Operation and maintenance management method and system for heating station

Abstract

The invention discloses an operation and maintenance management method and system for a heating station, and relates to the technical field of heating station operation and maintenance management, and the method comprises the steps: obtaining the physical topological structure information of each heating station in a heat supply pipe network and the historical heat load response information of a corresponding user side; according to the physical topological structure information and the historical thermal load response information, the comprehensive correlation degree between any two heating stations is determined; based on the comprehensive correlation degree, the heating stations which are stable in operation state and have typical representativeness are screened out to serve as characteristic heating stations; generating a remote operation and maintenance strategy for the non-characteristic heating station by using the historical operation and maintenance record and the operation stability performance of the characteristic heating station; in the operation process of the system, performing consistency verification on the real-time operation data of the non-characteristic heating station and the associated characteristic heating station, and performing remote processing on an abnormal meter according to a remote operation and maintenance strategy; and according to the consistency verification result and the remote processing feedback, a heat supply equipment regulation and control instruction is generated and issued to an execution mechanism.

Description

Technical Field

[0001] This invention relates to the field of operation and maintenance management technology for heating stations, and in particular to a method and system for operation and maintenance management of heating stations. Background Technology

[0002] Heating station operation and maintenance management technology refers to the comprehensive and continuous monitoring, control, and management of heating stations and their related facilities through the use of advanced information technology, communication technology, and automated control technology. Its purpose is to improve the energy efficiency ratio of the heating system, reduce energy consumption, enhance the quality and reliability of heating services, and simultaneously reduce operation and maintenance costs. This includes, but is not limited to, functions such as data acquisition and processing, equipment status monitoring, fault early warning and diagnosis, and remote control, to optimize the allocation of heating resources and enhance system flexibility and adaptability. Therefore, how to utilize advanced technologies to improve the intelligence level and safety of heating station operation and maintenance management has become one of the most pressing issues to be addressed.

[0003] In large-scale heating systems with a large number of heating stations, the stations are widely distributed and operate under complex and varied conditions. Applying a unified or independent operation and maintenance strategy to all stations would not only result in high computational overhead and low response efficiency, but also make it difficult to guarantee the accuracy of anomaly identification and the reliability of control decisions. Existing technologies lack a lightweight operation and maintenance mechanism for high-density heating station networks, especially lacking a method to automatically identify stable, typical, and representative characteristic heating stations among a massive number of stations. Consequently, it is impossible to generate reliable remote operation and maintenance strategies for other non-characteristic heating stations based on the historical performance and real-time data of these characteristic stations. At the same time, there is also a lack of an effective mechanism to continuously evaluate and adaptively update remote operation and maintenance strategies based on the dynamic changes in the actual operating status of non-characteristic heating stations. This leads to lagging operation and maintenance strategies, high misjudgment rates, and difficulty in supporting highly reliable remote intelligent operation and maintenance. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an operation and maintenance management method for heat stations to solve the problem that the existing technology lacks a lightweight operation and maintenance mechanism for high-density heat station networks, especially lacks a method to automatically identify stable, typical and representative characteristic heat stations among a large number of stations, and thus cannot generate reliable remote operation and maintenance strategies for other non-characteristic heat stations based on the historical performance and real-time data of these characteristic stations.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for the operation and maintenance management of a heating station, comprising: Obtain the physical topology information of each heating station in the heating network and the historical heat load response information of the corresponding user side; Based on the correlation between the physical topology information of the heating station and the historical heat load response information of the corresponding user side, when it is determined that a characteristic heating station needs to be constructed, the comprehensive correlation between any two heating stations is determined according to the physical topology information and the historical heat load response information. Based on the comprehensive correlation with other heating stations, heating stations with stable operation and typical representativeness were selected as characteristic heating stations. Continuously monitor the operating data of other heating stations besides the featured heating station, determine the changes in the operating data, and adjust or optimize the remote operation and maintenance strategy of the featured heating station based on the changes, thus forming a strategy update mechanism. During system operation, the real-time operating data of non-featured heat stations are checked for consistency with the associated feature heat stations. Based on the updated data of the remote operation and maintenance strategy of the feature heat stations and the operating data of the heat stations, the update method of the remote operation and maintenance strategy of the heat stations other than the feature heat stations is determined. Using the update method, a joint scheduling and processing scheme for all heat stations in the entire network is determined.

[0007] As a preferred embodiment of the operation and maintenance management method for heating stations described in this invention, the specific steps for determining the comprehensive correlation between any two heating stations based on physical topology information and historical heat load response information are as follows: Obtain hydraulic path information between any two heating stations in the heating network, wherein the hydraulic path information includes the length, inner diameter, roughness, and number of local resistance components of the connecting pipe section; Based on the hydraulic path information, calculate the total hydraulic path impedance between the two heating stations. ; in, It is obtained by summing the frictional resistance and local resistance of each pipe section; The total impedance of the hydraulic path The reciprocal of the physical connection strength The expression is: ; Obtain the historical secondary heat load sequences of the two heating stations under the same meteorological conditions and the same time window; The heat load sequence is normalized to eliminate dimensional differences; The minimum cumulative distance between two sequences is calculated using the dynamic time warping algorithm. ; The minimum cumulative distance Maximum regular distance from all heating stations in the network within the same time window Normalization is performed to obtain the normalized distance. ; Define load coordination for: ; The physical connection strength With the aforementioned load synergy Perform linear weighted fusion to obtain the comprehensive correlation degree R, which is expressed as: ; in, These are the weighting coefficients. For comprehensive correlation.

[0008] As a preferred embodiment of the operation and maintenance management method for heating stations described in this invention, the specific steps for selecting heating stations with stable operating conditions and typical representativeness as characteristic heating stations based on comprehensive correlation are as follows: For each heating station, calculate the arithmetic mean of its overall correlation with all other heating stations, and denot it as the average overall correlation. If the total number of heating stations is For any heating station Its average comprehensive correlation Represented as: ; Simultaneously acquire the time-series data of the secondary side water supply temperature of the heating station within a preset time window; The standard deviation of time-series data is calculated and denoted as the temperature fluctuation index. ; Set the first threshold With the second threshold ; When the average comprehensive correlation of a certain heating station Greater than And its temperature fluctuation index Less than When a heating station is determined to be in stable and representative operating condition, it is included in the characteristic heating station set.

[0009] As a preferred embodiment of the operation and maintenance management method for heat stations described in this invention, the specific steps for generating remote operation and maintenance strategies for non-characteristic heat stations by utilizing the historical operation and maintenance records and operational stability performance of characteristic heat stations are as follows: For each characteristic heat station in the set of characteristic heat stations, count its most recent consecutive The number of on-site maintenance operations performed within a unit time period is recorded as follows: ; Simultaneously, the total number of times data changes occurred within the same time period is counted and denoted as . ; Among them, the data change time is defined as the time when the relative deviation of the water supply temperature or return water pressure between adjacent monitoring times exceeds the preset change threshold. Based on the number of on-site maintenance operations With the number of data changes Construct the data credibility weight of this characteristic heat station. The expression is: ; in, To prevent extremely small positive numbers with a denominator of zero; For any non-featured heat station, find the feature heat station with the highest comprehensive correlation and obtain the data reliability weight of that feature heat station. ; Based on the data credibility weight The number of monitoring times for this non-characteristic thermal station within a unit time period is dynamically set. The higher the elevation, the higher the monitoring density; At the same time, a relative deviation threshold for meter anomaly detection is set. The higher the threshold, the stricter the judgment.

[0010] As a preferred embodiment of the operation and maintenance management method for heat stations described in this invention, the specific steps for performing consistency verification between the real-time operating data of non-characteristic heat stations and associated characteristic heat stations are as follows: At the current operating moment, the primary flow data of non-characteristic heat stations and their associated characteristic heat stations are acquired synchronously; Calculate the relative difference in primary flow rates between the two; if the relative difference is less than a preset comparable operating condition threshold, the two are considered to be in comparable operating conditions. Under this premise, the secondary water supply temperatures of both are obtained and denoted as follows: and ; Calculate the relative temperature difference between the two. The expression is: ; Obtain the data reliability weight of the associated feature heat station ; according to Dynamically determine the current consistency check threshold. ,in and There is a negative correlation; like If so, it is determined that the water supply temperature meter of the non-characteristic heating station is abnormal.

[0011] As a preferred embodiment of the operation and maintenance management method for heating stations described in this invention, the specific steps of performing remote processing on abnormal meters according to the remote operation and maintenance strategy are as follows: After determining that the meter of a non-characteristic heating station is abnormal, check whether the heating station is equipped with a backup monitoring channel. If a backup channel exists, the system will automatically switch to the backup channel to collect operational data and mark the original channel as pending on-site verification. If there is no backup channel, the weight of the operating data of the heating station will be reduced in the heating dispatch decision, and it will be prohibited from participating in the correlation calculation of characteristic heating stations. Simultaneously, a remote verification task is generated, which includes the anomaly type, occurrence time, associated feature station identifier, and suggested handling method, and is pushed to the task queue of the operation and maintenance management platform for operation and maintenance personnel to access and handle.

[0012] As a preferred embodiment of the operation and maintenance management method for heating stations described in this invention, the steps of generating heating equipment control instructions and issuing them to the execution mechanism based on consistency verification results and remote processing feedback, while simultaneously updating the remote operation and maintenance strategy and the screening results of characteristic heating stations, are as follows: The number of non-characteristic heating stations that were identified as having meter malfunctions within a preset time period is recorded as the number of malfunctioning stations. ; like If the risk trigger threshold is exceeded, the spatial distribution of abnormal heat stations will be further analyzed. Identify clustered areas in the heating network and locate the nearest primary network regulating valve upstream of the area; Based on the reference values ​​of secondary side parameters of characteristic heating stations in the region, the deviation between the current actual operating conditions and the reference operating conditions is calculated. Generate valve opening adjustment amount This causes the hydraulic distribution in the adjusted area to converge toward the baseline state. The adjustment amount The commands are encapsulated as control instructions and sent to the corresponding actuators via the communication network. In the next monitoring cycle after the control measures are implemented, operational data from all heating stations in the network will be collected again. The average comprehensive correlation of each heating station was recalculated based on the new data. Temperature fluctuation index ; According to the updated and Refresh the set of characteristic heat stations; Based on the latest data trust weights of the characteristic heat stations, regenerate the remote operation and maintenance strategies for all non-characteristic heat stations.

[0013] Secondly, the present invention provides an operation and maintenance management system for a heating station, comprising: The module includes a data fusion module, a correlation modeling module, a feature recognition module, a strategy generation module, a consistency verification module, and a closed-loop control module. The data fusion module is used to acquire the physical topology information of each heating station in the heating network and the historical heat load response information of the corresponding user side, and to perform standardized preprocessing on the two types of heterogeneous data to provide a unified input basis for subsequent correlation analysis. The correlation modeling module is used to calculate the total hydraulic path impedance between any two heating stations based on physical topology information, and use its reciprocal as the physical connection strength. At the same time, it calculates the dynamic time regularization distance of the load sequence based on historical heat load response information under the same meteorological and time period conditions, and normalizes it to obtain the load synergy. Then, it generates a comprehensive correlation degree through weighted fusion. The feature recognition module is used to calculate the average comprehensive correlation between each heat station and all other heat stations, and combine it with the standard deviation of its secondary side water supply temperature time series data to select heat stations with stable operation and typical representativeness based on preset dual threshold conditions, forming a set of feature heat stations. The strategy generation module is used to count the number of on-site maintenance and the number of data change times of each characteristic heat station in the recent unit time period, construct the data credibility weight, and dynamically configure the monitoring density and meter anomaly judgment threshold for the non-characteristic heat station with the highest correlation with it based on the weight, and generate differentiated remote maintenance strategies. The consistency verification module is used to determine whether non-characteristic heat stations and their associated characteristic heat stations are in comparable operating conditions during real-time system operation, and to calculate the relative deviation of their secondary water supply temperatures under the premise of comparability. It also dynamically sets the judgment threshold by combining the data credibility weight of the characteristic heat station, thereby realizing the automatic identification and marking of abnormal meters. The closed-loop control module is used to count the number of abnormal heat stations and their spatial distribution based on the consistency verification results, locate the upstream regulating valve and generate the valve opening adjustment amount when the risk threshold is triggered, and issue control instructions to the actuator.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the operation and maintenance management method for a heating station as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the operation and maintenance management method for a heating station as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By integrating the physical topology of the heating network with the dynamic heat load response characteristics on the user side, a comprehensive correlation model of heating stations with mechanistic support is constructed. This model accurately identifies stable and representative characteristic heating stations and uses this as a reliable benchmark to dynamically generate differentiated remote operation and maintenance strategies for non-characteristic heating stations. Through real-time consistency verification, automatic identification and remote handling of meter anomalies are achieved, and the actuators are linked to complete the active control of hydraulic conditions, forming a full-chain intelligent operation and maintenance system. This effectively improves the operational reliability of heating stations, the initiative in anomaly response, and the coordination of system scheduling, and effectively reduces the cost of manual inspection and the rate of invalid alarms. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the operation and maintenance management method for a heating station in Example 1.

[0019] Figure 2 This is a schematic diagram of the operation and maintenance management system used for the heating station in Example 1. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example, refer to Figure 1 and Figure 2 This embodiment of the invention provides a method for the operation and maintenance management of a heating station, comprising the following steps: S1. Obtain the physical topology information of each heating station in the heating network and the historical heat load response information of the corresponding user side; Furthermore, the node identifiers, spatial coordinates, and interconnections of each heating station are extracted from the geographic information system or hydraulic model database of the heating network to form a network topology map of the heating stations. For each connecting pipe segment, physical property parameters are collected, including pipe segment length, inner diameter, material roughness, type and number of local resistance components, and a hydraulic impedance parameter table including friction resistance and local resistance is established accordingly. Based on the building archive information of the heating station service area, obtain basic attribute data on the user side, including building type, building area, insulation level, heating method and number of heating users; Historical operating data of each heating station for the past year were retrieved from the heat metering system or SCADA platform. The time series of secondary side supply water temperature, return water temperature, instantaneous flow rate and cumulative heat were extracted and archived according to day, week, month and typical meteorological days. By combining the meteorological monitoring data of the same period, the historical heat load sequence is subjected to meteorological normalization processing to eliminate the interference of outdoor temperature, wind speed, sunshine and other factors on the original load, and generate a standardized historical heat load response sequence after removing the meteorological influence. The physical topology information is mapped and bound one-to-one with the standardized historical heat load response sequence according to the heat station identifier to form a structured input dataset; It should be noted that by extracting detailed physical topology information of each heating station in the heating network and historical heat load response information of the corresponding users, accurate basic data support can be provided for subsequent analysis. This not only helps to accurately calculate the comprehensive correlation between different heating stations, but also effectively improves the operating efficiency and stability of the heating network, ensures more rational heat distribution, and improves energy utilization.

[0024] S2. Determine the comprehensive correlation between any two heating stations based on physical topology information and historical heat load response information; Furthermore, the hydraulic path information between any two heating stations in the heating network is obtained. The hydraulic path information includes the length, inner diameter, roughness, and number of local resistance components of the connecting pipe section. Based on the hydraulic path information, calculate the total hydraulic path impedance between the two heating stations. ; in, It is obtained by summing the frictional resistance and local resistance of each pipe section; Total impedance of the hydraulic path The reciprocal of the physical connection strength The expression is: ; Obtain the historical secondary heat load sequences of the two heating stations under the same meteorological conditions and the same time window; The heat load sequence is normalized to eliminate dimensional differences; The minimum cumulative distance between two sequences is calculated using the dynamic time warping algorithm. ; Minimum cumulative distance Maximum regular distance from all heating stations in the network within the same time window Normalization is performed to obtain the normalized distance. ; Define load coordination for: ; Physical connection strength With load coordination Perform linear weighted fusion to obtain the comprehensive correlation degree R, which is expressed as: ; in, These are the weighting coefficients. To assess overall relevance; It should be noted that determining the comprehensive correlation between any two heating stations based on physical topology information and historical heat load response information can reveal the intrinsic connections and mutual influence between heating stations. This method helps to identify key nodes and weak links in the heating network, thereby enabling targeted optimization and adjustments to enhance the stability and reliability of the entire system.

[0025] S3. Based on the comprehensive correlation, select heat stations with stable operation and typical representativeness as characteristic heat stations; Furthermore, for each heating station, the arithmetic mean of its overall correlation with all other heating stations is calculated and denoted as the average overall correlation. If the total number of heating stations is For any heating station Its average comprehensive correlation Represented as: ; Simultaneously acquire the time-series data of the secondary side water supply temperature of the heating station within a preset time window; The standard deviation of time-series data is calculated and denoted as the temperature fluctuation index. ; Set the first threshold With the second threshold ; When the average comprehensive correlation of a certain heating station Greater than And its temperature fluctuation index Less than When a heating station is determined to be in stable and representative operating condition, it is included in the characteristic heating station set. It should be noted that selecting stable and representative heating stations as characteristic heating stations helps to build a reliable standard model library to guide the formulation of operation and maintenance strategies for non-characteristic heating stations. This can not only reduce overall operation and maintenance costs, but also improve the system's ability to cope with emergencies and ensure the quality and continuity of heating services.

[0026] S4. Utilize the historical operation and maintenance records and operational stability performance of characteristic heat stations to generate remote operation and maintenance strategies for non-characteristic heat stations; Furthermore, for each characteristic heat station in the set of characteristic heat stations, statistics are compiled on its most recent consecutive... The number of on-site maintenance operations performed within a unit time period is recorded as follows: ; Simultaneously, the total number of times data changes occurred within the same time period is counted and denoted as . ; Among them, the data change time is defined as the time when the relative deviation of the water supply temperature or return water pressure between adjacent monitoring times exceeds the preset change threshold. Based on the number of on-site maintenance operations With the number of data changes Construct the data credibility weight of this characteristic heat station. The expression is: ; in, To prevent extremely small positive numbers with a denominator of zero; For any non-featured heat station, find the feature heat station with the highest comprehensive correlation and obtain the data reliability weight of that feature heat station. ; Based on data credibility weight The number of monitoring times for this non-characteristic thermal station within a unit time period is dynamically set. The higher the elevation, the higher the monitoring density; At the same time, a relative deviation threshold for meter anomaly detection is set. The higher the threshold, the stricter the judgment threshold; It should be noted that by using the historical operation and maintenance records and operational stability performance of characteristic heat stations to generate remote operation and maintenance strategies for non-characteristic heat stations, effective monitoring and timely maintenance of all network equipment can be achieved. This effectively improves the speed of problem detection and resolution efficiency, reduces the risk of service interruption due to equipment failure, and reduces the number of unnecessary on-site inspections, saving human and material resources.

[0027] (The method for determining the adjustment scheme of the operation and maintenance strategy of the aforementioned characteristic heating station is as follows:) The heat stations other than the characteristic heat stations are designated as other heat stations. Based on the monitoring results of the operating data of the other heat stations, the changes in the operating data of the other heat stations are determined. Based on the aforementioned changes, the data change times in the monitoring times of the other heating stations are determined; Based on the data change times of other heating stations and the correlation factors between the other heating stations and the characteristic heating station, the adjustment plan for the operation strategy of the characteristic heating station is determined.

[0028] It is understandable that when the number of characteristic heat stations is obtained, and the proportion of the characteristic heat station among all heat stations is less than a preset proportion threshold, a preset strategy is adopted to determine the adjustment scheme of the operation and maintenance strategy of the characteristic heat station. That is, as long as there are other heat stations whose number of data change moments in the most recent preset time period does not meet the requirements, on-site operation and maintenance processing of the characteristic heat station is carried out. In one possible embodiment, as long as there are other heat stations whose proportion of data change moments in different unit time periods in the most recent month is not less than 0.5, on-site operation and maintenance processing of all characteristic heat stations is carried out, that is, meter verification processing is carried out.

[0029] It should be noted that the time of data change is the time when the deviation rate of the operating temperature or operating pressure of the previous monitoring time is greater than 3%. When the proportion of the characteristic heat station in the total number of heat stations is less than 0.2, a preset strategy is adopted to determine the adjustment plan of the operation and maintenance strategy of the characteristic heat station, with a unit time of 1 hour.

[0030] Furthermore, when the proportion of the characteristic heat station among all heat stations is not less than a preset proportion threshold, the correlation factor between the characteristic heat station and the other heat stations is determined. If the average value of the correlation factor between the characteristic heat station and the other heat stations is greater than a preset factor threshold, for example, 0.8, a preset strategy is adopted to determine the adjustment scheme of the operation and maintenance strategy of the characteristic heat station.

[0031] Additionally, it can be understood that if the average value of the correlation factor between the characteristic heat station and the other heat stations is not greater than a preset factor threshold, and based on the correlation factor with other heat stations, it is determined that there are no other heat stations with a correlation factor greater than the preset factor threshold for the characteristic heat station, then the second preset strategy is adopted to determine the adjustment scheme of the operation and maintenance strategy of the characteristic heat station. That is, when the sum of the correlation factors between the characteristic heat station and other heat stations whose number of data change moments in the most recent preset time period does not meet the requirements is greater than the factor preset value, for example, 2, then the on-site operation and maintenance processing of the characteristic heat station is carried out.

[0032] Furthermore, when there are other heat stations with a correlation factor greater than a preset factor threshold for the characteristic heat station, if the number of other heat stations with a correlation factor greater than the preset factor threshold meets the requirements, for example, not less than 3, then a preset strategy is adopted to determine the adjustment scheme of the operation and maintenance strategy of the characteristic heat station.

[0033] Additionally, it can be understood that if the number of other heating stations with a correlation factor greater than a preset factor threshold is insufficient, the comprehensive correlation factor of the characteristic heating station is determined based on the sum of the correlation factors between the characteristic heating station and other heating stations. Specifically, if the ranking result of the comprehensive correlation factor among all characteristic heating stations meets the requirements (e.g., within the top third), a preset strategy is used to determine the adjustment plan for the operation and maintenance strategy of the characteristic heating station. If the ranking result does not meet the requirements, a second preset strategy is used to determine the adjustment plan for the operation and maintenance strategy of the characteristic heating station. S5. During system operation, the real-time operating data of non-characteristic heat stations are checked for consistency with the associated characteristic heat stations, and abnormal meters are remotely processed according to the remote operation and maintenance strategy. Furthermore, at the current operating moment, the primary flow data of non-characteristic heat stations and their associated characteristic heat stations are acquired simultaneously; Calculate the relative difference in primary flow rates between the two; if the relative difference is less than the preset comparable operating condition threshold, the two are considered to be in comparable operating conditions. Under this premise, the secondary water supply temperatures of both are obtained and denoted as follows: and ; Calculate the relative temperature difference between the two. The expression is: ; Obtain the data credibility weight of the associated feature heat station ; according to Dynamically determine the current consistency check threshold. ,in and There is a negative correlation; like If so, it is determined that the water supply temperature meter of the non-characteristic heating station is abnormal; After determining that the meter of a non-characteristic heating station is abnormal, check whether the heating station is equipped with a backup monitoring channel. If a backup channel exists, the system will automatically switch to the backup channel to collect operational data and mark the original channel as pending on-site verification. If there is no backup channel, the weight of the operating data of the heating station will be reduced in the heating dispatch decision, and it will be prohibited from participating in the correlation calculation of characteristic heating stations. At the same time, a remote verification task is generated, which includes the anomaly type, occurrence time, associated feature station identifier, and suggested handling method. This task is then pushed to the task queue of the operation and maintenance management platform for operation and maintenance personnel to review and handle. It should be noted that during system operation, the real-time operating data of non-characteristic heating stations are checked for consistency with the associated characteristic heating stations, and anomaly handling is performed according to the remote operation and maintenance strategy. This can improve the system's level of automated management. The method can quickly locate and resolve potential problems without affecting normal heating services, ensuring the safety and stability of the heating network operation.

[0034] S6. Based on the consistency verification results and remote processing feedback, generate heating equipment control instructions and send them to the actuators, while updating the remote operation and maintenance strategy and the screening results of characteristic heating stations. Furthermore, the number of non-characteristic heating stations that are identified as having meter malfunctions within a preset time period is counted and recorded as the number of malfunctioning stations. ; like If the risk trigger threshold is exceeded, the spatial distribution of abnormal heat stations will be further analyzed. Identify clustered areas in the heating network and locate the nearest primary network regulating valve upstream of the area; Based on the reference values ​​of secondary side parameters of characteristic heating stations in the region, the deviation between the current actual operating conditions and the reference operating conditions is calculated. Generate valve opening adjustment amount This causes the hydraulic distribution in the adjusted area to converge toward the baseline state. Adjustment amount The commands are encapsulated as control instructions and sent to the corresponding actuators via the communication network. In the next monitoring cycle after the control measures are implemented, operational data from all heating stations in the network will be collected again. Based on the new data, the total hydraulic path impedance and load synergy between any two heating stations are recalculated, and an updated comprehensive correlation matrix is ​​generated to complete the reconstruction of the correlation model between heating stations. Based on the reconstructed correlation model, and combined with the real-time load demand, equipment status and hydraulic constraints of each heating station, an optimization model is constructed with the objective function of balanced heat supply, minimum energy consumption or maximum user satisfaction. Solve the optimization model to generate primary flow distribution, water supply temperature setting and regulating valve coordinated opening commands covering all heating stations in the network, forming a joint scheduling and processing scheme for the entire network; Subsequently, the average comprehensive correlation of each heating station was recalculated based on the newly collected data. Temperature fluctuation index ; According to the updated and Refresh the set of characteristic heat stations; Based on the latest data trust weights of the characteristic heat stations, regenerate the remote operation and maintenance strategies for all non-characteristic heat stations; It should be noted that generating control instructions for heating equipment and updating operation and maintenance strategies based on consistency verification results and remote processing feedback can dynamically adapt to changes in the operating status of the heating network, maintain continuous optimization of heating quality and service level. In addition, the method also promotes the rationalization of resource allocation, helps reduce energy consumption and operating costs, and achieves the goal of energy conservation and emission reduction.

[0035] This embodiment also provides an operation and maintenance management system for a heating station, including: The module includes a data fusion module, a correlation modeling module, a feature recognition module, a strategy generation module, a consistency verification module, and a closed-loop control module. The data fusion module is used to acquire the physical topology information of each heating station in the heating network and the historical heat load response information of the corresponding user side, and to perform standardized preprocessing on the two types of heterogeneous data to provide a unified input basis for subsequent correlation analysis. The correlation modeling module is used to calculate the total hydraulic path impedance between any two heating stations based on physical topology information, and use its reciprocal as the physical connection strength. At the same time, based on historical heat load response information, it calculates the dynamic time regularization distance of the load sequence under the same meteorological and time period conditions and normalizes it to obtain the load synergy. Then, it generates a comprehensive correlation degree through weighted fusion. The feature recognition module is used to calculate the average comprehensive correlation between each heating station and all other heating stations, and combine it with the standard deviation of its secondary side water supply temperature time series data. Based on the preset dual threshold conditions, it selects heating stations with stable operation and typical representativeness to form a set of feature heating stations. The strategy generation module is used to count the number of on-site maintenance and data change times of each characteristic heat station in the recent unit time period, construct the data credibility weight, and dynamically configure the monitoring density and meter anomaly judgment threshold for the non-characteristic heat station with the highest correlation based on the weight, and generate differentiated remote maintenance strategies. The consistency verification module is used to determine whether non-characteristic heat stations and their associated characteristic heat stations are in comparable operating conditions during real-time system operation. Under the premise of comparability, it calculates the relative deviation of the secondary water supply temperature between the two stations and dynamically sets the judgment threshold by combining the data credibility weight of the characteristic heat station to realize the automatic identification and marking of abnormal meters. The closed-loop control module is used to count the number of abnormal heat stations and their spatial distribution based on the consistency verification results, locate the upstream control valve when the risk threshold is triggered, generate the valve opening adjustment amount, and issue control instructions to the actuator.

[0036] This embodiment also provides a computer device applicable to the operation and maintenance management method of a heating station, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the operation and maintenance management method for a heating station as proposed in the above embodiment.

[0037] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0038] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the operation and maintenance management method for a heat station as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0039] In summary, this invention integrates the physical topology of the heating network with the dynamic heat load response characteristics on the user side to construct a comprehensive correlation model of heating stations with mechanistic support. This model accurately identifies stable and representative characteristic heating stations and uses this as a reliable benchmark to dynamically generate differentiated remote operation and maintenance strategies for non-characteristic heating stations. Through real-time consistency verification, it achieves automatic identification and remote handling of meter anomalies and coordinates with actuators to complete proactive hydraulic condition control, forming a full-chain intelligent operation and maintenance system. This effectively improves the operational reliability of heating stations, the proactiveness of anomaly response, and the coordination of system scheduling, while effectively reducing the cost of manual inspections and the rate of invalid alarms.

[0040] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for operation and maintenance management of a heating station, characterized in that: include: Obtain the physical topology information of each heating station in the heating network and the historical heat load response information of the corresponding user side; Based on the correlation between the physical topology information of the heating station and the historical heat load response information of the corresponding user side, when it is determined that a characteristic heating station needs to be constructed, the comprehensive correlation between any two heating stations is determined according to the physical topology information and the historical heat load response information. Based on the comprehensive correlation with other heating stations, heating stations with stable operation and typical representativeness were selected as characteristic heating stations. Continuously monitor the operating data of other heating stations besides the featured heating station, determine the changes in the operating data, and adjust or optimize the remote operation and maintenance strategy of the featured heating station based on the changes, thus forming a strategy update mechanism. During system operation, the real-time operating data of non-featured heat stations are checked for consistency with the associated feature heat stations. Based on the updated data of the remote operation and maintenance strategy of the feature heat stations and the operating data of the heat stations, the update method of the remote operation and maintenance strategy of the heat stations other than the feature heat stations is determined. Using the update method, a joint scheduling and processing scheme for all heat stations in the entire network is determined.

2. The operation and maintenance management method for a heating station as described in claim 1, characterized in that: The steps for determining the comprehensive correlation between any two heating stations based on physical topology information and historical heat load response information are as follows: Obtain hydraulic path information between any two heating stations in the heating network, wherein the hydraulic path information includes the length, inner diameter, roughness, and number of local resistance components of the connecting pipe section; Based on the hydraulic path information, calculate the total hydraulic path impedance between the two heating stations. ; in, It is obtained by summing the frictional resistance and local resistance of each pipe section; The total impedance of the hydraulic path The reciprocal of the physical connection strength The expression is: ; Obtain the historical secondary heat load sequences of the two heating stations under the same meteorological conditions and the same time window; The heat load sequence is normalized to eliminate dimensional differences; The minimum cumulative distance between two sequences is calculated using the dynamic time warping algorithm. ; The minimum cumulative distance Maximum regular distance from all heating stations in the network within the same time window Normalization is performed to obtain the normalized distance. ; Define load coordination for: ; The physical connection strength With the aforementioned load synergy Perform linear weighted fusion to obtain the comprehensive correlation degree R, which is expressed as: ; in, These are the weighting coefficients. For comprehensive correlation.

3. The operation and maintenance management method for a heating station as described in claim 2, characterized in that: The process of selecting stable and representative heat stations as characteristic heat stations based on comprehensive correlation is as follows: For each heating station, calculate the arithmetic mean of its overall correlation with all other heating stations, and denot it as the average overall correlation. If the total number of heating stations is For any heating station Its average comprehensive correlation Represented as: ; Simultaneously acquire the time-series data of the secondary side water supply temperature of the heating station within a preset time window; The standard deviation of time-series data is calculated and denoted as the temperature fluctuation index. ; Set the first threshold With the second threshold ; When the average comprehensive correlation of a certain heating station Greater than And its temperature fluctuation index Less than When a heating station is determined to be in stable and representative operating condition, it is included in the characteristic heating station set.

4. The operation and maintenance management method for a heating station as described in claim 3, characterized in that: The process of generating remote operation and maintenance strategies for non-characteristic heat stations by utilizing the historical operation and maintenance records and operational stability performance of characteristic heat stations involves the following steps: For each characteristic heat station in the set of characteristic heat stations, count its most recent consecutive The number of on-site maintenance operations performed within a unit time period is recorded as follows: ; Simultaneously, the total number of times data changes occurred within the same time period is counted and denoted as . ; Among them, the data change time is defined as the time when the relative deviation of the water supply temperature or return water pressure between adjacent monitoring times exceeds the preset change threshold. Based on the number of on-site maintenance operations With the number of data changes Construct the data credibility weight of this characteristic heat station. The expression is: ； in, To prevent extremely small positive numbers with a denominator of zero; For any non-featured heat station, find the feature heat station with the highest comprehensive correlation and obtain the data reliability weight of that feature heat station. ; Based on the data credibility weight The number of monitoring times for this non-characteristic thermal station within a unit time period is dynamically set. The higher the elevation, the higher the monitoring density; At the same time, a relative deviation threshold for meter anomaly detection is set. The higher the threshold, the stricter the judgment.

5. The operation and maintenance management method for a heating station as described in claim 4, characterized in that: The specific steps for verifying the consistency between the real-time operating data of non-featured heat stations and their associated feature heat stations are as follows: At the current operating moment, the primary flow data of non-characteristic heat stations and their associated characteristic heat stations are acquired synchronously; Calculate the relative difference in primary flow rates between the two; if the relative difference is less than a preset comparable operating condition threshold, the two are considered to be in comparable operating conditions. Under this premise, the secondary water supply temperatures of both are obtained and denoted as follows: and ; Calculate the relative temperature difference between the two. The expression is: ; Obtain the data reliability weight of the associated feature heat station ; according to Dynamically determine the current consistency check threshold. ,in and There is a negative correlation; like If so, it is determined that the water supply temperature meter of the non-characteristic heating station is abnormal.

6. The operation and maintenance management method for a heating station as described in claim 5, characterized in that: The specific steps for remotely processing abnormal meters according to the remote operation and maintenance strategy are as follows: After determining that the meter of a non-characteristic heating station is abnormal, check whether the heating station is equipped with a backup monitoring channel. If a backup channel exists, the system will automatically switch to the backup channel to collect operational data and mark the original channel as pending on-site verification. If there is no backup channel, the weight of the operating data of the heating station will be reduced in the heating dispatch decision, and it will be prohibited from participating in the correlation calculation of characteristic heating stations. Simultaneously, a remote verification task is generated, which includes the anomaly type, occurrence time, associated feature station identifier, and suggested handling method, and is pushed to the task queue of the operation and maintenance management platform for operation and maintenance personnel to access and handle.

7. The operation and maintenance management method for a heating station as described in claim 6, characterized in that: The steps are as follows: Based on the consistency verification results and remote processing feedback, a heating equipment control command is generated and sent to the execution mechanism. Simultaneously, the remote operation and maintenance strategy and the screening results of characteristic heating stations are updated. The number of non-characteristic heating stations that were identified as having meter malfunctions within a preset time period is recorded as the number of malfunctioning stations. ; like If the risk trigger threshold is exceeded, the spatial distribution of abnormal heat stations will be further analyzed. Identify clustered areas in the heating network and locate the nearest primary network regulating valve upstream of the area; Based on the reference values ​​of secondary side parameters of characteristic heating stations in the region, the deviation between the current actual operating conditions and the reference operating conditions is calculated. Generate valve opening adjustment amount This causes the hydraulic distribution in the adjusted area to converge toward the baseline state. The adjustment amount The commands are encapsulated as control instructions and sent to the corresponding actuators via the communication network. In the next monitoring cycle after the control measures are implemented, operational data from all heating stations in the network will be collected again. The average comprehensive correlation of each heating station was recalculated based on the new data. Temperature fluctuation index ; According to the updated and Refresh the set of characteristic heat stations; Based on the latest data trust weights of the characteristic heat stations, regenerate the remote operation and maintenance strategies for all non-characteristic heat stations.

8. An operation and maintenance management system for a heating station, based on the operation and maintenance management method for a heating station according to any one of claims 1 to 7, characterized in that: include: The module includes a data fusion module, a correlation modeling module, a feature recognition module, a strategy generation module, a consistency verification module, and a closed-loop control module. The data fusion module is used to acquire the physical topology information of each heating station in the heating network and the historical heat load response information of the corresponding user side, and to perform standardized preprocessing on the two types of heterogeneous data to provide a unified input basis for subsequent correlation analysis. The correlation modeling module is used to calculate the total hydraulic path impedance between any two heating stations based on physical topology information, and use its reciprocal as the physical connection strength. At the same time, it calculates the dynamic time regularization distance of the load sequence based on historical heat load response information under the same meteorological and time period conditions, and normalizes it to obtain the load synergy. Then, it generates a comprehensive correlation degree through weighted fusion. The feature recognition module is used to calculate the average comprehensive correlation between each heat station and all other heat stations, and combine it with the standard deviation of its secondary side water supply temperature time series data to select heat stations with stable operation and typical representativeness based on preset dual threshold conditions, forming a set of feature heat stations. The strategy generation module is used to count the number of on-site maintenance and the number of data change times of each characteristic heat station in the recent unit time period, construct the data credibility weight, and dynamically configure the monitoring density and meter anomaly judgment threshold for the non-characteristic heat station with the highest correlation with it based on the weight, and generate differentiated remote maintenance strategies. The consistency verification module is used to determine whether non-characteristic heat stations and their associated characteristic heat stations are in comparable operating conditions during real-time system operation, and to calculate the relative deviation of their secondary water supply temperatures under the premise of comparability. It also dynamically sets the judgment threshold by combining the data credibility weight of the characteristic heat station, thereby realizing the automatic identification and marking of abnormal meters. The closed-loop control module is used to count the number of abnormal heat stations and their spatial distribution based on the consistency verification results, locate the upstream regulating valve and generate the valve opening adjustment amount when the risk threshold is triggered, and issue control instructions to the actuator.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the operation and maintenance management method for a heating station as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the operation and maintenance management method for a heating station as described in any one of claims 1 to 7.

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