A centralized heating data analysis system for dispersed users based on the Internet of Things

Abstract

The invention discloses a centralized heating data analysis system for dispersed users based on the Internet of Things, relates to the technical field of centralized heating, and solves the technical problem that it is difficult to process and analyze sampled data of a heating system for each area and time stage of the heating system; the heating data is divided into different subsets according to the startup stage, stable operation stage and shutdown stage of the heating system through a data segmentation processing module; a normal data recognition model for each stage is established by extracting features from each subset of historical heating data; there may be significant differences in heating characteristics and demands in the startup stage, stable operation stage and shutdown stage, and these differences can be better captured by modeling each stage separately, thereby improving the prediction accuracy of the model; at the same time, in the stable operation stage, the state of the system is relatively stable and the data noise is less, which helps to improve the model's recognition ability for the normal state.

Description

Technical Field

[0001] The present invention belongs to the field of centralized heating, and in particular is a centralized heating data analysis system for dispersed users based on the Internet of Things. Background Art

[0002] With the acceleration of urbanization and the improvement of residents' living standards, centralized heating systems play an increasingly important role in modern urban heating. Traditional centralized heating systems usually rely on fixed heat sources and transmission pipelines, making it difficult to flexibly respond to the diverse heating needs of decentralized users. At the same time, with the development of Internet of Things (IoT) technology, smart devices that can collect and analyze large amounts of data in real time have become increasingly popular, providing new possibilities for optimizing heating management. In the centralized heating scenario of decentralized users, user needs vary greatly and environmental factors are complex, which poses challenges to traditional heating regulation methods. Through IoT technology, real-time monitoring and data collection of each user terminal, pipeline, and equipment can be achieved, thereby achieving more accurate and efficient heating management.

[0003] The centralized heating data analysis system for most dispersed users cannot process and analyze the sampling data of the heating system for each area and time stage through unified processing and analysis of the data. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a centralized heating data analysis system for dispersed users based on the Internet of Things, which is used to solve the technical problem that it is difficult to process and analyze the sampled data of the heating system for each area and time stage of the heating system.

[0005] To solve the above problems, the first aspect of the present invention provides a centralized heating data analysis system for decentralized users based on the Internet of Things, comprising: Intelligent zoning module: the heating system is divided into heat source monitoring area, heat transmission monitoring area and heat dissipation monitoring area, sampling nodes are set at the heat output position of the heat source monitoring area, the heat transmission monitoring area is divided into underground transmission area and floor transmission area, sampling nodes of different densities are set in the underground transmission area and floor transmission area, and terminal data sampling nodes are set in the heat dissipation monitoring area; Data collection module: collects heating data at sampling nodes in various areas of the heating system; collects pipeline temperature, pressure, sound and flow data in the underground transmission area of ​​the heat transmission monitoring area, and collects pipeline temperature, pressure, sound, video and flow data in the floor transmission area; Data segmentation processing module: divides the heating data into different subsets according to the startup phase, stable operation phase and shutdown phase of the heating system; extracts features from each subset of historical heating data and establishes a normal data recognition model for each phase; Real-time data comparison module: identifies real-time data with the normal data recognition model of the corresponding stage, and performs abnormal recognition analysis on abnormal data; Data analysis module: Dynamically evaluate the heat loss coefficient through the data collected by sampling nodes in the heat source monitoring area, heat transfer monitoring area and heat dissipation monitoring area. At the same time, make real-time predictions on user heating demand and adjust heating parameters based on the real-time prediction results of heating demand and the heat loss coefficient.

[0006] As a further solution of the present invention: the thermal transmission monitoring area is divided into an underground transmission area and a floor transmission area, and sampling nodes of different densities are set in the underground transmission area and the floor transmission area, including the following steps: The transmission pipelines buried underground in the heat transmission monitoring area are set as underground transmission areas, and the transmission pipelines located between above-ground buildings are set as floor transmission areas; For underground transmission areas, a sampling node is set up every 500 meters to collect pipeline temperature, pressure, sound and flow data; For the floor transmission area, a sampling node is set up every 10 meters to collect pipeline temperature, pressure, sound and flow data, and video sampling nodes are set up at the water distributor, water collector and floor main valve positions.

[0007] As a further solution of the present invention: heating data collection is performed at sampling nodes in various areas of the heating system, including the following steps: The pressure, flow rate and temperature data of heat source outlet and return water are collected through sampling nodes in the heat source monitoring area; Collect pipeline temperature, pressure, sound, video and flow data through sampling nodes in the thermal transmission monitoring area; The pressure, flow and temperature data of the inlet and return water of the heat dissipation terminal are collected through the sampling nodes in the heat dissipation monitoring area; The pipelines and sampling nodes in each area are numbered, and data labels are added to the heating data collected by the sampling nodes according to the sampling pipeline and node numbers and the sampling timestamp.

[0008] As a further solution of the present invention: the data segmentation processing module divides the heating data into different subsets according to the startup phase, stable operation phase and shutdown phase of the heating system, including the following steps: The stage from the heating system being in the off state to the stage when the sampling node in the heat dissipation monitoring area detects that the temperature reaches the predetermined temperature is set as the startup stage; The stage from when the temperature of the heating system is detected by the sampling node in the heat dissipation monitoring area to when the temperature reaches the predetermined temperature to when the heat source in the heat source monitoring area stops supplying heat is set as the stable operation stage; The process from when the heat source in the heat source monitoring area stops supplying heat to when the flow rate in the pipeline in the heat dissipation monitoring area drops to a preset threshold value is set as the shutdown stage; According to the settings of the startup phase, stable operation phase and shutdown phase of the heating system, the heating data is divided into different subsets according to the timestamp of the heating data, and the data in the subset is partitioned according to the data label in each subset, and the data belonging to the same sampling pipeline in the subset is divided into the same partition.

[0009] As a further solution of the present invention: by extracting features from each subset of historical heating data, a normal data recognition model for each stage is established, including the following steps: Obtain historical heating data, and mark the data of different areas of the heating system when they are operating normally as normal data, and mark the data of different areas of the heating system when they are operating abnormally as abnormal data, and divide the historical heating data into different data subsets according to the collection stage of the historical heating data; From the historical heating data subset, extract the sound and video data of the sampling nodes and collection pipelines in the heat transmission monitoring area; Extract the time domain features of the audio data of the pipeline, including mean, variance and peak value, convert the time domain signal of the audio into frequency domain representation through Fourier transform, and extract frequency domain features, including frequency width and power spectrum density; Mark the abnormal images in the image data of the water distributor, water collector and floor master valve position in the historical data, train the deep learning model with the marked image data, and identify whether the images of the water distributor, water collector and floor master valve position are abnormal; For the video data of the water distributor, water collector and floor master valve positions collected by the sampling nodes in the heat transfer monitoring area, an image frame is extracted every 50 frames, and the extracted image frame is input into the trained deep learning model to identify whether there is an abnormality; The sound data of the pipeline collected by the sampling node in the heat transfer monitoring area is replaced with the sound data of the pipeline in the time domain feature and frequency domain feature replacement subset, and the video data in the heat transfer monitoring area is replaced with the judgment result of whether the image is abnormal; The MLP multilayer perceptron is trained by using subset data of the startup phase, stable operation phase and shutdown phase of the heating system to identify the subset data of the startup phase, stable operation phase and shutdown phase as normal data or abnormal data.

[0010] As a further solution of the present invention: the data analysis module comprises: Heat loss assessment unit: dynamically assesses the heat loss coefficient through the flow and temperature data collected by sampling nodes in the heat source monitoring area, heat transfer monitoring area, and heat dissipation monitoring area; Heating demand analysis unit: Based on the pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area, as well as the historical heating records, it analyzes the user's heating demand and predicts the required heating amount in the heat dissipation monitoring area; Heating regulation unit: adjusts heating parameters according to the real-time forecast results of heating demand and the heat loss coefficient.

[0011] As a further solution of the present invention: dynamically evaluating the heat loss coefficient through the flow and temperature data collected by the sampling nodes in the heat source monitoring area, the heat transfer monitoring area and the heat dissipation monitoring area, including the following steps: Obtain subset data of the heat transfer monitoring area and the heat dissipation monitoring area corresponding to the heat source monitoring area; According to the flow data of each pipeline sampling node in the heat transmission monitoring area and the heat dissipation monitoring area, the mean of all pipeline flow detection data is calculated and used as pipeline flow data to dynamically evaluate the heat loss coefficient through the following formula: Among them, Rloss is the heat loss coefficient, Rs is the heat loss coefficient from the heat source monitoring area to the thermal transmission monitoring area; L0 is the total flow at the water outlet of the heat source monitoring area, L1 is the sum of the flow data of each pipeline in the thermal transmission monitoring area, T0 is the temperature at the water outlet of the heat source monitoring area, T1 is the mean of the temperature data of each pipeline in the thermal transmission monitoring area; Rz is the heat loss coefficient of the heat dissipation monitoring area in the thermal transmission monitoring area, L2 is the total flow at the water inlet of the heat dissipation monitoring area, T2 is the temperature at the water inlet of the heat dissipation monitoring area, and w1 and w2 are the weights of different heat loss coefficients respectively.

[0012] As a further solution of the present invention: based on the pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area and the historical heating records, the user's heating demand is analyzed and the required heating amount in the heat dissipation monitoring area is predicted, including the following steps: Obtain the pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area in different years and the same period in the historical heating data, as well as the heat supply and heating time in the historical heating records; The historical data is divided into different time intervals, and the pressure, flow and temperature data of the time interval are used as input in turn, and the corresponding heating data of the next time interval is used as output to train the RNN recurrent neural network model; The pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area in real time are input into the trained RNN recurrent neural network model to predict the required heating supply in the heat dissipation monitoring area.

[0013] As a further solution of the present invention: adjusting the heating parameters according to the real-time prediction result of the heating demand and the heat loss coefficient, including the following steps: According to the real-time forecast results of heating demand and the heat loss coefficient, and the temperatures at the water outlet and return water outlet of the heat source monitoring area are obtained, the target temperature at the water outlet of the heat source monitoring area is calculated by the following formula: Among them, Tout is the target temperature at the water outlet of the heat source monitoring area, Qout is the predicted value of the heat supply required in the heat dissipation monitoring area, Rloss is the heat loss coefficient, Qde is the current heat supply in the heat dissipation monitoring area, m is the flow rate at the water outlet of the heat source in the heat source monitoring area, unit: kg / s, c is the specific heat capacity of water, Tin is the temperature at the return water outlet of the heat source in the heat source monitoring area; According to the target temperature at the water outlet of the heat source monitoring area, the outlet water temperature of the heating parameter is adjusted.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention divides the heating data into different subsets according to the startup phase, stable operation phase and shutdown phase of the heating system through a data segmentation processing module; a normal data recognition model for each phase is established by extracting features from each subset of the historical heating data; there may be significant differences in the heating characteristics and demands in the startup phase, stable operation phase and shutdown phase, and these differences can be better captured by modeling each phase separately, thereby improving the prediction accuracy of the model; at the same time, in the stable operation phase, the state of the system is relatively stable and the data noise is less, which helps to improve the model's ability to recognize the normal state.

[0015] The present invention uses the data collected by the data analysis module through the sampling nodes of the heat source monitoring area, the heat transmission monitoring area and the heat dissipation monitoring area to dynamically evaluate the heat loss coefficient. At the same time, the user's heating demand is predicted in real time, and the heating parameters are adjusted according to the real-time prediction results of the heating demand and the heat loss coefficient. According to the real-time evaluated heat loss coefficient and user needs, the heating parameters can be accurately adjusted to reduce energy waste and improve the overall energy utilization efficiency. While meeting user needs, excessive heating is avoided and overall energy consumption is reduced. Real-time monitoring and dynamic adjustment can prevent system failures caused by overload or underload, thereby improving the reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of the system framework of the present invention; Figure 2 It is a schematic diagram of the framework of the data analysis module of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1-Figure 2 The first aspect of the present invention provides a centralized heating data analysis system for decentralized users based on the Internet of Things, including: Intelligent zoning module: the heating system is divided into heat source monitoring area, heat transmission monitoring area and heat dissipation monitoring area, sampling nodes are set at the heat output position of the heat source monitoring area, the heat transmission monitoring area is divided into underground transmission area and floor transmission area, sampling nodes of different densities are set in the underground transmission area and floor transmission area, and terminal data sampling nodes are set in the heat dissipation monitoring area; Data collection module: collects heating data at sampling nodes in various areas of the heating system; collects pipeline temperature, pressure, sound and flow data in the underground transmission area of ​​the heat transmission monitoring area, and collects pipeline temperature, pressure, sound, video and flow data in the floor transmission area; Data segmentation processing module: divides the heating data into different subsets according to the startup phase, stable operation phase and shutdown phase of the heating system; extracts features from each subset of historical heating data and establishes a normal data recognition model for each phase; Real-time data comparison module: identifies real-time data with the normal data recognition model of the corresponding stage, and performs abnormal recognition analysis on abnormal data; Data analysis module: Dynamically evaluate the heat loss coefficient through the data collected by sampling nodes in the heat source monitoring area, heat transfer monitoring area and heat dissipation monitoring area. At the same time, make real-time predictions on user heating demand and adjust heating parameters based on the real-time prediction results of heating demand and the heat loss coefficient.

[0020] Specifically, in this embodiment, the heat source monitoring area mainly monitors the output of the heat source equipment, and collects temperature, pressure and flow data in real time by setting sampling nodes.

[0021] Thermal transmission monitoring area: divided into underground transmission area and floor transmission area. Sampling nodes of different densities are set in these two areas to more accurately monitor the loss and efficiency in the thermal transmission process.

[0022] Heat dissipation monitoring area: mainly focuses on the performance of radiators or terminal devices. Terminal data sampling nodes are set in this area to obtain temperature and flow data in real time.

[0023] By using wireless communication technologies such as LoRa, Zigbee, Wi-Fi, etc., the collected data is sent to the central control system or cloud server in real time for unified processing and analysis of the data.

[0024] Through the data segmentation processing module, the heating data is divided into different subsets according to the startup phase, stable operation phase and shutdown phase of the heating system; by extracting features from each subset of the historical heating data, a normal data recognition model for each phase is established; there may be significant differences in the heating characteristics and demands in the startup phase, stable operation phase and shutdown phase. By modeling each phase separately, these differences can be better captured, thereby improving the prediction accuracy of the model; at the same time, in the stable operation phase, the system state is relatively stable and the data noise is less, which helps to improve the model's ability to recognize the normal state.

[0025] By identifying normal data at different stages, it is easy to quickly adjust the heating strategy according to the current system status, improve energy efficiency and reduce operating costs. Establishing a normal data recognition model facilitates timely detection of abnormal situations, prevents equipment failure or excessive energy consumption, and thus achieves proactive maintenance. Timely detection and handling of potential problems can reduce system downtime and improve the availability of the overall heating system.

[0026] The real-time data comparison module identifies the real-time data with the normal data recognition model of the corresponding stage, and performs abnormal recognition analysis on the abnormal data; through the comparative analysis of real-time monitoring and normal data models, abnormal data that deviates from the normal range can be quickly identified, thereby discovering potential faults or abnormal system operation at an early stage.

[0027] For follow-up, based on the data of abnormality identification, predictive maintenance strategies can be implemented to perform necessary inspections and repairs before failures occur, thereby reducing downtime.

[0028] The data analysis module uses data collected by sampling nodes in the heat source monitoring area, heat transfer monitoring area and heat dissipation monitoring area to dynamically evaluate the heat loss coefficient. At the same time, it makes real-time predictions of user heating demand and adjusts heating parameters based on the real-time prediction results of heating demand and the heat loss coefficient.

[0029] Based on the real-time evaluation of the heat loss coefficient and user needs, the heating parameters can be accurately adjusted to reduce energy waste and improve overall energy efficiency. While meeting user needs, overheating can be avoided to reduce overall energy consumption. Real-time monitoring and dynamic adjustment can prevent system failures caused by overload or underload, thereby improving system reliability.

[0030] In one embodiment of the present invention, the thermal transmission monitoring area is divided into an underground transmission area and a floor transmission area, and sampling nodes of different densities are set in the underground transmission area and the floor transmission area, including the following steps: The transmission pipelines buried underground in the heat transmission monitoring area are set as underground transmission areas, and the transmission pipelines located between above-ground buildings are set as floor transmission areas; For underground transmission areas, a sampling node is set up every 500 meters to collect pipeline temperature, pressure, sound and flow data; For the floor transmission area, a sampling node is set up every 10 meters to collect pipeline temperature, pressure, sound and flow data, and video sampling nodes are set up at the water distributor, water collector and floor main valve positions.

[0031] In one embodiment of the present invention, heating data collection is performed at sampling nodes in each area of ​​the heating system, including the following steps: The pressure, flow rate and temperature data of heat source outlet and return water are collected through sampling nodes in the heat source monitoring area; Collect pipeline temperature, pressure, sound, video and flow data through sampling nodes in the thermal transmission monitoring area; The pressure, flow and temperature data of the inlet and return water of the heat dissipation terminal are collected through the sampling nodes in the heat dissipation monitoring area; The pipelines and sampling nodes in each area are numbered, and data labels are added to the heating data collected by the sampling nodes according to the sampling pipeline and node numbers and the sampling timestamp.

[0032] Specifically, in this embodiment, a numbering rule is established: a unified numbering rule is formulated for the pipeline points in each area. The numbering should be concise and clear for easy identification. For example: heat source monitoring area: H1, H2, H3...; thermal transmission monitoring area: T1, T2, T3...; heat dissipation monitoring area: R1, R2, R3...; and set numbers for sampling points, and add data labels to the heating data collected by the sampling nodes according to the sampling pipeline and node numbers, as well as the sampling timestamp. For example, the data label added to the heating data in the thermal transmission monitoring area is: T1-001,2023-10-01T12:00:00Z.

[0033] In one embodiment of the present invention, the data segmentation processing module divides the heating data into different subsets according to the startup phase, stable operation phase and shutdown phase of the heating system, including the following steps: The stage from the heating system being in the off state to the stage when the sampling node in the heat dissipation monitoring area detects that the temperature reaches the predetermined temperature is set as the startup stage; The stage from when the temperature of the heating system is detected by the sampling node in the heat dissipation monitoring area to when the temperature reaches the predetermined temperature to when the heat source in the heat source monitoring area stops supplying heat is set as the stable operation stage; The process from when the heat source in the heat source monitoring area stops supplying heat to when the flow rate in the pipeline in the heat dissipation monitoring area drops to a preset threshold value is set as the shutdown stage; According to the settings of the startup phase, stable operation phase and shutdown phase of the heating system, the heating data is divided into different subsets according to the timestamp of the heating data, and the data in the subset is partitioned according to the data label in each subset, and the data belonging to the same sampling pipeline in the subset is divided into the same partition.

[0034] In one embodiment of the present invention, by extracting features from each subset of historical heating data, a normal data recognition model for each stage is established, including the following steps: Obtain historical heating data, and mark the data of different areas of the heating system when they are operating normally as normal data, and mark the data of different areas of the heating system when they are operating abnormally as abnormal data, and divide the historical heating data into different data subsets according to the collection stage of the historical heating data; From the historical heating data subset, extract the sound and video data of the sampling nodes and collection pipelines in the heat transmission monitoring area; Extract the time domain features of the audio data of the pipeline, including mean, variance and peak value, convert the time domain signal of the audio into frequency domain representation through Fourier transform, and extract frequency domain features, including frequency width and power spectrum density; Mark the abnormal images in the image data of the water distributor, water collector and floor master valve position in the historical data, train the deep learning model with the marked image data, and identify whether the images of the water distributor, water collector and floor master valve position are abnormal; For the video data of the water distributor, water collector and floor master valve positions collected by the sampling nodes in the heat transfer monitoring area, an image frame is extracted every 50 frames, and the extracted image frame is input into the trained deep learning model to identify whether there is an abnormality; The sound data of the pipeline collected by the sampling node in the heat transfer monitoring area is replaced with the sound data of the pipeline in the time domain feature and frequency domain feature replacement subset, and the video data in the heat transfer monitoring area is replaced with the judgment result of whether the image is abnormal; The MLP multilayer perceptron is trained by using subset data of the startup phase, stable operation phase and shutdown phase of the heating system to identify the subset data of the startup phase, stable operation phase and shutdown phase as normal data or abnormal data.

[0035] In one embodiment of the present invention, the data analysis module includes: Heat loss assessment unit: dynamically assesses the heat loss coefficient through the flow and temperature data collected by sampling nodes in the heat source monitoring area, heat transfer monitoring area, and heat dissipation monitoring area; Heating demand analysis unit: Based on the pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area, as well as the historical heating records, it analyzes the user's heating demand and predicts the required heating amount in the heat dissipation monitoring area; Heating regulation unit: adjusts heating parameters according to the real-time forecast results of heating demand and the heat loss coefficient.

[0036] In one embodiment of the present invention, the heat loss coefficient is dynamically evaluated by using the flow and temperature data collected by the sampling nodes in the heat source monitoring area, the heat transfer monitoring area, and the heat dissipation monitoring area, including the following steps: Obtain subset data of the heat transfer monitoring area and the heat dissipation monitoring area corresponding to the heat source monitoring area; According to the flow data of each pipeline sampling node in the heat transmission monitoring area and the heat dissipation monitoring area, the mean of all pipeline flow detection data is calculated and used as pipeline flow data to dynamically evaluate the heat loss coefficient through the following formula: Among them, Rloss is the heat loss coefficient, Rs is the heat loss coefficient from the heat source monitoring area to the thermal transmission monitoring area; L0 is the total flow at the water outlet of the heat source monitoring area, L1 is the sum of the flow data of each pipeline in the thermal transmission monitoring area, T0 is the temperature at the water outlet of the heat source monitoring area, T1 is the mean of the temperature data of each pipeline in the thermal transmission monitoring area; Rz is the heat loss coefficient of the heat dissipation monitoring area in the thermal transmission monitoring area, L2 is the total flow at the water inlet of the heat dissipation monitoring area, T2 is the temperature at the water inlet of the heat dissipation monitoring area, and w1 and w2 are the weights of different heat loss coefficients respectively.

[0037] Specifically, in this embodiment, by obtaining; data statistics of the total heat output of the heat source monitoring area in the historical data, and data statistics of the total heat obtained in the heat dissipation monitoring area corresponding to the heat source in the heat source monitoring area, the actual heat loss value is calculated, and the ratio of the actual heat loss value to the total heat output of the heat source monitoring area is compared with the heating loss coefficient calculated by the above formula, when w1 is set to 0.67 and w2 is set to 0.33.

[0038] In one embodiment of the present invention, based on the pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area and the historical heating records, the user's heating demand is analyzed and the required heating amount in the heat dissipation monitoring area is predicted, including the following steps: Obtain the pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area in different years and the same period in the historical heating data, as well as the heat supply and heating time in the historical heating records; The historical data is divided into different time intervals, and the pressure, flow and temperature data of the time interval are used as input in turn, and the corresponding heating data of the next time interval is used as output to train the RNN recurrent neural network model; The pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area in real time are input into the trained RNN recurrent neural network model to predict the required heating supply in the heat dissipation monitoring area.

[0039] In one embodiment of the present invention, adjusting heating parameters according to the real-time prediction result of heating demand and the heat loss coefficient includes the following steps: According to the real-time forecast results of heating demand and the heat loss coefficient, and the temperatures at the water outlet and return water outlet of the heat source monitoring area are obtained, the target temperature at the water outlet of the heat source monitoring area is calculated by the following formula: Among them, Tout is the target temperature at the water outlet of the heat source monitoring area, Qout is the predicted value of the heat supply required in the heat dissipation monitoring area, Rloss is the heat loss coefficient, Qde is the current heat supply in the heat dissipation monitoring area, m is the flow rate at the water outlet of the heat source in the heat source monitoring area, unit: kg / s, c is the specific heat capacity of water, Tin is the temperature at the return water outlet of the heat source in the heat source monitoring area; According to the target temperature at the water outlet of the heat source monitoring area, the outlet water temperature of the heating parameter is adjusted.

[0040] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A centralized heating data analysis system for decentralized users based on the Internet of Things, characterized in that: include: Intelligent zoning module: the heating system is divided into heat source monitoring area, heat transmission monitoring area and heat dissipation monitoring area, sampling nodes are set at the heat output position of the heat source monitoring area, the heat transmission monitoring area is divided into underground transmission area and floor transmission area, sampling nodes of different densities are set in the underground transmission area and floor transmission area, and terminal data sampling nodes are set in the heat dissipation monitoring area; Data collection module: collects heating data at sampling nodes in various areas of the heating system; collects pipeline temperature, pressure, sound and flow data in the underground transmission area of ​​the heat transmission monitoring area, and collects pipeline temperature, pressure, sound, video and flow data in the floor transmission area; Data segmentation processing module: divides the heating data into different subsets according to the startup phase, stable operation phase and shutdown phase of the heating system; extracts features from each subset of historical heating data and establishes a normal data recognition model for each phase; Real-time data comparison module: identifies real-time data with the normal data recognition model of the corresponding stage, and performs abnormal recognition analysis on abnormal data; Data analysis module: Dynamically evaluate the heat loss coefficient through the data collected by sampling nodes in the heat source monitoring area, heat transfer monitoring area and heat dissipation monitoring area. At the same time, make real-time predictions on user heating demand and adjust heating parameters based on the real-time prediction results of heating demand and the heat loss coefficient.

2. According to claim 1, a centralized heating data analysis system for dispersed users based on the Internet of Things is characterized in that: The thermal transmission monitoring area is divided into an underground transmission area and a floor transmission area, and sampling nodes of different densities are set in the underground transmission area and the floor transmission area, including the following steps: The transmission pipelines buried underground in the heat transmission monitoring area are set as underground transmission areas, and the transmission pipelines located between above-ground buildings are set as floor transmission areas; For underground transmission areas, a sampling node is set up every 500 meters to collect pipeline temperature, pressure, sound and flow data; For the floor transmission area, a sampling node is set up every 10 meters to collect pipeline temperature, pressure, sound and flow data, and video sampling nodes are set up at the water distributor, water collector and floor main valve positions.

3. According to the Internet of Things-based decentralized heating data analysis system of claim 1, it is characterized in that: Heating data collection is performed at sampling nodes in various areas of the heating system, including the following steps: The pressure, flow rate and temperature data of heat source outlet and return water are collected through sampling nodes in the heat source monitoring area; Collect pipeline temperature, pressure, sound, video and flow data through sampling nodes in the thermal transmission monitoring area; The pressure, flow and temperature data of the inlet and return water of the heat dissipation terminal are collected through the sampling nodes in the heat dissipation monitoring area; The pipelines and sampling nodes in each area are numbered, and data labels are added to the heating data collected by the sampling nodes according to the sampling pipeline and node numbers and the sampling timestamp.

4. According to the Internet of Things-based decentralized heating data analysis system of claim 1, it is characterized in that: The data segmentation processing module divides the heating data into different subsets according to the startup phase, stable operation phase and shutdown phase of the heating system, including the following steps: The stage from the heating system being in the off state to the stage when the sampling node in the heat dissipation monitoring area detects that the temperature reaches the predetermined temperature is set as the startup stage; The stage from when the temperature of the heating system is detected by the sampling node in the heat dissipation monitoring area to when the temperature reaches the predetermined temperature to when the heat source in the heat source monitoring area stops supplying heat is set as the stable operation stage; The process from when the heat source in the heat source monitoring area stops supplying heat to when the flow rate in the pipeline in the heat dissipation monitoring area drops to a preset threshold value is set as the shutdown stage; According to the settings of the startup phase, stable operation phase and shutdown phase of the heating system, the heating data is divided into different subsets according to the timestamp of the heating data, and the data in the subset is partitioned according to the data label in each subset, and the data belonging to the same sampling pipeline in the subset is divided into the same partition.

5. According to claim 4, a centralized heating data analysis system for dispersed users based on the Internet of Things is characterized in that: By extracting features from each subset of historical heating data, a normal data recognition model for each stage is established, including the following steps: Obtain historical heating data, and mark the data of different areas of the heating system when they are operating normally as normal data, and mark the data of different areas of the heating system when they are operating abnormally as abnormal data, and divide the historical heating data into different data subsets according to the collection stage of the historical heating data; From the historical heating data subset, extract the sound and video data of the sampling nodes and collection pipelines in the heat transmission monitoring area; Extract the time domain features of the audio data of the pipeline, including mean, variance and peak value, convert the time domain signal of the audio into frequency domain representation through Fourier transform, and extract frequency domain features, including frequency width and power spectrum density; Mark the abnormal images in the image data of the water distributor, water collector and floor master valve position in the historical data, train the deep learning model with the marked image data, and identify whether the images of the water distributor, water collector and floor master valve position are abnormal; For the video data of the water distributor, water collector and floor master valve positions collected by the sampling nodes in the heat transfer monitoring area, an image frame is extracted every 50 frames, and the extracted image frame is input into the trained deep learning model to identify whether there is an abnormality; The sound data of the pipeline collected by the sampling node in the heat transfer monitoring area is replaced with the sound data of the pipeline in the time domain feature and frequency domain feature replacement subset, and the video data in the heat transfer monitoring area is replaced with the judgment result of whether the image is abnormal; The MLP multilayer perceptron is trained by using subset data of the startup phase, stable operation phase and shutdown phase of the heating system to identify the subset data of the startup phase, stable operation phase and shutdown phase as normal data or abnormal data.

6. The centralized heating data analysis system for dispersed users based on the Internet of Things according to claim 1 is characterized in that: The data analysis module comprises: Heat loss assessment unit: dynamically assesses the heat loss coefficient through the flow and temperature data collected by sampling nodes in the heat source monitoring area, heat transfer monitoring area, and heat dissipation monitoring area; Heating demand analysis unit: Based on the pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area, as well as the historical heating records, it analyzes the user's heating demand and predicts the required heating amount in the heat dissipation monitoring area; Heating regulation unit: adjusts heating parameters according to the real-time forecast results of heating demand and the heat loss coefficient.

7. The centralized heating data analysis system for dispersed users based on the Internet of Things according to claim 6 is characterized in that: The heat loss coefficient is dynamically evaluated through the flow and temperature data collected by the sampling nodes in the heat source monitoring area, the heat transfer monitoring area, and the heat dissipation monitoring area, including the following steps: Obtain subset data of the heat transfer monitoring area and the heat dissipation monitoring area corresponding to the heat source monitoring area; According to the flow data of each pipeline sampling node in the heat transmission monitoring area and the heat dissipation monitoring area, the mean of all pipeline flow detection data is calculated and used as pipeline flow data to dynamically evaluate the heat loss coefficient through the following formula: Among them, Rloss is the heat loss coefficient, Rs is the heat loss coefficient from the heat source monitoring area to the thermal transmission monitoring area; L0 is the total flow at the water outlet of the heat source monitoring area, L1 is the sum of the flow data of each pipeline in the thermal transmission monitoring area, T0 is the temperature at the water outlet of the heat source monitoring area, T1 is the mean of the temperature data of each pipeline in the thermal transmission monitoring area; Rz is the heat loss coefficient of the heat dissipation monitoring area in the thermal transmission monitoring area, L2 is the total flow at the water inlet of the heat dissipation monitoring area, T2 is the temperature at the water inlet of the heat dissipation monitoring area, and w1 and w2 are the weights of different heat loss coefficients respectively.

8. The centralized heating data analysis system for dispersed users based on the Internet of Things according to claim 6 is characterized in that: Based on the pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area and the historical heating records, the user's heating demand is analyzed and the required heating amount in the heat dissipation monitoring area is predicted, including the following steps: Obtain the pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area in different years and the same period in the historical heating data, as well as the heat supply and heating time in the historical heating records; The historical data is divided into different time intervals, and the pressure, flow and temperature data of the time interval are used as input in turn, and the corresponding heating data of the next time interval is used as output to train the RNN recurrent neural network model; The pressure, flow and temperature data collected by the sampling nodes in the heat dissipation monitoring area in real time are input into the trained RNN recurrent neural network model to predict the required heating supply in the heat dissipation monitoring area.

9. The centralized heating data analysis system for dispersed users based on the Internet of Things according to claim 6 is characterized in that: According to the real-time forecast results of the heating demand and the heat loss coefficient, the heating parameters are adjusted, including the following steps: According to the real-time forecast results of heating demand and the heat loss coefficient, and the temperatures at the water outlet and return water outlet of the heat source monitoring area are obtained, the target temperature at the water outlet of the heat source monitoring area is calculated by the following formula: Among them, Tout is the target temperature at the water outlet of the heat source monitoring area, Qout is the predicted value of the heat supply required in the heat dissipation monitoring area, Rloss is the heat loss coefficient, Qde is the current heat supply in the heat dissipation monitoring area, m is the flow rate at the water outlet of the heat source in the heat source monitoring area, unit: kg / s, c is the specific heat capacity of water, Tin is the temperature at the return water outlet of the heat source in the heat source monitoring area; According to the target temperature at the water outlet of the heat source monitoring area, the outlet water temperature of the heating parameter is adjusted.

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