A device maintenance system and method for a heating pipe network

Abstract

The present application relates to the technical field of heat supply pipe network, and discloses a heat supply pipe network equipment maintenance system and method, wherein a data acquisition module divides the heat supply pipe network to be monitored into multiple heat supply pipe network monitoring areas, and acquires real-time heat supply data; a data analysis module extracts real-time heat supply data of the same type, constructs a real-time heat supply data chain, and divides the real-time heat supply data chain into a difference heat supply data chain and a normal heat supply data chain; a first processing module performs first processing on the normal heat supply data chain, and calculates a normal heat supply coefficient; a second processing module performs second processing on the difference heat supply data chain, and calculates a difference heat supply coefficient; and a risk maintenance module constructs a risk detection line, calculates a risk detection coefficient, and judges whether the heat supply pipe network to be monitored needs to be maintained, so that potential risks are determined by data analysis, the maintenance efficiency and accuracy are greatly improved, human errors are reduced, the service life of the heat supply pipe network is prolonged, the maintenance cost is reduced, and the system has wide market application prospects and promotion value.

Description

Technical Field

[0001] The present invention relates to the technical field of heating pipe networks, and in particular to an equipment maintenance system and method for a heating pipe network. Background Art

[0002] A heating network refers to the heating pipes that run from heat sources (such as boiler rooms, direct-fired power plants, and heating centers) to the heat inlets of buildings. Multiple heating pipes form a network that transports and distributes the heating medium. The heating network is a crucial component of urban centralized heating systems, and its design, construction, and operation must adhere to relevant technical standards and specifications to ensure a safe, reliable, and cost-effective heating system.

[0003] With the acceleration of urbanization, the operation of heating pipeline networks, as a vital component of urban infrastructure, directly impacts residents' quality of life and the city's energy security. Therefore, regular maintenance of heating pipelines to promptly identify and resolve potential issues is crucial for ensuring the stable operation of heating systems. Traditional methods for inspecting heating pipelines rely primarily on manual inspections and reactive repairs after failures occur. This approach is not only time-consuming and labor-intensive, but also makes it difficult to detect risks in the early stages of a problem, leading to interruptions in heating supply and wasted energy. Furthermore, manual inspections carry certain safety risks. Summary of the Invention

[0004] The embodiments of the present invention provide an equipment maintenance system and method for a heating pipe network, which uses data analysis to determine potential risks, greatly improves maintenance efficiency and accuracy, reduces human errors, helps to extend the service life of the heating pipe network, and reduces maintenance costs. It has broad market application prospects and promotion value.

[0005] In order to achieve the above-mentioned object, the present invention provides an equipment maintenance system for a heating pipe network, comprising:

[0006] A data acquisition module is used to determine the heating pipe network to be monitored, divide the heating pipe network to be monitored into multiple sub-heating pipe network monitoring areas, and obtain real-time heating data of each sub-heating pipe network monitoring area in real time;

[0007] a data analysis module, configured to extract real-time heating data of the same type from the real-time heating data corresponding to each sub-heating network monitoring area, construct a real-time heating data chain, analyze the real-time heating data chain, and divide the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis results;

[0008] a first processing module, configured to perform a first processing on the normal heating data chain, and calculate a normal heating coefficient of the normal heating data chain based on a result of the first processing;

[0009] A second processing module is used to perform a second processing on the differential heating data chain, and calculate a differential heating coefficient of the differential heating data chain based on the second processing result;

[0010] A risk maintenance module is used to construct a risk detection line for the heating network to be monitored based on all normal heating coefficients and differential heating coefficients, calculate the risk detection coefficient of the heating network to be monitored based on the risk detection line, and determine whether the heating network to be monitored needs maintenance based on the risk detection coefficient, wherein the risk detection line includes multiple risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links.

[0011] Furthermore, the data analysis module is used to:

[0012] The data analysis module is used to obtain a standard heating data range corresponding to each real-time heating data;

[0013] The data analysis module is used to compare the real-time heating data with the standard heating data range, and if the real-time heating data is within the standard heating data range, the corresponding real-time heating data is classified as the normal heating data chain;

[0014] The data analysis module is used to divide the corresponding real-time heating data into the difference heating data chain if the real-time heating data is not within the standard heating data range.

[0015] Furthermore, the first processing module is used to:

[0016] The first processing module is used to calculate the heating data variance of the real-time heating data on the normal heating data chain;

[0017] The first processing module is used to calculate the normal heating coefficient of the normal heating data link according to the following formula;

[0018] ;

[0019] Among them, a1 is the normal heating coefficient of the normal heating data chain, b1 is the number of real-time heating data on the normal heating data chain, f e is the e-th real-time heating data in the normal heating data chain, and f1 is the variance of the heating data.

[0020] Furthermore, the second processing module is used to:

[0021] The second processing module is used to extract the same real-time heating data from the differential heating data chain and obtain multiple real-time heating data sets;

[0022] The second processing module is used to count the number of first real-time heating data in the real-time heating data set;

[0023] The second processing module is used to extract a real-time heating data from all the real-time heating data sets respectively, and calculate the first real-time heating data set and value;

[0024] The second processing module is used to obtain preset real-time heating data, eliminate all real-time heating data sets that are smaller than the preset real-time heating data, and count the number of second real-time heating data in the remaining real-time heating data sets;

[0025] The second processing module is used to extract a real-time heating data from the remaining real-time heating data sets, and calculate the second real-time heating data and value;

[0026] The second processing module is used to calculate the difference heating coefficient of the difference heating data chain according to the first real-time heating data quantity, the second real-time heating data quantity, the first real-time heating data sum value and the second real-time heating data sum value.

[0027] Furthermore, the risk repair module is used to:

[0028] The risk inspection module is used to randomly determine two risk detection nodes and extract the corresponding first normal heating coefficient, first differential heating coefficient, second normal heating coefficient and second differential heating coefficient;

[0029] The risk maintenance module is used to calculate the absolute value of the difference between the first differential heating coefficient and the second differential heating coefficient;

[0030] The risk maintenance module is used to determine the maximum differential heating coefficient and the minimum differential heating coefficient from all differential heating coefficients, and calculate the differential heating coefficient extreme difference values ​​of the maximum differential heating coefficient and the minimum differential heating coefficient;

[0031] The risk inspection module is used to calculate the heating coefficient ratio of the absolute value of the heating coefficient difference and the heating coefficient extreme difference;

[0032] The risk maintenance module is used to calculate the absolute value of the normal heating coefficient difference between the first normal heating coefficient and the second normal heating coefficient;

[0033] The risk maintenance module is used to determine the maximum normal heating coefficient and the minimum normal heating coefficient from all normal heating coefficients, and calculate the normal heating coefficient extreme difference value of the maximum normal heating coefficient and the minimum normal heating coefficient;

[0034] The risk maintenance module is used to calculate the normal heating coefficient ratio of the absolute value of the heating coefficient difference and the extreme difference of the heating coefficient;

[0035] The risk maintenance module is used to calculate the product of the difference heating coefficient ratio and the normal heating coefficient ratio, and use it as the sub-risk detection coefficient of the heating network to be monitored;

[0036] The risk inspection module is used to analyze and calculate all remaining risk detection nodes to determine the sub-risk detection coefficients corresponding to every two risk detection nodes;

[0037] The risk inspection module is used to calculate the risk detection coefficient of the heating pipe network to be monitored based on all sub-risk detection coefficients.

[0038] Furthermore, the risk repair module is used to:

[0039] The risk inspection module is used to calculate the coefficient mean of all sub-risk detection coefficients;

[0040] The risk inspection module is used to classify all sub-risk detection coefficients that are smaller than the coefficient mean into a first sub-risk detection coefficient sequence;

[0041] The risk maintenance module is used to divide all sub-risk detection coefficients greater than or equal to the coefficient mean into a second sub-risk detection coefficient sequence;

[0042] The risk inspection module is used to calculate the first risk detection coefficient difference between each sub-risk detection coefficient in the first sub-risk detection coefficient sequence and the coefficient mean, and construct a first coefficient difference sequence;

[0043] The risk repair module is used to calculate the second risk detection coefficient difference between each sub-risk detection coefficient in the second sub-risk detection coefficient sequence and the coefficient mean, and construct a second coefficient difference sequence;

[0044] The risk repair module is used to randomly combine the first coefficient difference sequence and the second coefficient difference sequence in pairs to obtain multiple sub-coefficient difference sequences;

[0045] The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored based on all sub-coefficient difference sequences.

[0046] Furthermore, the risk repair module is used to:

[0047] The risk inspection module is used to calculate the risk detection coefficient of the heating pipe network to be monitored according to the following formula:

[0048] ;

[0049] Among them, y is the risk detection coefficient of the heating network to be monitored, n is the number of sub-coefficient difference sequences, g1 i is the first risk detection coefficient difference in the ith sub-coefficient difference sequence, g2 is the second risk detection coefficient difference in the ith sub-coefficient difference sequence, For all The minimum value in For all The maximum value in r 2 For all The variance of .

[0050] Furthermore, the risk repair module is used to:

[0051] The risk maintenance module is used to determine whether the monitored heating pipe network needs maintenance according to the relationship between the risk detection coefficient and the preset risk detection coefficient;

[0052] The risk maintenance module is used to determine that the monitored heating pipe network does not need maintenance when the risk detection coefficient is less than the preset risk detection coefficient;

[0053] The risk maintenance module is used to determine that the monitored heating pipe network needs maintenance when the risk detection coefficient is greater than or equal to the preset risk detection coefficient.

[0054] In order to achieve the above-mentioned object, the present invention further provides a method for repairing equipment in a heating pipe network, which is characterized by comprising:

[0055] Determine a heating network to be monitored, divide the heating network to be monitored into multiple sub-heating network monitoring areas, and obtain real-time heating data for each sub-heating network monitoring area in real time;

[0056] Extracting real-time heating data of the same type from the real-time heating data corresponding to each sub-heating pipe network monitoring area, and constructing a real-time heating data chain, analyzing the real-time heating data chain, and dividing the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis results;

[0057] performing a first processing on the normal heating data chain, and calculating a normal heating coefficient of the normal heating data chain based on a result of the first processing;

[0058] performing a second processing on the differential heating data chain, and calculating a differential heating coefficient of the differential heating data chain based on a result of the second processing;

[0059] A risk detection line of the heating network to be monitored is constructed based on all normal heating coefficients and differential heating coefficients, the risk detection coefficient of the heating network to be monitored is calculated based on the risk detection line, and whether the heating network to be monitored needs maintenance is judged based on the risk detection coefficient, wherein the risk detection line includes multiple risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] The present invention discloses an equipment maintenance system and method for a heating pipe network. The data acquisition module divides the heating pipe network to be monitored into multiple sub-heating pipe network monitoring areas to obtain real-time heating data; the data analysis module extracts real-time heating data of the same type, constructs a real-time heating data chain, and divides it into a differential heating data chain and a normal heating data chain; the first processing module performs a first processing on the normal heating data chain to calculate a normal heating coefficient; the second processing module performs a second processing on the differential heating data chain to calculate a differential heating coefficient; the risk maintenance module constructs a risk detection line, calculates a risk detection coefficient, determines whether the heating pipe network to be monitored needs maintenance, and uses data analysis to determine potential risks, thereby greatly improving maintenance efficiency and accuracy, reducing human errors, extending the service life of the heating pipe network, and reducing maintenance costs. The system has broad market application prospects and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0063] Figure 1 A schematic structural diagram of an equipment maintenance system for a heating pipe network according to an embodiment of the present invention is shown;

[0064] Figure 2 A flow chart of a method for repairing equipment in a heating pipe network according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0065] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0066] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0067] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0068] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0069] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0070] like Figure 1 As shown, an embodiment of the present invention discloses an equipment maintenance system for a heating pipe network, comprising:

[0071] A data acquisition module is used to determine the heating pipe network to be monitored, divide the heating pipe network to be monitored into multiple sub-heating pipe network monitoring areas, and obtain real-time heating data of each sub-heating pipe network monitoring area in real time;

[0072] a data analysis module, configured to extract real-time heating data of the same type from the real-time heating data corresponding to each sub-heating network monitoring area, construct a real-time heating data chain, analyze the real-time heating data chain, and divide the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis results;

[0073] a first processing module, configured to perform a first processing on the normal heating data chain, and calculate a normal heating coefficient of the normal heating data chain based on a result of the first processing;

[0074] A second processing module is used to perform a second processing on the differential heating data chain, and calculate a differential heating coefficient of the differential heating data chain based on the second processing result;

[0075] A risk maintenance module is used to construct a risk detection line for the heating network to be monitored based on all normal heating coefficients and differential heating coefficients, calculate the risk detection coefficient of the heating network to be monitored based on the risk detection line, and determine whether the heating network to be monitored needs maintenance based on the risk detection coefficient, wherein the risk detection line includes multiple risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links.

[0076] In this embodiment, the heating pipe network to be monitored is divided into regions according to the area of ​​the heating pipe network to be monitored, so as to ensure the uniformity of the monitoring area of ​​each sub-heating pipe network.

[0077] In this embodiment, the real-time heating data includes heating flow, heating pressure, heating temperature, heating heat, heating vibration, etc.

[0078] In this embodiment, the same type of real-time heating data is extracted, that is, the same real-time heating data is extracted from each sub-heating pipe network monitoring area, such as extracting the heating flow from each sub-heating pipe network monitoring area to build a real-time heating data chain, and extracting the heating pressure from each sub-heating pipe network monitoring area to build a real-time heating data chain.

[0079] The beneficial effects of the above technical solution are: the present invention uses data analysis to determine potential risks, greatly improves maintenance efficiency and accuracy, reduces human errors, extends the service life of the heating pipeline network, reduces maintenance costs, and has broad market application prospects and promotion value.

[0080] In some embodiments of the present application, the data analysis module is used to:

[0081] The data analysis module is used to obtain a standard heating data range corresponding to each real-time heating data;

[0082] The data analysis module is used to compare the real-time heating data with the standard heating data range, and if the real-time heating data is within the standard heating data range, the corresponding real-time heating data is classified as the normal heating data chain;

[0083] The data analysis module is used to divide the corresponding real-time heating data into the difference heating data chain if the real-time heating data is not within the standard heating data range.

[0084] In this embodiment, each real-time heating data corresponds to a standard heating data range, and the standard heating data range can be set according to actual conditions.

[0085] The beneficial effect of the above technical solution is: the present invention can divide the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the comparison between the real-time heating data and the standard heating data range, laying the foundation for the calculation of the normal heating coefficient and the differential heating coefficient.

[0086] In some embodiments of the present application, the first processing module is configured to:

[0087] The first processing module is used to calculate the heating data variance of the real-time heating data on the normal heating data chain;

[0088] The first processing module is used to calculate the normal heating coefficient of the normal heating data link according to the following formula;

[0089] ;

[0090] Among them, a1 is the normal heating coefficient of the normal heating data chain, b1 is the number of real-time heating data on the normal heating data chain, f e is the e-th real-time heating data in the normal heating data chain, and f1 is the variance of the heating data.

[0091] The beneficial effect of the above technical solution is: the present invention calculates the normal heating coefficient of the normal heating data chain based on the heating data variance of the real-time heating data on the normal heating data chain, which not only ensures the calculation accuracy of the normal heating coefficient, but also provides reliable data support for risk judgment of the heating pipeline network.

[0092] In some embodiments of the present application, the second processing module is configured to:

[0093] The second processing module is used to extract the same real-time heating data from the differential heating data chain and obtain multiple real-time heating data sets;

[0094] The second processing module is used to count the number of first real-time heating data in the real-time heating data set;

[0095] The second processing module is used to extract a real-time heating data from all the real-time heating data sets respectively, and calculate the first real-time heating data set and value;

[0096] The second processing module is used to obtain preset real-time heating data, eliminate all real-time heating data sets that are smaller than the preset real-time heating data, and count the number of second real-time heating data in the remaining real-time heating data sets;

[0097] The second processing module is used to extract a real-time heating data from the remaining real-time heating data sets, and calculate the second real-time heating data and value;

[0098] The second processing module is used to calculate the difference heating coefficient of the difference heating data chain according to the first real-time heating data quantity, the second real-time heating data quantity, the first real-time heating data sum value and the second real-time heating data sum value.

[0099] In this embodiment, if the differential heating data chain is temperature, the same real-time heating data is {25°C, 25°C}, {22°C, 22°C}, and {20°C, 20°C}. The first number of real-time heating data is 3, and one real-time heating data is extracted from each of them, namely 25°C, 22°C, and 20°C. The preset real-time heating data is 21°C, and the remaining real-time heating data sets are {25°C, 25°C} and {22°C, 22°C}. The second number of real-time heating data is 2, and one real-time heating data is extracted from each of the remaining real-time heating data sets, namely 25°C and 22°C. The above is illustrated by distance and is not specifically limited.

[0100] In this embodiment, the differential heating coefficient of the differential heating data link is calculated according to the following formula:

[0101] ;

[0102] Among them, u is the differential heating coefficient of the differential heating data chain, w1 is the number of first real-time heating data, w2 is the number of second real-time heating data, q1 is the sum value of the first real-time heating data, and q2 is the sum value of the second real-time heating data.

[0103] The beneficial effect of the above technical solution is: the present invention calculates the differential heating coefficient of the differential heating data chain based on the number of first real-time heating data, the number of second real-time heating data, the sum of the first real-time heating data and the sum of the second real-time heating data, thereby ensuring the calculation accuracy and convenience of the differential heating coefficient, avoiding calculation errors caused by manual participation, and providing reliable data support for risk judgment of the heating pipeline network.

[0104] In some embodiments of the present application, the risk repair module is used to:

[0105] The risk inspection module is used to randomly determine two risk detection nodes and extract the corresponding first normal heating coefficient, first differential heating coefficient, second normal heating coefficient and second differential heating coefficient;

[0106] The risk maintenance module is used to calculate the absolute value of the difference between the first differential heating coefficient and the second differential heating coefficient;

[0107] The risk maintenance module is used to determine the maximum differential heating coefficient and the minimum differential heating coefficient from all differential heating coefficients, and calculate the differential heating coefficient extreme difference values ​​of the maximum differential heating coefficient and the minimum differential heating coefficient;

[0108] The risk inspection module is used to calculate the heating coefficient ratio of the absolute value of the heating coefficient difference and the heating coefficient extreme difference;

[0109] The risk maintenance module is used to calculate the absolute value of the normal heating coefficient difference between the first normal heating coefficient and the second normal heating coefficient;

[0110] The risk maintenance module is used to determine the maximum normal heating coefficient and the minimum normal heating coefficient from all normal heating coefficients, and calculate the normal heating coefficient extreme difference value of the maximum normal heating coefficient and the minimum normal heating coefficient;

[0111] The risk maintenance module is used to calculate the normal heating coefficient ratio of the absolute value of the heating coefficient difference and the extreme difference of the heating coefficient;

[0112] The risk maintenance module is used to calculate the product of the difference heating coefficient ratio and the normal heating coefficient ratio, and use it as the sub-risk detection coefficient of the heating network to be monitored;

[0113] The risk inspection module is used to analyze and calculate all remaining risk detection nodes to determine the sub-risk detection coefficients corresponding to every two risk detection nodes;

[0114] The risk inspection module is used to calculate the risk detection coefficient of the heating pipe network to be monitored based on all sub-risk detection coefficients.

[0115] In this embodiment, the difference between the first differential heating coefficient and the second differential heating coefficient is calculated, and then the absolute value is taken to obtain the absolute value of the differential heating coefficient difference.

[0116] In this embodiment, the difference between the maximum differential heating coefficient and the minimum differential heating coefficient is calculated to obtain the differential heating coefficient extreme difference value.

[0117] In this embodiment, the ratio of the absolute value of the heating coefficient difference to the extreme difference of the heating coefficient is calculated to obtain the differential heating coefficient ratio.

[0118] In this embodiment, the difference between the first normal heating coefficient and the second normal heating coefficient is calculated, and then the absolute value is taken to obtain the absolute value of the normal heating coefficient difference.

[0119] In this embodiment, the difference between the maximum normal heating coefficient and the minimum normal heating coefficient is calculated to obtain the normal heating coefficient extreme difference value.

[0120] In this embodiment, the ratio of the absolute value of the heating coefficient difference to the extreme difference of the heating coefficient is calculated to obtain the normal heating coefficient ratio.

[0121] In this embodiment, the calculation method of the sub-risk detection coefficients corresponding to the remaining two risk detection nodes is consistent with the above, and will not be repeated here.

[0122] The beneficial effect of the above technical solution is that the present invention obtains multiple sub-risk detection coefficients by calculation, providing a calculation basis for the risk detection coefficient.

[0123] In some embodiments of the present application, the risk repair module is used to:

[0124] The risk inspection module is used to calculate the coefficient mean of all sub-risk detection coefficients;

[0125] The risk inspection module is used to classify all sub-risk detection coefficients that are smaller than the coefficient mean into a first sub-risk detection coefficient sequence;

[0126] The risk maintenance module is used to divide all sub-risk detection coefficients greater than or equal to the coefficient mean into a second sub-risk detection coefficient sequence;

[0127] The risk inspection module is used to calculate the first risk detection coefficient difference between each sub-risk detection coefficient in the first sub-risk detection coefficient sequence and the coefficient mean, and construct a first coefficient difference sequence;

[0128] The risk repair module is used to calculate the second risk detection coefficient difference between each sub-risk detection coefficient in the second sub-risk detection coefficient sequence and the coefficient mean, and construct a second coefficient difference sequence;

[0129] The risk repair module is used to randomly combine the first coefficient difference sequence and the second coefficient difference sequence in pairs to obtain multiple sub-coefficient difference sequences;

[0130] The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored based on all sub-coefficient difference sequences.

[0131] In this embodiment, the first risk detection coefficient difference and the second risk detection coefficient difference are both positive numbers.

[0132] In this embodiment, if there is an uncombined first risk detection coefficient difference value or a uncombined second risk detection coefficient difference value, the uncombined first risk detection coefficient difference value or the uncombined second risk detection coefficient difference value is deleted.

[0133] The beneficial effect of the above technical solution is: the present invention calculates the risk detection coefficient of the heating network to be monitored based on all sub-coefficient difference sequences, provides a basis for judging whether the heating network to be monitored needs to be repaired, improves the accuracy and efficiency of judgment, realizes real-time judgment, and avoids the phenomenon of missed judgment.

[0134] In some embodiments of the present application, the risk repair module is used to:

[0135] The risk inspection module is used to calculate the risk detection coefficient of the heating pipe network to be monitored according to the following formula:

[0136] ;

[0137] Among them, y is the risk detection coefficient of the heating network to be monitored, n is the number of sub-coefficient difference sequences, g1 i is the first risk detection coefficient difference in the ith sub-coefficient difference sequence, g2 is the second risk detection coefficient difference in the ith sub-coefficient difference sequence, For all The minimum value in For all The maximum value in r 2 For all The variance of .

[0138] In some embodiments of the present application, the risk repair module is used to:

[0139] The risk maintenance module is used to determine whether the monitored heating pipe network needs maintenance according to the relationship between the risk detection coefficient and the preset risk detection coefficient;

[0140] The risk maintenance module is used to determine that the monitored heating pipe network does not need maintenance when the risk detection coefficient is less than the preset risk detection coefficient;

[0141] The risk maintenance module is used to determine that the monitored heating pipe network needs maintenance when the risk detection coefficient is greater than or equal to the preset risk detection coefficient.

[0142] In this embodiment, the preset risk detection coefficient is preferably 8, which is used to measure whether the monitored heating pipe network needs to be repaired and processed. The specific setting can also be made according to actual conditions.

[0143] The beneficial effects of the above technical solution are: the present invention uses data analysis to determine potential risks, greatly improves maintenance efficiency and accuracy, reduces human errors, extends the service life of the heating pipeline network, reduces maintenance costs, and has broad market application prospects and promotion value.

[0144] In order to further illustrate the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0145] Correspondingly, such as Figure 2 As shown, the present application also provides a method for repairing equipment in a heating pipe network, comprising:

[0146] S110: Determine a heating network to be monitored, divide the heating network to be monitored into multiple sub-heating network monitoring areas, and obtain real-time heating data of each sub-heating network monitoring area in real time;

[0147] S120: extracting real-time heating data of the same type from the real-time heating data corresponding to each sub-heating pipe network monitoring area, constructing a real-time heating data chain, analyzing the real-time heating data chain, and dividing the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis result;

[0148] S130: performing a first processing on the normal heating data chain, and calculating a normal heating coefficient of the normal heating data chain based on a result of the first processing;

[0149] S140: performing a second processing on the differential heating data chain, and calculating a differential heating coefficient of the differential heating data chain based on the second processing result;

[0150] S150: Construct a risk detection line for the heating network to be monitored based on all normal heating coefficients and differential heating coefficients, calculate the risk detection coefficient of the heating network to be monitored based on the risk detection line, and determine whether the heating network to be monitored needs maintenance based on the risk detection coefficient, wherein the risk detection line includes multiple risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links.

[0151] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0152] While the present invention has been described above with reference to exemplary embodiments, various modifications may be made and equivalent components may be substituted without departing from the scope of the present invention. In particular, the various features of the disclosed embodiments may be combined with one another in any manner, provided no structural conflicts exist. These combinations are not fully described in this specification for reasons of space and resource conservation.

[0153] Those skilled in the art will understand that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will still be able to modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A heating network equipment maintenance system, characterized in that: include: A data acquisition module is used to determine the heating pipe network to be monitored, divide the heating pipe network to be monitored into multiple sub-heating pipe network monitoring areas, and obtain real-time heating data of each sub-heating pipe network monitoring area in real time; a data analysis module, configured to extract real-time heating data of the same type from the real-time heating data corresponding to each sub-heating network monitoring area, construct a real-time heating data chain, analyze the real-time heating data chain, and divide the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis results; a first processing module, configured to perform a first processing on the normal heating data chain, and calculate a normal heating coefficient of the normal heating data chain based on a result of the first processing; A second processing module is used to perform a second processing on the differential heating data chain, and calculate a differential heating coefficient of the differential heating data chain based on the second processing result; A risk maintenance module is configured to construct a risk detection line for the heating network to be monitored based on all normal heating coefficients and differential heating coefficients, calculate the risk detection coefficient of the heating network to be monitored based on the risk detection line, and determine whether the heating network to be monitored requires maintenance based on the risk detection coefficient, wherein the risk detection line includes a plurality of risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links; The first processing module is used to calculate the heating data variance of the real-time heating data on the normal heating data chain; The first processing module is used to calculate the normal heating coefficient of the normal heating data link according to the following formula; ； Among them, a1 is the normal heating coefficient of the normal heating data chain, b1 is the number of real-time heating data on the normal heating data chain, f e is the e-th real-time heating data on the normal heating data chain, and f1 is the variance of the heating data; The second processing module is used to extract the same real-time heating data from the differential heating data chain and obtain multiple real-time heating data sets; The second processing module is used to count the number of first real-time heating data in the real-time heating data set; The second processing module is used to extract a real-time heating data from all the real-time heating data sets respectively, and calculate the first real-time heating data set and value; The second processing module is used to obtain preset real-time heating data, eliminate all real-time heating data sets that are smaller than the preset real-time heating data, and count the number of second real-time heating data in the remaining real-time heating data sets; The second processing module is used to extract a real-time heating data from the remaining real-time heating data sets, and calculate the second real-time heating data and value; The second processing module is used to calculate the difference heating coefficient of the difference heating data chain according to the first real-time heating data quantity, the second real-time heating data quantity, the first real-time heating data sum value and the second real-time heating data sum value.

2. The equipment maintenance system of the heating pipe network according to claim 1, characterized in that: The data analysis module is used to: The data analysis module is used to obtain a standard heating data range corresponding to each real-time heating data; The data analysis module is used to compare the real-time heating data with the standard heating data range, and if the real-time heating data is within the standard heating data range, the corresponding real-time heating data is classified as the normal heating data chain; The data analysis module is used to divide the corresponding real-time heating data into the difference heating data chain if the real-time heating data is not within the standard heating data range.

3. The equipment maintenance system of the heating pipe network according to claim 1, characterized in that: The risk maintenance module is used to: The risk inspection module is used to randomly determine two risk detection nodes and extract the corresponding first normal heating coefficient, first differential heating coefficient, second normal heating coefficient and second differential heating coefficient; The risk maintenance module is used to calculate the absolute value of the difference between the first differential heating coefficient and the second differential heating coefficient; The risk maintenance module is used to determine the maximum differential heating coefficient and the minimum differential heating coefficient from all differential heating coefficients, and calculate the differential heating coefficient extreme difference values ​​of the maximum differential heating coefficient and the minimum differential heating coefficient; The risk inspection module is used to calculate the heating coefficient ratio of the absolute value of the heating coefficient difference and the heating coefficient extreme difference; The risk maintenance module is used to calculate the absolute value of the normal heating coefficient difference between the first normal heating coefficient and the second normal heating coefficient; The risk maintenance module is used to determine the maximum normal heating coefficient and the minimum normal heating coefficient from all normal heating coefficients, and calculate the normal heating coefficient extreme difference value of the maximum normal heating coefficient and the minimum normal heating coefficient; The risk maintenance module is used to calculate the normal heating coefficient ratio of the absolute value of the heating coefficient difference and the extreme difference of the heating coefficient; The risk maintenance module is used to calculate the product of the difference heating coefficient ratio and the normal heating coefficient ratio, and use it as the sub-risk detection coefficient of the heating network to be monitored; The risk inspection module is used to analyze and calculate all remaining risk detection nodes to determine the sub-risk detection coefficients corresponding to every two risk detection nodes; The risk inspection module is used to calculate the risk detection coefficient of the heating pipe network to be monitored based on all sub-risk detection coefficients.

4. The equipment maintenance system of the heating pipe network according to claim 3, characterized in that: The risk maintenance module is used to: The risk inspection module is used to calculate the coefficient mean of all sub-risk detection coefficients; The risk inspection module is used to classify all sub-risk detection coefficients that are smaller than the coefficient mean into a first sub-risk detection coefficient sequence; The risk maintenance module is used to divide all sub-risk detection coefficients greater than or equal to the coefficient mean into a second sub-risk detection coefficient sequence; The risk inspection module is used to calculate the first risk detection coefficient difference between each sub-risk detection coefficient in the first sub-risk detection coefficient sequence and the coefficient mean, and construct a first coefficient difference sequence; The risk repair module is used to calculate the second risk detection coefficient difference between each sub-risk detection coefficient in the second sub-risk detection coefficient sequence and the coefficient mean, and construct a second coefficient difference sequence; The risk repair module is used to randomly combine the first coefficient difference sequence and the second coefficient difference sequence in pairs to obtain multiple sub-coefficient difference sequences; The risk inspection module is used to calculate the risk detection coefficient of the heating network to be monitored based on all sub-coefficient difference sequences.

5. The equipment maintenance system of the heating pipe network according to claim 4, characterized in that: The risk maintenance module is used to: The risk inspection module is used to calculate the risk detection coefficient of the heating pipe network to be monitored according to the following formula: ； Among them, y is the risk detection coefficient of the heating network to be monitored, n is the number of sub-coefficient difference sequences, g1 i is the first risk detection coefficient difference in the ith sub-coefficient difference sequence, g2 is the second risk detection coefficient difference in the ith sub-coefficient difference sequence, For all The minimum value in For all The maximum value in r 2 For all The variance of .

6. The equipment maintenance system of the heating pipe network according to claim 1, characterized in that: The risk maintenance module is used to: The risk maintenance module is used to determine whether the monitored heating pipe network needs maintenance according to the relationship between the risk detection coefficient and the preset risk detection coefficient; The risk maintenance module is used to determine that the monitored heating pipe network does not need maintenance when the risk detection coefficient is less than the preset risk detection coefficient; The risk maintenance module is used to determine that the monitored heating pipe network needs maintenance when the risk detection coefficient is greater than or equal to the preset risk detection coefficient.

7. A method for repairing equipment in a heating pipe network, applied to the equipment repair system in a heating pipe network according to any one of claims 1 to 6, characterized in that: include: Determine a heating network to be monitored, divide the heating network to be monitored into multiple sub-heating network monitoring areas, and obtain real-time heating data for each sub-heating network monitoring area in real time; Extracting real-time heating data of the same type from the real-time heating data corresponding to each sub-heating pipe network monitoring area, and constructing a real-time heating data chain, analyzing the real-time heating data chain, and dividing the real-time heating data chain into a differential heating data chain and a normal heating data chain based on the analysis results; performing a first processing on the normal heating data chain, and calculating a normal heating coefficient of the normal heating data chain based on a result of the first processing; performing a second processing on the differential heating data chain, and calculating a differential heating coefficient of the differential heating data chain based on a result of the second processing; A risk detection line of the heating network to be monitored is constructed based on all normal heating coefficients and differential heating coefficients, the risk detection coefficient of the heating network to be monitored is calculated based on the risk detection line, and whether the heating network to be monitored needs maintenance is judged based on the risk detection coefficient, wherein the risk detection line includes multiple risk detection nodes, each risk detection node includes a normal heating coefficient and a corresponding differential heating coefficient, and the number of risk detection nodes is consistent with the number of real-time heating data links.

Images