An intelligent management system based on integrated management and control of heat supply and a working method thereof

Abstract

This invention discloses an intelligent management system and its operating method based on integrated heating control. The intelligent management system includes a data warehouse, an operation management system, a production management system, a monitoring management system, an equipment and facility management system, a billing management system, and a customer service management system. The operation management system provides functions for heat network balancing, energy consumption analysis, and operation scheduling; the production management system provides online inspection, auditing, and maintenance functions; the monitoring management system provides data monitoring and video monitoring functions; the equipment and facility management system provides equipment ledger and spare parts management functions; the billing management system provides user files and billing receipt functions; and the customer service management system provides customer service and auxiliary customer service decision-making functions. This invention achieves data sharing among various functional modules through a data warehouse, reducing heating operation energy efficiency, improving operation and management efficiency, and enhancing customer service quality, thus having significant promotional value in the heating field.

Description

Technical Field

[0001] This invention belongs to the field of intelligent heating control technology, specifically relating to an intelligent management system and its working method based on integrated heating control. Background Technology

[0002] Urban centralized heating systems mainly consist of three parts: heat sources, heating networks, and heat users. They utilize heat sources such as thermal power plants and district boiler rooms, and then transmit the heat to users through heating pipelines and other facilities. With continuous economic development, accelerated urbanization, and the gradual improvement of people's living standards, the scale of urban heating pipelines has become increasingly large, which has greatly increased the difficulty of managing and controlling the heating system.

[0003] Currently, the overall level of intelligent management and control in grassroots heating enterprises is relatively low. Production and operation management still relies on manual, extensive methods, with inadequate functions in areas such as billing and customer service. Insufficient and unupdated record-keeping information leads to information asymmetry, resulting in missed or incorrect orders. This increases the complexity of operations, lowers the overall service quality, and causes a sharp increase in enterprise management costs. In particular, information gaps exist between different management modules of the heating system, leading to blind spots in business operations and a passive approach to customer communication, hindering the provision of accurate and efficient services.

[0004] In the field of intelligent heating management and control technology, existing patented technologies include: (1) the technology with application number 202310704190.4, which is a comprehensive intelligent urban heating management system based on big data analysis, which introduces meteorological information into the analysis and management of heating losses to achieve precise heating, thereby solving the problems of poor heating experience and failure to achieve the expected heating effect caused by the current heating system not taking into account the influence of climate; (2) the technology with patent number ZL202011052825.X, which is a heating intelligent management system and working method that includes management modules such as heat network monitoring, heat metering, customer service and billing. The data collected by the heating network system at all levels, the metering data of heat users, the room temperature data, the billing data, the customer service data and the video data are uniformly classified, managed and analyzed. This assists the scheduling decision module in making unified decisions and scheduling for each system of the heating network, so as to reduce heat loss and achieve energy conservation and emission reduction. (3) The technology with patent number ZL202211683365.X is a smart urban heating management system based on the Internet of Things. It opens up the data connection channels between heating network information monitoring, operation adjustment and maintenance management, and unifies the management of operation and maintenance, thereby ensuring the safe and efficient heating of the heating system. In summary, the existing technology has only realized the data interconnection of some management modules of the heating system, which has improved the management efficiency to a certain extent. However, it still cannot fundamentally solve the problem of low efficiency and blind development of various businesses of the heating system caused by the inability to share data among management modules, and the inability to provide high-quality services to heat users. Summary of the Invention

[0005] The purpose of this invention is to overcome the above-mentioned shortcomings in the prior art and to provide a reasonably designed intelligent management system and its working method based on integrated heating control.

[0006] The technical solution adopted by this invention to solve the above problems is: an intelligent management system based on integrated heating control, characterized in that the intelligent management system includes: a data warehouse, an operation management system, a production management system, a monitoring management system, an equipment and facility management system, a billing management system, and a customer service management system. The data warehouse classifies, integrates, and stores data from the operation management system, production management system, monitoring management system, equipment and facility management system, billing management system, and customer service management system. All of these systems can access all the data stored in the data warehouse.

[0007] The operation and management system includes a heat network balancing module, an energy consumption analysis module, an intelligent decision-making module, and an operation scheduling module. The heat network balancing module, energy consumption analysis module, intelligent decision-making module, and operation scheduling module transmit all the data they generate to the data warehouse for storage, and can call up all the data stored in the data warehouse.

[0008] The production management system includes an online inspection module, an online audit module, an intelligent diagnosis module, and a maintenance module. The online inspection module, online audit module, intelligent diagnosis module, and maintenance module transmit all the data they generate to the data warehouse for storage, and can call up all the data stored in the data warehouse.

[0009] The monitoring and management system includes a data monitoring module, a video monitoring module, and an intelligent analysis module. The data monitoring module, video monitoring module, and intelligent analysis module transmit all the data they generate to the data warehouse for storage, and can call up all the data stored in the data warehouse.

[0010] The equipment and facility management system includes an equipment ledger module, a spare parts management module, and an intelligent evaluation module. The equipment ledger module, spare parts management module, and intelligent evaluation module transmit all the data they generate to the data warehouse for storage, and can call all the data stored in the data warehouse.

[0011] The charging management system includes a user profile module, a charging receipt module, and an intelligent report module. All data generated by the user profile module, the charging receipt module, and the intelligent report module are transmitted to the data warehouse for storage, and can call all data stored in the data warehouse.

[0012] The customer service management system includes a business processing module, an auxiliary decision-making module, and an intelligent evaluation module. All data generated by each of these modules is transmitted to a data warehouse for storage, and all data stored in the data warehouse can be accessed.

[0013] Furthermore, the working method of the intelligent management system is as follows:

[0014] For the operation and management system, the heat network balancing module analyzes the balance of flow and heat distribution in each pipeline of the heat network based on the real-time heat load demand of the heat users connected to each pipeline of the heat network, using real-time data from each measuring point of the heat network, and feeds the analysis results back to the intelligent decision-making module. The energy consumption analysis module uses real-time data from each measuring point of the heat network and the operating data of each equipment to statistically analyze the heat consumption and water consumption indicators of the heat network from three dimensions: the heat source station, the heating station, and the heated buildings; statistically analyze the electricity consumption indicators of the heat network from two dimensions: the heat source station and the heating station; and statistically analyze the heat loss and water loss indicators of the heat network from two dimensions: the primary network and the secondary network, and feeds the statistical analysis results back to the intelligent decision-making module. The intelligent decision-making module first analyzes the heat load of heat users based on meteorological parameters, target room temperature, heat user characteristics, and building characteristics using machine learning algorithms. The system first calculates the real-time demand forecast value, then corrects the real-time demand forecast value of heat users using the measured room temperature, resulting in the actual real-time demand value of heat users. This actual real-time demand value of heat users is then transmitted to the heat network balancing module. Next, based on the actual real-time demand value of heat users, the system formulates a heat network operation scheduling strategy using real-time data from various measuring points in the heat network and the operating characteristics of each device. The system then corrects the heat network operation scheduling strategy using the analysis results from the heat network balancing module and the energy consumption analysis module, and then feeds the correction results back to the operation scheduling module. The operation scheduling module remotely controls each valve and pump in the heat network system according to the corrected heat network operation scheduling strategy, realizing on-demand heating of the heat network system, and feeds back the real-time data from each measuring point and the operating data of each device after remote control to the heat network balancing module and the energy consumption analysis module.

[0015] For the production management system, the online inspection module first formulates daily inspection tasks based on the heating production plan, the operating status of various equipment in the heating network, abnormal events in the heating network, and rain and snow weather phenomena. Next, it evaluates and analyzes the workload and effectiveness of the inspection personnel, and assigns daily inspection tasks to each inspection personnel based on the evaluation and analysis results. Then, it remotely acquires the inspection results information from the inspection personnel online and feeds the inspection results information back to the intelligent diagnostic module. Similarly, the online auditing module formulates auditing tasks based on the heating production plan, abnormal equipment operating events, sudden abnormal events in the heating network, and the payment information and heating usage of heat users. Next, it evaluates and analyzes the workload and effectiveness of the auditing personnel, and assigns auditing tasks to each auditing personnel based on the evaluation and analysis results. The system remotely acquires inspection results from inspectors online and feeds them back to the intelligent diagnostic module. Based on the operating status of each piece of equipment in the heating network, real-time data from each measuring point, inspection results, and inspection results, the intelligent diagnostic module uses machine learning algorithms to diagnose and analyze whether the heating network is operating normally. It identifies existing defects and fault events in the heating network system online and assesses potential defects and fault events, then formulates defect elimination and maintenance tasks. These tasks are then fed back to the maintenance module. The maintenance module first evaluates and analyzes the workload and effectiveness of maintenance personnel, and assigns defect elimination and maintenance tasks to each maintenance personnel based on the evaluation results. It then remotely acquires the maintenance information recorded by the maintenance personnel during the completion of defect elimination and maintenance work.

[0016] For the equipment and facility management system, the equipment ledger module is used to statistically analyze, retrieve, and update basic and usage information of equipment and facilities. It establishes standardized equipment and facility information archives using a unified equipment and facility code, and simultaneously feeds back the basic and usage information to the intelligent evaluation module. Basic information includes the name, specifications, parameters, manufacturer, and instruction manual of the equipment and facility. Usage information includes the number of equipment and facilities in operation, installation and completion information, operational anomalies and malfunctions, troubleshooting and maintenance information, replacement frequency, replacement time, and replacement reason. The spare parts management module statistically analyzes, retrieves, and updates the inventory of spare parts for equipment and facilities. Inventory spare parts information refers to the basic information and inventory quantity of equipment and facilities used as spare parts. Based on the operational status of the equipment and facilities and the product quality of the equipment manufacturers fed back by the intelligent evaluation module, it generates inventory demand lists and procurement plans for the equipment and facilities. The intelligent evaluation module uses machine learning algorithms to diagnose and evaluate the operational status of the equipment and facilities and the product quality of the equipment manufacturers based on the basic and usage information, and then feeds back the operational status and product quality of the equipment and facilities to the spare parts management module.

[0017] For the monitoring and management system, the data monitoring module collects real-time measured data from various instrument and meter measuring points in the heating network system and operational data from various equipment and facilities. It also issues real-time alarms for abnormal data during the operation of the heating network system and feeds the monitoring data back to the intelligent analysis module. The video monitoring module collects real-time video image data from various monitoring points in the heating network system and issues real-time alarms for video image loss and image occlusion. It also feeds the monitoring video images back to the intelligent analysis module. The intelligent analysis module performs early warning analysis on the monitoring data and feeds the data early warning analysis results back to the data monitoring module. It also performs early warning analysis on the monitoring video images for flame detection, perimeter security, failure to wear safety helmets, and violations of operating procedures, and feeds the video image early warning analysis results back to the video monitoring module.

[0018] For the billing management system, the user profile module is used to collect, retrieve, and update the basic and business information of heat users. It establishes standardized user information profiles using a unified heat user code and feeds back the basic and business information of heat users to the intelligent reporting module. Basic information includes the heat user's personal information, heating type, heating area, and geographical location; business information includes the heat user's heating status, payment status, invoice status, and fee adjustment information. The billing and invoice module is used for heat user payment management, invoice management, and heating supply / shutdown / activation management, and feeds back the heat user's business processing status to the user profile module. The intelligent reporting module automatically generates business reports and provides graphical displays and year-on-year and month-on-month comparisons of business data based on the heat user's basic and business information.

[0019] For the customer service management system, the business processing module manages heat user inquiries, repair requests, and complaints. Based on heat user service requests, it generates service request work orders, which are then assigned to different staff for processing, supervision, and follow-up. The module also feeds back the processing information to the intelligent evaluation module. The decision support module, based on identified heat user call requests, first automatically matches similar standard question-and-answer knowledge from the data warehouse and displays it on the customer service interface to assist staff. Secondly, it automatically matches heat user information files, regional heating network operation monitoring data, heating network maintenance information, and other heat user call requests from the data warehouse and displays this information on the customer service interface to assist staff. The module also feeds back the heat user call request information and processing results to the intelligent evaluation module. Finally, the intelligent evaluation module uses machine learning algorithms to evaluate staff efficiency and service quality, as well as the good behavior level of heat users, based on the business processing information, heat user call request information, and processing results.

[0020] Furthermore, the operation management system updates the heat load demand of connected heat users, the valve opening and closing status of the heat network pipeline model, and the heat network operation scheduling strategy in real time based on the data information generated by the online inspection module and online audit module of the production management system obtained from the data warehouse.

[0021] Furthermore, the charging management system uses data information obtained from the online inspection module and online audit module of the production management system obtained from the data warehouse to carry out real-time heating supply suspension or activation, heating area increase or decrease, and fee payment or refund.

[0022] Furthermore, the production management system corrects daily inspection tasks in real time based on data information generated by the intelligent evaluation module of the equipment and facility management system obtained from the data warehouse.

[0023] Furthermore, the production management system uses data information generated by the data monitoring module, video monitoring module, and intelligent analysis module of the monitoring management system obtained from the data warehouse to correct daily inspection and audit tasks in real time.

[0024] Furthermore, the equipment and facility management system updates the basic information and usage information of the equipment and facilities in real time based on the data information generated by the maintenance module of the production management system obtained from the data warehouse.

[0025] Furthermore, the customer service management system uses data information obtained from the data warehouse from the operation management system, production management system, and billing management system to assist customer service personnel in providing call answering services to hot users.

[0026] Compared with existing technologies, this invention has the following advantages and effects: First, it achieves data sharing among modules such as operation management, production management, and service management. Firstly, data support from production management further optimizes operation scheduling decisions, taps into the energy-saving potential of the heating system, and further reduces heating energy consumption. Secondly, mutual data support among operation, production, and service management modules reduces the repetitiveness and blindness of operation management work, promotes improved business management efficiency, and reduces enterprise operation management costs. Thirdly, real-time data from operation and production modules efficiently assists service decision-making, improving the accuracy and efficiency of customer service and enhancing the overall quality of customer service. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the intelligent management system based on integrated heating control in this invention. Detailed Implementation

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following embodiments.

[0029] See Figure 1 This embodiment relates to an intelligent management system based on integrated heating control, including: a data warehouse, an operation management system, a production management system, a monitoring management system, an equipment and facility management system, a billing management system, and a customer service management system. The data warehouse classifies, integrates, and stores data from the operation management system, production management system, monitoring management system, equipment and facility management system, billing management system, and customer service management system. All of these systems can access all the data stored in the data warehouse.

[0030] The operation and management system includes a heat network balancing module, an energy consumption analysis module, an intelligent decision-making module, and an operation scheduling module. All data generated by the heat network balancing module, energy consumption analysis module, intelligent decision-making module, and operation scheduling module are transmitted to the data warehouse for storage, and all data stored in the data warehouse can be accessed.

[0031] The production management system includes an online inspection module, an online audit module, an intelligent diagnosis module, and a maintenance module. All data generated by the online inspection module, online audit module, intelligent diagnosis module, and maintenance module are transmitted to the data warehouse for storage, and all data stored in the data warehouse can be accessed.

[0032] The monitoring and management system includes a data monitoring module, a video monitoring module, and an intelligent analysis module. All data generated by the data monitoring module, video monitoring module, and intelligent analysis module are transmitted to the data warehouse for storage, and all data stored in the data warehouse can be accessed.

[0033] The equipment and facility management system includes an equipment ledger module, a spare parts management module, and an intelligent evaluation module. All data generated by the equipment ledger module, spare parts management module, and intelligent evaluation module are transmitted to the data warehouse for storage, and can access all data stored in the data warehouse.

[0034] The fee management system includes a user profile module, a fee receipt module, and an intelligent report module. All data generated by the user profile module, the fee receipt module, and the intelligent report module are transmitted to the data warehouse for storage, and can access all data stored in the data warehouse.

[0035] The customer service management system includes a business processing module, a decision support module, and an intelligent evaluation module. All data generated by each of these modules is transmitted to a data warehouse for storage, and all data stored in the data warehouse can be accessed.

[0036] The working method of the intelligent management system is as follows:

[0037] For the operation and management system, the heat network balancing module analyzes the balance of flow and heat distribution in each pipeline of the heat network based on the real-time heat load demand of the heat users connected to each pipeline of the heat network, using real-time data from each measuring point of the heat network, and feeds the analysis results back to the intelligent decision-making module. The energy consumption analysis module uses real-time data from each measuring point of the heat network and the operating data of each equipment to statistically analyze the heat consumption and water consumption indicators of the heat network from three dimensions: the heat source station, the heating station, and the heated buildings; statistically analyze the electricity consumption indicators of the heat network from two dimensions: the heat source station and the heating station; and statistically analyze the heat loss and water loss indicators of the heat network from two dimensions: the primary network and the secondary network, and feeds the statistical analysis results back to the intelligent decision-making module. The intelligent decision-making module first analyzes the heat load of heat users based on meteorological parameters, target room temperature, heat user characteristics, and building characteristics using machine learning algorithms. The system first calculates the real-time demand forecast value, then corrects the real-time demand forecast value of heat users using the measured room temperature, resulting in the actual real-time demand value of heat users. This actual real-time demand value of heat users is then transmitted to the heat network balancing module. Next, based on the actual real-time demand value of heat users, the system formulates a heat network operation scheduling strategy using real-time data from various measuring points in the heat network and the operating characteristics of each device. The system then corrects the heat network operation scheduling strategy using the analysis results from the heat network balancing module and the energy consumption analysis module, and then feeds the correction results back to the operation scheduling module. The operation scheduling module remotely controls each valve and pump in the heat network system according to the corrected heat network operation scheduling strategy, realizing on-demand heating of the heat network system, and feeds back the real-time data from each measuring point and the operating data of each device after remote control to the heat network balancing module and the energy consumption analysis module.

[0038] For the production management system, the online inspection module first formulates daily inspection tasks based on the heating production plan, the operating status of various equipment in the heating network, abnormal events in the heating network, and rain and snow weather phenomena. Next, it evaluates and analyzes the workload and effectiveness of the inspection personnel, and assigns daily inspection tasks to each inspection personnel based on the evaluation and analysis results. Then, it remotely acquires the inspection results information from the inspection personnel online and feeds the inspection results information back to the intelligent diagnostic module. Similarly, the online auditing module formulates auditing tasks based on the heating production plan, abnormal equipment operating events, sudden abnormal events in the heating network, and the payment information and heating usage of heat users. Next, it evaluates and analyzes the workload and effectiveness of the auditing personnel, and assigns auditing tasks to each auditing personnel based on the evaluation and analysis results. The system remotely acquires inspection results from inspectors online and feeds them back to the intelligent diagnostic module. Based on the operating status of each piece of equipment in the heating network, real-time data from each measuring point, inspection results, and inspection results, the intelligent diagnostic module uses machine learning algorithms to diagnose and analyze whether the heating network is operating normally. It identifies existing defects and fault events in the heating network system online and assesses potential defects and fault events, then formulates defect elimination and maintenance tasks. These tasks are then fed back to the maintenance module. The maintenance module first evaluates and analyzes the workload and effectiveness of maintenance personnel, and assigns defect elimination and maintenance tasks to each maintenance personnel based on the evaluation results. It then remotely acquires the maintenance information recorded by the maintenance personnel during the completion of defect elimination and maintenance work.

[0039] For the equipment and facility management system, the equipment ledger module is used to statistically analyze, retrieve, and update basic and usage information of equipment and facilities. It establishes standardized equipment and facility information archives using a unified equipment and facility code, and simultaneously feeds back the basic and usage information to the intelligent evaluation module. Basic information includes the name, specifications, parameters, manufacturer, and instruction manual of the equipment and facility. Usage information includes the number of equipment and facilities in operation, installation and completion information, operational anomalies and malfunctions, troubleshooting and maintenance information, replacement frequency, replacement time, and replacement reason. The spare parts management module statistically analyzes, retrieves, and updates the inventory of spare parts for equipment and facilities. Inventory spare parts information refers to the basic information and inventory quantity of equipment and facilities used as spare parts. Based on the operational status of the equipment and facilities and the product quality of the equipment manufacturers fed back by the intelligent evaluation module, it generates inventory demand lists and procurement plans for the equipment and facilities. The intelligent evaluation module uses machine learning algorithms to diagnose and evaluate the operational status of the equipment and facilities and the product quality of the equipment manufacturers based on the basic and usage information, and then feeds back the operational status and product quality of the equipment and facilities to the spare parts management module.

[0040] For the monitoring and management system, the data monitoring module collects real-time measured data from various instrument and meter measuring points in the heating network system and operational data from various equipment and facilities. It also issues real-time alarms for abnormal data during the operation of the heating network system and feeds the monitoring data back to the intelligent analysis module. The video monitoring module collects real-time video image data from various monitoring points in the heating network system and issues real-time alarms for video image loss and image occlusion. It also feeds the monitoring video images back to the intelligent analysis module. The intelligent analysis module performs early warning analysis on the monitoring data and feeds the data early warning analysis results back to the data monitoring module. It also performs early warning analysis on the monitoring video images for flame detection, perimeter security, failure to wear safety helmets, and violations of operating procedures, and feeds the video image early warning analysis results back to the video monitoring module.

[0041] For the billing management system, the user profile module is used to collect, retrieve, and update the basic and business information of heat users. It establishes standardized user information profiles using a unified heat user code and feeds back the basic and business information of heat users to the intelligent reporting module. Basic information includes the heat user's personal information, heating type, heating area, and geographical location; business information includes the heat user's heating status, payment status, invoice status, and fee adjustment information. The billing and invoice module is used for heat user payment management, invoice management, and heating supply / shutdown / activation management, and feeds back the heat user's business processing status to the user profile module. The intelligent reporting module automatically generates business reports and provides graphical displays and year-on-year and month-on-month comparisons of business data based on the heat user's basic and business information.

[0042] For the customer service management system, the business processing module manages heat user inquiries, repair requests, and complaints. Based on heat user service requests, it generates service request work orders, which are then assigned to different staff for processing, supervision, and follow-up. The module also feeds back the processing information to the intelligent evaluation module. The decision support module, based on identified heat user call requests, first automatically matches similar standard question-and-answer knowledge from the data warehouse and displays it on the customer service interface to assist staff. Secondly, it automatically matches heat user information files, regional heating network operation monitoring data, heating network maintenance information, and other heat user call requests from the data warehouse and displays this information on the customer service interface to assist staff. The module also feeds back the heat user call request information and processing results to the intelligent evaluation module. Finally, the intelligent evaluation module uses machine learning algorithms to evaluate staff efficiency and service quality, as well as the good behavior level of heat users, based on the business processing information, heat user call request information, and processing results.

[0043] In this embodiment, the operation management system updates the heat load demand of connected heat users, the valve opening and closing status of the heat network pipeline model, and the heat network operation scheduling strategy in real time based on the data information generated by the online inspection module and online audit module of the production management system obtained from the data warehouse.

[0044] In this embodiment, the charging management system uses data information obtained from the online inspection module and online audit module of the production management system obtained from the data warehouse to carry out real-time heating supply suspension or activation, heating area increase or decrease, and fee payment or refund.

[0045] In this embodiment, the production management system corrects daily inspection tasks in real time based on data information generated by the intelligent evaluation module of the equipment and facilities management system obtained from the data warehouse.

[0046] In this embodiment, the production management system uses data information obtained from the data monitoring module, video monitoring module, and intelligent analysis module of the monitoring management system obtained from the data warehouse to correct daily inspection and audit tasks in real time.

[0047] In this embodiment, the equipment and facility management system updates the basic information and usage information of the equipment and facilities in real time based on the data information generated by the maintenance module of the production management system obtained from the data warehouse.

[0048] In this embodiment, the customer service management system uses data from the operation management system, production management system, and billing management system obtained from the data warehouse to assist customer service personnel in providing call answering services to hot users.

[0049] Any content not described in detail in this specification is prior art known to those skilled in the art.

[0050] Although the present invention has been disclosed above with reference to embodiments, it is not intended to limit the scope of protection of the present invention. Any modifications and refinements made by those skilled in the art without departing from the concept and scope of the present invention shall fall within the scope of protection of the present invention.

Claims

1. A method for operating an intelligent management system based on integrated heating control, wherein the intelligent management system based on integrated heating control comprises: The system comprises a data warehouse, an operations management system, a production management system, a monitoring and management system, an equipment and facilities management system, a billing management system, and a customer service management system. The data warehouse categorizes, integrates, and stores data from these systems. All of these systems can access all the data stored in the data warehouse. The operation and management system includes a heat network balancing module, an energy consumption analysis module, an intelligent decision-making module, and an operation scheduling module. The heat network balancing module, energy consumption analysis module, intelligent decision-making module, and operation scheduling module transmit all the data they generate to the data warehouse for storage, and can call all the data stored in the data warehouse. The production management system includes an online inspection module, an online audit module, an intelligent diagnosis module, and a maintenance module. The online inspection module, online audit module, intelligent diagnosis module, and maintenance module transmit all the data they generate to the data warehouse for storage and can call up all the data stored in the data warehouse. The monitoring and management system includes a data monitoring module, a video monitoring module, and an intelligent analysis module. The data monitoring module, video monitoring module, and intelligent analysis module transmit all the data they generate to the data warehouse for storage and can call up all the data stored in the data warehouse. The equipment and facility management system includes an equipment ledger module, a spare parts management module, and an intelligent evaluation module. The equipment ledger module, spare parts management module, and intelligent evaluation module transmit all the data they generate to the data warehouse for storage and can call all the data stored in the data warehouse. The charging management system includes a user profile module, a charging receipt module, and an intelligent report module. All data generated by the user profile module, the charging receipt module, and the intelligent report module are transmitted to the data warehouse for storage, and can call all data stored in the data warehouse. The customer service management system includes a business processing module, a decision support module, and an intelligent evaluation module. All data generated by each module is transmitted to a data warehouse for storage, and all data stored in the data warehouse can be accessed. The system's operating method is as follows: For the operation and management system, the heat network balancing module analyzes the balance of flow and heat distribution in each pipeline of the heat network based on the real-time heat load demand of the heat users connected to each pipeline of the heat network, using real-time data from each measuring point of the heat network, and feeds the analysis results back to the intelligent decision-making module. The energy consumption analysis module uses real-time data from each measuring point of the heat network and the operating data of each equipment to statistically analyze the heat consumption and water consumption indicators of the heat network from three dimensions: the heat source station, the heating station, and the heated buildings; statistically analyze the electricity consumption indicators of the heat network from two dimensions: the heat source station and the heating station; and statistically analyze the heat loss and water loss indicators of the heat network from two dimensions: the primary network and the secondary network, and feeds the statistical analysis results back to the intelligent decision-making module. The intelligent decision-making module first analyzes the heat load of heat users based on meteorological parameters, target room temperature, heat user characteristics, and building characteristics using machine learning algorithms. The system first calculates the real-time demand forecast value, then corrects the real-time demand forecast value of heat users using the measured room temperature, resulting in the actual real-time demand value of heat users. This actual real-time demand value of heat users is then transmitted to the heat network balancing module. Next, based on the actual real-time demand value of heat users, the system formulates a heat network operation scheduling strategy using real-time data from various measuring points in the heat network and the operating characteristics of each device. The system then corrects the heat network operation scheduling strategy using the analysis results from the heat network balancing module and the energy consumption analysis module, and then feeds the correction results back to the operation scheduling module. The operation scheduling module remotely controls each valve and pump in the heat network system according to the corrected heat network operation scheduling strategy, realizing on-demand heating of the heat network system, and feeds back the real-time data from each measuring point and the operating data of each device after remote control to the heat network balancing module and the energy consumption analysis module. For the production management system, the online inspection module first formulates daily inspection tasks based on the heating production plan, the operating status of various equipment in the heating network, abnormal events in the heating network, and rain and snow weather phenomena. Next, it evaluates and analyzes the workload and effectiveness of the inspection personnel, and assigns daily inspection tasks to each inspection personnel based on the evaluation and analysis results. Then, it remotely acquires the inspection results information from the inspection personnel online and feeds the inspection results information back to the intelligent diagnostic module. Similarly, the online auditing module formulates auditing tasks based on the heating production plan, abnormal equipment operating events, sudden abnormal events in the heating network, and the payment information and heating usage of heat users. Next, it evaluates and analyzes the workload and effectiveness of the auditing personnel, and assigns auditing tasks to each auditing personnel based on the evaluation and analysis results. The system remotely acquires inspection results from inspectors online and feeds them back to the intelligent diagnostic module. Based on the operating status of each piece of equipment in the heating network, real-time data from each measuring point, inspection results, and inspection results, the intelligent diagnostic module uses machine learning algorithms to diagnose and analyze whether the heating network is operating normally. It identifies existing defects and fault events in the heating network system online and assesses potential defects and fault events, then formulates defect elimination and maintenance tasks. These tasks are then fed back to the maintenance module. The maintenance module first evaluates and analyzes the workload and effectiveness of maintenance personnel, and assigns defect elimination and maintenance tasks to each maintenance personnel based on the evaluation results. It then remotely acquires the maintenance information recorded by the maintenance personnel during the completion of defect elimination and maintenance work. For the equipment and facility management system, the equipment ledger module is used to statistically analyze, retrieve, and update basic and usage information of equipment and facilities. It establishes standardized equipment and facility information archives using a unified equipment and facility code, and simultaneously feeds back the basic and usage information to the intelligent evaluation module. Basic information includes the name, specifications, parameters, manufacturer, and instruction manual of the equipment and facility. Usage information includes the number of equipment and facilities in operation, installation and completion information, operational anomalies and malfunctions, troubleshooting and maintenance information, replacement frequency, replacement time, and replacement reason. The spare parts management module statistically analyzes, retrieves, and updates the inventory of spare parts for equipment and facilities. Inventory spare parts information refers to the basic information and inventory quantity of equipment and facilities used as spare parts. Based on the operational status of the equipment and facilities and the product quality of the equipment manufacturers fed back by the intelligent evaluation module, it generates inventory demand lists and procurement plans for the equipment and facilities. The intelligent evaluation module uses machine learning algorithms to diagnose and evaluate the operational status of the equipment and facilities and the product quality of the equipment manufacturers based on the basic and usage information, and then feeds back the operational status and product quality of the equipment and facilities to the spare parts management module. For the monitoring and management system, the data monitoring module collects real-time measured data from various instrument and meter measuring points in the heating network system and operational data from various equipment and facilities. It also issues real-time alarms for abnormal data during the operation of the heating network system and feeds the monitoring data back to the intelligent analysis module. The video monitoring module collects real-time video image data from various monitoring points in the heating network system and issues real-time alarms for video image loss and image occlusion. It also feeds the monitoring video images back to the intelligent analysis module. The intelligent analysis module performs early warning analysis on the monitoring data and feeds the data early warning analysis results back to the data monitoring module. It also performs early warning analysis on the monitoring video images for flame detection, perimeter security, failure to wear safety helmets, and violations of operating procedures, and feeds the video image early warning analysis results back to the video monitoring module. For the billing management system, the user profile module is used to collect, retrieve, and update the basic and business information of heat users. It establishes standardized user information profiles using a unified heat user code and feeds back the basic and business information of heat users to the intelligent reporting module. Basic information includes the heat user's personal information, heating type, heating area, and geographical location; business information includes the heat user's heating status, payment status, invoice status, and fee adjustment information. The billing and invoice module is used for heat user payment management, invoice management, and heating supply / shutdown / activation management, and feeds back the heat user's business processing status to the user profile module. The intelligent reporting module automatically generates business reports and provides graphical displays and year-on-year and month-on-month comparisons of business data based on the heat user's basic and business information. For the customer service management system, the business processing module manages heat user inquiries, repair requests, and complaints. Based on heat user service requests, it generates service request work orders, which are then assigned to different staff for processing, supervision, and follow-up. The module also feeds back the processing information to the intelligent evaluation module. The decision support module, based on identified heat user call requests, first automatically matches similar standard question-and-answer knowledge from the data warehouse and displays it on the customer service interface to assist staff. Secondly, it automatically matches heat user information files, regional heating network operation monitoring data, heating network maintenance information, and other heat user call requests from the data warehouse and displays this information on the customer service interface to assist staff. The module also feeds back the heat user call request information and processing results to the intelligent evaluation module. Finally, the intelligent evaluation module uses machine learning algorithms to evaluate staff efficiency and service quality, as well as the good behavior level of heat users, based on the business processing information, heat user call request information, and processing results.

2. The working method of the intelligent management system based on integrated heating control according to claim 1, characterized in that, The operation management system updates the heat load demand of connected heat users, the valve opening and closing status of the heat network pipeline model, and the heat network operation scheduling strategy in real time based on the data information generated by the online inspection module and online audit module of the production management system obtained from the data warehouse.

3. The working method of the intelligent management system based on integrated heating control according to claim 1, characterized in that, The billing management system uses data from the online inspection module and online audit module of the production management system obtained from the data warehouse to carry out real-time heating supply suspension or activation, heating area increase or decrease, and fee payment or refund.

4. The working method of the intelligent management system based on integrated heating control according to claim 1, characterized in that, The production management system corrects daily inspection tasks in real time based on data information generated by the intelligent evaluation module of the equipment and facility management system obtained from the data warehouse.

5. The working method of the intelligent management system based on integrated heating control according to claim 1, characterized in that, The production management system uses data from the data monitoring module, video monitoring module, and intelligent analysis module of the monitoring management system obtained from the data warehouse to correct daily inspection and audit tasks in real time.

6. The working method of the intelligent management system based on integrated heating control according to claim 1, characterized in that, The equipment and facility management system updates the basic information and usage information of the equipment and facilities in real time based on the data information generated by the maintenance module of the production management system obtained from the data warehouse.

7. The working method of the intelligent management system based on integrated heating control according to claim 1, characterized in that, The customer service management system uses data from the operation management system, production management system, and billing management system obtained from the data warehouse to assist customer service personnel in providing call answering services to hot users.

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