Intelligent monitoring method and system for central heating pipe network and electronic equipment

Abstract

The invention relates to the technical field of heat supply network monitoring, in particular to an intelligent monitoring system and method for a centralized heat supply network, and the method specifically comprises the steps: obtaining the basic installation information of a pipeline in the centralized heat supply network; acquiring target state data of the pipeline, and acquiring real-time state data and scale settlement data of the pipeline through a sensor network; calculating a scale accumulation threat value at the pipeline bend; calculating a pipeline heat loss coefficient of the heat supply network; and according to the basic installation information of the pipeline and the heat loss coefficient of the pipeline, the working efficiency regional stability index of the central heating pipe network is evaluated, and meanwhile, the operation suggestion of the heating pipe network is output in a targeted mode. The problem that in the prior art, when a heat source in a heat supply pipe network is transported from a heat supply main pipeline to a heat supply branch pipeline at the position where a user is located, the heat supply stability is difficult to quantitatively evaluate due to different branch pipeline structures is solved.

Description

Technical Field

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

[0002] In modern urban heating systems, the heating network undertakes the important task of efficiently transporting the heat energy produced by the heat source to various users, such as residential, commercial and industrial users. The design and operation of the heating network is crucial to ensure the stability and efficiency of heating. However, in actual operation, due to the significant differences in the structure of the heating network and the design of the branch pipelines at the user end, it is difficult to quantify the heating stability and heating effect, which in turn affects the user's heating comfort and energy efficiency. Regarding how to improve the stability of the heating area of ​​the "last kilometer of heating" of the heating network, it is urgent to develop a set of systematic evaluation methods and adjustment mechanisms to improve the intelligence level and service quality of the heating system.

[0003] To this end, the technical problem to be solved by the present invention is: in the prior art, when the heat source of the heating network is transported from the heating main pipeline to the heating branch pipeline at the user's location, due to the significant differences in the structure of the heating network and the design of the branch pipeline at the user end, the heating stability and heating effect are difficult to quantify and evaluate, which in turn affects the user's heating comfort and energy utilization efficiency. Summary of the invention

[0004] The present invention provides an intelligent monitoring system, method and electronic equipment for a centralized heating network, so as to solve the technical problem in the prior art that when the heat source of the heating network is transported from the heating main pipeline to the heating branch pipeline at the user's location, the heating stability and heating effect are difficult to be quantitatively evaluated due to the significant difference between the structure of the heating network and the design of the branch pipeline at the user end, thereby affecting the heating comfort and energy utilization efficiency of the user.

[0005] The specific plan is as follows:

[0006] A method for intelligent monitoring of a central heating network comprises the following steps:

[0007] Step S1: obtaining basic installation information of pipelines in the central heating network, calculating the tortuosity of the branch pipelines, evaluating the tortuosity level of the branch pipelines according to the tortuosity, and obtaining the scale accumulation probability weight corresponding to the tortuosity level;

[0008] Step S2: Obtain target state data of the pipeline, and obtain real-time state data and scale precipitation data of the pipeline through a sensor network;

[0009] Step S3: Substituting the data obtained in step S2 into the calculation strategy of the scale accumulation threat value at the pipe bend to calculate the scale accumulation threat value at the pipe bend;

[0010] Step S4: Substituting the scale accumulation threat value into the heat loss coefficient calculation strategy of the pipeline of the heating network to calculate the heat loss coefficient of the pipeline of the heating network;

[0011] Step S5: According to the basic installation information of the pipeline, the heat loss coefficient of the pipeline, and the scale accumulation probability weight, the work efficiency regional stability index of the centralized heating network is evaluated, and the instability threshold of the heating network is preset. When the work efficiency regional stability index is greater than the instability threshold, the work efficiency regional stability index is arranged in descending order, and targeted operation suggestions for the heating network are output at the same time.

[0012] The step S1 comprises the following specific steps:

[0013] S11: Extract the pipeline structure information of each branch pipeline, including: the total length data sl of the branch pipeline, the total number data I of the pipeline elbows of the branch pipeline, the angle data θ of the pipeline elbows of the branch pipeline i , the pipe elbow radius B of the branch pipe and the branch pipe diameter data DL;

[0014] S12: Importing the pipeline structure information into the branch pipeline tortuosity calculation strategy to obtain the branch pipeline tortuosity. The branch pipeline tortuosity calculation strategy is: Where e represents the tortuosity of the branch pipe, θ i represents the angle data at the i-th pipe elbow; θ′ represents the angle data at the branch pipe with θ i The angle data with the smallest difference;

[0015] S13: extract the tortuosity of the branch pipeline, and evaluate the tortuosity level of the branch pipeline according to the tortuosity of the branch pipeline; when e∈[E0,E1], the branch pipeline is evaluated to be slightly tortuous, wherein the probability weight of scale accumulation of the slightly tortuous level is g1;

[0016] When e∈(E1,E2], the branch pipeline is assessed to belong to the general tortuosity level, where the scale accumulation probability weight of the general tortuosity level is g2;

[0017] When e∈(E2,E3], the branch pipeline is evaluated as belonging to the special tortuosity level, where the scale accumulation of the special tortuosity level

[0018] The cumulative probability weight is g3;

[0019] S14: extracting pipeline usage information of each branch pipeline, specifically including: actual pipeline usage time l, cumulative pipeline installation time h, and pipeline replacement cycle L.

[0020] In step S3, the scale accumulation threat value calculation strategy is as follows:

[0021]

[0022] Among them, w i is the scale accumulation threat value at the i-th pipe elbow; SC is the scale threat coefficient, SC = 1.88;

[0023] d i (t) is the thickness of the scale layer at the i-th pipe elbow at the monitoring time point t;

[0024] Q t ,Q mb They are the actual flow rate of the pipeline and the standard flow rate of the pipeline when it leaves the factory;

[0025] v(t) represents the velocity reduction factor of the fluid at the pipe elbow at the monitoring time point t; f represents the velocity reduction factor of the fluid at the pipe elbow at the monitoring time point t;

[0026] Friction factor at the elbow;

[0027] γ and δ are the influence coefficients of scale thickness and fluid flow rate, respectively.

[0028] The step S4 comprises the following specific steps:

[0029] S41: extract the pipeline usage information and construct a pipeline aging function, wherein the pipeline aging function J(l,h,L) is: l is the actual service time of the pipeline, h is the cumulative installation time of the pipeline, L is the replacement cycle of the pipeline, μ1 and μ2 are the aging factor ratios;

[0030] S42: Substituting the scale accumulation threat value and the pipeline aging function into the pipeline heat loss coefficient calculation strategy of the heating network to calculate the pipeline heat loss coefficient of the heating network. The pipeline heat loss coefficient calculation strategy is as follows:

[0031]

[0032] Among them, q i is the heat loss coefficient of the pipe at the i-th pipe elbow;

[0033] U is the thermal conductivity coefficient; S i is the liquid flow cross-sectional area at the i-th pipe elbow;

[0034] T insid ,T outside They are the temperature data of the inner surface and the temperature data of the outer surface of the pipe elbow respectively;

[0035] r i is the thermal resistance coefficient of the scale layer at the i-th pipe elbow; r i′ is the thermal resistance coefficient of the pipe at the i-th pipe elbow;

[0036] R is the thermal resistance coefficient of the straight pipe;

[0037] k θ is the bend loss coefficient of the pipe elbow, k 45 =0.8, k 90 =1.5;

[0038] The thermal resistance coefficient of the scale layer at the pipe elbow is r i The calculation strategy is as follows:

[0039]

[0040] Where C is the thermal conductivity of scale.

[0041] The step S5 comprises the following specific steps:

[0042] S51: extracting basic installation information of pipelines and heat loss coefficient of pipelines;

[0043] S52: According to S51 and S1, the work efficiency regional stability index of the centralized heating network is evaluated. The evaluation strategy of the work efficiency regional stability index Φ is specifically as follows:

[0044]

[0045] Where x and i are both subscripts, X represents the total number of branch pipes in the heating area;

[0046] g(x) represents the scale accumulation probability weight corresponding to the tortuosity level of the x-th branch pipeline, g(x)∈(g1,g2,g3);

[0047] S53: presetting an instability threshold of the heating network, and when the working efficiency area stability index is greater than the instability threshold, arranging the working efficiency area stability indexes in descending order, and preferentially optimizing the fluid flow rate or fluid temperature of the heating network for branch pipes in the heating area with a low working efficiency area stability index;

[0048] S54: Continue to evaluate the work efficiency area stability index of the optimized heating area in the next monitoring period; when the work efficiency area stability index is still greater than the unstable threshold in the next monitoring period, introduce a water quality treatment device into the heating pipe network system to soften the scale.

[0049] A centralized heating pipe network intelligent monitoring system comprises a basic information acquisition module, a status data acquisition module, a scale risk calculation module, a pipe network loss assessment module and a pipe network operation suggestion visualization module; the basic information acquisition module, the status data acquisition module and the pipe network loss assessment module are all connected to the scale risk calculation module; the pipe network loss assessment module is also connected to the pipe network operation suggestion visualization module.

[0050] An electronic device includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned centralized heating pipe network intelligent monitoring system and method by calling the computer program stored in the memory.

[0051] An electronic device includes a computer-readable storage medium storing an erasable computer program; when the computer program runs on the computer device, the computer device executes the above-mentioned intelligent monitoring system and method for a centralized heating pipe network.

[0052] The beneficial effects of the present invention are:

[0053] The present invention provides a method for intelligent monitoring of a centralized heating network. By calculating the scale threat value, the heat loss coefficient and the work efficiency, a standardized evaluation method and an intelligent optimization mechanism are established, and operation suggestions for the heating network can be output in a targeted manner. An intelligent monitoring system for a centralized heating network is provided. By collecting and calculating pipeline data and scale data, real-time monitoring of the heating network is achieved, the heating service can be accurately adjusted, and personalized optimization can be performed for different users, thereby improving service stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of the method of the present invention.

[0055] Figure 2 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the implementation of the present invention, not all of the implementations. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] like Figure 1 As shown, the present invention provides a method for intelligent monitoring of a centralized heating network, which specifically includes the following steps:

[0058] Step S1: obtaining basic installation information of pipelines in the central heating network, calculating the tortuosity of the branch pipelines, evaluating the tortuosity level of the branch pipelines according to the tortuosity, and obtaining the scale accumulation probability weight corresponding to the tortuosity level;

[0059] Step S2: Obtain target state data of the pipeline, and obtain real-time state data and scale precipitation data of the pipeline through a sensor network;

[0060] Step S3: Substituting the data obtained in step S2 into the calculation strategy of the scale accumulation threat value at the pipe bend to calculate the scale accumulation threat value at the pipe bend;

[0061] Step S4: Substituting the scale accumulation threat value into the heat loss coefficient calculation strategy of the pipeline of the heating network to calculate the heat loss coefficient of the pipeline of the heating network;

[0062] Step S5: According to the basic installation information of the pipeline, the heat loss coefficient of the pipeline, and the scale accumulation probability weight, the work efficiency regional stability index of the centralized heating network is evaluated, and the instability threshold of the heating network is preset. When the work efficiency regional stability index is greater than the instability threshold, the work efficiency regional stability index is arranged in descending order, and targeted operation suggestions for the heating network are output at the same time.

[0063] The present invention provides an intelligent monitoring method for a centralized heating network. By calculating the scale threat value, the heat loss coefficient and the work efficiency, a standardized evaluation method and an intelligent optimization mechanism are established, so that real-time monitoring of the heating network can be achieved and targeted operation suggestions for the heating network can be output. In this way, the heating service can be accurately adjusted, and personalized optimization can be performed for different users, thereby improving service stability.

[0064] The step S1 comprises the following specific steps:

[0065] S11: Extract the pipeline structure information of each branch pipeline, including: the total length data sl of the branch pipeline, the total number data I of the pipeline elbows of the branch pipeline, the angle data θ of the pipeline elbows of the branch pipeline i , the pipe elbow radius B of the branch pipe and the branch pipe diameter data DL;

[0066] S12: Importing the pipeline structure information into the branch pipeline tortuosity calculation strategy to obtain the branch pipeline tortuosity. The branch pipeline tortuosity calculation strategy is: Where e represents the tortuosity of the branch pipe, θ i represents the angle data at the i-th pipe elbow; θ′ represents the angle data at the branch pipe with θ i; wherein, it should be noted that, when calculating, the pipe elbow radius B of the branch pipe and the branch pipe diameter data DL use the values ​​of the corresponding branch pipe for different branch pipes. For example, in one embodiment, one branch pipe is evaluated, and the pipe elbow radius B of the branch pipe and the branch pipe diameter data DL used at this time are a fixed value. In another embodiment, another branch pipe is evaluated, and the pipe elbow radius B of the branch pipe and the branch pipe diameter data DL used at this time are another fixed value. The values ​​in these two embodiments may be the same or different, and are determined according to the specific branch pipe used. Once a branch pipe is determined, the values ​​of the pipe elbow radius B of the branch pipe and the branch pipe diameter data DL are also determined;

[0067] S13: extracting the tortuosity of the branch pipeline, and evaluating the tortuosity level of the branch pipeline according to the tortuosity of the branch pipeline;

[0068] When e∈[E0,E1], the branch pipeline is assessed to be of the slightly tortuous grade, where the probability weight of scale accumulation of the slightly tortuous grade is g1;

[0069] When e∈(E1,E2], the branch pipeline is assessed to belong to the general tortuosity level, where the scale accumulation probability weight of the general tortuosity level is g2;

[0070] When e∈(E2,E3], the branch pipeline is evaluated to belong to the special tortuosity level, wherein the scale accumulation probability weight of the special tortuosity level is g3; the threshold is determined in advance according to the properties of the branch pipeline itself, the tortuosity level of the branch pipeline is divided, the tortuosity range corresponding to different tortuosity levels and the scale accumulation probability weights corresponding to different tortuosity levels are determined, and when the calculated tortuosity falls within a certain tortuosity range, the tortuosity level and scale accumulation probability weight corresponding to the range are determined accordingly;

[0071] S14: extracting pipeline usage information of each branch pipeline, specifically including: actual pipeline usage time l, cumulative pipeline installation time h, and pipeline replacement cycle L.

[0072] The data acquired in step S2 is the data required for the formula calculation in steps S3 and S4, specifically:

[0073] Target state data refers to the data in the factory state, including the standard flow rate Q of the pipeline in the factory state mb ;

[0074] Real-time status data including actual pipeline flow Q t , the velocity reduction factor v(t) of the fluid at the pipe elbow, the friction factor f of the fluid at the pipe elbow, the liquid flow cross-sectional area S at the pipe elbow i, the temperature data of the inner surface of the pipe elbow T insid , the temperature data of the outer surface of the pipe elbow T outside , are all data obtained by actual measurement using the instrument, and also include the thermal conductivity coefficient U, which refers to the properties of the fluid itself and is related to temperature; the fluid flow influence coefficient δ is a set proportional coefficient, which is a specific value, and the pipe thermal resistance coefficient r at the pipe elbow determined according to the pipe material i ' and the thermal resistance coefficient R of the straight pipe;

[0075] Scale deposition data include scale threat coefficient SC, scale thickness influence coefficient γ, scale thermal conductivity C, and scale layer thickness d of pipe elbows. i (t).

[0076] In step S3, the scale accumulation threat value calculation strategy is as follows:

[0077]

[0078] Among them, w i is the scale accumulation threat value at the i-th pipe elbow; SC is the scale threat coefficient, SC = 1.88;

[0079] d i (t) is the thickness of the scale layer at the i-th pipe elbow at the monitoring time point t;

[0080] Q t ,Q mb They are the actual flow rate of the pipeline and the standard flow rate of the pipeline when it leaves the factory;

[0081] v(t) represents the velocity reduction factor of the fluid at the pipe elbow at the monitoring time point t; f is the friction factor of the fluid at the pipe elbow;

[0082] γ and δ are the influence coefficients of scale thickness and fluid flow rate, respectively.

[0083] The step S4 comprises the following specific steps:

[0084] S41: extract the pipeline usage information and construct a pipeline aging function, wherein the pipeline aging function J(l,h,L) is: l is the actual service time of the pipeline, h is the cumulative installation time of the pipeline, L is the replacement cycle of the pipeline, μ1 and μ2 are the aging factor ratios;

[0085] S42: Substituting the scale accumulation threat value and the pipeline aging function into the pipeline heat loss coefficient calculation strategy of the heating network to calculate the pipeline heat loss coefficient of the heating network. The pipeline heat loss coefficient calculation strategy is as follows:

[0086]

[0087] Among them, q i is the heat loss coefficient of the pipe at the i-th pipe elbow;

[0088] U is the thermal conductivity coefficient; S i is the liquid flow cross-sectional area at the i-th pipe elbow;

[0089] T insid ,T outside They are the temperature data of the inner surface and the temperature data of the outer surface of the pipe elbow respectively;

[0090] r i is the thermal resistance coefficient of the scale layer at the i-th pipe elbow; r i ′ is the thermal resistance coefficient of the pipe at the i-th pipe elbow; R is the thermal resistance coefficient of the straight pipe;

[0091] k θ is the bend loss coefficient of the pipe elbow, k 45 =0.8, k 90 =1.5;

[0092] The thermal resistance coefficient of the scale layer at the pipe elbow is r i The calculation strategy is as follows:

[0093]

[0094] Where C is the thermal conductivity of scale.

[0095] In this embodiment, the thermal conductivity C of the scale is obtained by simulation using the finite element analysis method. Here, a method for obtaining the thermal conductivity of the scale is given, as follows:

[0096] a. Establish geometric model

[0097] Create scale geometry model: Create a geometric model of the scale layer in the finite element software. Assuming that the scale is evenly distributed, it can be represented by a simple geometric shape, a cylinder.

[0098] Determine the pipe size: including the inner diameter, wall thickness and thickness of the scale layer;

[0099] b. Select material properties, boundary conditions, and initial conditions

[0100] Material properties of scale: Select an appropriate material model for scale, including thermal conductivity, specific heat capacity, and density;

[0101] Boundary conditions: Set the boundary conditions of the model, which in this example are the temperatures at both ends of the pipe;

[0102] Initial conditions: Set the initial temperature distribution, which is uniform in this embodiment.

[0103] After step a and step b are completed, finite element analysis is performed as follows:

[0104] S421: Meshing and mesh generation: Divide the geometric model into small finite element meshes, especially using the finest mesh in the scale layer;

[0105] S422: Input the thermal conductivity and other thermophysical properties of the scale of each small unit cell into the finite element software, including specific heat capacity and density;

[0106] S423: setting the heat conduction equation to ▽(C▽Te)=0, where C is the thermal conductivity of the scale layer and Te is the temperature; and performing a steady-state analysis on the heat conduction equation;

[0107] S424: running simulation finite element software to obtain the temperature distribution of the scale layer, and analyzing the simulation results, including the distribution of temperature gradient and heat flow;

[0108] S425: Through the simulation results, the thermal conductivity of the scale layer is finally inferred to input into the calculation.

[0109] The step S5 comprises the following specific steps:

[0110] S51: extracting basic installation information of pipelines and heat loss coefficient of pipelines;

[0111] S52: According to S51 and S1, the work efficiency regional stability index of the centralized heating network is evaluated. The evaluation strategy of the work efficiency regional stability index Φ is specifically as follows:

[0112]

[0113] Where x and i are both subscripts, X represents the total number of branch pipes in the heating area;

[0114] g(x) represents the scale accumulation probability weight corresponding to the tortuosity level of the x-th branch pipeline, g(x)∈(g1,g2,g3);

[0115] S53: presetting an instability threshold of the heating network, and when the working efficiency area stability index is greater than the instability threshold, arranging the working efficiency area stability indexes in descending order, and preferentially optimizing the fluid flow rate or fluid temperature of the heating network for branch pipes in the heating area with a low working efficiency area stability index;

[0116] S54: Continue to evaluate the work efficiency area stability index of the optimized heating area in the next monitoring period; in the next monitoring period, when the work efficiency area stability index is still greater than the unstable threshold, introduce a water quality treatment device into the heating network system to soften the scale.

[0117] In this embodiment, the water treatment device includes adding anti-scaling agent and softening water treatment.

[0118] like Figure 2 As shown, the present invention provides a centralized heating pipe network intelligent monitoring system, comprising the following modules: a basic information acquisition module, a state data acquisition module, a scale risk calculation module, a pipe network loss assessment module and a pipe network operation suggestion visualization module; the basic information acquisition module, the state data acquisition module and the pipe network loss assessment module are all connected to the scale risk calculation module; the pipe network loss assessment module is also connected to the pipe network operation suggestion visualization module;

[0119] The basic information acquisition module is used to obtain basic installation information of pipelines in the central heating network;

[0120] The state data acquisition module is used to obtain the target state data of the pipeline, and obtain the real-time state data and scale precipitation data of the pipeline through the sensor network;

[0121] The scale risk calculation module is used to calculate the scale accumulation threat value at the pipe bend;

[0122] The pipe network loss assessment module is used to calculate the pipe heat loss coefficient of the heating pipe network;

[0123] The network operation suggestion visualization module evaluates the work efficiency regional stability index of the centralized heating network according to the basic installation information of the pipeline and the pipeline heat loss coefficient, and presets the instability threshold of the heating network. When the work efficiency regional stability index is greater than the threshold, the work efficiency regional stability index is arranged in descending order, and at the same time, targeted operation suggestions for the heating network are output.

[0124] The present invention can understand and dynamically monitor the pipe network structure in detail through the basic information acquisition module and the status data acquisition module, which helps to identify and solve the problems of uneven heating, increased heat loss and delayed system response.

[0125] The present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned centralized heating pipe network intelligent monitoring system and method by calling the computer program stored in the memory.

[0126] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement a centralized heating network intelligent monitoring system and method provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0127] The present invention provides a computer-readable storage medium on which an erasable computer program is stored; when the computer program is run on a computer device, the computer device executes the above-mentioned intelligent monitoring system and method for a centralized heating pipe network.

[0128] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0129] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0130] The present invention provides an intelligent monitoring system, method and electronic equipment for a centralized heating pipe network. Through the acquisition of information by a basic information acquisition module and a status data acquisition module, the calculation of the scale threat value, the calculation of the heat loss coefficient and the evaluation of the working efficiency, a standardized evaluation method and an intelligent optimization mechanism are established, which can understand and dynamically monitor the pipe network structure in detail, help to identify and solve the problems of uneven heating, increased heat loss and system response delay, comprehensively consider the influence of branch pipes on the user side, and make up for the deficiencies of the existing technology in design and monitoring through real-time monitoring and dynamic adjustment, and significantly improve the overall performance of the heating system.

[0131] The technical means disclosed in the scheme of the present invention are not limited to the technical means disclosed in the above-mentioned implementation mode, but also include technical schemes composed of any combination of the above-mentioned technical features. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also regarded as the protection scope of the present invention.

Claims

1. A method for intelligent monitoring of a central heating network, characterized in that: The specific steps include: Step S1: obtaining basic installation information of pipelines in the central heating network, calculating the tortuosity of the branch pipelines, evaluating the tortuosity level of the branch pipelines according to the tortuosity, and obtaining the scale accumulation probability weight corresponding to the tortuosity level; Step S2: Obtain target state data of the pipeline, and obtain real-time state data and scale precipitation data of the pipeline through a sensor network; Step S3: Substituting the data obtained in step S2 into the calculation strategy of the scale accumulation threat value at the pipe bend to calculate the scale accumulation threat value at the pipe bend; Step S4: Substituting the scale accumulation threat value into the heat loss coefficient calculation strategy of the pipeline of the heating network to calculate the heat loss coefficient of the pipeline of the heating network; Step S5: According to the basic installation information of the pipeline, the heat loss coefficient of the pipeline, and the scale accumulation probability weight, the work efficiency regional stability index of the centralized heating network is evaluated, and the instability threshold of the heating network is preset. When the work efficiency regional stability index is greater than the instability threshold, the work efficiency regional stability index is arranged in descending order, and targeted operation suggestions for the heating network are output at the same time.

2. The intelligent monitoring method for a central heating network according to claim 1, characterized in that: The step S1 comprises the following specific steps: S11: Extract the pipeline structure information of each branch pipeline, including: the total length data sl of the branch pipeline, the total number data I of the pipeline elbows of the branch pipeline, the angle data θ of the pipeline elbows of the branch pipeline i , the pipe elbow radius B of the branch pipe and the branch pipe diameter data DL; S12: Importing the pipeline structure information into the branch pipeline tortuosity calculation strategy to obtain the branch pipeline tortuosity. The branch pipeline tortuosity calculation strategy is: Where e represents the tortuosity of the branch pipe, θ i represents the angle data at the i-th pipe elbow; θ′ represents the angle data at the branch pipe with θ i The angle data with the smallest difference; S13: extract the tortuosity of the branch pipeline, and evaluate the tortuosity level of the branch pipeline according to the tortuosity of the branch pipeline; when e∈[E0,E1], the branch pipeline is evaluated to be slightly tortuous, wherein the probability weight of scale accumulation of the slightly tortuous level is g1; When e∈(E1,E2], the branch pipeline is assessed to belong to the general tortuosity level, where the scale accumulation probability weight of the general tortuosity level is g2; When e∈(E2,E3], the branch pipeline is evaluated as belonging to the special tortuosity level, where the scale accumulation probability weight of the special tortuosity level is g3; S14: extracting pipeline usage information of each branch pipeline, specifically including: actual pipeline usage time l, cumulative pipeline installation time h, and pipeline replacement cycle L.

3. The intelligent monitoring method for a central heating network according to claim 1, characterized in that: In step S3, the scale accumulation threat value calculation strategy is as follows: Among them, w i is the scale accumulation threat value at the i-th pipe elbow; SC is the scale threat coefficient, SC = 1.88; d i (t) is the thickness of the scale layer at the i-th pipe elbow at the monitoring time point t; Q t ,Q mb They are the actual flow rate of the pipeline and the standard flow rate of the pipeline when it leaves the factory; v(t) represents the velocity reduction factor of the fluid at the pipe elbow at the monitoring time point t; f is the friction factor of the fluid at the pipe elbow; γ and δ are the influence coefficients of scale thickness and fluid flow rate, respectively.

4. The intelligent monitoring method for a central heating network according to claim 1, characterized in that: The step S4 comprises the following specific steps: S41: extract the pipeline usage information and construct a pipeline aging function, wherein the pipeline aging function J(l,h,L) is: l is the actual service time of the pipeline, h is the cumulative installation time of the pipeline, L is the replacement cycle of the pipeline, μ1 and μ2 are the aging factor ratios; S42: Substituting the scale accumulation threat value and the pipeline aging function into the pipeline heat loss coefficient calculation strategy of the heating network to calculate the pipeline heat loss coefficient of the heating network. The pipeline heat loss coefficient calculation strategy is as follows: Among them, q i is the heat loss coefficient of the pipe at the i-th pipe elbow; U is the thermal conductivity coefficient; S i is the liquid flow cross-sectional area at the i-th pipe elbow; T insid ,T outside They are the temperature data of the inner surface and the temperature data of the outer surface of the pipe elbow respectively; r i is the thermal resistance coefficient of the scale layer at the i-th pipe elbow; r i ′ is the thermal resistance coefficient of the pipe at the i-th pipe elbow; R is the thermal resistance coefficient of the straight pipe; k θ is the bend loss coefficient of the pipe elbow, k 45 =0.8, k 90 =1.5; The thermal resistance coefficient of the scale layer at the pipe elbow is r i The calculation strategy is as follows: Where C is the thermal conductivity of scale.

5. The intelligent monitoring method for a central heating network according to claim 1, characterized in that: The step S5 comprises the following specific steps: S51: extracting basic installation information of pipelines and heat loss coefficient of pipelines; S52: According to S51 and S1, the work efficiency regional stability index of the centralized heating network is evaluated. The evaluation strategy of the work efficiency regional stability index Φ is specifically as follows: Where x and i are both subscripts, X represents the total number of branch pipes in the heating area; g(x) represents the scale accumulation probability weight corresponding to the tortuosity level of the x-th branch pipeline, g(x)∈(g1,g2,g3); S53: presetting an instability threshold of the heating network, and when the working efficiency area stability index is greater than the instability threshold, arranging the working efficiency area stability indexes in descending order, and preferentially optimizing the fluid flow rate or fluid temperature of the heating network for branch pipes in the heating area with a low working efficiency area stability index; S54: Continue to evaluate the work efficiency regional stability index of the optimized heating area in the next monitoring period; When the working efficiency area stability index is still greater than the unstable threshold in the next monitoring period, a water quality treatment device is introduced into the heating network system to soften the scale.

6. An intelligent monitoring system for a central heating network, characterized in that: It includes basic information acquisition module, status data collection module, scale risk calculation module, pipe network loss assessment module and pipe network operation suggestion visualization module; The basic information acquisition module, the status data acquisition module, and the pipe network loss assessment module are all connected to the scale risk calculation module; the pipe network loss assessment module is also connected to the pipe network operation suggestion visualization module.

7. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned centralized heating pipe network intelligent monitoring system and method by calling the computer program stored in the memory.

8. The electronic device according to claim 7, characterized in that: It includes a computer-readable storage medium storing an erasable computer program; when the computer program is run on a computer device, the computer device executes the above-mentioned intelligent monitoring system and method for a centralized heating pipe network.

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