A long-distance heat supply pipe network heat preservation drag reduction and intelligent monitoring system

Abstract

The application belongs to the field of heat supply pipeline monitoring, and discloses a long-distance heat supply pipeline network heat preservation and drag reduction and intelligent monitoring system. The medium state data, environment and structure data and equipment operation data of the long-distance heat supply pipeline network are uniformly collected and pretreated, so that the heat state, the pipe wall structure state and the equipment working condition in the pipeline network operation can be comprehensively analyzed on the basis of the same data, and the problem that only the temperature, pressure or flow single parameter is monitored in the prior art is avoided. On this basis, the medium heat force feature, the pipe wall resistance feature and the equipment operation state feature are obtained through feature extraction, and the heat preservation efficiency evaluation index and the drag reduction state evaluation index are calculated, so that the heat preservation performance degradation degree and the fluid conveying resistance degree of the pipeline network can be reflected simultaneously.

Description

Technical Field

[0001] This invention belongs to the field of heating pipeline monitoring, specifically relating to a long-distance heating pipeline network insulation and drag reduction and intelligent monitoring system. Background Technology

[0002] Long-distance heating pipelines are a crucial link in centralized heating systems, and their operational efficiency and safety stability depend primarily on pipeline insulation performance, hydraulic transport resistance, and the operating status of equipment along the route. Existing long-distance heating pipelines typically employ multi-layered composite insulation structures to reduce heat loss during heat medium transport. Temperature, pressure, and flow sensors are installed at key nodes in the network, along with pumps, valves, compensators, and other actuators or regulators, forming a data acquisition and monitoring system for the heating pipeline network. This enables real-time monitoring and remote adjustment of medium temperature, pressure, flow rate, and equipment operating conditions. Furthermore, existing technologies incorporate anti-corrosion coatings, insulation layer inspection, and cathodic protection to maintain pipeline structural integrity, thereby ensuring the thermal quality and hydraulic stability of the heat medium during long-distance transport.

[0003] However, existing long-distance heating pipeline networks still largely separate insulation monitoring and drag reduction control. On the one hand, current systems typically monitor thermal insulation performance and hydraulic transport resistance as two independent indicators. For example, they use temperature deviations to determine abnormal heat loss and pressure or flow rate changes to determine abnormal resistance. This fails to adequately consider the impact of medium temperature drops on fluid viscosity, friction coefficient, and hydraulic transport conditions, nor does it fully account for the potential for increased heat loss and changes in flow resistance caused by moisture, damage, or aging of the insulation layer. Therefore, existing technologies struggle to establish a correlation between insulation effectiveness and drag reduction, and cannot accurately reveal the synergistic changes between the thermal and hydraulic states of long-distance heating pipeline networks.

[0004] On the other hand, existing monitoring methods mostly rely on a single threshold for alarm judgment, such as triggering anomaly prompts only when the temperature is below a set value or the pressure loss exceeds a set value. This method can only reflect whether a single parameter exceeds the limit, and it is difficult to quantify the degree of deviation between the decline in thermal insulation performance and the increase in flow resistance. When the pipeline network is in the early stage of declining thermal insulation performance and deteriorating hydraulic conditions, but individual indicators have not yet significantly exceeded the limit, existing systems are prone to missed or delayed judgments, and cannot timely identify the hidden overall performance decline problem caused by the mismatch between thermal insulation and drag reduction functions.

[0005] Meanwhile, existing control systems typically employ independent adjustment logic, adjusting the heat source temperature or medium flow rate based on temperature deviation, and adjusting the pump frequency or valve opening based on pressure deviation. This type of control fails to integrate the results of insulation performance analysis with the results of drag reduction analysis. When abnormal heat loss or resistance occurs in the pipeline network, it often only allows for passive, single-parameter adjustments, making it difficult to generate thermodynamic and hydraulic synergistic optimization commands based on the overall operating status of the pipeline network. This, in turn, affects the overall transmission efficiency and dynamic balance capability of long-distance heating pipeline networks. Summary of the Invention

[0006] The purpose of this invention is to overcome the problem that the existing long-distance heating pipeline network insulation monitoring and drag reduction control are disconnected, making it difficult to accurately quantify the coupling imbalance between the decline in insulation performance and the increase in flow resistance, and to provide a long-distance heating pipeline network insulation drag reduction and intelligent monitoring system.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a long-distance heating pipeline network insulation and drag reduction and intelligent monitoring system, comprising: The data acquisition module is used to collect medium status data, environmental and structural data, and equipment operation data of the heating pipeline network, and to preprocess the collected medium status data, environmental and structural data, and equipment operation data. The feature extraction module is used to extract features from the preprocessed medium state data, environmental and structural data, and equipment operation data to obtain medium thermal characteristics, pipe wall resistance characteristics, and equipment operation status characteristics. The thermal insulation efficiency analysis module is used to analyze the thermal insulation efficiency of the pipeline network based on the thermal characteristics of the medium, and obtain the thermal insulation efficiency evaluation index. The drag reduction status analysis module is used to analyze the drag reduction status of the pipeline network based on the characteristics of pipe wall resistance and equipment operating status, and obtain the drag reduction status evaluation index. The comprehensive diagnostic module is used to perform correlation analysis between the thermal insulation performance evaluation index and the drag reduction status evaluation index, construct the feature mapping relationship between the thermal insulation performance evaluation index and the drag reduction status evaluation index, calculate the correlation deviation under actual operating conditions, evaluate the degree of coupling imbalance between the thermal insulation and drag reduction functions of the pipeline network based on the correlation deviation, and output the comprehensive diagnostic results of thermal insulation and drag reduction of the pipeline network. The intelligent control module is used to generate and issue corresponding dynamic thermal and hydraulic control commands based on the comprehensive diagnostic results of thermal insulation and drag reduction, and to coordinately optimize the thermal insulation and drag reduction of the pipeline network.

[0008] A further improvement of the present invention is that the data acquisition module acquires data through a group of multi-source sensing devices deployed at key nodes of a long-distance heating pipeline network; The medium status data includes the real-time temperature, real-time pressure, instantaneous flow rate, and medium velocity of the medium inside the pipeline network; Environmental and structural data include the outer surface temperature of the pipe wall, the temperature of the soil around the pipe, the soil moisture content, the groundwater level, and the structural integrity indicators of the pipe's anti-corrosion and insulation layer. Equipment operation data includes the operating frequency and power consumption of water pumps in the pipeline network, the on / off status and opening degree of valves, and the displacement deformation of compensators.

[0009] A further improvement of this invention lies in the following specific method for the data acquisition module to preprocess the acquired medium state data, environmental and structural data, and equipment operation data: The raw sensing signals acquired by the multi-source sensing device group at heterogeneous sampling frequencies are time-aligned. Outlier removal is performed on the time-aligned raw sensing signal; The original sensing signal after outlier removal is imputed to create a preprocessed dataset with a unified spatiotemporal reference.

[0010] A further improvement of this invention is that the feature extraction module performs friction difference calculation on the real-time temperature, real-time pressure and instantaneous flow rate in the preprocessed medium state data to obtain the temperature drop rate and pressure drop gradient per unit pipe length, which are used as the thermal characteristics of the medium.

[0011] A further improvement of this invention is that the feature extraction module performs state scoring calculations on the soil moisture content and the integrity index of the anti-corrosion and heat insulation layer in the preprocessed environmental and structural data, and performs hydraulic inversion calculations in combination with the medium flow velocity data to obtain the actual friction coefficient and the thermal resistance attenuation rate of the insulation layer, which are used as pipe wall resistance features.

[0012] A further improvement of the present invention is that the feature extraction module calculates the efficiency ratio of the pump operating frequency and power consumption data in the preprocessed equipment operation data, and performs linkage matching calculation on the valve opening and compensator displacement data to obtain the pump operating efficiency deviation value and the valve compensator action synchronization rate as equipment operating status features.

[0013] A further improvement of the present invention is that the thermal insulation efficiency analysis module calculates the deviation between the temperature drop rate per unit pipe length in the thermal characteristics of the medium and the standard temperature drop benchmark value under the design conditions of the pipeline network, and obtains the abnormal temperature drop ratio. Based on the pressure drop gradient in the thermodynamic characteristics of the medium and the instantaneous flow rate of the medium inside the pipe network, the actual heat loss power of the current pipe section is calculated in combination with the physical parameters of the pipe section. The heat loss deviation is obtained by calculating the ratio of the difference between the actual heat loss power and the theoretical heat loss power. The thermal insulation performance evaluation index is obtained by weighted fusion calculation of the temperature drop anomaly ratio and heat loss deviation.

[0014] A further improvement of this invention is that the drag reduction state analysis module calculates the ratio between the actual friction coefficient along the pipe wall resistance characteristics and the theoretical friction coefficient under the standard operating conditions of the pipeline network to obtain the pipe wall roughness surge coefficient. The negative normalization mapping calculation of the pump operating efficiency deviation value and valve compensator action synchronization rate in the equipment operating status characteristics yields the equipment operating resistance loss rate. The drag reduction status evaluation index is obtained by weighted and fused calculation of the pipe wall roughness surge coefficient and the equipment operating resistance loss rate.

[0015] A further improvement of this invention is that the comprehensive diagnostic module acquires historical data on the unit pipe length temperature drop rate, actual heat loss power, actual friction coefficient, and equipment operating resistance loss rate of the pipeline network under healthy design conditions, and constructs a standard collaborative benchmark curve with the thermal insulation performance evaluation index as the horizontal axis and the drag reduction state evaluation index as the vertical axis. Extract the thermal insulation performance evaluation index and drag reduction performance evaluation index under the current actual operating conditions, calculate the shortest Euclidean distance from the coordinate point corresponding to the current actual operating conditions to the standard synergistic reference curve, and normalize the shortest Euclidean distance to obtain the correlation deviation. When the correlation deviation is greater than or equal to the preset imbalance alarm threshold, a comprehensive diagnostic result for thermal insulation and drag reduction is generated, which includes the coordinate direction of the deviation from the standard collaborative benchmark curve and the location information of the abnormal source. The intelligent control module generates hydraulic or thermal dynamic control commands based on the anomaly source location information and associated deviation, and then merges and issues the hydraulic and thermal dynamic control commands after synchronizing their execution times.

[0016] Secondly, this invention provides a method for heat preservation, drag reduction, and intelligent monitoring of long-distance heating pipelines, comprising the following steps: Real-time acquisition of media status data, environmental and structural data, and equipment operation data of the heating network; and preprocessing of the acquired media status data, environmental and structural data, and equipment operation data. Feature extraction is performed on the preprocessed medium state data, environmental and structural data, and equipment operation data to obtain the medium thermal characteristics, pipe wall resistance characteristics, and equipment operation state characteristics. Based on the thermal characteristics of the medium, the thermal insulation performance of the pipeline network is analyzed to obtain the thermal insulation performance evaluation index. Based on the characteristics of pipe wall resistance and equipment operating status, the drag reduction status of the pipeline network is analyzed to obtain the drag reduction status evaluation index. The thermal insulation performance evaluation index and the drag reduction status evaluation index are correlated and analyzed to construct the characteristic mapping relationship between the two indices. The correlation deviation under actual operating conditions is calculated, and the degree of coupling imbalance between the thermal insulation and drag reduction functions of the pipeline network is evaluated based on the correlation deviation. The comprehensive diagnostic results of thermal insulation and drag reduction of the pipeline network are then output. Based on the comprehensive diagnostic results of thermal insulation and drag reduction, corresponding dynamic control commands for thermal and hydraulic systems are generated and issued to optimize the thermal insulation and drag reduction of the pipeline network in a coordinated manner.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention unifies the collection and preprocessing of medium state data, environmental and structural data, and equipment operation data for long-distance heating pipelines. This allows for comprehensive analysis of the thermal state, pipe wall structure, and equipment operating conditions within the pipeline network, avoiding the problem of isolated monitoring of single parameters such as temperature, pressure, or flow rate in existing technologies. Based on this, feature extraction yields medium thermal characteristics, pipe wall resistance characteristics, and equipment operating state characteristics. Insulation performance evaluation indices and drag reduction performance evaluation indices are calculated separately, simultaneously reflecting the degree of degradation in pipeline insulation performance and the degree of fluid transport obstruction. By performing correlation analysis between the insulation performance evaluation indices and the drag reduction performance evaluation indices, this invention constructs a feature mapping relationship between them and calculates the correlation deviation under actual operating conditions. This quantifies the degree of coupling imbalance between pipeline insulation and drag reduction functions, thereby promptly identifying problems such as decreased insulation performance, increased hydraulic resistance, and the hidden overall performance degradation caused by their combined effects. Finally, based on the comprehensive diagnostic results, dynamic control commands for thermal and hydraulic systems are generated and issued, enabling the system to transform from passive adjustment of a single parameter to coordinated optimization control of thermal and hydraulic systems. This is beneficial for improving the overall transmission efficiency, operational stability, and fault early warning capabilities of long-distance heating networks. Attached Figure Description

[0018] Figure 1 This is a system diagram of the present invention; Figure 2 This is a flowchart of the present invention. Detailed Implementation

[0019] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0020] Example 1: See Figure 1 A long-distance heating pipeline insulation and drag reduction and intelligent monitoring system includes: The data acquisition module is used to collect media status data, environmental and structural data, and equipment operation data of the heating pipeline network, and to preprocess the collected media status data, environmental and structural data, and equipment operation data.

[0021] The feature extraction module is used to extract features from the preprocessed medium state data, environmental and structural data, and equipment operation data to obtain medium thermal characteristics, pipe wall resistance characteristics, and equipment operation status characteristics.

[0022] The thermal insulation efficiency analysis module is used to analyze the thermal insulation efficiency of the pipeline network based on the thermal characteristics of the medium, and obtain the thermal insulation efficiency evaluation index.

[0023] The drag reduction status analysis module is used to analyze the drag reduction status of the pipeline network based on the characteristics of pipe wall resistance and equipment operating status, and obtain the drag reduction status evaluation index.

[0024] The comprehensive diagnostic module is used to perform correlation analysis between the thermal insulation performance evaluation index and the drag reduction status evaluation index, construct the feature mapping relationship between the thermal insulation performance evaluation index and the drag reduction status evaluation index, calculate the correlation deviation under actual operating conditions, evaluate the degree of coupling imbalance between the thermal insulation and drag reduction functions of the pipeline network based on the correlation deviation, and output the comprehensive diagnostic results of thermal insulation and drag reduction of the pipeline network.

[0025] The intelligent control module is used to generate and issue corresponding dynamic thermal and hydraulic control commands based on the comprehensive diagnostic results of thermal insulation and drag reduction, and to coordinately optimize the thermal insulation and drag reduction of the pipeline network.

[0026] Example 2: See Figure 2 A method for thermal insulation, drag reduction, and intelligent monitoring of long-distance heating pipelines includes the following steps: S1 collects real-time data on the medium status, environment and structure, and equipment operation of the heating network, and preprocesses the collected data.

[0027] S2 extracts features from the preprocessed medium state data, environmental and structural data, and equipment operation data to obtain medium thermal characteristics, pipe wall resistance characteristics, and equipment operation status characteristics.

[0028] S3. Based on the thermal characteristics of the medium, the thermal insulation performance of the pipeline network is analyzed to obtain the thermal insulation performance evaluation index.

[0029] S4. Based on the characteristics of pipe wall resistance and equipment operating status, the drag reduction status of the pipeline network is analyzed to obtain the drag reduction status evaluation index.

[0030] S5 performs correlation analysis between the thermal insulation performance evaluation index and the drag reduction status evaluation index, constructs a feature mapping relationship between the two indices, calculates the correlation deviation under actual operating conditions, evaluates the degree of coupling imbalance between the thermal insulation and drag reduction functions of the pipeline network based on the correlation deviation, and outputs the comprehensive diagnostic results of the thermal insulation and drag reduction of the pipeline network.

[0031] S6 generates and issues corresponding dynamic thermal and hydraulic control commands based on the comprehensive diagnostic results of thermal insulation and drag reduction, and performs collaborative optimization of thermal insulation and drag reduction of the pipeline network.

[0032] Example 3: In this embodiment, the data acquisition module is used to collect media status data, environmental and structural data, and equipment operation data of the long-distance heating pipeline network, and to preprocess the above data. Specifically, the data acquisition module collects data through a group of multi-source sensing devices deployed at key nodes of the long-distance heating pipeline network.

[0033] The media status data is collected in real time by temperature and flow sensors, including real-time temperature, pressure, instantaneous flow rate, and velocity of the media within the pipeline network. Environmental and structural data are collected through pipe wall surface temperature detection devices, soil temperature and humidity detection devices, and anti-corrosion layer integrity detection devices. This data includes pipe wall surface temperature, surrounding soil temperature, soil moisture content, groundwater level, and structural integrity indicators of the pipeline's anti-corrosion and insulation layer. Equipment operation data is collected through monitoring terminals for pumps, valves, and compensators. This data includes the operating frequency and power consumption of pumps in the pipeline network, the on / off status and opening degree of valves, and the displacement deformation of compensators.

[0034] After obtaining the aforementioned multi-source sensing data, the data acquisition module sequentially performs time-series alignment, outlier removal, and missing value interpolation on the raw sensing signals acquired at heterogeneous sampling frequencies to form a preprocessed dataset with a unified spatiotemporal reference.

[0035] Specifically, addressing the issue of heterogeneous sampling frequencies, high-frequency sampling data of medium flow velocity and pressure in the pipeline network are used as a benchmark. Linear interpolation is employed to map low-frequency collected ambient temperature and soil moisture data onto high-frequency time series data, constructing a spatiotemporal dataset with a unified timestamp. The linear interpolation formula is:

[0036] in, Indicates the time after interpolation The corresponding data values, and Representing time and time respectively Two adjacent valid sampling times, and Representing time respectively and The corresponding data value.

[0037] For outlier removal, a sliding window statistical method is used, with a window length set to [value missing]. Calculate the mean of the data within a window using 10 sampling points. with standard deviation :

[0038]

[0039] When a certain data point A data point is identified as a random outlier and removed if the following conditions are met:

[0040] For data imputation with missing values, taking advantage of the strong temporal correlation of heating network operation data, an interpolation algorithm based on Lagrange multinomials is employed, according to the missing time... adjacent An interpolation polynomial is constructed using valid data points to calculate and fill in missing real-time temperature or flow rates. The Lagrange interpolation formula is:

[0041]

[0042] Indicates missing moments interpolated value, Indicates the first Valid sampling time The corresponding data values, Indicates the first A number of Lagrange basis functions. Through the above processing, the data acquisition module outputs a continuous, complete, and standardized dataset that can be used for subsequent intelligent monitoring and analysis.

[0043] The feature extraction module is used to extract features from the preprocessed medium state data, environmental and structural data, and equipment operation data to obtain medium thermal characteristics, pipe wall resistance characteristics, and equipment operation status characteristics.

[0044] Based on the thermal characteristics of the medium, the feature extraction module locates upstream and downstream monitoring nodes according to the pipeline topology, calculates the temperature and pressure differences between upstream and downstream nodes, and divides these differences by the pipeline length between the nodes to obtain the temperature drop rate and pressure drop gradient per unit pipe length. The formula for calculating the temperature drop rate per unit pipe length is:

[0045] The formula for calculating the pressure drop gradient is:

[0046] in, This indicates the temperature drop rate per unit pipe length. This indicates the temperature of the medium at the upstream monitoring node. This indicates the temperature of the medium at the downstream monitoring node. This represents the pressure gradient. This indicates the medium pressure at the upstream monitoring node. Indicates the medium pressure at the downstream monitoring node. This indicates the length of the pipe section between upstream and downstream monitoring nodes.

[0047] To address the pipe wall resistance characteristics, the feature extraction module calculates the state score of soil moisture content and the integrity index of the anti-corrosion and insulation layer in the preprocessed environmental and structural data. It then combines this with the medium flow velocity data and uses Darcy's formula for hydraulic inversion calculations to obtain the actual friction coefficient and the thermal resistance attenuation rate of the insulation layer. The actual friction coefficient can be obtained through inversion using the Darcy-Weisbach relation.

[0048] in, This represents the actual friction coefficient along the friction path. Indicates the inner diameter of the pipe. Indicates the pressure loss in the pipe section. Indicates the density of the medium. Indicates the length of the pipe section. This indicates the flow rate of the medium.

[0049] The thermal resistance attenuation rate of the insulation layer can be calculated using the following formula:

[0050] in, Indicates the thermal resistance attenuation rate of the insulation layer. Indicates the thermal resistance of the insulation layer under design or healthy conditions. This indicates the actual thermal resistance of the insulation layer, determined under the current operating conditions based on a comprehensive assessment of factors such as the pipe wall outer surface temperature, soil temperature, soil moisture content, and the integrity of the anti-corrosion and insulation layer.

[0051] Based on the characteristics of equipment operation status, the feature extraction module calculates the efficiency ratio of pump operating frequency and power consumption data in the preprocessed equipment operation data, and performs linkage matching calculations on valve opening and compensator displacement data to obtain the pump operating efficiency deviation value and valve compensator action synchronization rate. The pump operating efficiency deviation value can be calculated according to the following formula:

[0052] in, This indicates the deviation value of the water pump's operating efficiency. This indicates the theoretical operating efficiency at the current frequency and flow rate, obtained from the pump performance curve. This indicates the actual operating efficiency calculated based on the measured power consumption.

[0053] Actual operating efficiency It can be determined using the following formula:

[0054] in, Represents gravitational acceleration. Indicates the flow rate of the medium. Indicates the pump head. This indicates the actual measured power consumption of the water pump.

[0055] The synchronization rate of valve compensator action is obtained by comparing the consistency of the valve opening change curve with the displacement change curve of adjacent compensators in the time dimension, and can be represented by the waveform correlation coefficient:

[0056] in, Indicates the synchronization rate of valve compensator operation. Indicates the first Valve opening at each sampling time, This represents the average valve opening. Indicates the first The compensator displacement at each sampling time. This represents the average displacement of the compensator. The closer it is to 1, the more synchronized the valve opening change is with the compensator displacement change; The lower the value, the more abnormal the device's linkage status.

[0057] The thermal insulation efficiency analysis module is used to analyze and calculate the thermal insulation efficiency of the pipeline network based on the thermal characteristics of the medium, and obtain the thermal insulation efficiency evaluation index.

[0058] Specifically, the thermal insulation efficiency analysis module will analyze the temperature drop rate per unit pipe length in the thermal characteristics of the medium. Compared with the standard temperature drop benchmark value under pipeline design conditions The deviation was calculated to obtain the temperature drop anomaly ratio. :

[0059] in, Indicates the ratio of abnormal temperature drops. This indicates the current temperature drop rate per unit pipe length. This represents the standard temperature drop reference value under the design conditions of the pipeline network.

[0060] Meanwhile, the thermal insulation efficiency analysis module calculates the actual heat loss power of the current pipe section based on the pressure drop gradient in the thermodynamic characteristics of the medium, the instantaneous flow rate of the medium inside the pipe network, and the physical parameters of the pipe section. The actual heat loss power can be calculated according to the following formula:

[0061] in, This indicates the actual heat loss power of the current pipe section. Indicates the specific heat capacity of the medium. Indicates the instantaneous flow rate of the medium. This indicates the temperature of the medium at the upstream monitoring node. This indicates the temperature of the medium at the downstream monitoring node.

[0062] Actual heat loss power With theoretical heat loss power The difference ratio is calculated to obtain the heat loss deviation. :

[0063] in, Indicates the degree of heat loss deviation. This represents the standard heat loss power under theoretical design conditions.

[0064] Subsequently, the thermal insulation efficiency analysis module presets the temperature drop anomaly ratio based on the different emphasis requirements of long-distance heating pipeline networks for temperature control accuracy and energy consumption control. Deviation from heat loss The weighting coefficients are used to calculate the thermal insulation performance index using a weighted fusion method. :

[0065] in, Indicates the thermal insulation performance evaluation index. The weighting coefficient represents the ratio of abnormal temperature drops. The weighting coefficient represents the deviation of heat loss, and Thermal insulation performance evaluation index Used to characterize the degree of degradation of the actual thermal insulation performance of the pipeline network relative to the design baseline. The larger the value, the more significant the degradation in the insulation performance of the pipeline network.

[0066] The drag reduction status analysis module is used to analyze and calculate the drag reduction status of the pipeline network based on the characteristics of pipe wall resistance and equipment operating status, and obtain the drag reduction status evaluation index.

[0067] Specifically, the drag reduction state analysis module will analyze the actual friction coefficient along the pipe wall resistance characteristics. Theoretical friction coefficient under standard operating conditions of pipeline network The ratio calculation yields the pipe wall roughness surge coefficient. :

[0068] in, This represents the coefficient of increase in pipe wall roughness. This represents the actual friction coefficient along the friction path. This represents the theoretical friction coefficient under standard operating conditions of the pipeline network. The larger the value, the more pronounced the scaling on the pipe wall, the increased roughness of the inner wall, or the increased flow resistance.

[0069] Subsequently, the drag reduction state analysis module will analyze the pump operating efficiency deviation value in the equipment operating status characteristics. Synchronization rate with valve compensator operation The equipment operating resistance loss rate is obtained by performing negative normalization mapping calculation. :

[0070] in, Indicates the equipment operating resistance loss rate. This represents the normalized result of the pump operating efficiency deviation. This indicates the normalized result of the valve compensator's synchronization rate. and These represent the weighting coefficients corresponding to the pump efficiency deviation and the valve compensator synchronization rate, respectively. The greater the deviation in pump operating efficiency, or the lower the synchronization rate of valve compensator operation, the higher the equipment operating resistance loss rate. The larger.

[0071] Subsequently, the drag reduction state analysis module, based on the preset weights of the impact of pipe wall friction and equipment failure on fluid transport in long-distance pipeline networks, uses a linear weighted summation algorithm to calculate the pipe wall roughness surge coefficient. With equipment operating resistance loss rate By combining the results, we obtain the drag reduction state evaluation index. :

[0072] in, This represents the drag reduction state assessment index. The weighting coefficient represents the coefficient of increase in pipe wall roughness. The weighting coefficient represents the equipment's operating resistance loss rate, and Drag Reduction State Assessment Index Used to characterize the degree of obstruction in the overall fluid transport process of a pipeline network. The larger the value, the higher the resistance to fluid transport in the pipeline network.

[0073] The comprehensive diagnostic module is used to evaluate the thermal insulation performance index. With drag reduction state assessment index Correlation analysis is performed to construct the feature mapping relationship between the two, and the correlation deviation under actual operating conditions is calculated. Based on the correlation deviation, the degree of coupling imbalance between pipeline insulation and drag reduction functions is assessed, and the comprehensive diagnostic results of pipeline insulation and drag reduction are output.

[0074] Specifically, the comprehensive diagnostic module acquires historical data on the temperature drop per unit pipe length, actual heat loss power, actual friction coefficient, and equipment operating resistance loss rate of the pipeline network under healthy design conditions, and calculates the corresponding thermal insulation performance evaluation index under historical healthy conditions. With drag reduction state assessment index And construct a thermal insulation performance evaluation index The horizontal axis represents the index evaluated under reduced drag conditions. The standard co-reference curve is used as the vertical axis.

[0075] The standard synergistic baseline curve can be represented as:

[0076] Where, f( This indicates the ideal linkage between the thermal insulation performance evaluation index and the drag reduction state evaluation index under healthy design conditions.

[0077] During actual operation, the comprehensive diagnostic module extracts the thermal insulation performance evaluation index calculated in real time. With drag reduction state assessment index Use this as the actual operating coordinate point:

[0078] Subsequently, the comprehensive diagnostic module calculates the actual operating coordinates. To standard coherent baseline curve Shortest Euclidean distance :

[0079] in, and This represents the coordinates of any reference point on the standard collaborative reference curve.

[0080] The shortest Euclidean distance Normalization is performed to obtain the correlation deviation. :

[0081] in, Indicates the degree of correlation deviation. This indicates the preset maximum allowable deviation or the maximum reference deviation in historical samples. (Association Deviation) It is used to characterize the degree of deviation of the current operating state from the ideal coordinated state of thermal insulation and drag reduction.

[0082] The comprehensive diagnostic module will correlate deviation. Compared with the preset imbalance alarm threshold Compare: when When this occurs, it is determined that the degree of coupling imbalance in the pipeline network exceeds the limit; when At that time, it was determined that the coupling imbalance in the pipeline network did not exceed the limit.

[0083] when At that time, the comprehensive diagnostic module further analyzes the actual operating coordinates. The deviation quadrant and direction relative to the standard reference curve. (If the thermal insulation performance evaluation index...) Abnormally high drag reduction state assessment index If there is no synchronous change, it is determined that there is abnormal heat loss due to moisture, damage, aging, or thermal resistance reduction of the insulation layer; if the drag reduction status assessment index Abnormally high thermal insulation performance evaluation index If the change is small, it indicates abnormal hydraulic resistance caused by pipe wall scaling, increased inner wall roughness, decreased pump efficiency, abnormal valve operation, or abnormal compensator linkage; if the insulation performance evaluation index With drag reduction state assessment index If the temperature rises abnormally at the same time, it is determined that the pipeline network is in a state of complex coupling imbalance due to the superposition of heat loss and hydraulic resistance.

[0084] The comprehensive diagnostic results generated by the comprehensive diagnostic module include correlation deviation. The specific coordinate direction of the deviation from the standard collaborative baseline curve, the anomaly type, and the pipeline anomaly source location information obtained based on the deviation direction inversion. When At that time, the comprehensive diagnostic module indicated that the coupling imbalance of the pipeline network did not exceed the limit, and continued to monitor the coupling imbalance of the pipeline network.

[0085] The intelligent control module is used to generate and issue corresponding dynamic thermal and hydraulic control commands based on the comprehensive diagnostic results of thermal insulation and drag reduction, thereby coordinating and optimizing the thermal insulation and drag reduction of the pipeline network.

[0086] Specifically, the intelligent control module extracts the location information of abnormal sources and the correlation deviation from the comprehensive diagnostic results of thermal insulation and drag reduction. When the anomaly source location information indicates that the current anomaly is a pipe wall resistance anomaly, the intelligent control module determines the anomaly based on the correlation deviation. Compared with the current actual friction coefficient Calculate the pressure loss that needs to be reduced. The pressure loss that needs to be compensated can be calculated using the following formula:

[0087] in, This indicates the pressure loss value that needs to be compensated for or reduced. This represents the hydraulic control correction coefficient. This represents the actual friction coefficient along the friction path. This represents the theoretical coefficient of friction. Indicates the length of the pipe section. Indicates the inner diameter of the pipe. Indicates the density of the medium. This indicates the flow rate of the medium.

[0088] The intelligent control module calculates a pump frequency correction value that can cover the pressure loss increment based on the pump characteristic curve, and generates valve opening adjustment parameters in conjunction with the valve operating status, forming a hydraulic dynamic control command to increase the pump frequency and valve opening. The pump frequency correction value and valve opening correction value can be expressed as:

[0089]

[0090] in, This indicates the pump frequency correction value. This indicates the valve opening correction value. This represents the pump frequency adjustment coefficient. This indicates the valve opening adjustment coefficient.

[0091] When the anomaly source location information indicates that the current anomaly is a heat loss anomaly, the intelligent control module determines the anomaly based on the correlation deviation. Compared with the current unit pipe length temperature drop rate Calculate the required calorie intake. The required calorie intake can be expressed as:

[0092] in, This indicates the amount of calories that need to be replenished. This represents the thermal regulation correction coefficient. Indicates the density of the medium. Indicates the specific heat capacity of the medium. Indicates the instantaneous flow rate of the medium. This indicates the current temperature drop rate per unit pipe length. This represents the standard temperature drop reference value. Indicates the length of the pipe section.

[0093] The intelligent control module converts the required heat value into an increase in the medium temperature setpoint and a proportional increase in the circulation flow rate, generating dynamic thermodynamic control commands to increase the medium temperature setpoint and the medium flow rate. The increase in the medium temperature setpoint can be expressed as:

[0094] The percentage increase in circulating flow can be expressed as:

[0095] in, This indicates the increase in the medium temperature setpoint. This indicates the percentage increase in circulating flow. This represents the flow regulation coefficient.

[0096] After generating hydraulic and thermal dynamic control commands, the intelligent control module synchronizes the execution time based on the response time constants of the network's hydraulic and thermal operating conditions. If the hydraulic response time constant is... The thermodynamic response time constant is The execution delay difference can then be expressed as:

[0097] The intelligent control module determines the execution delay difference. The order and execution time of hydraulic dynamic control commands and thermal dynamic control commands are set so that the hydraulic conditions are established first or stabilized synchronously with the thermal conditions, and then the hydraulic dynamic control commands and thermal dynamic control commands are issued in combination, thereby achieving coordinated optimization of insulation and drag reduction in long-distance heating pipelines.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A long-distance heating pipeline insulation, drag reduction, and intelligent monitoring system, characterized in that, include: The data acquisition module is used to collect medium status data, environmental and structural data, and equipment operation data of the heating pipeline network, and to preprocess the collected medium status data, environmental and structural data, and equipment operation data. The feature extraction module is used to extract features from the preprocessed medium state data, environmental and structural data, and equipment operation data to obtain medium thermal characteristics, pipe wall resistance characteristics, and equipment operation status characteristics. The thermal insulation efficiency analysis module is used to analyze the thermal insulation efficiency of the pipeline network based on the thermal characteristics of the medium, and obtain the thermal insulation efficiency evaluation index. The drag reduction status analysis module is used to analyze the drag reduction status of the pipeline network based on the characteristics of pipe wall resistance and equipment operating status, and obtain the drag reduction status evaluation index. The comprehensive diagnostic module is used to perform correlation analysis between the thermal insulation performance evaluation index and the drag reduction status evaluation index, construct the feature mapping relationship between the thermal insulation performance evaluation index and the drag reduction status evaluation index, calculate the correlation deviation under actual operating conditions, evaluate the degree of coupling imbalance between the thermal insulation and drag reduction functions of the pipeline network based on the correlation deviation, and output the comprehensive diagnostic results of thermal insulation and drag reduction of the pipeline network. The intelligent control module is used to generate and issue corresponding dynamic thermal and hydraulic control commands based on the comprehensive diagnostic results of thermal insulation and drag reduction, and to coordinately optimize the thermal insulation and drag reduction of the pipeline network.

2. The long-distance heating pipeline insulation, drag reduction, and intelligent monitoring system according to claim 1, characterized in that, The data acquisition module collects data through a group of multi-source sensing devices deployed at key nodes of the long-distance heating pipeline network. The medium status data includes the real-time temperature, real-time pressure, instantaneous flow rate, and medium velocity of the medium inside the pipeline network; Environmental and structural data include the outer surface temperature of the pipe wall, the temperature of the soil around the pipe, the soil moisture content, the groundwater level, and the structural integrity indicators of the pipe's anti-corrosion and insulation layer. Equipment operation data includes the operating frequency and power consumption of water pumps in the pipeline network, the on / off status and opening degree of valves, and the displacement deformation of compensators.

3. The long-distance heating pipeline insulation, drag reduction, and intelligent monitoring system according to claim 2, characterized in that, The specific methods for preprocessing the acquired media status data, environmental and structural data, and equipment operation data by the data acquisition module are as follows: The raw sensing signals acquired by the multi-source sensing device group at heterogeneous sampling frequencies are time-aligned. Outlier removal is performed on the time-aligned raw sensing signal; The original sensing signal after outlier removal is imputed to create a preprocessed dataset with a unified spatiotemporal reference.

4. The long-distance heating pipeline insulation, drag reduction, and intelligent monitoring system according to claim 3, characterized in that, The feature extraction module calculates the friction difference along the pipe from the real-time temperature, real-time pressure, and instantaneous flow rate in the preprocessed medium state data to obtain the temperature drop rate and pressure drop gradient per unit pipe length, which are used as the thermodynamic characteristics of the medium.

5. The long-distance heating pipeline insulation, drag reduction, and intelligent monitoring system according to claim 3, characterized in that, The feature extraction module calculates the state score of the soil moisture content and the integrity index of the anti-corrosion and thermal insulation layer in the preprocessed environmental and structural data, and performs hydraulic inversion calculations in combination with the medium flow velocity data to obtain the actual friction coefficient and thermal resistance attenuation rate of the insulation layer, which are used as pipe wall resistance features.

6. The long-distance heating pipeline insulation, drag reduction, and intelligent monitoring system according to claim 3, characterized in that, The feature extraction module calculates the efficiency ratio of pump operating frequency and power consumption data in the preprocessed equipment operation data, and performs linkage matching calculation on valve opening and compensator displacement data to obtain the pump operating efficiency deviation value and valve compensator action synchronization rate as equipment operating status features.

7. The long-distance heating pipeline insulation, drag reduction, and intelligent monitoring system according to claim 4, characterized in that, The thermal insulation efficiency analysis module calculates the deviation between the temperature drop rate per unit pipe length in the thermal characteristics of the medium and the standard temperature drop benchmark value under the design conditions of the pipeline network, and obtains the abnormal temperature drop ratio. Based on the pressure drop gradient in the thermodynamic characteristics of the medium and the instantaneous flow rate of the medium inside the pipe network, the actual heat loss power of the current pipe section is calculated in combination with the physical parameters of the pipe section. The heat loss deviation is obtained by calculating the ratio of the difference between the actual heat loss power and the theoretical heat loss power. The thermal insulation performance evaluation index is obtained by weighted fusion calculation of the temperature drop anomaly ratio and heat loss deviation.

8. The long-distance heating pipeline insulation, drag reduction, and intelligent monitoring system according to claim 1, characterized in that, The drag reduction state analysis module calculates the ratio of the actual friction coefficient along the pipe wall resistance characteristics to the theoretical friction coefficient under the standard operating conditions of the pipeline network, and obtains the pipe wall roughness surge coefficient. The negative normalization mapping calculation of the pump operating efficiency deviation value and valve compensator action synchronization rate in the equipment operating status characteristics yields the equipment operating resistance loss rate. The drag reduction status evaluation index is obtained by weighted and fused calculation of the pipe wall roughness surge coefficient and the equipment operating resistance loss rate.

9. A long-distance heating pipeline insulation, drag reduction, and intelligent monitoring system according to claim 1, characterized in that, The comprehensive diagnostic module acquires historical data on the unit pipe length temperature drop rate, actual heat loss power, actual friction coefficient, and equipment operating resistance loss rate of the pipeline network under healthy design conditions, and constructs a standard collaborative benchmark curve with the thermal insulation performance evaluation index as the horizontal axis and the drag reduction status evaluation index as the vertical axis. Extract the thermal insulation performance evaluation index and drag reduction performance evaluation index under the current actual operating conditions, calculate the shortest Euclidean distance from the coordinate point corresponding to the current actual operating conditions to the standard synergistic reference curve, and normalize the shortest Euclidean distance to obtain the correlation deviation. When the correlation deviation is greater than or equal to the preset imbalance alarm threshold, a comprehensive diagnostic result for thermal insulation and drag reduction is generated, which includes the coordinate direction of the deviation from the standard collaborative benchmark curve and the location information of the abnormal source. The intelligent control module generates hydraulic or thermal dynamic control commands based on the anomaly source location information and associated deviation, and then merges and issues the hydraulic and thermal dynamic control commands after synchronizing their execution times.

10. A method for heat preservation, drag reduction, and intelligent monitoring of long-distance heating pipeline networks, characterized in that, Includes the following steps: Real-time acquisition of media status data, environmental and structural data, and equipment operation data of the heating network; and preprocessing of the acquired media status data, environmental and structural data, and equipment operation data. Feature extraction is performed on the preprocessed medium state data, environmental and structural data, and equipment operation data to obtain the medium thermal characteristics, pipe wall resistance characteristics, and equipment operation state characteristics. Based on the thermal characteristics of the medium, the thermal insulation performance of the pipeline network is analyzed to obtain the thermal insulation performance evaluation index. Based on the characteristics of pipe wall resistance and equipment operating status, the drag reduction status of the pipeline network is analyzed to obtain the drag reduction status evaluation index. The thermal insulation performance evaluation index and the drag reduction status evaluation index are correlated and analyzed to construct the characteristic mapping relationship between the two indices. The correlation deviation under actual operating conditions is calculated, and the degree of coupling imbalance between the thermal insulation and drag reduction functions of the pipeline network is evaluated based on the correlation deviation. The comprehensive diagnostic results of thermal insulation and drag reduction of the pipeline network are then output. Based on the comprehensive diagnostic results of thermal insulation and drag reduction, corresponding dynamic control commands for thermal and hydraulic systems are generated and issued to optimize the thermal insulation and drag reduction of the pipeline network in a coordinated manner.

Images