CN-120488146-B - Intelligent management system and method for steam pipe network based on digitization

CN120488146BCN 120488146 BCN120488146 BCN 120488146BCN-120488146-B

Abstract

The invention discloses a digital-based intelligent management system and method for a steam pipe network, which relate to the technical field of monitoring and management of the steam pipe network and comprise a steam pipe network data monitoring module, a data re-monitoring module, a scaling data acquisition and analysis module and an abnormal data transmission module, wherein the steam pipe network data monitoring module is used for carrying out data acquisition and monitoring on the steam pipe network, analyzing the abnormal condition of the pipe network data and acquiring the time information of the abnormal occurrence of the pipe network, the data re-monitoring module is used for analyzing the judgment accuracy when the pipe network data is acquired at the historical sampling frequency and judging the abnormal condition, the optimal frequency range is set, whether the pipe network data is re-monitored or not is selected, the current time of scaling abnormality of the pipe network is predicted through the scaling data acquisition and analysis module, the reason of the abnormal occurrence of the pipe network is analyzed and abnormal data transmission is carried out through the abnormal data transmission module, and the influence of untimely uploading of the modified data of the steam pipe network is reduced.

Inventors

CHEN ZHENXIANG
QIANG LIANG
LU SHUNJIE

Assignees

常州艾肯技术有限公司

Dates

Publication Date: 20260508
Application Date: 20250507

Claims (9)

1. A steam pipe network intelligent management method based on digitalization is characterized by comprising the following steps: s1, data acquisition and monitoring are carried out on a steam pipe network, abnormal conditions of the pipe network data are analyzed, and time information of occurrence of pipe network abnormality is obtained; s2, acquiring the current pipe network data sampling frequency, analyzing the judging accuracy rate when the pipe network data is acquired at the historical sampling frequency and the abnormal condition is judged, setting the optimal frequency range, and selecting whether to re-monitor the pipe network data according to the comparison result between the current sampling frequency and the range; s3, collecting and analyzing historical data of scaling phenomenon of the pipe network, and predicting time of scaling abnormality of the current pipe network; s4, analyzing the cause of pipe network abnormality and transmitting abnormal data by comparing the occurrence time and the predicted time of the pipe network abnormality; The S2 comprises the steps of obtaining the sampling frequency of the current pipe network parameter as g, collecting the sampling frequency set of the pipe network parameter which is set in the past as F= { F 1 ,F 2 ,...F m }, m represents the number of the sampling frequency items which are set in the past, obtaining the pipe network parameter which is set in the past and is randomly acquired by one sampling frequency F i , calculating the comprehensive resistance coefficient of the steam pipe network according to the pipe network parameter, analyzing the comprehensive resistance coefficient of the steam pipe network and judging the abnormal frequency of the steam pipe network as D, confirming the correct judging result as D after verification, obtaining the judging accuracy rate when the pipe network data are collected by the historical sampling frequency F i and the abnormal condition is judged as R i = D/D, obtaining the judging accuracy rate set when the pipe network data are collected by different historical sampling frequencies and the abnormal condition is judged as R= { R 1 ,R 2 ,...R m }, arranging the m sampling frequencies from large to small according to the corresponding judging accuracy rates and randomly grouping, obtaining a random grouping result, wherein the judging accuracy rate set corresponding to each group of sampling frequencies is p= { p 1 ,p 2 ,...p r }, the judging accuracy rate corresponding to the average value of the random grouping result, and the maximum reference grouping result is selected as the reference grouping result, Obtaining a maximum value F (1,max) in the sampling frequency of the first group and a minimum value F (1,min) in the sampling frequency of the first group in a grouping result with the maximum reference degree, and setting an optimal sampling frequency range as [ F (1,min) ,F (1,max) ]; The S3 comprises the steps of collecting the used time of a current steam pipe network as Y, obtaining the time length set of T= { T 1 ,T 2 ,...T w } needed by the thickness of the scale exceeding a thickness threshold value after the other random steam pipe network with the used time as Y previously carries out scale cleaning each time, w represents the times of carrying out scale cleaning on the corresponding steam pipe network, obtaining the last time of cleaning the scale of the current steam pipe network as the xth cleaning before the current time, wherein the time of the xth cleaning is U, if x is smaller than or equal to w, predicting the time of the thickness exceeding the thickness threshold value of the current steam pipe network as U+T x ' ,T x ' =T x after the xth cleaning, and if x is more than w and w+q=x, establishing a scaling abnormal time pre-judging model: Predicting that the time for the scaling thickness of the current steam pipe network to exceed the thickness threshold value after the xth cleaning is U+T x ' , In order to smooth the coefficient of the coefficient, , For the length of time that the current steam pipe network has the fouling thickness exceeding the thickness threshold after the w+q-1 cleaning, A duration index smooth value required by the scaling thickness exceeding a thickness threshold value after the w+q-1 cleaning of the current steam pipe network is represented; The S4 comprises the steps of predicting that the reason for abnormality of the current pipe network is that the pipe network is transformed if the current time is earlier than U+T x ' , and otherwise, predicting that the reason for abnormality of the current pipe network is that scale formation abnormality of the pipe network is generated.
2. The intelligent management method of the steam pipe network based on the digitization is characterized in that S1 comprises the steps of collecting pipe network parameters, obtaining comprehensive resistance coefficients of the steam pipe network according to pipe network parameters through calculation, obtaining a comprehensive resistance coefficient set of the steam pipe network according to pipe network parameters collected at the current sampling frequency, wherein the comprehensive resistance coefficient set is K= { K1, K2,..Kn } of the steam pipe network, collecting n times of data altogether, if coefficients in the set K are in rising trend, forming data points by the sampling time and the comprehensive resistance coefficients calculated at the corresponding time, performing straight line fitting on the data points to generate a comprehensive resistance coefficient change straight line, wherein the comprehensive resistance coefficient change straight line represents a straight line of the steam pipe network, the slope of the obtained straight line is B, setting a slope threshold value to be B, comparing B with B, and judging that the steam pipe network is not abnormal, otherwise, judging that the steam pipe network is abnormal, and obtaining the time for judging that the steam pipe network is abnormal.
3. The intelligent management method of the steam pipe network based on the digitization of claim 2 is characterized by judging whether g is in an optimal sampling frequency range, if so, predicting that the current steam pipe network is abnormal, if not, predicting that the current steam pipe network is abnormal, judging that the current steam pipe network is abnormal, selecting one data sampling frequency from the optimal sampling frequency range as an adjusted data sampling frequency, re-acquiring and monitoring pipe network parameters according to the adjusted data sampling frequency, calculating a comprehensive resistance coefficient of the steam pipe network according to the re-acquired data, analyzing whether the current steam pipe network is abnormal, and if still judging that the current steam pipe network is abnormal, updating the time for judging that the steam pipe network is abnormal.
4. The intelligent management method of the steam pipe network based on the digitization is characterized in that if the reason of the abnormality of the current pipe network is predicted to be the occurrence of the transformation of the pipe network, abnormal data and reason predicted data of the current pipe network are transmitted to a pipe network monitoring terminal and a measure prompt is made, if the current pipe network is predicted to be transformed, a steam unit is prompted to upload pipe network transformation information to a system, if the reason of the abnormality of the current pipe network is predicted to be the occurrence of the scaling abnormality of the pipe network, the abnormal data and reason predicted data of the current pipe network are transmitted to the pipe network monitoring terminal and a measure prompt is made, a patrol robot is prompted to perform scaling monitoring on the pipe network, and cleaning operation is performed after the scaling abnormality is monitored.
5. The intelligent management system based on the digital steam pipe network is applied to the intelligent management method based on the digital steam pipe network as claimed in claim 1, and is characterized by comprising a steam pipe network data monitoring module, a data re-monitoring module, a scaling data acquisition and analysis module and an abnormal data transmission module; The steam pipe network data monitoring module is used for carrying out data acquisition and monitoring on the steam pipe network, analyzing the abnormal condition of the pipe network data and obtaining the time information of occurrence of pipe network abnormality; Acquiring the current pipe network data sampling frequency through the data re-monitoring module, analyzing the judging accuracy rate when the pipe network data is acquired at the historical sampling frequency and the abnormal condition is judged, setting the optimal frequency range, and selecting whether to re-monitor the pipe network data according to the comparison result between the current sampling frequency and the range; the historical data of the scaling phenomenon of the pipe network is collected and analyzed through the scaling data collection and analysis module, and the time of scaling abnormality of the current pipe network is predicted; and analyzing the cause of the pipe network abnormality and carrying out abnormal data transmission by comparing the occurrence time and the predicted time of the pipe network abnormality through the abnormal data transmission module.
6. The intelligent management system of the steam pipe network based on the digitization of claim 5, wherein the steam pipe network data monitoring module comprises a pipe network parameter acquisition unit, a parameter variation analysis unit and an abnormal time acquisition unit; The pipe network parameter acquisition unit is used for acquiring the comprehensive resistance coefficient of the steam pipe network; The parameter change analysis unit is used for analyzing the change condition of the comprehensive resistance coefficient of the steam pipe network and judging whether the steam pipe network is abnormal or not according to the change condition; The abnormal time acquisition unit is used for acquiring abnormal time of the steam pipe network.
7. The intelligent management system based on the digital steam pipe network of claim 6, wherein the data re-monitoring module comprises a sampling frequency acquisition unit, a data monitoring misjudgment analysis unit and a data re-monitoring planning unit; the sampling frequency acquisition unit is used for acquiring the sampling frequency of the pipe network parameters required by the calculation of the current steam pipe network comprehensive resistance coefficient data when the steam pipe network is judged to be abnormal; The data monitoring misjudgment analysis unit is used for collecting pipe network parameters at different sampling frequencies in the past, calculating according to the pipe network parameters to obtain a steam pipe network comprehensive resistance coefficient, analyzing the steam pipe network comprehensive resistance coefficient, judging the number of times that a judgment result is correct after verification, collecting pipe network data at a historical sampling frequency according to the number of times information analysis and judging the judgment accuracy when in an abnormal condition, and comparing whether the current sampling frequency is in the optimal sampling frequency range according to the judgment accuracy; And the data re-monitoring planning unit is used for selecting one data sampling frequency from the optimal sampling frequency range as the adjusted data sampling frequency if the current steam pipe network abnormality judgment error is predicted, re-collecting and monitoring pipe network parameters with the adjusted data sampling frequency, calculating the comprehensive resistance coefficient of the steam pipe network according to the re-collected data, and analyzing whether the current steam pipe network is abnormal or not.
8. The intelligent management system of steam pipe network based on digitization of claim 7, wherein the fouling data acquisition and analysis module comprises a usage data acquisition unit, a fouling data acquisition unit, and a fouling time prediction unit; the usage data acquisition unit is used for acquiring the usage time data of the current steam pipe network; The system comprises a scale data acquisition unit, a scale control unit and a scale control unit, wherein the scale data acquisition unit is used for acquiring the times that the previous scale thickness of a steam pipe network exceeds a thickness threshold value, which is the same as the current service time of the steam pipe network, the thickness threshold value is set by the system, the scale position of the pipe network needs to be cleaned when the scale thickness of the steam pipe network is monitored to exceed the thickness threshold value, and the time that the scale thickness of the steam pipe network exceeds the thickness threshold value after each cleaning is acquired; the scaling time prediction unit is used for obtaining the time of the steam pipe network for cleaning the scaling part last time before the current time, obtaining the time of the last cleaning to the xth cleaning, and predicting the time of the scaling thickness of the current steam pipe network exceeding a thickness threshold value after the xth cleaning.
9. The intelligent management system of the steam pipe network based on the digitization of claim 8, wherein the abnormal data transmission module comprises a time comparison unit, a pipe network abnormal tracing unit and a suggestion measure transmission and prompting unit; the time comparison unit is used for comparing the current time with the time when the predicted scaling thickness of the current steam pipe network exceeds a thickness threshold value; The pipe network abnormality tracing unit is used for predicting the reason for abnormality of the current pipe network to be the improvement of the pipe network if the current time is earlier than the predicted time, otherwise, predicting the reason for abnormality of the current pipe network to be the scale formation abnormality of the pipe network; The recommended measure transmission and prompting unit is used for transmitting the abnormal data and the reason prejudging data of the current pipe network to the pipe network monitoring terminal and prompting measures, wherein if the reason for prejudging the abnormality of the current pipe network is that the pipe network is reformed, prompting to verify whether the current pipe network is reformed, if so, prompting to upload pipe network reforming information to the system by a steam unit, and if the reason for prejudging the abnormality of the current pipe network is that the pipe network is abnormal in scale formation, prompting to arrange the inspection robot to perform scale formation monitoring on the pipe network, and performing cleaning operation after the scale formation abnormality is monitored.

Description

Intelligent management system and method for steam pipe network based on digitization Technical Field The invention relates to the technical field of steam pipe network monitoring management, in particular to a digital-based intelligent management system and method for a steam pipe network. Background The intelligent management platform of the steam pipe network is a platform for intelligently managing and optimizing the steam pipe network by utilizing modern information technology such as the Internet of things, big data, cloud computing and the like, and the intelligent management platform of the steam pipe network is used for monitoring the data of the steam pipe network, so that the abnormal condition of the pipe network can be found and solved in time; If the steam pipe network is modified by a steam unit, the modification information needs to be uploaded to the intelligent management platform in time so as to help the platform to acquire updated pipe network structure data and monitor the modified pipe network, however, after the steam pipe network is modified, the intelligent management platform of the steam pipe network may not receive or not receive the pipe network modification information in time, and the intelligent management platform of the steam pipe network can collect comprehensive resistance coefficient parameters of the steam pipe network to predict whether a pipeline is modified in advance, but because similar amplitude changes of the comprehensive resistance coefficient of the steam pipe network are also possibly caused by pipe wall scaling problems, the problem of misjudgment may occur in the pre-judging process, a pre-judging mode for predicting the pipe modification phenomenon through analysis of the parameter of the comprehensive resistance coefficient of the steam pipe network is lacking in the prior art, and the situation that the pipe modification misjudgment may occur is not considered, so that the accuracy of the pre-judging result of the pipe network modification cannot be improved while the intelligent management platform is helped to timely discover the possible modification phenomenon on the premise that the pipe network modification information is not received in time. Disclosure of Invention The invention aims to provide a digital-based intelligent management system and method for a steam pipe network, which are used for solving the problems in the prior art. In order to achieve the aim, the invention provides the technical scheme that the intelligent management system based on the digital steam pipe network comprises a steam pipe network data monitoring module, a data re-monitoring module, a scaling data acquisition and analysis module and an abnormal data transmission module; The steam pipe network data monitoring module is used for carrying out data acquisition and monitoring on the steam pipe network, analyzing the abnormal condition of the pipe network data and obtaining the time information of occurrence of pipe network abnormality; Acquiring the current pipe network data sampling frequency through the data re-monitoring module, analyzing the judging accuracy rate when the pipe network data is acquired at the historical sampling frequency and the abnormal condition is judged, setting the optimal frequency range, and selecting whether to re-monitor the pipe network data according to the comparison result between the current sampling frequency and the range; the historical data of the scaling phenomenon of the pipe network is collected and analyzed through the scaling data collection and analysis module, and the time of scaling abnormality of the current pipe network is predicted; and analyzing the cause of the pipe network abnormality and carrying out abnormal data transmission by comparing the occurrence time and the predicted time of the pipe network abnormality through the abnormal data transmission module. Preferably, the steam pipe network data monitoring module comprises a pipe network parameter acquisition unit, a parameter change analysis unit and an abnormal time acquisition unit; the pipe network parameter acquisition unit is used for acquiring the comprehensive resistance coefficient of the steam pipe network, and the comprehensive resistance coefficient can be obtained through calculation through joint monitoring of basic parameters such as pressure, flow, temperature and the like; The parameter change analysis unit is used for analyzing the change condition of the comprehensive resistance coefficient of the steam pipe network and judging whether the steam pipe network is abnormal or not according to the change condition; The abnormal time acquisition unit is used for acquiring abnormal time of the steam pipe network. Preferably, the data re-monitoring module comprises a sampling frequency acquisition unit, a data monitoring misjudgment analysis unit and a data re-monitoring planning unit; the sampling frequency acquisition unit is used for acquiring the