A multi-dimensional operation data intelligent processing system and method for a heat supply pipe network

Abstract

The present application belongs to the technical field of intelligent heating, and discloses a multi-dimensional operation data intelligent processing system and method for a heating pipe network, which comprises a three-layer collaborative architecture including a data acquisition and transmission module, a central processing module and a distributed execution module. The data acquisition and transmission module is responsible for collecting the operation parameters of the pipe network and the outdoor environment parameters, and realizes the bidirectional data transfer between devices. The central processing module standardizes the original data, generates a standardized data set and calculates the building heat load prediction value, and solves the optimal regulation and control parameters of the pipe network by taking the heat load demand as a constraint. The distributed execution module adjusts the pipe network hydraulic working condition according to the regulation and control parameters, and iteratively updates the prediction model by returning the adjusted data, thereby realizing the multi-dimensional collection, analysis and dynamic regulation of the heating pipe network data, and effectively improving the uneven heating phenomenon of the pipe network and reducing the heating energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of intelligent heating technology, specifically relating to an intelligent processing system and method for multi-dimensional operation data of heating pipeline networks. Background Technology

[0002] Heating networks are critical infrastructure connecting heat sources and users in urban energy systems, and their operational efficiency directly affects urban energy utilization and the quality of heating for residents. With the continuous advancement of urbanization and increasingly stringent building energy conservation requirements, the scale of heating networks is expanding daily, significantly increasing the complexity of system operation and management.

[0003] Currently, traditional heating network management methods generally suffer from the following problems. First, energy efficiency is low. Traditional heating systems mostly adopt a unified supply operation mode, making it difficult to differentiate adjustments based on the actual heat demand of different areas and buildings, resulting in widespread overheating and significant energy waste. Second, heating quality is inconsistent. Due to a lack of comprehensive perception and precise control of the operating parameters of each node in the network, uneven heating is a prominent issue. Third, operation and control mainly rely on manual experience. Operators manually set parameters such as water supply temperature, pressure, and flow rate based on temperature changes, resulting in crude control, delayed response, and an inability to achieve refined adjustment. Furthermore, the existing heating systems lack sufficient information technology capabilities, lacking the ability to systematically collect, transmit, and deeply analyze operational data.

[0004] The existing traditional heating management and control model lacks a mechanism for collaborative processing of data across the entire region, distributed intelligent control, and hierarchical data security protection. It cannot rely on real-time data from the pipeline network to dynamically solve for the optimal control parameters. Load forecasting and hydraulic optimization calculations are concentrated in the central equipment. Control delays are high under concurrent operation of multiple heat exchange stations. There is no standardized processing and encrypted storage process for operating data, making it difficult to meet the multiple needs of energy-saving and balanced heating of the pipeline network, early warning of faults, and secure management and control of thermal data. Summary of the Invention

[0005] The purpose of this invention is to solve the problem that the existing heating network lacks a comprehensive intelligent data processing mechanism, which makes it impossible to dynamically optimize the hydraulic conditions of the network to achieve balanced and energy-saving heating. This invention provides a multi-dimensional intelligent data processing system and method for heating networks.

[0006] To achieve the above objectives, the present invention employs the following technical solution: This invention proposes a multi-dimensional intelligent data processing system for heating network operations, comprising: The data acquisition and transmission module is used to collect operating parameters of the heating pipeline network and outdoor environmental parameters, forward the collected parameters to the central processing module, and simultaneously receive the optimal control parameters transmitted by the central processing module and forward them to the distributed execution module, realizing bidirectional data transfer. The central processing module receives parameters from the data acquisition and transmission module, performs standardized processing to generate a standardized pipeline network operation dataset, calculates the predicted heat load of each building based on the standardized pipeline network operation dataset, solves for the optimal control parameters of the pipeline network using the predicted heat load as a constraint, and outputs the optimal control parameters to the data acquisition and transmission module. The distributed execution module receives the optimal control parameters forwarded by the data acquisition and transmission module, drives the network pumps and electric regulating valves to perform hydraulic condition regulation based on the optimal control parameters, and transmits the regulated operating data to the central processing module for iterative updates to the heat load prediction model.

[0007] Preferably, the data acquisition and transmission module includes a temperature sensor, a pressure sensor, a flow meter, and a heat meter installed at the heat source, heat exchange station, and pipeline node; the temperature sensor outputs a 4~20mA current signal; the pressure sensor outputs a 4~20mA current signal.

[0008] Preferably, the data transmission network between the data acquisition and transmission module and the central processing module is a passive optical network, including an optical line terminal, an optical network unit, and an optical distribution network.

[0009] Preferably, the central processing module has a built-in heat load prediction calculation unit, a pipeline pressure loss coupling calculation unit, an anomaly judgment unit, and an encrypted storage unit; the heat load prediction calculation unit receives the pipeline operation dataset and outputs the heat load prediction values ​​for each building, and receives the adjusted operation data returned by the distributed execution module to iteratively update the internal prediction model; the pipeline pressure loss coupling calculation unit uses the heat load prediction value as a flow constraint to solve for the optimal opening degree of the regulating valve of each building.

[0010] Preferably, when the central processing module solves for the optimal control parameters of the pipeline network, it uses the predicted heat load as a constraint condition, specifically including the following steps: Obtain the topology data of the secondary transmission and distribution network and the predicted heat load of each building; Based on the topology data, pressure loss calculation models for regulating valves, pipelines, buildings and heat exchangers in the heat exchange station and secondary side pipe network are established respectively. Based on the flow direction of the heat medium, the various pressure loss calculation models are integrated to establish an overall pressure loss model for the heat exchange station and the secondary side pipeline network. With the goal of reducing the overall pressure loss, the predicted heat load of each building as the flow constraint for each building, and the valve opening of each building as the adjustment parameter, the optimal valve opening value of each building is solved as the optimal control parameter for the pipeline network.

[0011] Preferably, the distributed execution module consists of multiple distributed control units; a single distributed control unit can interact with up to six adjacent distributed control units to obtain building load data, and the interacting data is synchronously transmitted back to the central processing module to supplement the modeling data.

[0012] Preferably, the distributed execution module includes distributed control units located in each building or heating area, and the distributed control unit includes a data exchange port, a storage module, a computing core, and a drive signal interaction module; The data exchange port is used for data communication with other distributed control units. A single distributed control unit can communicate with up to six adjacent distributed control units through the data exchange port. The storage module is used to store device operating parameters and performance data; The computing core is used to perform control calculations based on the optimal control parameters and output control commands. The drive signal interaction module is used to transmit control commands to the drive components of the corresponding valve.

[0013] Preferably, the central processing module is also used to perform hierarchical encryption storage of operating parameters, pipeline operation datasets, heat load forecasts, and optimal control parameters according to data security levels.

[0014] The present invention proposes an intelligent processing method for multi-dimensional operation data of heating pipe networks, comprising the following steps: The data acquisition and transmission module collects operating parameters of the heating network and outdoor environmental parameters, and forwards the collected parameters to the central processing module. The central processing module receives the collected parameters and performs standardized processing to generate a standardized pipeline network operation dataset. The central processing module calculates the predicted heat load of each building based on the standardized pipeline network operation dataset, solves for the optimal control parameters of the pipeline network using the predicted heat load as a constraint, and sends the optimal control parameters to the data acquisition and transmission module. The data acquisition and transmission module forwards the optimal control parameters to the distributed execution module; the distributed execution module drives the pipeline pumps and electric regulating valves to complete the hydraulic condition adjustment based on the optimal control parameters. The distributed execution module collects the adjusted pipeline operation data and sends it back to the central processing module. The central processing module uses the returned operation data to iteratively update the heat load prediction model, providing an updated model for the next round of load prediction calculations.

[0015] Preferably, the standardization process includes removing abnormal noise data outside the measurement range, unifying the dimensions of multiple types of parameters, linearly completing short-term missing acquisition values, and adding timestamps and equipment numbers to the processed data to generate a standardized pipeline network operation dataset.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention proposes an intelligent processing system for multi-dimensional operational data of heating pipe networks. It collects various operational parameters of the heating pipe network and outdoor environmental parameters, enabling the acquisition and real-time bidirectional transmission of multi-dimensional operational data. This effectively solves the problems of incomplete data collection, delayed information transmission, and low informatization levels in traditional heating systems. Simultaneously, the central processing module standardizes and organizes the raw collected data to form a standardized pipe network operation dataset. Based on this dataset, it accurately calculates the predicted heat load values ​​for each building, using the actual heat demand of each building as a constraint to solve for the optimal control parameters of the pipe network. On this basis, the distributed execution module precisely drives the pipe network pumps and electric regulating valves to complete hydraulic condition regulation according to the optimal control parameters, achieving refined on-demand heating of the pipe network. At the same time, the adjusted actual operational data is fed back to the central processing module for continuous iteration and updating of the heat load prediction model. This allows the system regulation to dynamically adapt to changes in ambient temperature, user heating habits, and pipe network conditions, solving the problems existing in the prior art. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a diagram of the architecture of the intelligent heating control system of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0021] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0022] The present invention will now be described in further detail with reference to the accompanying drawings: Heating networks play a crucial role in urban energy systems. However, traditional heating network management methods often suffer from energy waste and operational instability, severely hindering the efficiency and sustainable development of heating systems. To address this issue, this invention proposes a multi-dimensional operational data processing system for heating networks.

[0023] This invention aims to improve the processing level and operational efficiency of heating pipe networks. The system utilizes sensor technology to monitor parameters at each node of the network in real time, and uses algorithms for data analysis and decision-making to achieve refined control of the heating pipe network. This innovative design not only improves the energy utilization efficiency of the heating system but also effectively responds to various abnormal situations during network operation, thereby ensuring the stable operation of the system.

[0024] This invention proposes a multi-dimensional intelligent data processing system for heating network operations, comprising: The data acquisition and transmission module is used to collect operating parameters of the heating pipeline network and outdoor environmental parameters, forward the collected parameters to the central processing module, and simultaneously receive the optimal control parameters transmitted by the central processing module and forward them to the distributed execution module, realizing bidirectional data transfer. The central processing module receives parameters from the data acquisition and transmission module, performs standardized processing to generate a standardized pipeline network operation dataset, calculates the predicted heat load of each building based on the standardized pipeline network operation dataset, solves for the optimal control parameters of the pipeline network using the predicted heat load as a constraint, and outputs the optimal control parameters to the data acquisition and transmission module. The distributed execution module receives the optimal control parameters forwarded by the data acquisition and transmission module, drives the network pumps and electric regulating valves to perform hydraulic condition regulation based on the optimal control parameters, and transmits the regulated operating data to the central processing module for iterative updates to the heat load prediction model.

[0025] The data acquisition and transmission module includes temperature sensors, pressure sensors, flow meters, and heat meters installed at heat sources, heat exchange stations, and pipeline nodes; the temperature sensors output a 4~20mA current signal; the pressure sensors output a 4~20mA current signal.

[0026] The data transmission network between the data acquisition and transmission module and the central processing module is a passive optical network, which includes an optical line terminal, an optical network unit, and an optical distribution network.

[0027] The central processing module includes a built-in heat load prediction calculation unit, a pipeline pressure loss coupling calculation unit, an anomaly judgment unit, and an encrypted storage unit. The heat load prediction calculation unit receives the pipeline operation dataset and outputs the heat load prediction values ​​for each building. It also receives the adjusted operation data returned by the distributed execution module and iteratively updates the internal prediction model. The pipeline pressure loss coupling calculation unit uses the heat load prediction value as a flow constraint to solve for the optimal opening degree of the regulating valves in each building.

[0028] When the central processing module solves for the optimal control parameters of the pipeline network, it uses the predicted heat load as a constraint condition, and specifically includes the following steps: Obtain the topology data of the secondary transmission and distribution network and the predicted heat load of each building; Based on the topology data, pressure loss calculation models for regulating valves, pipelines, buildings and heat exchangers in the heat exchange station and secondary side pipe network are established respectively. Based on the flow direction of the heat medium, the various pressure loss calculation models are integrated to establish an overall pressure loss model for the heat exchange station and the secondary side pipeline network. With the goal of reducing the overall pressure loss, the predicted heat load of each building as the flow constraint for each building, and the valve opening of each building as the adjustment parameter, the optimal valve opening value of each building is solved as the optimal control parameter for the pipeline network.

[0029] The distributed execution module consists of multiple distributed control units; a single distributed control unit can interact with up to six adjacent distributed control units to obtain building load data, and the interactive data is synchronously transmitted back to the central processing module to supplement the modeling data.

[0030] The distributed execution module includes distributed control units located in each building or heating area. The distributed control unit includes a data exchange port, a storage module, a computing core, and a drive signal interaction module. The data exchange port is used for data communication with other distributed control units. A single distributed control unit can communicate with up to six adjacent distributed control units through the data exchange port. The storage module is used to store device operating parameters and performance data; The computing core is used to perform control calculations based on the optimal control parameters and output control commands. The drive signal interaction module is used to transmit control commands to the drive components of the corresponding valve.

[0031] The central processing module is also used to perform hierarchical encryption storage of operating parameters, pipeline operation datasets, heat load forecasts, and optimal control parameters according to data security levels.

[0032] The system is described in detail below: First, the composition of the multi-dimensional operation data processing system for the heating network. This invention integrates an environmentally friendly heat source with a high-efficiency heating network, promoting the realization of customized heating models through integration with network technology. In this system, intelligent heating equipment plays a fundamental role, while network technology provides powerful support for the innovation and advancement of the heating system. The aim of intelligent heating is to customize heating solutions according to user needs, thereby eliminating the energy waste and heat loss problems of traditional heating methods and greatly improving energy efficiency.

[0033] To maximize the utilization of the heating resources of the heat source plant, accurately manage the heat exchange station and its secondary pipeline network, and centrally monitor the heating area within the service range of the heat source plant, a three-layer architecture control system was constructed. This system comprises a data acquisition and transmission module, a distributed execution module, a passive optical network, and a central processing module. The system includes a hardware layer, a network layer, and a software layer, as detailed below. Figure 1 As shown.

[0034] The data acquisition and transmission module and the distributed execution module, serving as infrastructure, constitute the hardware foundation of the system, encompassing various facilities and connectors within the heat exchange station and its secondary-side transmission network. This layer, composed of security monitoring cameras, heat exchange equipment, water delivery equipment, control valves, various sensing elements, operating actuators, and control units, is responsible for collecting and integrating key information such as flow rate, heat energy, and temperature, and issuing operating commands to the primary-side valves of the plate heat exchanger and the feedwater pump.

[0035] The central processing module, as the software core of the system, integrates multiple modules such as the heat load prediction calculation unit, the pipeline pressure loss coupling calculation unit, and the user operation interface. It is also equipped with corresponding hardware facilities such as servers, displays, printers, and uninterruptible power supplies, realizing a variety of functions such as centralized monitoring, remote operation, abnormal alarms, intelligent services, cost management, and data analysis and storage of the heating system.

[0036] The network layer (corresponding to the data transmission network in the data acquisition and transmission module) connects the data acquisition and transmission module, the distributed execution module, and the central processing module. It is responsible for transmitting the data from the heat exchange station and its secondary network to the management center in real time, quickly, and securely, thereby connecting the entire system into a highly efficient whole.

[0037] Second, data collection and monitoring: With technological advancements, the intelligent transformation of heating systems is deepening, particularly in data collection and monitoring. This aspect is undoubtedly the core of advancing the intelligentization of heating networks. First, selecting suitable sensing elements and monitoring tools is a crucial step, such as deploying temperature and pressure sensors and flow recording devices. These tools must possess excellent accuracy and durability to ensure the authenticity and reliability of the acquired data. In urban heating systems, these sensing and monitoring devices are scientifically deployed at key nodes such as heat sources, heat exchange stations, and pipelines to ensure comprehensive monitoring of the entire heating process. Next, the collected data is transmitted reliably and in real-time to the central processing module via wired or wireless means. Once at the processing center, the data undergoes systematic organization, processing, and in-depth analysis to generate a standardized pipeline operation dataset. Finally, based on the collected and monitored data, the heating system is continuously improved and upgraded by adjusting operating parameters and optimizing system structure to achieve efficient energy allocation and utilization.

[0038] In heating pipeline networks, information collection and monitoring encompass multiple steps, including selecting appropriate sensing elements and monitoring instruments, their deployment and positioning, information collection and transmission, data processing and analysis, the construction of alarm systems and decision support, and system improvement and enhancement. Relying on these methods, the application of intelligent technology in heating pipeline networks can achieve precise control over the heating system, enhance its robustness and reliability, and inject strong momentum into the effective operation and sustainable development of the heating industry.

[0039] In the structural design of the intelligent heating management system, the underlying hardware facilities mainly consist of heat exchange stations and their auxiliary secondary network monitoring devices (corresponding to data acquisition and transmission modules), including cascaded servers, multi-channel video recording devices, security monitoring cameras, a host computer for heat load prediction and calculation units, programmable logic controllers, interactive operation interfaces, distributed control units, regulating valves, frequency converters, various sensors and measuring instruments, etc.

[0040] The heat load forecasting calculation unit's host computer receives two types of data as input parameters for the forecasting model: weather information and historical heat usage records. This host computer not only processes the data but also sends the sub-building heat load forecast values ​​for each building to the distributed control units of the control valves in each building, using these as constraints for valve control optimization.

[0041] At the software level, programs related to heat load forecasting and pipeline hydraulic optimization can be remotely transmitted via the network layer to the forecasting host and distributed control units at the hardware layer. These units then execute predictive analysis and control strategy optimization tasks for their respective areas. This approach aims to alleviate the burden on the central processing module for heat demand forecasting and control optimization calculations when multiple heat exchange stations at a heat source plant are operating simultaneously. Furthermore, thanks to the inherent advantages of a distributed architecture, the system reduces computation time, thereby facilitating rapid online response for heat demand forecast-based optimized control strategies.

[0042] The distributed control unit comprises four main components: a data exchange port, a memory module, a computing core, and a drive signal interaction module. It can exchange information with other distributed control units via the data exchange port, with a single control unit capable of connecting to up to six neighboring control units. The memory module is pre-loaded with standardized datasets to record machine performance indicators and operational data. The computing core is responsible for executing various complex simulation and calculation tasks to output necessary results. The drive signal interaction module connects to the drive components of the mechanical equipment, transmitting control commands generated by the computing core to the drive components to execute specific actions. Furthermore, the distributed control units of similar mechanical equipment execute completely identical program code; only the parameters for the specific equipment need to be input for the program to start and run normally.

[0043] The distribution box, as an integrated unit, encompasses a programmable logic controller (PLC), an interactive control panel, an uninterruptible power supply (UPS), and industrial-grade network switching equipment. The PLC must reserve at least 20% of its channels as backup to ensure that the central processing unit's occupancy does not exceed 20% during program execution. The control panel uses a color touchscreen display of the same brand as the PLC, equipped with a communication interface, capable of adjusting proportional-integral-PID parameters, displaying real-time machine operating status and key parameters, and activating a fault alarm system by setting warning thresholds. The UPS has a capacity of 2 kVA, containing 12V and 24V lithium-ion batteries, ensuring at least half an hour of backup time, and includes a fault indication function. The network switch is an 8-port industrial-grade gigabit device with a switching capacity of 20 gigabits per second and a packet forwarding rate of 14.88 megabits per second, providing eight 10 / 100 / 1000BASE-T Ethernet interfaces and two gigabit small form factor (SFP) optical module interfaces, while also supporting switch stacking.

[0044] The security enclosure is an integrated device, housing a digital video recording system, a large-capacity storage unit, and a cascaded processing unit. The digital video recording system has four network signal access channels, supports video recording up to 6 megapixels with a resolution of 1920×1080 / 60Hz, and features historical data playback with a 90-day data retention period. The cascaded processing unit is compatible with ONVIF (Open Network Video Interface) and standard communication protocols. The surveillance camera can automatically track and switch feeds, monitor device status in real time, and issue security alarms. Image clarity is no less than 1080P, and it features 23x optical magnification. It also boasts intelligent operation functions such as automatic detection and tracking, automatic alarm, 360° continuous rotation, and 180° automatic flip.

[0045] The water pipe temperature sensor has a measurement accuracy of 0.2%, with an output current signal between 4 and 20 mA; the water pipe pressure sensor has a measurement accuracy of 0.5%, also outputting a 4-20 mA signal; the ambient temperature and humidity sensor has an accuracy of 0.3% for temperature measurement and between 3% and 10% for humidity measurement, with an output voltage signal range of 0-10 volts; the flow meter outputs a 4-20 mA signal, with an accuracy of 2% when the flow rate is below 1 m / s and 3% when the flow rate exceeds 1 m / s; the heat meter can be powered by AC 220V or an external 24V power supply; the electric regulating valve's control signal is 4-20 mA or DC 1-5 volts or 2-10 volts, and the feedback signal can be selected as 4-20 mA or DC. The actuator power supply is 2~10 volts and 220V DC. The frequency converter of the water supply pump should be equipped with closed-loop control function of PID logic, and the frequency converter of the circulating pump should be able to communicate with the PLC and the distributed controller. The primary side electric regulating valve can exchange data with the PLC, and the valves of each building unit can exchange data with the PLC and the distributed controller respectively.

[0046] Third, heating network optimization and control strategies based on intelligent algorithms: The intelligent algorithm-based optimization control strategy for heating networks utilizes advanced algorithm technology to achieve intelligent management and optimized regulation of heating networks. Traditional heating network management often relies on experience and rules, failing to fully utilize data and operational status within the network, resulting in low system efficiency. In contrast, the intelligent algorithm-based optimization control strategy fully leverages real-time monitoring data from the network, combining mathematical models and optimization algorithms to achieve intelligent regulation of the heating system, thereby improving the energy efficiency and operational stability of the heating network.

[0047] The intelligent algorithm-based heating network optimization control strategy enables intelligent scheduling of the heating system. By establishing a mathematical model of the network's operating status and combining it with real-time monitoring data, the intelligent algorithm can analyze parameters such as the network's load and temperature distribution in real time, and perform intelligent scheduling based on the actual situation of the network. This achieves optimized control of parameters such as hot water temperature and flow rate to meet users' heating needs and minimize system energy consumption.

[0048] The intelligent algorithm-based heating network optimization control strategy enables intelligent diagnosis and prediction of the heating system. The intelligent algorithm can analyze and process network operation data in real time, detect potential faults and anomalies in network operation, and predict faults by establishing predictive models, allowing for proactive repair measures to ensure the stable operation of the heating system.

[0049] The intelligent algorithm-based heating network optimization control strategy can also achieve dynamic optimization of the heating system. The intelligent algorithm can dynamically adjust the optimization control strategy according to the real-time operation of the network and environmental changes, adapting to changes in the operating state of the heating system and ensuring its stability and safety.

[0050] The intelligent algorithm-based optimization control strategy for heating pipe networks is an effective means of managing heating systems. This strategy enables intelligent scheduling, diagnosis, prediction, and dynamic optimization of the heating pipe network, thereby improving the operational efficiency and energy utilization efficiency of the heating system. Through precise data analysis and the application of intelligent algorithms, this strategy can promptly identify problems in the network operation and propose solutions, providing crucial technical support for the sustainable development of heating pipe networks.

[0051] Fourth, distributed optimized control of valves in each building: To improve the heating accuracy of heating systems and address uneven heating at the user end, this invention proposes a load forecasting-based solution. Utilizing a distributed system architecture, it intelligently controls the water supply regulating valves of each building. By collecting topology data and load forecast data from the secondary distribution network, it deeply analyzes the interaction between pressure loss, resistance, and flow rate of key facilities in the heat exchange station and secondary network (such as regulating valves, pipelines, buildings, and heat exchangers), thereby establishing a pressure loss calculation model for each facility. Based on the heat medium flow direction, these models are integrated to form an overall pressure loss model for the heat exchange station and its secondary network. Supported by the distributed architecture, with the optimization objective of reducing the overall pressure loss of the heat exchange station and secondary network, constrained by building flow demand, and using valve opening as an adjustment parameter, a distributed optimization model is constructed.

[0052] Fifth, heat users without high-rise buildings: For heating users without high-rise buildings, the constant pressure should be controlled at no less than 30 m water column to ensure that hot water can be effectively delivered to every user, whether upstairs or downstairs, thereby achieving heating standards and providing comfortable indoor temperatures. For older residential areas where cast iron radiators are used as terminals, the pressure-bearing capacity of the cast iron radiators needs to be considered, and attention should be paid to the safety of aging internal heating pipe networks. Since heating facilities in older residential areas may have certain aging and wear, when selecting and installing radiators, it is essential to ensure that they can withstand potential pressure fluctuations and changes in the system, and to conduct regular inspections and maintenance of the pipe network to ensure its safe and reliable operation. When the pipe pressure is between 30 and 38 m water column, pipe purging should be avoided to reduce the content of air and oxidizing substances in the system, thereby better protecting the pipe network and related equipment and extending their service life. Based on the pressure drop of high-temperature users, the secondary pipe network of the water distribution station is pre-designed with a hot water pipe pressure of 45 m water column and a return water pipe pressure of 33 m water column to fully meet the heating needs of users. When designing and planning a pipeline system, it is necessary to comprehensively analyze the hot water demand and water supply of all users to ensure that the diameter and design of the pipeline meet the flow requirements of the overall system, thereby ensuring the efficient operation and service quality of the heating system.

[0053] Sixth, ensure the data security of the heating network: With the continuous accumulation and frequent use of information in heating pipeline networks, ensuring the security of this data has become an urgent challenge. The key is to build a robust data protection system, which is fundamental to protecting heating pipeline network data from infringement. Taking urban heating supply companies as an example, these companies need to introduce a series of targeted data protection strategies and rules, establish a data protection responsibility system, and effectively implement data security measures. Furthermore, they need to raise employees' awareness of the importance of data security and strengthen their ability to prevent data leakage through training. Moreover, in the data transmission and storage stages, the use of encryption technology is the core approach to ensuring data security. By encoding information using encryption methods, even if the data is stolen, unauthorized individuals cannot decipher its contents.

[0054] The present invention proposes an intelligent processing method for multi-dimensional operation data of heating pipe networks, comprising the following steps: Step 1: Collect heating network operating parameters and outdoor environmental parameters through the data acquisition and transmission module, and forward the collected parameters to the central processing module; Step 2: The central processing module receives the collected parameters and performs standardized processing to generate a standardized pipeline network operation dataset; Step 3: The central processing module calculates the predicted heat load of each building based on the standardized pipeline network operation dataset, solves for the optimal control parameters of the pipeline network using the predicted heat load as a constraint, and sends the optimal control parameters to the data acquisition and transmission module. Step 4: The data acquisition and transmission module forwards the optimal control parameters to the distributed execution module; the distributed execution module drives the pipeline pumps and electric regulating valves to complete the hydraulic condition adjustment based on the optimal control parameters. Step 5: The distributed execution module collects the adjusted pipeline network operation data and sends it back to the central processing module; the central processing module uses the returned operation data to iteratively update the heat load prediction model, providing an updated model for the next round of load prediction calculation.

[0055] Standardization processing includes removing abnormal noise data outside the measurement range, unifying the dimensions of multiple types of parameters, linearly completing short-term missing data acquisition values, and adding timestamps and equipment numbers to the processed data to generate a standardized pipeline network operation dataset.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-dimensional intelligent data processing system for heating network operation, characterized in that, include: The data acquisition and transmission module is used to collect operating parameters of the heating pipeline network and outdoor environmental parameters, forward the collected parameters to the central processing module, and simultaneously receive the optimal control parameters transmitted by the central processing module and forward them to the distributed execution module, realizing bidirectional data transfer. The central processing module receives parameters from the data acquisition and transmission module, performs standardized processing to generate a standardized pipeline network operation dataset, calculates the predicted heat load of each building based on the standardized pipeline network operation dataset, solves for the optimal control parameters of the pipeline network using the predicted heat load as a constraint, and outputs the optimal control parameters to the data acquisition and transmission module. The distributed execution module receives the optimal control parameters forwarded by the data acquisition and transmission module, drives the network pumps and electric regulating valves to perform hydraulic condition regulation based on the optimal control parameters, and transmits the regulated operating data to the central processing module for iterative updates to the heat load prediction model.

2. The intelligent processing system for multi-dimensional operation data of heating pipe networks according to claim 1, characterized in that, The data acquisition and transmission module includes temperature sensors, pressure sensors, flow meters, and heat meters installed at heat sources, heat exchange stations, and pipeline nodes; the temperature sensors output a 4~20mA current signal; the pressure sensors output a 4~20mA current signal.

3. The intelligent processing system for multi-dimensional operation data of heating pipe networks according to claim 1, characterized in that, The data transmission network between the data acquisition and transmission module and the central processing module is a passive optical network, which includes an optical line terminal, an optical network unit, and an optical distribution network.

4. The intelligent processing system for multi-dimensional operation data of heating pipe networks according to claim 1, characterized in that, The central processing module includes a built-in heat load prediction calculation unit, a pipeline pressure loss coupling calculation unit, an anomaly judgment unit, and an encrypted storage unit. The heat load prediction calculation unit receives the pipeline operation dataset and outputs the heat load prediction values ​​for each building. It also receives the adjusted operation data returned by the distributed execution module and iteratively updates the internal prediction model. The pipeline pressure loss coupling calculation unit uses the heat load prediction value as a flow constraint to solve for the optimal opening degree of the regulating valves in each building.

5. The intelligent processing system for multi-dimensional operation data of heating pipe networks according to claim 1, characterized in that, When the central processing module solves for the optimal control parameters of the pipeline network, it uses the predicted heat load as a constraint condition, and specifically includes the following steps: Obtain the topology data of the secondary transmission and distribution network and the predicted heat load of each building; Based on the topology data, pressure loss calculation models for regulating valves, pipelines, buildings and heat exchangers in the heat exchange station and secondary side pipe network are established respectively. Based on the flow direction of the heat medium, the various pressure loss calculation models are integrated to establish an overall pressure loss model for the heat exchange station and the secondary side pipeline network. With the goal of reducing the overall pressure loss, the predicted heat load of each building as the flow constraint for each building, and the valve opening of each building as the adjustment parameter, the optimal valve opening value of each building is solved as the optimal control parameter for the pipeline network.

6. The intelligent processing system for multi-dimensional operation data of heating pipe networks according to claim 1, characterized in that, The distributed execution module consists of multiple distributed control units; a single distributed control unit can interact with up to six adjacent distributed control units to obtain building load data, and the interactive data is synchronously transmitted back to the central processing module to supplement the modeling data.

7. The intelligent processing system for multi-dimensional operation data of heating pipe networks according to claim 1, characterized in that, The distributed execution module includes distributed control units located in each building or heating area. The distributed control unit includes a data exchange port, a storage module, a computing core, and a drive signal interaction module. The data exchange port is used for data communication with other distributed control units. A single distributed control unit can communicate with up to six adjacent distributed control units through the data exchange port. The storage module is used to store device operating parameters and performance data; The computing core is used to perform control calculations based on the optimal control parameters and output control commands. The drive signal interaction module is used to transmit control commands to the drive components of the corresponding valve.

8. The intelligent processing system for multi-dimensional operation data of heating pipe networks according to claim 1, characterized in that, The central processing module is also used to perform hierarchical encryption storage of operating parameters, pipeline operation datasets, heat load forecasts, and optimal control parameters according to data security levels.

9. A method for intelligent processing of multi-dimensional operational data of a heating network, characterized in that, The intelligent processing system for multi-dimensional operational data of heating pipe networks as described in any one of claims 1 to 8 includes the following steps: The data acquisition and transmission module collects operating parameters of the heating network and outdoor environmental parameters, and forwards the collected parameters to the central processing module. The central processing module receives the collected parameters and performs standardized processing to generate a standardized pipeline network operation dataset. The central processing module calculates the predicted heat load of each building based on the standardized pipeline network operation dataset, solves for the optimal control parameters of the pipeline network using the predicted heat load as a constraint, and sends the optimal control parameters to the data acquisition and transmission module. The data acquisition and transmission module forwards the optimal control parameters to the distributed execution module; The distributed execution module drives the network pumps and electric regulating valves to complete the hydraulic condition regulation based on the optimal control parameters. The distributed execution module collects the adjusted pipeline network operation data and sends it back to the central processing module. The central processing module uses the backhauled operational data to iteratively update the heat load prediction model, providing an updated model for the next round of load prediction calculations.

10. The intelligent processing method for multi-dimensional operation data of heating pipe networks according to claim 9, characterized in that, Standardization processing includes removing abnormal noise data outside the measurement range, unifying the dimensions of multiple types of parameters, linearly completing short-term missing data acquisition values, and adding timestamps and equipment numbers to the processed data to generate a standardized pipeline network operation dataset.

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