An online pipeline management method and system based on the degree of heating correlation

Abstract

The present invention relates to the technical field of pipeline heating, and more specifically, to an online pipeline management method and system based on the degree of heating correlation. The solution includes setting flow sensors inside the pipeline to form internal segmented flow data; embedding sensors outside the pipeline to monitor the leakage flow at the buried position; extracting the internal segmented flow data to determine whether there is a night anomaly, and if so, sending the positioning corresponding to the night anomaly to the monitoring personnel; dividing all partitions to form several independent partitions, and each independent partition consists of several sub-partitions; judging whether early warning is needed according to the leakage flow monitored at the buried position, and if so, sending an early warning signal to the corresponding position for sub-region and segmented early warning. In the solution of the present invention, by setting internal and external flow monitoring devices, online independent and joint analysis of pipeline sub-regions is carried out to realize efficient, reliable and highly utilized abnormal analysis and early warning of data pipelines.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline heating, and more specifically, to an online pipeline management method and system based on the degree of heating correlation. Background Art

[0002] Currently, pipeline heating mainly provides heat energy for residences. The common method of directly burying heating pipelines has the following advantages: the working steel pipe is made of steel, with sufficient mechanical strength and excellent corrosion resistance; the outer protective layer is high-density polyethylene, with good heat resistance and corrosion resistance; there is also an anti-corrosion layer made of fiberglass or other materials outside the working steel pipe, with good anti-corrosion performance; there is a certain gap between the working steel pipe and the insulation layer, which can not only play a heat insulation role but also release space when the working steel pipe expands and contracts due to temperature changes; this structure can not only play a good heat insulation role but also effectively prevent the corrosion of the pipeline by water, moisture, and other corrosive liquids and gases.

[0003] Before the technology of the present invention, the existing method for analyzing directly buried pipelines mainly obtains information from leak detection sensors through sensors to confirm the pipeline status. On the one hand, the information is not comprehensive enough, and on the other hand, the correlation between each pipeline is not considered, resulting in characteristics such as incomplete pipeline analysis information, and there is an urgent need for upgrading and optimization. Summary of the Invention

[0004] In view of the above problems, the present invention proposes an online pipeline management method and system based on the degree of heating correlation. In the solution of the present invention, by setting internal and external flow monitoring devices, independent and combined analysis of pipelines is carried out online without much zoning, realizing efficient, reliable, and highly utilized data pipeline anomaly analysis and early warning.

[0005] According to the first aspect of the embodiments of the present invention, an online pipeline management method based on the degree of heating correlation is provided.

[0006] In one or more embodiments, preferably, the online pipeline management method based on the degree of heating correlation includes:

[0007] Set flow sensors inside the pipeline to form internal segmented flow data;

[0008] Embed sensors outside the pipeline to monitor the leakage flow at the buried position;

[0009] Extract the internal segmented flow data, determine whether there is a night anomaly, and if so, send the location corresponding to the night anomaly to the monitoring personnel;

[0010] Divide all the partitions to form several independent partitions, and each of the independent partitions is composed of several sub-partitions;

[0011] Judge whether early warning is needed according to the leakage flow rate at the monitored embedding position, and if so, send an early warning signal to the corresponding position.

[0012] Conduct zoned and segmented early warning according to the independent zones.

[0013] In one or more embodiments, preferably, the internal flow sensors of the pipeline are arranged to form internal segmented flow data, which specifically includes:

[0014] Put the flow sensors into the pipeline at preset intervals.

[0015] Set the corresponding operating pipeline route for the flow sensors.

[0016] Collect the flow information at different pipeline positions and different times on the preset operating pipeline route as the internal segmented flow data.

[0017] In one or more embodiments, preferably, the sensors are embedded outside the pipeline to monitor the leakage flow rate at the embedding position, which specifically includes:

[0018] Set up observation points of the pipeline online, embed sensors at each observation position for collecting leakage flow rate.

[0019] Extract the leakage flow rate according to the preset sampling period as the leakage flow rate at the monitored embedding position.

[0020] In one or more embodiments, preferably, extract the internal segmented flow data, judge whether there is a night anomaly, and if so, send the positioning corresponding to the night anomaly to the monitoring personnel, which specifically includes:

[0021] Set the night time period, and extract the internal segmented flow data according to the night time period as the night data.

[0022] Calculate the internal segmented flow data by using the first calculation formula.

[0023] Judge that there is a night anomaly if the second calculation formula is satisfied, otherwise there is no night anomaly.

[0024] The first calculation formula is:

[0025] A = Min(a)

[0026] Wherein, A is the internal segmented flow data of the i-th day, a is the night data, and Min(i-th day)() is a function for extracting the minimum value of the night data of the i-th day.

[0027] The second calculation formula is:

[0028] A < B

[0029] Among them, B is a preset judgment flow margin.

[0030] In one or more embodiments, preferably, dividing all the partitions to form several independent partitions, and each of the independent partitions consists of several partitions, specifically including:

[0031] Setting the partitioning principle of the independent partition as the third calculation formula and the fourth calculation formula;

[0032] If any two adjacent partitions satisfy the third calculation formula and the fourth calculation formula, they belong to the same independent partition;

[0033] Dividing all the pipeline monitoring positions into several of the independent partitions;

[0034] The third calculation formula is:

[0035] (Maxc - MINc) - (Maxd - MINd) < Y

[0036] Among them, Maxc is the maximum value of the intraday flow of partition c, MINc is the minimum value of the intraday flow of partition c, Maxd is the maximum value of the intraday flow of partition d, MINd is the minimum value of the intraday flow of partition d, Y is the fluctuation comparison margin, and the intraday flow specifically refers to within 24 hours before the current moment;

[0037] The fourth calculation formula is:

[0038] c ∈ {D}

[0039] Among them, {D} is the set of adjacent nodes of partition d.

[0040] In one or more embodiments, preferably, judging whether a warning is needed according to the leakage flow of the monitoring and burial position, and if so, sending a warning signal to the corresponding position, specifically including:

[0041] Obtaining the leakage flow of the monitoring and burial position;

[0042] Calculating the attenuation index by using the fifth calculation formula;

[0043] Judging whether the attenuation index satisfies the sixth calculation formula, and if so, setting a separate warning for the pipeline at the corresponding monitoring and burial position;

[0044] If it does not satisfy the sixth calculation formula, no processing is performed;

[0045] The fifth calculation formula is:

[0046] E = f1 ÷ f0

[0047] Wherein, E is the attenuation exponent, f0 is the leakage flow rate one hour ago, and f1 is the current leakage flow rate;

[0048] The sixth calculation formula is:

[0049] E > Y

[0050] Wherein, Y is the attenuation margin.

[0051] In one or more embodiments, preferably, the sub-region segmented early warning according to the independent region specifically includes:

[0052] Obtain the independent region, online extract the flow data and leakage flow rate of each sub-region within the independent region, and calculate the real-time leakage rate by using the seventh calculation formula;

[0053] Calculate the sub-region volatility of the corresponding independent region by using the eighth calculation formula;

[0054] When the sub-region volatility exceeds 50%, set the leakage margin to 20%;

[0055] When the sub-region volatility does not exceed 50%, set the leakage margin to 10%;

[0056] If the ninth calculation formula is satisfied, start the alarm for the corresponding independent region, and if not, do not process;

[0057] The seventh calculation formula is:

[0058] s = Σf 1j ÷Σvj

[0059] vj is the flow data of the jth sub-region, is the leakage flow rate of the jth sub-region at the current moment, Σf 1j is the sum of all the leakage flow rates extracted within a certain independent region, Σvj is the sum of all the flow data extracted within a certain independent region, and s is the real-time leakage rate;

[0060] The eighth calculation formula is:

[0061] P = 2×[Max(ll) - Min(ll)]÷[Max(ll) + Min(ll)]

[0062] Wherein, P is the sub-region volatility, ll is the internal segmented flow data, Max(ll) is the maximum value of the internal segmented flow data in the recent 20 days, and Min(ll) is the minimum value of the internal segmented flow data in the recent 20 days;

[0063] The ninth calculation formula is:

[0064] s > r

[0065] where r is the leakage margin.

[0066] According to a second aspect of an embodiment of the present invention, an on-line pipeline management system based on the degree of heating correlation is provided.

[0067] In one or more embodiments, preferably, the on-line pipeline management system based on the degree of heating correlation includes:

[0068] A sensing setting module for setting flow sensors inside the pipeline to form internal segmented flow data;

[0069] An on-line acquisition module for embedding sensors outside the pipeline to monitor the leakage flow at the buried position;

[0070] A night analysis module for extracting the internal segmented flow data, determining whether there is a night anomaly, and if so, sending the positioning corresponding to the night anomaly to the monitoring personnel;

[0071] An association division module for dividing all partitions to form several independent partitions, each of the independent partitions being composed of several partitions;

[0072] A leakage warning module for determining whether a warning is needed according to the leakage flow at the monitored buried position, and if so, sending a warning signal to the corresponding position;

[0073] A constraint alarm module for performing zoned and segmented warnings according to the independent partitions.

[0074] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described in any one of the first aspects of the embodiments of the present invention is implemented.

[0075] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided, including a memory and a processor, where the memory is used to store one or more computer program instructions, and wherein the one or more computer program instructions are executed by the processor to implement the method described in any one of the first aspects of the embodiments of the present invention.

[0076] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0077] In the solution of the present invention, the relevance of the pipeline position is considered, and on-line pipeline leakage analysis and warning are performed.

[0078] In the solution of the present invention, the cooperation between internal sensing and external sensing is considered to achieve efficient warning based on the leakage rate.

[0079] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the written description, claims, as well as the drawings.

[0080] The technical solution of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0081] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative efforts.

[0082] Figure 1 is a flowchart of an online pipeline management method based on the degree of heat supply correlation according to an embodiment of the present invention.

[0083] Figure 2 is a flowchart of setting a flow sensor inside a pipeline to form internal segmented flow data in an online pipeline management method based on the degree of heat supply correlation according to an embodiment of the present invention.

[0084] Figure 3 is a flowchart of embedding a sensor outside the pipeline to monitor the leakage flow at the embedding position in an online pipeline management method based on the degree of heat supply correlation according to an embodiment of the present invention.

[0085] Figure 4 is a flowchart of extracting the internal segmented flow data, determining whether there is a night anomaly, and if so, sending the location corresponding to the night anomaly to the monitoring personnel in an online pipeline management method based on the degree of heat supply correlation according to an embodiment of the present invention.

[0086] Figure 5 is a flowchart of dividing all partitions to form several independent partitions, where each independent partition consists of several partitions in an online pipeline management method based on the degree of heat supply correlation according to an embodiment of the present invention.

[0087] Figure 6 is a flowchart of determining whether a warning is needed based on the leakage flow at the monitored embedding position, and if so, sending a warning signal to the corresponding position in an online pipeline management method based on the degree of heat supply correlation according to an embodiment of the present invention.

[0088] Figure 7It is a flowchart of performing zoned and segmented early warning according to the independent zones in an online pipeline management method based on the degree of heating correlation in an embodiment of the present invention.

[0089] Figure 8 It is a structural diagram of an online pipeline management system based on the degree of heating correlation in an embodiment of the present invention.

[0090] Figure 9 It is a structural diagram of an electronic device in an embodiment of the present invention. Detailed implementation manners

[0091] In some processes described in the specification, claims and the above-mentioned drawings of the present invention, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0092] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0093] S801, 1

[0094] S802, 1

[0095] Currently, pipeline heating mainly provides heat energy for residences. The common method of directly burying the heating pipeline has the following advantages: the working steel pipe is a steel pipe, which has sufficient mechanical strength and excellent corrosion resistance; the outer protective layer is high-density polyethylene, which has good heat resistance and corrosion resistance; there is also an anti-corrosion layer made of fiberglass or other materials outside the working steel pipe, which has good anti-corrosion performance; there is a certain gap between the working steel pipe and the insulation layer, which can not only play a heat preservation role, but also release space when the working steel pipe expands and contracts thermally; this structure can not only play a good heat preservation role, but also effectively prevent the corrosion of water, moisture and other corrosive liquids and gases to the pipeline.

[0096] Prior to the technology of the present invention, the existing direct buried pipeline analysis method mainly obtained information from side leakage sensors through sensors to confirm the pipeline status. On the one hand, the information was not comprehensive enough, and on the other hand, the correlation between the various pipelines was not considered. The pipeline analysis information was incomplete, and it was in urgent need of upgrading and optimization.

[0097] In the embodiment of the present invention, an online pipeline management method and system based on the degree of heat supply correlation is provided. In the solution of the present invention, by setting internal and external flow monitoring equipment, independent and joint analysis of the pipeline is performed online, so as to realize efficient, reliable and high-utilization data pipeline abnormality analysis and early warning.

[0098] According to a first aspect of an embodiment of the present invention, there is provided an online pipeline management method based on a heat supply correlation degree.

[0099] Figure 1 The present invention is a flowchart of an online pipeline management method based on the degree of heating correlation according to an embodiment of the present invention.

[0100] In one or more embodiments, preferably, the online pipeline management method based on the degree of heating correlation comprises:

[0101] S101. Setting a flow sensor inside the pipeline to form internal segment flow data;

[0102] S102. Pre-embed a sensor outside the pipeline to monitor the leakage flow at the buried location;

[0103] S103. Extract the internal segment traffic data to determine whether there is a nighttime anomaly, and if so, send the location corresponding to the nighttime anomaly to the monitoring personnel;

[0104] S104. Divide all partitions to form a number of independent partitions, each of which is composed of a number of partitions;

[0105] S105. Determine whether an early warning is required based on the leakage flow of the monitoring buried position, and if necessary, send an early warning signal to the corresponding position;

[0106] S106. Perform zone-by-zone and segment-by-segment early warning according to the independent zones.

[0107] In the embodiment of the present invention, in the pipeline heating analysis, the core is to perform correlation analysis on the leakage problem in the current pipeline. First, it is determined how the internal leakage sensor is set up. Secondly, according to the preliminary analysis of the pipeline, the corresponding external online collection method is set to complete the night analysis. On this basis, the intraday analysis is carried out. The core of the intraday analysis is the regional division, and the online leakage analysis of the association between single points and partitions is carried out according to the leakage situation.

[0108] Figure 2 The present invention is a flowchart of an online pipeline management method based on the degree of heating correlation, which is a method for setting flow sensors inside a pipeline to form internal segmented flow data.

[0109] like Figure 2 As shown, in one or more embodiments, preferably, the flow sensor is set inside the pipeline to form internal segment flow data, specifically including:

[0110] S201. Place a flow sensor into the interior of the pipeline at a preset interval;

[0111] S202. Setting a corresponding operation pipeline line for the flow sensor;

[0112] S203. Collect flow information at different pipeline locations and at different times on the preset running pipeline route as internal segment flow data.

[0113] In an embodiment of the present invention, after obtaining the pipeline, a flow sensor is placed at the entrance of the pipeline according to a preset cycle. The flow sensor can record the flow of the area it flows through online. At the same time, a device for controlling the moving direction is pre-arranged at a key bifurcation position on the flow sensor, so that the flow sensor can move forward on a preset route inside the pipeline, thereby forming corresponding internal segment flow data.

[0114] Figure 3 The present invention is a flowchart of an online pipeline management method based on the degree of heat supply correlation according to an embodiment of the present invention, in which a sensor is pre-buried outside a pipeline to monitor the leakage flow at the buried position.

[0115] like Figure 3 As shown, in one or more embodiments, preferably, the pre-buried sensor outside the pipeline monitors the leakage flow at the buried position, specifically including:

[0116] S301, setting up observation points of the pipeline online, and burying sensors at each observation position to collect leakage flow;

[0117] S302: extracting the leakage flow rate according to a preset sampling period as the leakage flow rate of the monitoring buried position.

[0118] In the embodiment of the present invention, some key connections on the outside of the pipeline can be pre-set with several core-position pipeline leakage collection devices, and some information leakage information can also be obtained from these collection devices, and the leakage information is the leakage flow.

[0119] Figure 4It is a flowchart of extracting the internal segmented flow data in an online pipeline management method based on the degree of heating correlation in an embodiment of the present invention, determining whether there is a nighttime anomaly, and if so, sending the location corresponding to the nighttime anomaly to the monitoring personnel.

[0120] As Figure 4 shown, in one or more embodiments, preferably, the extracting the internal segmented flow data, determining whether there is a nighttime anomaly, and if so, sending the location corresponding to the nighttime anomaly to the monitoring personnel specifically includes:

[0121] S401. Set the nighttime time period, and extract the internal segmented flow data according to the nighttime time period as nighttime data;

[0122] S402. Calculate the internal segmented flow data using the first calculation formula;

[0123] S403. Determine that there is a nighttime anomaly if the second calculation formula is satisfied, otherwise there is no nighttime anomaly;

[0124] The first calculation formula is:

[0125] A = Min(a)

[0126] where A is the internal segmented flow data of the i-th day, a is the nighttime data, and Min(i-th day)() is a function for extracting the minimum value of the nighttime data of the i-th day;

[0127] The second calculation formula is:

[0128] A < B

[0129] where B is a preset judgment flow margin.

[0130] In an embodiment of the present invention, the nighttime flow mainly refers to the pipeline flow information obtained during the nighttime time period. Since managers are more likely to be negligent during the nighttime flow observation, continuous reminders are needed. In this case, first, the historical data of the fluctuation of the internal segmented flow data is analyzed online, and then the result of the nighttime flow anomaly analysis is formed.

[0131] Figure 5 It is a flowchart of dividing all partitions to form several independent partitions, and each of the independent partitions is composed of several partitions in an online pipeline management method based on the degree of heating correlation in an embodiment of the present invention.

[0132] As Figure 5 shown, in one or more embodiments, preferably, the dividing all partitions to form several independent partitions, and each of the independent partitions is composed of several partitions specifically includes:

[0133] S501, setting the division principle of the independent partition to the third calculation formula and the fourth calculation formula;

[0134] S502: If any two adjacent partitions satisfy the third calculation formula and the fourth calculation formula, they belong to the same independent partition;

[0135] S503, dividing all pipeline monitoring locations into a plurality of independent partitions;

[0136] The third calculation formula is:

[0137] (Maxc-MINc)-(Maxd-MINd) <Y

[0138] Among them, Maxc is the maximum value of the intraday flow of partition c, MINc is the minimum value of the intraday flow of partition c, Maxd is the maximum value of the intraday flow of partition d, MINd is the minimum value of the intraday flow of partition d, Y is the fluctuation comparison margin, and the intraday flow specifically refers to the 24 hours before the current time;

[0139] The fourth calculation formula is:

[0140] c∈{D}

[0141] Where {D} is the set of adjacent nodes of partition d.

[0142] In an embodiment of the present invention, the core of traffic partitioning is to screen according to a preset formula to determine that any two adjacent partitions belong to the same group of partitions, and then divide them into a number of traffic association groups. These traffic association groups are the basis for subsequent leakage reminders and alarms. The minimum unit of the partition is a preset monitoring buried position. Secondly, considering the geographical location, each partition contains only one monitoring buried position.

[0143] Figure 6 It is a flowchart of an online pipeline management method based on the degree of heat supply correlation in an embodiment of the present invention, which determines whether an early warning is needed according to the leakage flow of the monitored buried position, and if necessary, sends an early warning signal to the corresponding position.

[0144] like Figure 6 As shown, in one or more embodiments, preferably, judging whether an early warning is needed according to the leakage flow of the monitoring buried position, and sending an early warning signal to the corresponding position if necessary, specifically includes:

[0145] S601, obtaining the leakage flow of the monitoring buried position;

[0146] S602, calculating the attenuation index using the fifth calculation formula;

[0147] S603. Determine whether the attenuation index satisfies the sixth calculation formula. If it is satisfied, set a separate warning for the pipeline at the corresponding monitored and buried position.

[0148] S604. If the sixth calculation formula is not satisfied, no processing is performed.

[0149] The fifth calculation formula is:

[0150] E = f1÷f0

[0151] where E is the attenuation index, f0 is the leakage flow rate one hour ago, and f1 is the current leakage flow rate.

[0152] The sixth calculation formula is:

[0153] E > Y

[0154] where Y is the attenuation margin.

[0155] In the embodiment of the present invention, the leakage amount analysis mainly performs attenuation analysis on each leakage flow rate according to the leakage flow rate at the monitored and buried position obtained by online acquisition. If there is no attenuation, it is considered an effective leakage point and needs to be warned separately. If there is no effective leakage point, no separate warning is required, but secondary analysis needs to be performed according to the independent region to determine whether a warning is required.

[0156] Figure 7 It is a flowchart of segmented warning according to the independent partition in an online pipeline management method based on the degree of heating association in an embodiment of the present invention.

[0157] As Figure 7 shown, in one or more embodiments, preferably, the segmented warning according to the independent partition specifically includes:

[0158] S701. Obtain the independent partition, extract the flow rate data and leakage flow rate of each partition in the independent partition online, and calculate the real-time leakage rate using the seventh calculation formula.

[0159] S702. Calculate the partition volatility of the corresponding independent partition using the eighth calculation formula.

[0160] S703. When the partition volatility exceeds 50%, set the leakage margin to 20%.

[0161] S704. When the partition volatility does not exceed 50%, set the leakage margin to 10%.

[0162] S705. If the ninth calculation formula is satisfied, start the alarm for the corresponding independent partition. If not, no processing is performed.

[0163] The seventh calculation formula is as follows:

[0164] s = Σf 1j ÷Σvj

[0165] where vj is the flow data of the j-th partition, and is the leakage flow of the j-th partition at the current moment. Σf 1j is the sum of all leakage flows extracted within a certain independent partition, Σvj is the sum of all flow data extracted within a certain independent partition, and s is the real-time leakage rate;

[0166] The eighth calculation formula is as follows:

[0167] P = 2×[Max(ll) - Min(ll)]÷[Max(ll) + Min(ll)]

[0168] where P is the partition volatility, ll is the internal segmented flow data, Max(ll) is the maximum value of the internal segmented flow data in the past 20 days, and Min(ll) is the minimum value of the internal segmented flow data in the past 20 days;

[0169] The ninth calculation formula is as follows:

[0170] s > r

[0171] where r is the leakage margin.

[0172] In the embodiment of the present invention, after obtaining the corresponding independent region, the attenuation rate within each partition can be analyzed separately according to the situation of the independent partition; in addition, it is also necessary to analyze in combination with the fluctuation situation in the historical data, and for the independent region with excessive flow fluctuation, the corresponding alarm margin is also set differently.

[0173] According to the second aspect of the embodiment of the present invention, an online pipeline management system based on the degree of heat supply association is provided.

[0174] Figure 8 is the structural diagram of an online pipeline management system based on the degree of heat supply association according to an embodiment of the present invention.

[0175] In one or more embodiments, preferably, the online pipeline management system based on the degree of heat supply association includes:

[0176] A sensing setting module 801, configured to set a flow sensor inside the pipeline to form internal segmented flow data;

[0177] An online acquisition module 802, configured to embed a sensor outside the pipeline to monitor the leakage flow at the buried position;

[0178] A night analysis module 803 is used to extract the internal segmented traffic data, determine whether there is a night anomaly, and if so, send the location corresponding to the night anomaly to the monitoring personnel.

[0179] An association division module 804 is used to divide all the partitions to form several independent partitions, and each of the independent partitions is composed of several partitions.

[0180] A leakage warning module 805 is used to determine whether a warning is needed according to the leakage flow rate at the monitored buried position, and if so, send a warning signal to the corresponding position.

[0181] A constraint alarm module 806 is used to perform a partitioned and segmented warning according to the independent partition.

[0182] In an embodiment of the present invention, through a series of modular designs, a system applicable to different structures is realized. The system can achieve a closed-loop, reliable, and efficient execution through collection, analysis, and control.

[0183] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described in any one of the first aspects of the embodiments of the present invention is realized.

[0184] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Figure 9 It is a structural diagram of an electronic device in an embodiment of the present invention. Figure 9 The shown electronic device is a general online pipeline management device based on the degree of heating association. The electronic device can be a smart phone, a tablet computer, or other devices. As shown, the electronic device 900 includes a processor 901 and a memory 902. Among them, the processor 901 is electrically connected to the memory 902. The processor 901 is the control center of the terminal 900, connects various parts of the entire terminal through various interfaces and lines, executes various functions of the terminal and processes data by running or calling computer programs stored in the memory 902, and calling data stored in the memory 902, so as to perform overall monitoring of the terminal.

[0185] In this embodiment, the processor 901 in the electronic device 900 will load the instructions corresponding to the processes of one or more computer programs into the memory 902 according to the following steps, and the processor 901 will run the computer programs stored in the memory 902 to implement various functions: set up flow sensors inside the pipeline to form internal segmented flow data; embed sensors outside the pipeline to monitor the leakage flow at the buried position; extract the internal segmented flow data, determine whether there is a nighttime anomaly, and if so, send the location corresponding to the nighttime anomaly to the monitoring personnel; divide all the partitions to form several independent partitions, and each of the independent partitions consists of several sub-partitions; determine whether a warning is needed according to the leakage flow at the monitored buried position, and if so, send a warning signal to the corresponding position; issue warnings in a partitioned and segmented manner according to the independent partitions.

[0186] The memory 902 can be used to store computer programs and data. The computer programs stored in the memory 902 contain instructions that can be executed in the processor. The computer programs can form various functional modules. The processor 901 executes various functional applications and data processing by calling the computer programs stored in the memory 902.

[0187] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0188] In the solution of the present invention, the relevance of the pipeline position is considered to perform online pipeline leakage analysis and warning.

[0189] In the solution of the present invention, the cooperation between internal sensing and external sensing is considered to achieve efficient warning based on the leakage rate.

[0190] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) that contain computer-usable program code.

[0191] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0192] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0193] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0194] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. An online pipeline management method based on the degree of heating correlation, characterized in that The method includes: Setting up a flow sensor inside the pipeline to form internal segmented flow data; Embedding sensors outside the pipeline to monitor the leakage flow at the buried position; Extracting the internal segmented flow data, determining whether there is a night anomaly, and if so, sending the location corresponding to the night anomaly to the monitoring personnel; Dividing all the partitions to form several independent partitions, and each of the independent partitions consists of several partitions; Judging whether a warning is needed according to the leakage flow at the monitored buried position, and if so, sending a warning signal to the corresponding position; Conducting zoned and segmented warnings according to the independent partitions; Among them, the extracting the internal segmented flow data, determining whether there is a night anomaly, and if so, sending the location corresponding to the night anomaly to the monitoring personnel specifically includes: Setting a night time period, and extracting the internal segmented flow data according to the night time period as night data; Calculating the internal segmented flow data using the first calculation formula; Judging that there is a night anomaly if the second calculation formula is satisfied, otherwise there is no night anomaly; The first calculation formula is: A = Min(a) Where A is the internal segmented flow data on the i-th day, a is the night data, and Min() is a function for extracting the minimum value of the night data on the i-th day among the data on the i-th day; The second calculation formula is: A < B Where B is a preset judgment flow margin; Among them, the dividing all the partitions to form several independent partitions, and each of the independent partitions consists of several partitions specifically includes: Setting the division principles of the independent partitions as the third calculation formula and the fourth calculation formula; If any two adjacent partitions satisfy the third calculation formula and the fourth calculation formula, they belong to the same independent partition; Dividing all the pipeline monitoring positions into several of the independent partitions; The third calculation formula is: (Maxc - MINc) - (Maxd - MINd) < Y Where Maxc is the maximum value of the daily flow in partition c, MINc is the minimum value of the daily flow in partition c, Maxd is the maximum value of the daily flow in partition d, MINd is the minimum value of the daily flow in partition d, Y is the fluctuation comparison margin, and the daily flow specifically refers to within 24 hours before the current moment; The fourth calculation formula is: c ∈ {D} Where {D} is the set of adjacent nodes of partition d; Among them, the judging whether a warning is needed according to the leakage flow at the monitored buried position, and if so, sending a warning signal to the corresponding position specifically includes: Obtaining the leakage flow at the monitored buried position; Calculating the attenuation index using the fifth calculation formula; Judging whether the attenuation index satisfies the sixth calculation formula, and if so, setting a separate warning for the pipeline at the corresponding monitored buried position; If the sixth calculation formula is not satisfied, no processing is performed; The fifth calculation formula is: E = f1 ÷ f0 Where E is the attenuation index, f0 is the leakage flow one hour ago, and f1 is the current leakage flow; The sixth calculation formula is: E > YY Where YY is the attenuation margin; Among them, the conducting zoned and segmented warnings according to the independent partitions specifically includes: Obtain the independent partition, extract the traffic data and leakage flow of each partition within the independent partition online, and calculate the real-time leakage rate using the seventh calculation formula; Calculate the partition volatility of the corresponding independent partition using the eighth calculation formula; When the partition volatility exceeds 50%, set the leakage margin to 20%; When the partition volatility does not exceed 50%, set the leakage margin to 10%; If the ninth calculation formula is satisfied, activate the alarm for the corresponding independent partition, and if not, do nothing; The seventh calculation formula is: s = Σf 1j ÷Σvj $v_j$ is the flow data of the $j$-th partition, and is the leakage flow of the $j$-th partition at the current moment, $\sum f$ 1j is the sum of all the extracted leakage flows within a certain independent partition, $\sum v_j$ is the sum of all the extracted flow data within a certain independent partition, and $s$ is the real-time leakage rate; The eighth calculation formula is: P = 2×[Max(ll) - Min(ll)]÷[Max(ll) + Min(ll)] where P is the partition volatility, ll is the internal segmented traffic data, Max(ll) is the maximum value of the internal segmented traffic data in the past 20 days, and Min(ll) is the minimum value of the internal segmented traffic data in the past 20 days; The ninth calculation formula is: s>r where r is the leakage margin.

2. The online pipeline management method based on the degree of heating correlation according to claim 1, wherein The setting of the flow sensor inside the pipeline to form internal segmented traffic data specifically includes: Put the flow sensor into the pipeline at preset intervals; Set the corresponding operating pipeline route for the flow sensor; Collect the flow information at different pipeline positions and different times on the preset operating pipeline route as the internal segmented traffic data.

3. The online pipeline management method based on the degree of heating correlation as claimed in claim 1, characterized in that The embedding of sensors outside the pipeline to monitor the leakage flow at the embedding position specifically includes: Set the observation points of the pipeline online, embed sensors at each observation position for collecting the leakage flow; Extract the leakage flow according to the preset sampling period as the leakage flow for monitoring the embedding position.

4. An online pipeline management system based on the degree of heating correlation, characterized in that, This system is used to implement the method described in any one of claims 1-3. The system includes: A sensing setting module for setting the flow sensor inside the pipeline to form internal segmented traffic data; An online acquisition module for embedding sensors outside the pipeline to monitor the leakage flow at the embedding position; A night analysis module for extracting the internal segmented traffic data, judging whether there is a night anomaly, and if so, sending the location corresponding to the night anomaly to the monitoring personnel; An association division module for dividing all partitions to form several independent partitions, and each independent partition consists of several partitions; A leakage warning module for judging whether early warning is needed according to the leakage flow at the monitoring embedding position, and if so, sending an early warning signal to the corresponding position; A constraint alarm module for performing partitioned and segmented early warning according to the independent partition.

5. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, The computer program instructions, when executed by the processor, implement the method described in any one of claims 1-3.

6. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store one or more computer program instructions, and the one or more computer program instructions are executed by the processor to implement the method described in any one of claims 1-3.

Images