Heat supply pipe network

Abstract

The embodiment of the invention discloses a heat supply pipe network which comprises at least one heat supply pipeline, at least one heat power generator and a thermal balance adjusting unit. At least one monitor is arranged in the heat distribution pipeline, and the monitor is used for sensing state data of a fluid medium in the heat distribution pipeline; the heat power generator is used for converting heat energy in the heat distribution pipeline into electric energy and supplying power to the monitor; and the thermal balance adjusting unit is deployed on a background server and is used for scheduling external heat supply equipment to adjust the thermal balance of the fluid medium in the heat supply pipe network based on the sensing data obtained by the monitor. The pressure, the flow and the temperature of a fluid medium in a pipeline are sensed; and the pipeline thermodynamic equilibrium state is monitored in real time and adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of a heating system, and more specifically, to a thermal power pipe network. Background Art

[0002] At present, the urban thermal network may encounter the following problems during operation: pipeline corrosion. Due to long-term exposure to high temperature, high pressure or corrosive media, the pipeline material may be chemically eroded or electrochemically corroded, reducing its service life; low energy efficiency. If the thermal insulation performance is poor, the heat loss will increase, resulting in energy waste and affecting the heating effect; troubleshooting is difficult. Old pipelines may have pipeline leakage, blockage or pressure imbalance, which are more complicated to locate and repair; pipelines are often mistakenly excavated, resulting in production safety accidents. Therefore, new technologies are urgently needed to solve the above problems. Summary of the invention

[0003] The object of the present invention is to provide a thermal network to solve at least one of the problems existing in the prior art.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] The present invention provides a thermal power network, comprising:

[0006] At least one thermal pipeline, at least one thermal generator and a thermal balance adjustment unit;

[0007] At least one monitor is arranged in the thermal pipeline, and the monitor is used to sense the state data of the fluid medium in the thermal pipeline;

[0008] The thermal generator is used to convert the thermal energy in the thermal pipeline into electrical energy to supply power to the monitor;

[0009] The thermal balance adjustment unit is deployed on a background server and is used to schedule external heating equipment to adjust the thermal balance of the fluid medium in the thermal network based on the sensing data obtained by the monitor.

[0010] Optionally, the monitor includes a pressure flow sensor and a temperature sensor;

[0011] The pressure flow sensor is used to sense the pressure and flow of the fluid medium in the thermal pipeline;

[0012] The temperature sensor is used to sense the temperature of the fluid medium in the thermal pipeline.

[0013] Optionally, the thermal balance adjustment unit includes a simulation modeling module, a prediction analysis module, an optimization scheduling module, an instruction generation module and an instruction issuing module;

[0014] The simulation modeling module is used to establish a neural network model of the thermal network according to the sensing data of the monitor and the structure and parameters of each pipeline in the thermal network, and calculate the gradient of the loss function to each parameter through the back propagation algorithm to adjust the weight and bias parameters of the neural network model;

[0015] The prediction and analysis module is used to analyze and diagnose the sensing data according to the neural network model of the thermal network and the heating demand;

[0016] The optimization scheduling module is used to generate a control plan for the thermal balance of the thermal network according to the prediction analysis results and the heating target;

[0017] The instruction generation module is used to generate scheduling instructions according to the control scheme, including temperature, pressure, flow, water level, voltage and current control parameters;

[0018] The instruction issuing module is used to issue the dispatching instruction to the external heating equipment to adjust the thermal balance of the fluid medium in the thermal network.

[0019] Optionally, the thermal pipeline further includes a first self-cleaning module and a second self-cleaning module;

[0020] The first self-cleaning module is arranged at the pipe mouth of the thermal pipeline, and is used to clean the inside of the thermal pipeline with high-pressure steam;

[0021] The second self-cleaning module is arranged inside the thermal pipeline and is used to generate a high-energy electric field to clean the inside of the thermal pipeline.

[0022] Optionally, the thermal pipeline further includes a main control device, which includes an MCU processor and a wireless communication module.

[0023] The MCU processor is used to process the data obtained by different sensors using a multi-data fusion protocol;

[0024] The wireless communication module is used to send the data processed by the MCU processor to the thermal balance adjustment unit.

[0025] Optionally, the thermal pipeline further includes a vibration sensor;

[0026] The vibration sensor is used to collect vibration signals outside the thermal pipeline. If the amplitude of the vibration signal is greater than a preset threshold, an audible and visual alarm is issued, and the alarm information is also transmitted to the main control device.

[0027] Optionally, the thermal pipeline further comprises at least one first thermal imager and at least one second thermal imager;

[0028] The first thermal imager and the second thermal imager have the same structure;

[0029] The first thermal imager is used to sense the temperature of the fluid medium inside the thermal pipeline and transmit the sensing result to the main control device;

[0030] The second thermal imager is used to sense the ambient temperature outside the thermal pipeline and transmit the sensing result to the main control device;

[0031] Optionally, the first thermal imager includes a first infrared lens, a first infrared detector and a first signal processing circuit; the second thermal imager includes a second infrared lens, a second infrared detector and a second signal processing circuit;

[0032] The first infrared lens is used to collect the first infrared radiation signal emitted by the fluid medium inside the thermal pipeline onto the first infrared detector; the first infrared detector is used to convert the first infrared radiation signal into a first electrical signal; the first signal processing circuit is used to process the first electrical signal and send it to the main control device.

[0033] The second infrared lens is used to collect the second infrared radiation signal emitted from the outside of the thermal pipe onto the second infrared detector; the second infrared detector is used to convert the second infrared radiation signal into a second electrical signal; the second signal processing circuit is used to process the second electrical signal and send it to the main control device.

[0034] Optionally, the thermal pipeline includes, from inside to outside, a self-cleaning layer, a high-temperature power generation layer, an inner thermal imaging layer, a heat insulation layer, a monitoring circuit layer, a low-temperature power generation layer, an outer thermal imaging layer, an anti-excavation detection layer and an insulating protection layer;

[0035] Among them, the second self-cleaning module is arranged in the self-cleaning layer; the thermal generator uses the high-temperature power generation layer and the low-temperature power generation layer to convert the temperature difference and heat flow into a DC power supply, and stores the DC power supply in the battery of the thermal generator; the first thermal imager is arranged in the inner thermal imaging layer, and the second thermal imager is arranged in the outer thermal imaging layer; the circuit of the monitor is arranged in the monitoring circuit layer; the vibration sensor is arranged in the anti-excavation detection layer.

[0036] Optionally, the background server further includes a display, and the display is used to display the operating status, control scheme, optimization results and fault alarm information of the thermal network.

[0037] The beneficial effects of the present invention are as follows:

[0038] The present invention utilizes electromagnetic effect to remove dirt to achieve self-cleaning, and regularly self-cleans the inner wall of the thermal pipe; it senses the pressure, flow and temperature of the fluid medium in the thermal pipe in real time to monitor the thermal balance state in the thermal pipe network in real time and adjust the thermal pipe network; it issues an alarm in real time when a leak occurs in the thermal pipe network; the present invention also has an anti-excavation function, and when there is excavation near the thermal pipe, it will issue an on-site sound and light alarm and wirelessly report the alarm information. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0040] Figure 1 A schematic diagram of the operation of a thermal network in one embodiment of the present invention is shown.

[0041] Figure 2 A neural network model diagram of the thermal network of the present invention is shown.

[0042] Figure 3 The composition diagram of the thermal network neural network module of the present invention is shown.

[0043] Figure 4 A cross-sectional view of a thermal pipeline in one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0044] In order to more clearly illustrate the present invention, the following embodiments and accompanying drawings are provided. Figure 1-4 The present invention is further described. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the following specific description is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0045] In one embodiment, a thermal network includes:

[0046] At least one thermal pipeline, at least one thermal generator and a thermal balance adjustment unit;

[0047] At least one monitor is arranged in the thermal pipeline, and the monitor is used to sense the state data of the fluid medium in the thermal pipeline;

[0048] The thermal generator is used to convert the thermal energy in the thermal pipeline into electrical energy to supply power to the monitor;

[0049] The thermal balance adjustment unit is deployed on a background server and is used to schedule external heating equipment to adjust the thermal balance of the fluid medium in the thermal network based on the sensing data obtained by the monitor.

[0050] Specifically, the thermal generator uses the thermoelectric effect to power the monitor. The hot spot effect, also known as the Seebeck effect, is a phenomenon that uses temperature difference to directly convert thermal energy into electrical energy. This phenomenon is based on the principle that when a contact point composed of two different materials is heated at one end and cooled at the other end, an electromotive force is formed. This process involves the migration of electrons from a high temperature area to a low temperature area under a temperature gradient, resulting in the flow of current. A thermoelectric generator (TEG) is a device made using this principle, which is usually composed of a series of alternating thermoelectric elements. When heat is transferred through these elements, it drives the movement of charges, thereby generating electricity. This power generation technology is particularly suitable for thermal pipe networks. The thermal generator uses the thermoelectric effect to generate electricity. It consists of a heat source, a semiconductor material, a cold source, and an electrode to form a device that can convert thermal energy into electrical energy. The high-temperature power generation module is a solid-state semiconductor device that converts temperature difference and heat flow into a useful DC power supply. Its structure usually includes a copper strip and a semiconductor (such as a silicon block), and uses the Seebeck effect to generate voltage, drive current and generate useful power at the load. Specifically, the movement of charge carriers in the semiconductor causes the charge carriers to accumulate on one side, generating an electric potential proportional to the temperature difference. The output DC voltage is 0-24V, charging the lithium battery and storing the generated electricity in the lithium battery pack.

[0051] The present invention can sense the pressure, flow rate and temperature of the fluid medium in the thermal pipeline; monitor the thermal balance state in the thermal pipeline network in real time, and adjust the thermal pipeline network.

[0052] In one embodiment,

[0053] The monitor includes a pressure flow sensor and a temperature sensor;

[0054] The pressure flow sensor is used to sense the pressure and flow of the fluid medium in the thermal pipeline;

[0055] The temperature sensor is used to sense the temperature of the fluid medium in the thermal pipeline.

[0056] The present invention can sense the pressure, flow rate and temperature of the fluid medium in the thermal pipeline; monitor the thermal balance state in the thermal pipeline network in real time, and adjust the thermal pipeline network.

[0057] Specifically, sensor technology is a core component of modern science and technology. It involves multiple disciplines such as physics, chemistry, and biology. It is used to sense various signals or changes in the environment and convert them into electrical signals or other forms of data. Common sensors include temperature sensors, pressure sensors, light sensors, magnetic sensors, gas sensors, etc. They are widely used in industrial automation, the Internet of Things, medical equipment, homes, and other fields. The working principles of sensors vary. Some rely on changes in the physical properties of substances (such as changes in resistance), while others obtain information by detecting specific electromagnetic waves or particles. Their role is to monitor environmental conditions in real time, such as temperature rise, pressure increase, changes in light intensity, etc., and then transmit this information to the control system or computer for processing.

[0058] The pressure flow sensor uses a flow pressure sensor, and its structure mainly includes a pressure-sensitive element, a signal conditioning circuit, and an output interface. The pressure-sensitive element is used to sense the change in pressure and convert it into an electrical signal. The signal conditioning circuit amplifies and filters the electrical signal to output a more stable and accurate signal. The output interface is used to output the processed signal to the main control device. The capacitive pressure-sensitive element is used to sense pressure by using the characteristic that the capacitance value of the capacitive element changes with strain. It consists of a structure consisting of two metal electrodes and a medium.

[0059] The temperature sensor uses a thermistor sensor, and its working principle is based on the characteristic that the resistance of the thermistor changes with temperature. The thermistor sensor uses a positive temperature coefficient (PTC) thermistor, and its thermistor resistance increases with the increase of temperature. This characteristic enables the thermistor sensor to be used for sensing and detecting temperature. Structurally, the thermistor sensor consists of a thermistor, a probe (which may be made of a metal shell or a plastic shell, etc.), an extension lead, and a metal terminal or a connector. This structure enables the sensor to maintain good stability and accuracy, high sensitivity, and fast response speed.

[0060] In one embodiment, the thermal balance adjustment unit includes a simulation modeling module, a prediction analysis module, an optimization scheduling module, an instruction generation module and an instruction issuing module;

[0061] The simulation modeling module is used to establish a neural network model of the thermal network according to the sensing data of the monitor and the structure and parameters of each pipeline in the thermal network, and calculate the gradient of the loss function to each parameter through the back propagation algorithm to adjust the weight and bias parameters of the neural network model;

[0062] The prediction and analysis module is used to analyze and diagnose the sensing data according to the neural network model of the thermal network and the heating demand;

[0063] The optimization scheduling module is used to generate a control plan for the thermal balance of the thermal network according to the prediction analysis results and the heating target;

[0064] The instruction generation module is used to generate scheduling instructions according to the control scheme, including temperature, pressure, flow, water level, voltage and current control parameters;

[0065] The instruction issuing module is used to issue the dispatching instruction to the external heating equipment to adjust the thermal balance of the fluid medium in the thermal network.

[0066] In one embodiment, the thermal pipeline further includes a first self-cleaning module and a second self-cleaning module;

[0067] The first self-cleaning module is arranged at the pipe mouth of the thermal pipeline, and is used to clean the inside of the thermal pipeline with high-pressure steam;

[0068] The second self-cleaning module is arranged inside the thermal pipeline and is used to generate a high-energy electric field to clean the inside of the thermal pipeline.

[0069] Specifically, the first self-cleaning module uses high-pressure steam to clean the inside of the pipe. The high-pressure pump pressurizes ordinary tap water to a pressure of several hundred to several thousand bar, converting it into high-temperature steam. The high-pressure steam is quickly ejected through a special nozzle to form high-speed flowing fine steam particles. Due to its high temperature, it can instantly soften and decompose various stains. The advantages of high-pressure steam cleaning are that it has no chemical pollution, is environmentally friendly, and can deeply clean and kill bacteria and viruses.

[0070] The second cleaning module is set in the self-cleaning layer to generate a high-energy electric field. It uses ionization technology to break the chemical bonds in the pollutant molecules or atoms on the inner wall of the pipe network, thereby generating charged particles. The main components of these charged particles are ions, free radicals or electrons, which are more likely to react with other substances or be removed. Through ionization and decomposition, pollutants are converted into inorganic substances, organic substances and gases, achieving the purpose of removing pollutants, which is efficient and environmentally friendly.

[0071] In one embodiment, the thermal pipeline further includes a vibration sensor;

[0072] The vibration sensor is used to collect vibration signals from the outside of the thermal pipeline. If the amplitude of the vibration signal is greater than a preset threshold, an audible and visual alarm is issued, and the alarm information is also transmitted to the main control device;

[0073] Specifically, a highly sensitive vibration sensor is used to collect noise data near the pipe network, and the amplitude and spectrum analysis of the noise signal are analyzed to determine whether there is construction excavation near the pipe. When the noise value near the thermal pipe network exceeds the threshold, the frequency information of the noise is obtained through spectrum analysis, and it is determined whether it is excavation through intelligent analysis. If it is determined to be excavation, an audible and visual alarm is issued, and the alarm information is wirelessly transmitted to the rear monitoring system for timely intervention to avoid the occurrence of pipe excavation accidents. Acoustic vibration sensing technology is a non-contact detection technology that uses the characteristics of sound waves to sense the vibration state of a physical system. This technology usually involves emitting sound waves to a target object, and then analyzing the vibration information of the object, such as frequency, amplitude, and phase, by receiving the changes in the reflected sound waves. The vibration sensor can be set as a highly sensitive piezoelectric accelerometer, which can convert the vibration signal generated by excavators and other equipment during excavation into an electrical signal and issue an alarm in time. When there is construction excavation near the thermal pipeline, the vibration signal can be collected immediately and an audio and visual alarm can be issued. At the same time, the alarm information can be wirelessly transmitted to the server through the main control device, allowing timely intervention to avoid pipe excavation accidents.

[0074] In one embodiment, the thermal conduit further comprises at least one first thermal imager and at least one second thermal imager;

[0075] The first thermal imager and the second thermal imager have the same structure;

[0076] The first thermal imager is used to sense the temperature of the fluid medium inside the thermal pipeline and transmit the sensing result to the main control device;

[0077] The second thermal imager is used to sense the ambient temperature outside the thermal pipeline and transmit the sensing result to the main control device.

[0078] In one embodiment, each thermal pipeline is provided with a main control device, and the main control device includes an MCU processor and a wireless communication module.

[0079] The MCU processor is used to perform preprocessing, calibration, filtering, compression and encryption operations on the data obtained by different sensors using a multi-data fusion protocol;

[0080] The wireless communication module is used to send the data processed by the MCU processor to the thermal balance adjustment unit.

[0081] In one embodiment, the background server further includes a display, which is used to display the operating status, control scheme, optimization results and fault alarm information of the thermal network, and receive user input and feedback.

[0082] In one embodiment, if Figure 1As shown, a schematic diagram of the operation of a thermal pipeline is shown.

[0083] Thermal pipes 1 are used to provide heating for cities and are generally buried underground.

[0084] The thermal generator 2 and the main control module (MCU processor 5 and wireless communication module 3) are arranged on one side of the thermal pipeline 1. In fact, a thermal generator 2 and a main control module are arranged for a thermal pipeline of 150-200 meters; the second self-cleaning module 4 is arranged on the inner wall of the thermal pipeline 1; the pressure flow sensor 6 and the temperature sensor 7 are arranged on the inner wall of the thermal pipeline 1, or attached to the self-cleaning layer of the thermal pipeline 1; for example, the pressure flow sensor 6 and the temperature sensor 7 are placed 50 meters apart, and the distance between the pressure flow sensor 6 and the temperature sensor 7 is 25 meters; the vibration sensor 8 and the thermal imager 9 are placed on the outer wall of the thermal pipeline, one is placed 50 meters apart.

[0085] Specifically, in actual operation, the thermal generator 2 generates electricity through the thermoelectric effect, and is composed of a heat source, a semiconductor material, a cold source, and an electrode, forming a device that can convert thermal energy into electrical energy. The generated electrical energy is stored in a large-capacity lithium battery pack to power the thermal pipeline 1. The self-cleaning module 4 uses the electromagnetic effect to remove dirt to achieve self-cleaning and clean the inner wall of the thermal pipeline 1; the pressure flow sensor 6 senses the pressure data and flow data of the fluid medium in the thermal pipeline; the temperature sensor 7 senses the temperature data of the fluid medium in the thermal pipeline; the vibration sensor 8 collects the vibration data outside the thermal pipeline; the thermal imager 9 is used to sense the temperature of the fluid medium inside the thermal pipeline and transmit the sensing result to the main control device; it is also used to sense the ambient temperature outside the thermal pipeline and transmit the sensing result to the main control device; the MCU processor 5 uses a multi-data fusion protocol to pre-process, calibrate, filter, compress, and encrypt the data obtained by different sensors, and sends it to the server through the wireless communication module 3.

[0086] Among them, the pressure flow sensor 6 adopts a capacitive pressure-sensitive sensor, which senses pressure by using the characteristic that the capacitance value of the capacitor element changes with strain. It is composed of a structure consisting of two metal electrodes and a medium, and can convert pressure into an electrical signal, thereby obtaining a pressure value, and using an electromagnetic sensor to sense the change in flow. The temperature sensor 7 adopts a thermistor sensor, which uses a positive temperature coefficient thermistor, and its thermistor resistance increases with increasing temperature; the vibration sensor 8 uses a high-sensitivity acceleration sensor, which adopts a piezoelectric structure, and can convert the vibration signal generated by excavators and other equipment during excavation into an electrical signal, and timely alarm through the MCU processor. The thermal imager 9 includes an infrared lens, an infrared detector and a signal processing circuit.

[0087] In one embodiment, thermal model technology is a scientific tool for simulating and analyzing heat transfer processes, and is mainly used in building energy-saving design, industrial process optimization, and energy system research. It is based on the thermodynamic principles in physics, such as heat transfer, convection, radiation, and conduction, and uses mathematical models to predict and control the thermal performance of the system. In architectural design, thermal models can help engineers evaluate the temperature distribution, energy consumption, and comfort inside a building, thereby improving the selection of insulation materials and spatial layout. In industrial production, thermal models can optimize the design of equipment cooling systems, improve energy efficiency, and reduce production costs.

[0088] The thermal balance adjustment unit uses a neural network to adjust the thermal balance of the thermal network, improve the network operation efficiency, and reduce heat loss. The thermal network is a heat transmission network that mainly uses water as the medium. The typical structure is as follows: Figure 2 The operation of the thermal network Ω must meet the basic laws of the network: ① The total flow of heat medium flowing into and out of the node is equal; ② The sum of the heat medium pressure head losses of each pipeline in the closed loop is zero.

[0089] In one embodiment, the workflow of the neural network mainly includes the following steps:

[0090] Data preprocessing: Clean, normalize and standardize the raw data of the thermal network to eliminate noise and inconsistency in the data.

[0091] Design the network structure according to the specific task and data characteristics, including the number of input layers, hidden layers and output layers and the number of neurons in each layer, and determine the network's training function, activation function, loss function and other parameters to establish a neural network model.

[0092] The model training process is guided by optimization algorithms such as gradient descent, and the gradient of the loss function for each parameter is calculated through the back-propagation algorithm, and parameters such as weights and biases are adjusted to make the output results of the model closer to the actual results.

[0093] Use the trained neural network model to predict new data and select the appropriate prediction result output method according to the task type.

[0094] In one embodiment, the neural network model of the thermal network is as follows: Figure 3 shown.

[0095] The input layer receives new input and the hidden state of the previous moment, calculates a new input information, and decides how to update the current memory unit; the formula is i t =σ(W i *[h t-1 ,x t ]+b i ), where it is the output of the input gate, W i is the weight matrix, b i is the bias of the input layer, σ is the activation function, and h t-1 is short-term memory, x t For event information;

[0096] The hidden layer evaluates whether the memory of the previous moment needs to be forgotten, and decides which information should be discarded based on the input and the current hidden state. The formula is f t =σ(W f *[h t-1 ,x t ]+b f ), where f t is the output of the forget gate, W f is the weight matrix, b f is the bias of the hidden layer and σ is the activation function.

[0097] The output layer determines what information is extracted from the current state as output, which can be part of the entire cell state or the result after a layer of nonlinear transformation; the formula is o t =σ(W o *[h t-1 ,x t ]+b o ), where o t is the output of the output gate, W o is the weight matrix, b o is the bias of the output layer and σ is the activation function.

[0098] In one embodiment, if Figure 4 As shown, a cross-sectional view of a self-learning and self-repairing pipeline, the pipeline includes from the inside to the outside: a self-cleaning layer 11, a high-temperature power generation layer 12, an inner thermal imaging layer 13, a heat insulation layer 14, a monitoring circuit layer 15, a low-temperature power generation layer 16, an outer thermal imaging layer 17, an anti-excavation detection layer 18 and an insulating protection layer 19. Among them, the second self-cleaning module 4 in the above embodiment is arranged in the self-cleaning layer 11 to generate a high-energy electric field, and uses ionization technology to break the chemical bonds in the pollutant molecules or atoms on the inner wall of the pipe network, thereby generating charged particles;

[0099] The thermal generator 2, the high temperature power generation layer 12, the low temperature power generation layer 16 and the battery form a thermoelectric effect generator, which generates electricity using the thermoelectric effect. The thermal generator 2 uses the high temperature power generation layer 12 and the low temperature power generation layer 16 to convert the temperature difference and heat flow into a DC power source; and stores the DC power source in the battery of the thermal generator 2; the high temperature power generation layer 12 is a heat source, and the semiconductor material, the cold source and the electrode are arranged in the low temperature power generation layer 16 to form a device that can convert thermal energy into electrical energy, and convert the temperature difference and heat flow into a useful DC power source; its structure includes a copper strip and a semiconductor (such as a silicon block), and uses the Seebeck effect to generate voltage, drive current and generate useful power at the load. The movement of carriers in the semiconductor causes the charge carriers to accumulate on one side, thereby generating an electric potential proportional to the temperature difference, and outputting electrical energy to be stored in the battery. A plurality of thermal imagers 9 are arranged on the inner thermal imaging layer 13 and the outer thermal imaging layer 17 to respectively monitor the temperature of the fluid medium inside the thermal pipeline and the ambient temperature outside the pipeline in real time, thereby providing effective data for the thermal balance of the thermal pipe network system; the thermal insulation layer 14 is used to divide the thermal pipeline into a high-temperature part and a low-temperature part; the circuit of the pressure flow sensor 6, the circuit of the temperature sensor 7 and the circuit of the vibration sensor 8 are arranged on the monitoring circuit layer 15; the vibration sensor 8 is arranged on the anti-excavation detection layer 18; the insulating protective layer 19 is used to ensure the corrosion resistance of the thermal pipeline and is also used to insulate the thermal pipeline 1.

[0100] In the description of the present invention, it should be noted that the terms "upper", "lower", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0101] It should also be noted that, in the description of the present invention, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0102] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A thermal network, characterized in that: include: At least one thermal pipeline, at least one thermal generator and a thermal balance adjustment unit; At least one monitor is arranged in the thermal pipeline, and the monitor is used to sense the state data of the fluid medium in the thermal pipeline; The thermal generator is used to convert the thermal energy in the thermal pipeline into electrical energy to supply power to the monitor; The thermal balance adjustment unit is deployed on a background server and is used to schedule external heating equipment to adjust the thermal balance of the fluid medium in the thermal network based on the sensing data obtained by the monitor.

2. The thermal network according to claim 1, characterized in that: The monitor includes a pressure flow sensor and a temperature sensor; The pressure flow sensor is used to sense the pressure and flow of the fluid medium in the thermal pipeline; The temperature sensor is used to sense the temperature of the fluid medium in the thermal pipeline.

3. The thermal network according to claim 1, characterized in that: The thermal balance adjustment unit includes a simulation modeling module, a prediction analysis module, an optimization scheduling module, an instruction generation module and an instruction issuing module; The simulation modeling module is used to establish a neural network model of the thermal network according to the sensing data of the monitor and the structure and parameters of each pipeline in the thermal network, and calculate the gradient of the loss function for each parameter through the back propagation algorithm to adjust the weight and bias parameters of the neural network model; The prediction and analysis module is used to analyze and diagnose the sensing data according to the neural network model of the thermal network and the heating demand; The optimization scheduling module is used to generate a control plan for the thermal balance of the thermal network according to the prediction analysis results and the heating target; The instruction generation module is used to generate scheduling instructions according to the control scheme, including temperature, pressure, flow, water level, voltage and current control parameters; The instruction issuing module is used to issue the dispatching instruction to the external heating equipment to adjust the thermal balance of the fluid medium in the thermal network.

4. The thermal network according to claim 2, characterized in that: The thermal pipeline also includes a first self-cleaning module and a second self-cleaning module; The first self-cleaning module is arranged at the pipe mouth of the thermal pipeline, and is used to clean the inside of the thermal pipeline with high-pressure steam; The second self-cleaning module is arranged inside the thermal pipeline and is used to generate a high-energy electric field to clean the inside of the thermal pipeline.

5. The thermal network according to claim 4, characterized in that: The thermal pipeline also includes a main control device, which includes an MCU processor and a wireless communication module. The MCU processor is used to process the data obtained by different sensors using a multi-data fusion protocol; The wireless communication module is used to send the data processed by the MCU processor to the thermal balance adjustment unit.

6. The thermal network according to claim 5, characterized in that: The thermal pipeline also includes a vibration sensor; The vibration sensor is used to collect vibration signals outside the thermal pipeline. If the amplitude of the vibration signal is greater than a preset threshold, an audible and visual alarm is issued, and the alarm information is also transmitted to the main control device.

7. The thermal network according to claim 6, characterized in that: The thermal conduit further includes at least one first thermal imager and at least one second thermal imager; The first thermal imager and the second thermal imager have the same structure; The first thermal imager is used to sense the temperature of the fluid medium inside the thermal pipeline and transmit the sensing result to the main control device; The second thermal imager is used to sense the ambient temperature outside the thermal pipeline and transmit the sensing result to the main control device.

8. The thermal network according to claim 7, characterized in that: The first thermal imager includes a first infrared lens, a first infrared detector and a first signal processing circuit; the second thermal imager includes a second infrared lens, a second infrared detector and a second signal processing circuit; The first infrared lens is used to collect the first infrared radiation signal emitted by the fluid medium inside the thermal pipeline onto the first infrared detector; the first infrared detector is used to convert the first infrared radiation signal into a first electrical signal; the first signal processing circuit is used to process the first electrical signal and send it to the main control device; The second infrared lens is used to collect the second infrared radiation signal emitted from the outside of the thermal pipe onto the second infrared detector; the second infrared detector is used to convert the second infrared radiation signal into a second electrical signal; the second signal processing circuit is used to process the second electrical signal and send it to the main control device.

9. The thermal network according to claim 8, characterized in that: The thermal pipeline includes, from the inside to the outside, a self-cleaning layer, a high-temperature power generation layer, an inner thermal imaging layer, a heat insulation layer, a monitoring circuit layer, a low-temperature power generation layer, an outer thermal imaging layer, an anti-excavation detection layer and an insulating protection layer; Among them, the second self-cleaning module is arranged in the self-cleaning layer; the thermal generator uses the high-temperature power generation layer and the low-temperature power generation layer to convert the temperature difference and heat flow into a DC power supply, and stores the DC power supply in the battery of the thermal generator; the first thermal imager is arranged in the inner thermal imaging layer, and the second thermal imager is arranged in the outer thermal imaging layer; the circuit of the monitor is arranged in the monitoring circuit layer; the vibration sensor is arranged in the anti-excavation detection layer.

10. The thermal network according to claim 1, characterized in that: The backend server also includes a display, which is used to display the operating status, control scheme, optimization results and fault alarm information of the thermal network.

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