A method and system for identifying and correcting characteristic parameters of a hydraulic online simulation model

Abstract

The application provides a feature parameter identification correction method and system of a hydraulic online simulation model, belongs to the technical field of data processing, and specifically comprises the following steps: determining joint scheduling processing data of different state change pipe networks by using historical scheduling data of a heat supply pipe network; determining scheduling influence conditions of the heat supply pipe network between heat supply stations of different state change pipe networks in different joint scheduling processing times; determining correlation coefficients of different state change pipe networks based on the scheduling influence conditions; determining correlation conditions of a change pipe network group formed by different state change pipe networks based on the correlation coefficients of different state change pipe networks; and determining a feature parameter identification correction method of a hydraulic simulation model based on the correlation conditions, so that the reliability of load scheduling processing is improved.

Description

Technical Field

[0001] This invention belongs to the field of parameter identification technology in the operation status analysis and model optimization of heating pipe networks, specifically involving a method and system for identifying and correcting characteristic parameters of a hydraulic online simulation model. Background Technology

[0002] In the operation and management of heating networks, hydraulic simulation models are the core tool for load scheduling and state identification. Existing technologies typically assess the operating status of the heating system by constructing hydraulic simulation models and combining them with network monitoring data. For example, in invention patent application CN202411639677.X, "Hydraulic Optimization Method and Device for Heating Networks, and Medium," model parameter optimization is achieved by matching historical data with real-time monitoring data; CN202411260710.8, "Initial Parameter Generation Method Based on NUMAP Steady-State Calculation," utilizes steady-state calculation to generate initial parameters to improve model accuracy.

[0003] As heating networks operate for longer periods, factors such as scaling on pipe walls and valve aging can cause dynamic changes in parameters such as network resistance coefficients and flow distribution. Current technologies often rely on periodic manual calibration or fixed threshold triggering for parameter updates in hydraulic simulation models, failing to respond in real-time to subtle changes in network conditions, leading to a decline in model prediction accuracy over time.

[0004] Existing solutions lack systematic verification of the distribution density of monitoring devices and the validity of the data. For example, when monitoring devices are sparsely deployed or malfunction, abnormal data can directly affect the identification results of model parameters. However, existing technologies have not established a bias filtering mechanism based on the distribution of monitoring devices, resulting in errors in the parameter correction results.

[0005] The state changes of different areas in the heating network are often coupled (e.g., the flow adjustment in a certain area will affect the pressure distribution of adjacent networks). However, existing technologies often divide the network into independent units for parameter correction, without considering the joint scheduling impact between network states, which makes the model unable to accurately reflect the real operating state of complex networks.

[0006] To address the aforementioned issues, the key to improving the reliability of hydraulic simulation models lies in developing a parameter correction method based on real-time monitoring data and historical scheduling data that can dynamically identify changes in pipeline network status, filter monitoring deviations, and quantify the impact of pipeline network correlations. Summary of the Invention

[0007] In view of this, the purpose of this invention is to overcome the shortcomings of existing hydraulic simulation models, such as untimely parameter correction and low accuracy due to changes in pipeline network status, monitoring deviations, and lack of correlation analysis. This invention provides a method and system for identifying and correcting characteristic parameters of an online hydraulic simulation model. The aim is to accurately identify pipelines with changing status and quantify the correlation coefficients between pipelines by dynamically analyzing heating pipeline network operation data and monitoring device reliability. Combined with monitoring device distribution density verification and an adaptive correction decision mechanism, this achieves accurate identification and dynamic correction of characteristic parameters of the online hydraulic simulation model. This improves the model's adaptability to dynamic changes in the pipeline network, ensures data reliability, quantifies correlation effects, and optimizes correction decisions, thereby enhancing the intelligence and reliability of heating pipeline network load scheduling.

[0008] To achieve the above objectives, the first aspect of the present invention provides a method for identifying and correcting characteristic parameters of an online hydraulic simulation model, comprising:

[0009] Based on the changes in monitoring data and original data of different heating networks during the scheduling and operation process, the status change network in the heating network is determined;

[0010] Based on the distribution data of monitoring devices in different state-changing pipeline networks, determine the type and location of monitoring devices in different state-changing pipeline networks. If, based on the type and location of monitoring devices in different state-changing pipeline networks, it is determined that there are no monitoring deviation pipeline networks in the state-changing pipeline network, proceed to the next step.

[0011] Based on historical scheduling data of the heating network, joint scheduling processing data of different state-changing networks are determined, and the scheduling impact of heating networks between heat stations of different state-changing networks in different joint scheduling processing times is determined. Based on the scheduling impact, the correlation coefficient of different state-changing networks is determined.

[0012] Based on the correlation coefficients of different state-changing pipe networks, the correlation of changing pipe network groups composed of different state-changing pipe networks is determined, and the characteristic parameter identification and correction method of the hydraulic simulation model is determined based on the correlation.

[0013] Furthermore, the original data is the operating data of the heating network corresponding to the update of the characteristic parameters of the hydraulic simulation model.

[0014] Furthermore, the method for determining the state-changing pipeline network in the heating pipeline network is as follows:

[0015] Based on the changes in monitoring data and original data during the scheduling and operation of the heating network, the deviation between the identification results of the hydraulic simulation model and the original data under different heating flow rates is determined.

[0016] Based on the aforementioned deviation, the variation in the heating pipeline under different heating flow rates is determined;

[0017] Whether the heating network is a state-changing network is determined by the changes in the heating pipeline under different heating flow rates.

[0018] Furthermore, the variable heating pipeline refers to the heating pipeline in a heating network where the deviation between the identification result of the hydraulic simulation model and the original data under the heating flow rate is greater than the preset data deviation.

[0019] Furthermore, when there is no state-changing pipeline in the heating network, there is no need to perform characteristic parameter identification and correction processing of the hydraulic simulation model.

[0020] Furthermore, the variable pipeline group is constructed by freely combining different variable pipelines in different states.

[0021] Furthermore, the method for determining the characteristic parameter identification and correction method of the hydraulic simulation model is as follows:

[0022] Based on the correlation of different state pipe network groups, determine the average value of the correlation coefficient between state-changing pipe networks in different changing pipe network groups;

[0023] Based on the average value of the correlation coefficients, the variable pipeline network groups whose average value of the correlation coefficients is greater than the preset correlation coefficient threshold are identified as the associated pipeline network groups.

[0024] Based on the number of associated pipe network groups, a method for identifying and correcting the characteristic parameters of the hydraulic simulation model is determined.

[0025] Furthermore, based on the number of associated pipe network groups, a method for identifying and correcting the characteristic parameters of the hydraulic simulation model is determined, specifically including:

[0026] When the number of associated pipe network groups exceeds a preset threshold for the number of associated pipe network groups, it is determined that the characteristic parameters of the hydraulic simulation model need to be identified and corrected.

[0027] When the number of associated pipe network groups is not greater than the preset threshold for the number of associated pipe network groups, it is determined that there is no need to perform the identification and correction processing of the feature parameters of the hydraulic simulation model.

[0028] A second aspect of the present invention provides a feature parameter identification and correction system for an online hydraulic simulation model. This system is used to execute the aforementioned feature parameter identification and correction method for an online hydraulic simulation model, comprising:

[0029] The pipeline division module is used to divide the heating pipeline network into different heating pipeline networks;

[0030] The status identification module is used to determine the status changes of the pipeline network based on the changes in the monitoring data and the original data.

[0031] The monitoring and verification module is used to determine whether there are monitoring deviations in the pipeline network with changing status based on the distribution data of the monitoring devices.

[0032] The correlation analysis module is used to calculate the number of times the joint scheduling of pipelines in different states changes based on historical scheduling data, determine the correlation coefficient based on the proportion of the number of times the influence occurs, and construct a group of changing pipelines based on the correlation coefficient.

[0033] The parameter correction module is used to determine the characteristic parameter identification and correction method of the hydraulic simulation model based on the correlation of the changing pipe network group.

[0034] The present invention, by adopting the above technical solution, has at least the following beneficial effects:

[0035] Based on the type and location of monitoring devices for different state-changing pipeline networks, it is determined whether there are monitoring deviation pipeline networks in the state-changing pipeline networks. This enables the determination of the monitoring reliability of state-changing pipeline networks from the setting data of the monitoring devices. It also lays the foundation for the identification and correction method of characteristic parameters of differentiated hydraulic simulation models based on the differences in the monitoring reliability of state-changing pipeline networks, and ensures the reliability of identifying the operating status of heating pipeline networks during operation scheduling and processing.

[0036] Based on the correlation of different state-changing pipe network groups, a characteristic parameter identification and correction method for the hydraulic simulation model is determined. This method fully considers the correlation between different state-changing pipe networks due to the overlap of heating pipe networks, and further considers the mutual influence between different state-changing pipe networks in the load scheduling process due to differences in their correlation. Thus, the characteristic parameter identification and correction method for the hydraulic simulation model is determined from the perspective of load scheduling requirements. Attached Figure Description

[0037] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0038] Figure 1 This is a flowchart of a method for identifying and correcting characteristic parameters of an online hydraulic simulation model.

[0039] Figure 2 This is a flowchart illustrating the method for determining the state-changing network within a heating network.

[0040] Figure 3 This is a flowchart illustrating a method for determining whether a monitoring deviation exists in a pipeline network undergoing status changes.

[0041] Figure 4 This is a flowchart illustrating the method for determining the characteristic parameter identification and correction method of a hydraulic simulation model. Detailed Implementation

[0042] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many ways and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0043] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0044] In this application, based on the monitoring reliability of the monitoring devices in the state-changing pipeline network and the correlation between different state-changing pipeline networks, a differentiated characteristic parameter identification and correction method for the hydraulic online simulation model is determined, thereby improving the timeliness of updating the characteristic parameters of the hydraulic online simulation model of the heating pipeline network.

[0045] Example 1

[0046] To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, a method for identifying and correcting characteristic parameters of an online hydraulic simulation model is provided, specifically including:

[0047] S1 determines the heating network with changing status by comparing the monitoring data with the original data during the scheduling and operation of different heating networks.

[0048] Furthermore, the original data is the operating data of the heating network corresponding to the update of the characteristic parameters of the hydraulic simulation model.

[0049] It should be noted that, as Figure 2 As shown, the method for determining the state-changing pipeline network in the heating network is as follows:

[0050] Based on the changes in monitoring data and original data during the scheduling and operation of the heating network, the deviation between the identification results of the hydraulic simulation model and the original data under different heating flow rates is determined.

[0051] Based on the aforementioned deviation, the variation in the heating pipeline under different heating flow rates is determined;

[0052] Whether the heating network is a state-changing network is determined by the changes in the heating pipeline under different heating flow rates.

[0053] Furthermore, the variable heating pipeline refers to the heating pipeline in a heating network where the deviation between the identification result of the hydraulic simulation model and the original data under the heating flow rate is greater than the preset data deviation.

[0054] It is understandable that when the number of variable heating pipes exceeds the preset threshold for the number of variable pipes, the heating network is determined to be a state-variable network.

[0055] It should be noted that when there is no state-changing pipeline in the heating network, there is no need to perform characteristic parameter identification and correction processing of the hydraulic simulation model.

[0056] In another possible embodiment, the method for determining the state-changing network in the heating network is as follows:

[0057] S11 uses the changes in monitoring data and original data of the heating network during the scheduling and operation process to determine the deviation between the identification results of the hydraulic simulation model of the heating network and the original data under different heating flow rates. Based on the deviation, the variable heating pipelines under different heating flow rates are determined. According to the number of variable heating pipelines under different heating flow rates, the data deviation coefficient under different heating flow rates is determined.

[0058] S12 determines the distribution data of different heating pipelines under different heating flow rates, and based on the distribution data of different heating pipelines under different heating flow rates, determines the identification anomaly coefficient of different heating pipelines.

[0059] S13 determines the network monitoring anomaly coefficient of the heating network based on the data deviation coefficient under different heating flow rates and the identification anomaly coefficient of different heating pipelines, and determines whether the heating network is a state-changing network based on the network monitoring anomaly coefficient.

[0060] It should be further explained that the input and output quantities of the aforementioned data deviation coefficient, anomaly identification coefficient, and pipeline monitoring anomaly coefficient are fixed and can be determined using mathematical models of the analytic hierarchy process, expert scoring, or neural network models.

[0061] Specifically, before proceeding to step S12, it is necessary to further determine whether there are variable heating pipelines, whether the number of variable heating pipelines under different heating flow rates meets the requirements, and whether the average value of the data deviation coefficient under different heating flow rates meets the requirements.

[0062] Specifically, when there are no variable heating pipelines, it can be directly determined that the heating network is not a state-changing network. However, if and only if there are variable heating pipelines, it is necessary to further determine whether the number of variable heating pipelines under different heating flow rates meets the requirements. When the number of variable heating pipelines is greater than the heating flow rate of the preset variable pipeline number threshold, the heating network is determined to be a state-changing network.

[0063] Even if the number of variable heating pipes under different heating flow rates meets the requirements, if the average value of the data deviation coefficient under different heating flow rates is greater than a certain threshold, the heating network can still be directly determined as a variable state network. Only when all the above conditions are met will the process proceed to step S12.

[0064] It should be further explained that before proceeding to step S13, it is necessary to further determine whether there are heating pipes that are identified as having a number of variable heating pipes that do not meet the requirements under different heating flow rates, and whether the number of variable heating pipes with a large abnormality coefficient meets the requirements. The specific judgment on whether the requirements are met can be determined by using a threshold method.

[0065] It is understandable that when there are heating pipes identified as variable heating pipes under different heating flow rates that exceed a certain threshold, the heating network is directly identified as a variable state network. Even if there are no heating pipes identified as variable heating pipes under different heating flow rates that exceed a certain threshold, if there is an identification anomaly coefficient that is too large, that is, if the number of variable heating pipes within the preset range exceeds a certain threshold, it can still be identified as a variable state network.

[0066] Furthermore, only when there are no heating pipes identified as having insufficient quantity of variable heating pipes under different heating flow rates, and when the quantity of variable heating pipes with a large identification anomaly coefficient meets the requirements, will the process proceed to step S13.

[0067] S2 uses the distribution data of monitoring devices for different state-changing pipeline networks to determine the type and location of monitoring devices for different state-changing pipeline networks. Based on the type and location of monitoring devices for different state-changing pipeline networks, if it is determined that there is no monitoring deviation pipeline network in the state-changing pipeline network, proceed to the next step.

[0068] Specifically, such as Figure 3As shown, determining that there is no monitoring deviation pipeline network in the state-changing pipeline network specifically includes:

[0069] Based on the type and location of monitoring devices for different state-changing pipeline networks, the number of monitoring devices to be installed in different heating pipelines within the state-changing pipeline network is determined.

[0070] The density of monitoring devices in different heating pipelines is determined based on the ratio of the number of monitoring devices to the length of the heating pipeline.

[0071] The monitoring device is set to heat supply pipes with a density less than a preset density threshold as monitoring deviation pipes, and the number of monitoring deviation pipes is used to determine whether the state change pipe network belongs to the monitoring deviation pipe network.

[0072] Furthermore, when the number of monitored deviation pipelines is greater than a preset monitoring deviation number threshold, the state change pipeline network is determined to belong to the monitoring deviation pipeline network.

[0073] It should be noted that when the data domain of the pipeline network with changing status belongs to the monitoring deviation pipeline network, it is determined that the characteristic parameters of the hydraulic simulation model need to be identified and corrected.

[0074] S3 uses historical scheduling data of the heating network to determine joint scheduling processing data for different state-changing networks, determines the scheduling impact of heating networks between heat stations of different state-changing networks in different joint scheduling processing times, and determines the correlation coefficient of different state-changing networks based on the scheduling impact.

[0075] Specifically, the scheduling impact of the heating network includes heating networks where different heating networks affect each other's heating flow rate during scheduling.

[0076] Specifically, the method for determining the correlation coefficient of the state-changing pipeline network is as follows:

[0077] Based on the aforementioned scheduling impact, determine the number of scheduling processes that affect the heating flow rate between heat exchange stations in different state-changing pipeline networks during scheduling processes, and use these as the associated scheduling process counts.

[0078] The correlation coefficient between different state-changing pipelines is determined based on the proportion of the number of associated scheduling processes in the historical scheduling processes of the heating pipeline network.

[0079] S4 determines the correlation of the changing pipe network groups composed of different state-changing pipe networks based on the correlation coefficients of the different state-changing pipe networks, and determines the characteristic parameter identification and correction method of the hydraulic simulation model based on the correlation.

[0080] Furthermore, the variable pipeline group is constructed by freely combining different variable pipelines in different states.

[0081] Specifically, such as Figure 4 As shown, the method for determining the characteristic parameter identification and correction method of the hydraulic simulation model is as follows:

[0082] Based on the correlation of different state pipe network groups, determine the average value of the correlation coefficient between state-changing pipe networks in different changing pipe network groups;

[0083] Based on the average value of the correlation coefficients, the variable pipeline network groups whose average value of the correlation coefficients is greater than the preset correlation coefficient threshold are identified as the associated pipeline network groups.

[0084] Based on the number of associated pipe network groups, a method for identifying and correcting the characteristic parameters of the hydraulic simulation model is determined.

[0085] Furthermore, based on the number of associated pipe network groups, a method for identifying and correcting the characteristic parameters of the hydraulic simulation model is determined, specifically including:

[0086] When the number of associated pipe network groups exceeds a preset threshold for the number of associated pipe network groups, it is determined that the characteristic parameters of the hydraulic simulation model need to be identified and corrected.

[0087] When the number of associated pipe network groups is not greater than the preset threshold for the number of associated pipe network groups, it is determined that there is no need to perform the identification and correction processing of the feature parameters of the hydraulic simulation model.

[0088] In another possible embodiment, the method for determining the characteristic parameter identification and correction method of the hydraulic simulation model is as follows:

[0089] S41 determines the average value of the correlation coefficient between state-changing pipelines in different state-changing pipeline groups based on the correlation of different state-changing pipeline groups, and identifies changing pipeline groups whose average correlation coefficient is greater than a preset correlation coefficient threshold as associated pipeline groups based on the average value of the correlation coefficient.

[0090] S42 determines the variation correlation coefficient of different associated pipe network groups based on the correlation coefficient between different state-changing pipe networks in different associated pipe network groups, and in combination with the number of state-changing pipe networks in the associated pipe network groups.

[0091] S43 determines the scheduling influence coefficient of the state-changing pipeline network based on the change correlation coefficient of different associated pipeline network groups, and determines the characteristic parameter identification and correction method of the hydraulic simulation model based on the scheduling influence coefficient.

[0092] Optionally, before proceeding to step S42, it is also necessary to determine whether there are associated pipe network groups, whether the number of associated pipe network groups meets the requirements, and whether the number of state-changing pipe networks in different associated pipe network groups meets the requirements.

[0093] Specifically, when there are no associated pipe network groups, it is determined that no feature parameter identification and correction processing of the hydraulic simulation model is required. When there are associated pipe network groups, it is necessary to further determine whether the number of associated pipe network groups is too large. Specifically, when the number of associated pipe network groups is greater than a certain threshold, it indicates that during the scheduling process, the operating status of the state-changing pipe network cannot be determined, so feature parameter identification and correction processing of the hydraulic simulation model is required.

[0094] Even if the number of associated pipe network groups is not large, it is still necessary to further determine whether there are associated pipe network groups with a large number of pipe networks with changes in status. Specifically, for associated pipe network groups with a number of pipe networks with changes in status greater than a fixed threshold, the impact on the accuracy of the entire scheduling process is greater. Therefore, it is necessary to identify and correct the characteristic parameters of the hydraulic simulation model.

[0095] Step S42 is performed only if there is no associated network group with a number of network states changing greater than a fixed threshold.

[0096] Understandably, before proceeding to step S43, it is necessary to further determine whether there are any associated pipeline groups whose variable correlation coefficients do not meet the requirements and whether the number of associated pipeline groups with large variable correlation coefficients meets the requirements. Only when the above conditions are met will the process proceed to step S43. In addition, the variable correlation coefficient and scheduling influence coefficient can be constructed using the hierarchical analysis method or a trained neural network model.

[0097] Specifically, when there are associated pipe network groups whose variable correlation coefficients do not meet the requirements (i.e., associated pipe network groups whose variable correlation coefficients are greater than a certain threshold), it is directly determined that the characteristic parameters of the hydraulic simulation model need to be identified and corrected. When there are no associated pipe network groups whose variable correlation coefficients do not meet the requirements, it is necessary to further determine whether the number of associated pipe network groups with large variable correlation coefficients meets the requirements. Specifically, it can be determined whether the number of associated pipe network groups whose variable correlation coefficients are within the preset range of variable correlation coefficients is greater than a fixed threshold. When the number of associated pipe network groups with large variable correlation coefficients does not meet the requirements, it is directly determined that the characteristic parameters of the hydraulic simulation model need to be identified and corrected.

[0098] Example 2

[0099] This embodiment provides a feature parameter identification and correction system for an online hydraulic simulation model. This system is used to execute the aforementioned feature parameter identification and correction method for an online hydraulic simulation model, including:

[0100] The pipeline division module is used to divide the heating pipeline network into different heating pipeline networks. Based on the heating station, its corresponding heating pipeline network is divided into independent sub-pipeline units. The division results form heating pipeline network topology data for subsequent state change analysis.

[0101] The status identification module is used to determine the status change of the pipeline network based on the changes in monitoring data and original data; it acquires the monitoring data and original data (operational data when the characteristic parameters of the hydraulic simulation model are updated) of the heating pipeline network during scheduling and operation, and determines the deviation between the identification results of the hydraulic simulation model and the original data under different heating flow rates by analyzing the changes in the two data, thereby identifying the changing heating pipelines and the status change of the pipeline network.

[0102] The monitoring and verification module is used to determine whether there are monitoring deviation pipelines in the status change pipeline network based on the distribution data of monitoring devices; based on the distribution data (type and location) of monitoring devices in the status change pipeline network, it calculates the setting density of monitoring devices, marks heating pipelines with a density less than a preset threshold as monitoring deviation pipelines, and determines whether the status change pipeline network belongs to the monitoring deviation pipeline network based on the number of monitoring deviation pipelines.

[0103] The correlation analysis module is used to calculate the number of times joint scheduling affects pipelines in different states based on historical scheduling data. The correlation coefficient is determined by the proportion of these impacts, and a network of changing states is constructed using these correlation coefficients. Joint scheduling processing data for pipelines in different states is extracted from historical scheduling data of heating networks. The scheduling impact between heating stations in each state of changing pipeline network is analyzed, and the correlation coefficient is determined by the proportion of the number of associated scheduling processes to the total number of historical scheduling processes. Based on these correlation coefficients, network of changing states is freely combined to construct network of changing states groups, and the correlation within these groups is analyzed.

[0104] The parameter correction module is used to determine the characteristic parameter identification and correction method of the hydraulic simulation model based on the correlation of changing pipe network groups. It calculates the average correlation coefficient of the changing pipe network groups, identifies groups with an average value greater than a preset threshold as associated pipe network groups, and determines whether to trigger the identification and correction process of the hydraulic simulation model's characteristic parameters based on the number of associated pipe network groups (correction is performed if the number exceeds the preset threshold, otherwise no correction is performed).

[0105] The pipeline network division module outputs the sub-pipeline network division results to the status identification module; after the status identification module determines the pipeline network with changing status, it transmits the data to the monitoring and verification module; after the monitoring and verification module completes the reliability verification, the data that meets the conditions enters the correlation analysis module; the correlation analysis module generates the correlation coefficient and the correlation status of the changing pipeline network group, and transmits it to the parameter correction module; the parameter correction module performs characteristic parameter identification and correction based on the correlation status, and feeds it back to the hydraulic simulation model.

[0106] Example 3

[0107] On the other hand, this application provides a computer system that stores a computer program. When the computer program is executed in the computer, it causes the computer to execute the above-mentioned method for identifying and correcting feature parameters of an online hydraulic simulation model.

[0108] Example 4

[0109] On the other hand, this application provides a computer program product, characterized in that the computer program product stores instructions, which, when executed by a computer, cause the computer to implement the above-described method for identifying and correcting feature parameters of an online hydraulic simulation model.

[0110] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for identifying and correcting characteristic parameters of a hydraulic online simulation model, characterized in that, Comprise: With the change of the original data and the monitoring data of the heating pipe network in the scheduling operation process, determine the state change pipe network in the heating pipe network; With the distribution data of the monitoring device of the different state change pipe network, determine the type and setting position of the monitoring device of the different state change pipe network, and based on the type and setting position of the monitoring device of the different state change pipe network, determine that when there is no monitoring deviation pipe network in the state change pipe network, enter the next step; With the historical scheduling data of the heating pipe network, determine the joint scheduling processing data of the different state change pipe network, determine the scheduling influence situation of the heating pipe network between the heating stations of the different state change pipe network in the different joint scheduling processing times, and based on the scheduling influence situation, determine the correlation coefficient of the different state change pipe network; Based on the correlation coefficient of the different state change pipe network, determine the correlation situation of the change pipe network group composed of the different state change pipe network, and based on the correlation situation, determine the characteristic parameter identification correction method of the hydraulic simulation model; The original data is the operation data of the heating pipe network corresponding to the update of the characteristic parameters of the hydraulic simulation model; The method for determining the state change pipe network in the heating pipe network is: With the change of the original data and the monitoring data of the heating pipe network in the scheduling operation process, determine the deviation amount of the identification result of the hydraulic simulation model and the original data under different heating flow of the heating pipe network; Based on the deviation amount, determine the change heating pipe under different heating flow; According to the change heating pipe under different heating flow, determine whether the heating pipe network is a state change pipe network; The change heating pipe is a heating pipe in the heating pipe network whose deviation amount of the identification result of the hydraulic simulation model and the original data under the heating flow is greater than a preset data deviation amount; The method for determining the characteristic parameter identification correction method of the hydraulic simulation model is: With the correlation situation of the different state pipe network group, determine the average value of the correlation coefficient between the state change pipe networks of the different change pipe network group; Based on the average value of the correlation coefficient, the change pipe network group with an average value of the correlation coefficient greater than a preset correlation coefficient threshold is regarded as a correlation pipe network group; According to the number of the correlation pipe network group, determine the characteristic parameter identification correction method of the hydraulic simulation model; According to the number of the correlation pipe network group, determine the characteristic parameter identification correction method of the hydraulic simulation model, specifically including: When the number of the correlation pipe network group is greater than a preset correlation pipe network group number threshold, it is determined that the identification correction processing of the characteristic parameters of the hydraulic simulation model is needed; When the number of the correlation pipe network group is not greater than a preset correlation pipe network group number threshold, it is determined that the identification correction processing of the characteristic parameters of the hydraulic simulation model is not needed.

2. The method of claim 1, wherein the method is characterized by, When there is no state change pipe network in the heating pipe network, the characteristic parameter identification correction processing of the hydraulic simulation model is not needed.

3. The method of claim 1, wherein the method is characterized by, The change pipe network group is constructed by freely combining different state change pipe networks.

4. A system for identifying and correcting characteristic parameters of a hydraulic online simulation model, characterized in that, The system is used for executing the feature parameter identification correction method of the online hydraulic simulation model according to any one of claims 1 to 3, and comprises: a pipe network division module, configured to divide the heat supply pipe network into different heat supply pipe networks; a state identification module, configured to determine state change pipe networks based on change of the monitoring data and the original data; a monitoring verification module, configured to determine whether there is a monitoring deviation pipe network in the state change pipe network based on distribution data of the monitoring device; a correlation analysis module, configured to calculate joint scheduling influence times of different state change pipe networks based on historical scheduling data, determine a correlation coefficient based on influence time proportion, and construct a change pipe network group through the correlation coefficient; a parameter correction module, configured to determine the feature parameter identification correction method of the hydraulic simulation model based on correlation of the change pipe network group.

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