is kept, the same measuring point on the pipe network is tested for multiple times, and the average data of the heat loss rate is measured.

In some embodiments, the implementation of the current optimal control mode for central heating further comprises a method for controlling the thickness of the insulating layer, and the reasonable thickness of the insulating layer is determined according to economic conditions, and the method comprises the following steps:

s1, acquiring historical data of heat loss of the centralized heat supply pipe network under different heat preservation layer thicknesses and different pipe network water supply temperatures, and establishing an optimal heat preservation layer thickness data set according to big data analysis;

s2, adjusting the thickness of the heat preservation layer on the current pipe network to the optimal heat preservation layer thickness according to the water supply temperature of the pipe network and based on the big data analysis result, wherein the optimal heat preservation layer thickness comprises the thickness of the pipe line heat preservation layer which is most suitable for the current conditions and is selected under the conditions that the optimal heat preservation layer thickness is closest to the economic budget and a certain heat loss rate is kept, and generally, the higher the heat preservation layer thickness is, the lower the heat loss is.

In one embodiment, the pipe network water supply temperature adjusting mode is realized through an electric heating layer which is preset between a pipeline and a heat preservation layer and used for heating the pipeline, the pipe network water supply temperature is adjusted through the electric heating pipeline, and the water supply temperature is monitored in real time by using a temperature sensor when the water supply temperature is adjusted.

In another embodiment, the adjusting mode of the water supply temperature of the pipe network is realized by serially connecting heat exchangers on pipelines, the water supply temperature of the pipe network is adjusted in a heat exchange mode, and the water supply temperature is monitored in real time by using a temperature sensor when the water supply temperature is adjusted.

The two heating modes of adopting electrical heating to promote pipe network water supply temperature and adopting the mode of heat exchanger heat exchange to promote pipe network water supply temperature are all applicable to this embodiment, compare, do benefit to more through the mode of heat exchange and practice thrift the cost, more do benefit to the implementation, convenient to overhaul and maintain simultaneously.

Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.