Intelligent diagnosis management method and device for regional boiler room cluster pipe network
By monitoring and processing the operating status data of the regional boiler room cluster heating pipeline network, calculating the probability of failure points and generating strategies, the problem of low intelligence of intelligent diagnosis management is solved, and the precise positioning and efficient handling of faults are achieved.
Patent Information
- Application Number
- CN202510476329.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-16
- Publication Date
- 2025-08-08
AI Technical Summary
In the prior art, the intelligent level of intelligent diagnosis management of regional boiler room cluster pipeline network is low, resulting in poor management results.
The operating status of the heating pipeline network is monitored through sensors, data is obtained and preprocessed, the probability of failure points is calculated, fault processing strategies are generated, and target fault clusters and edge abnormal points are identified using clustering algorithms to generate accurate fault processing strategies.
It realizes accurate positioning of faults and accurate fault handling, improving diagnostic management efficiency.
Smart Images
Figure CN120449028A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to intelligent management technology, and in particular to an intelligent diagnosis and management method and device for a regional boiler room cluster pipe network. Background Art
[0002] Regional boiler room clusters can cover an area of tens of square kilometers, with pipeline lengths exceeding 100 kilometers. This requires promoting the transformation of heating systems towards high efficiency and low carbon, as well as intelligent diagnosis to reduce energy consumption and emissions.
[0003] At present, the intelligent diagnosis and management of regional boiler room cluster pipelines is mainly carried out through manual inspections, and diagnosis and maintenance are provided based on experience.
[0004] However, the above methods have a low level of intelligence, resulting in poor targeted management effects. Summary of the Invention
[0005] Based on the above problems, the present invention is proposed to provide a method and device for intelligent diagnosis and management of regional boiler room cluster pipe networks that overcome the above problems or at least partially solve the above problems.
[0006] According to one aspect of the present invention, a method for intelligent diagnosis and management of a regional boiler room cluster pipe network is provided, comprising the following steps: Monitor the operating status of the heating pipe network of the regional boiler room cluster through sensors and obtain operating status data; Preprocessing the operating status data to obtain preprocessed operating status data; Based on the pre-processed operating status data, the probability corresponding to the fault point of the heating pipe network of the regional boiler room cluster is obtained; According to the probability corresponding to the fault point, a fault handling strategy is generated to perform intelligent diagnosis and management of the regional boiler room cluster pipe network based on the fault handling strategy.
[0007] Optionally, based on the pre-processed operating status data, obtaining the probability corresponding to the failure point of the heating pipe network of the regional boiler room cluster includes: Obtain thermal parameters, power parameters, and water quality and equipment operating parameters. Thermal parameters include supply water temperature, return water temperature, specific heat capacity, and hot water flow in each branch pipe network. Power parameters include the operating frequency of the circulating water pump, supply water pressure, and return water pressure. Water quality and equipment operating parameters include water pH, water hardness, and boiler operating load. Based on thermal and power parameters as well as water quality and equipment operating parameters, the probability of failure points in the heating network of the regional boiler room cluster is calculated.
[0008] Optionally, the formula for calculating the probability P corresponding to the failure point of the heating network of the regional boiler room cluster is as follows: Where c1 represents the specific heat capacity, Q represents the hot water flow in each branch pipe network, and Q d represents the theoretical heat flow, ΔT represents the temperature difference of the pipe network, f represents the operating frequency of the circulating water pump, f d Indicates the optimal operating frequency of the circulating water pump, pH indicates the acidity and alkalinity of the water, PH d Indicates neutral pH value, H indicates water hardness, H d Indicates standard water hardness, L indicates boiler operating load, L d Indicates the rated operating load of the boiler, ΔP d Indicates the theoretical pipe network pressure difference, T s Indicates the water supply temperature, T r Indicates the return water temperature, ΔT d Indicates the expected network temperature difference, P s Indicates water supply pressure, P r represents the return water pressure, and a, b, c2, d, e, e, g, and h are weight coefficients.
[0009] Optionally, the weight coefficient satisfies the following formula: a+b+c²+d+e+g+h=1 Among them, a represents the first weight coefficient, b represents the second weight coefficient, c2 represents the third weight coefficient, d represents the fourth weight coefficient, e represents the fifth weight coefficient, g represents the sixth weight coefficient, and h represents the seventh weight coefficient.
[0010] Optionally, a fault handling strategy is generated based on the probability corresponding to the fault point, including: Construct clusters based on the probability corresponding to the fault points; Target fault clusters and edge anomalies are identified in the clustering clusters, and fault handling strategies are generated based on the target fault clusters and edge anomalies.
[0011] According to another aspect of the present invention, there is provided an intelligent diagnosis and management device for a regional boiler room cluster pipe network, comprising: a first acquisition module for monitoring the operating status of a heating pipe network of a regional boiler room cluster through a sensor and acquiring operating status data; A data processing module is used to pre-process the operation status data to obtain pre-processed operation status data; The second acquisition module is used to obtain the probability corresponding to the fault point of the heating pipe network of the regional boiler room cluster based on the pre-processed operating status data; The diagnosis management module is used to generate a fault handling strategy according to the probability corresponding to the fault point, so as to perform intelligent diagnosis and management of the regional boiler room cluster pipe network based on the fault handling strategy.
[0012] According to the solution of the present invention, in the present invention, the operating status of the heating network of the regional boiler room cluster is first monitored by sensors to obtain operating status data, and then the operating status data is preprocessed to obtain preprocessed operating status data, so as to obtain the probability corresponding to the fault point of the heating network of the regional boiler room cluster based on the preprocessed operating status data, which not only can accurately locate the fault, but also obtain the accurate probability corresponding to the fault point; then, according to the probability corresponding to the fault point, a fault handling strategy is generated to execute intelligent diagnosis and management of the regional boiler room cluster network based on the fault handling strategy, which can improve the accuracy of the fault handling strategy and thereby improve the efficiency and effect of diagnosis and management. BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Figure 1 A flowchart of a method for intelligent diagnosis and management of a regional boiler room cluster pipe network according to an embodiment of the present invention is shown; Figure 2 A structural block diagram of a regional boiler room cluster pipe network intelligent diagnosis and management device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION
[0014] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0015] like Figure 1 As shown, this embodiment proposes a method for intelligent diagnosis and management of a regional boiler room cluster pipe network, including the following steps: Step S101: monitor the operating status of the heating pipe network of the regional boiler room cluster through sensors to obtain operating status data.
[0016] Among them, the operating status data includes thermal parameters, power parameters, and water quality and equipment operating parameters. Among them, thermal parameters include water supply temperature, return water temperature, specific heat capacity and hot water flow in each branch pipe network. Power parameters include the operating frequency of the circulating water pump, water supply pressure and return water pressure. Water quality and equipment operating parameters include water pH, water hardness and boiler operating load.
[0017] Water supply temperature: refers to the temperature of hot water flowing out of the boiler room and entering the pipe network system. It directly affects the heating effect. Generally, during the heating season, the water supply temperature of the hot water boiler may be around 80℃-95℃.
[0018] Return water temperature: It is the temperature of hot water that flows back to the boiler room after being dissipated by the user. Under normal circumstances, the return water temperature will be about 15℃-25℃ lower than the supply water temperature. For example, when the supply water is 90℃, the return water may be 65℃-75℃.
[0019] Specific heat capacity: refers to the amount of heat absorbed (or released) when the temperature of a unit mass of hot water increases (or decreases) by 1 degree Celsius.
[0020] Hot water flow in each branch pipe network: used to monitor whether the heating supply in each area is balanced. By adjusting the valves and other equipment on the branch pipe network, the flow of each branch can be adjusted to achieve hydraulic balance.
[0021] The operating frequency of the circulating water pump determines the circulation speed of hot water, usually between 30Hz-50Hz.
[0022] Water supply pressure: The pressure that ensures that hot water can overcome resistance in the pipe network and be smoothly delivered to each user end. Generally speaking, the water supply pressure of the primary pipe network may be between 1.0MPa-1.6MPa, and the water supply pressure of the secondary pipe network is relatively low, at around 0.6MPa-1.0MPa.
[0023] Return water pressure: The pressure of return water when flowing in the pipe network is usually lower than the supply water pressure, with a difference of about 0.2MPa-0.4MPa, to ensure that hot water can flow back to the boiler room normally.
[0024] Boiler operating load: Boiler operating load refers to the actual amount of steam generated by the boiler per unit time or the heat output capacity.
[0025] Step S102: pre-processing the running status data to obtain pre-processed running status data.
[0026] Preprocessing at least includes data cleaning and data conversion. Data cleaning includes missing value processing and outlier processing. Data conversion includes format conversion and encoding conversion.
[0027] Step S103: Based on the pre-processed operating status data, the probability corresponding to the fault point of the heating pipe network of the regional boiler room cluster is obtained.
[0028] In order to obtain the probability corresponding to the fault point of the heating network of the regional boiler room cluster based on the preprocessed operating status data, it is necessary to first obtain the thermal parameters, power parameters, water quality and equipment operating parameters. Among them, the thermal parameters include the supply water temperature, return water temperature, specific heat capacity and the hot water flow in each branch pipe network; the power parameters include the operating frequency of the circulating water pump, the supply water pressure and the return water pressure; the water quality and equipment operating parameters include the water pH, water hardness and the operating load of the boiler. Then, based on the thermal parameters, power parameters, water quality and equipment operating parameters, the probability corresponding to the fault point of the heating network of the regional boiler room cluster is calculated.
[0029] The formula for calculating the probability P of a failure point in the heating network of a regional boiler room cluster is as follows: Where c1 represents the specific heat capacity, Q represents the hot water flow in each branch pipe network, and Q d represents the theoretical heat flow, ΔT represents the temperature difference of the pipe network, f represents the operating frequency of the circulating water pump, f d Indicates the optimal operating frequency of the circulating water pump, pH indicates the acidity and alkalinity of the water, PH d Indicates neutral pH value, H indicates water hardness, H d Indicates standard water hardness, L indicates boiler operating load, L d Indicates the rated operating load of the boiler, ΔP d Indicates the theoretical pipe network pressure difference, T s Indicates the water supply temperature, T r Indicates the return water temperature, ΔT d Indicates the expected network temperature difference, P s Indicates water supply pressure, P r represents the return water pressure, and a, b, c2, d, e, g, and h are weight coefficients.
[0030] Among them, the weight coefficient satisfies the following formula: a+b+c²+d+e+g+h=1 Among them, a represents the first weight coefficient, b represents the second weight coefficient, c2 represents the third weight coefficient, d represents the fourth weight coefficient, e represents the fifth weight coefficient, g represents the sixth weight coefficient, and h represents the seventh weight coefficient.
[0031] This application is based on multi-parameter heating pipe network failure probability calculation, which can accurately locate high-risk fault points and dynamically optimize fault strategies according to the probability of fault points, thereby improving the efficiency and effectiveness of fault diagnosis and management.
[0032] Step S104: Generate a fault handling strategy according to the probability corresponding to the fault point, and perform intelligent diagnosis and management of the regional boiler room cluster pipe network based on the fault handling strategy.
[0033] In order to generate a fault handling strategy based on the probability corresponding to the fault point, it is necessary to first build clusters based on the probability corresponding to the fault point, then identify the target fault clusters and edge anomalies in the clusters, and generate a fault handling strategy based on the target fault clusters and edge anomalies.
[0034] Clusters can be constructed using K-means or hierarchical clustering algorithms, with fault point probability as the core dimension and characteristic parameters combined to construct a multidimensional space. Determine the number of clusters: Use the silhouette coefficient to select the optimal number of clusters (e.g., 3 to 5 clusters).
[0035] Fault points are mapped into a multidimensional space and grouped according to the similarity of probability and characteristic parameters. Fault points with high probability and high impact tend to cluster in the same cluster (target fault cluster), while points with low probability but high risk may become marginal outliers.
[0036] Calculate the average probability and average impact range of each cluster, and select clusters with high probability and large impact range as target clusters. For example, a cluster with an average probability greater than 80% and an average impact range covering more than three key subsystems.
[0037] The isolation forest algorithm is used to identify outliers outside the cluster, focusing on two types of outliers: low-probability, high-impact points (such as a core failure with a probability of 10% but affecting the entire system); and fluctuating outliers (probability fluctuations exceeding a threshold may indicate potential risks).
[0038] For the target fault cluster, a first-level response is immediately triggered, and resources (such as expert teams and spare parts reserves) are allocated on a priority basis.
[0039] For edge anomalies: Low-probability, high-impact points: formulate emergency plans and conduct continuous monitoring; For fluctuating anomalies: analyze the causes of fluctuations (such as environmental changes) and adjust the monitoring frequency.
[0040] This application generates a fault handling strategy based on the probability corresponding to the fault point, and performs intelligent diagnosis and management of the regional boiler room cluster pipe network based on the fault handling strategy. By accurately locating the root cause of the fault, shortening the response time, and improving the accuracy of the fault handling strategy, the efficiency and effectiveness of the intelligent diagnosis and management of the regional boiler room cluster pipe network are improved.
[0041] To sum up, in this embodiment, the operating status of the heating network of the regional boiler room cluster is first monitored by sensors to obtain operating status data, and then the operating status data is preprocessed to obtain preprocessed operating status data, so that based on the preprocessed operating status data, the probability corresponding to the fault point of the heating network of the regional boiler room cluster is obtained, which not only can accurately locate the fault, but also obtain the accurate probability corresponding to the fault point; then, according to the probability corresponding to the fault point, a fault handling strategy is generated, and the intelligent diagnosis and management of the regional boiler room cluster network is executed based on the fault handling strategy, which can improve the accuracy of the fault handling strategy and thus improve the efficiency and effect of diagnosis and management.
[0042] Figure 2 An embodiment of the present invention shows a regional boiler room cluster pipe network intelligent diagnosis and management device. Figure 2 As shown, the device includes: The first acquisition module 201 is used to monitor the operating status of the heating network of the regional boiler room cluster through sensors and obtain operating status data; The data processing module 202 is used to pre-process the operation status data to obtain pre-processed operation status data; The second acquisition module 203 is used to obtain the probability corresponding to the fault point of the heating pipe network of the regional boiler room cluster based on the pre-processed operating status data; The diagnosis management module 204 is used to generate a fault handling strategy according to the probability corresponding to the fault point, so as to perform intelligent diagnosis management of the regional boiler room cluster pipe network based on the fault handling strategy.
[0043] Optionally, the second acquisition module 203 is further configured to acquire thermal parameters, power parameters, and water quality and equipment operating parameters, wherein the thermal parameters include the supply water temperature, return water temperature, specific heat capacity, and hot water flow rate in each branch pipe network; the power parameters include the operating frequency, supply water pressure, and return water pressure of the circulating water pump; and the water quality and equipment operating parameters include water pH, water hardness, and boiler operating load. Based on thermal and power parameters as well as water quality and equipment operating parameters, the probability of failure points in the heating network of the regional boiler room cluster is calculated.
[0044] Optionally, the formula for calculating the probability P corresponding to the failure point of the heating network of the regional boiler room cluster is as follows: Where c1 represents the specific heat capacity, Q represents the hot water flow in each branch pipe network, and Q d represents the theoretical heat flow, ΔT represents the temperature difference of the pipe network, f represents the operating frequency of the circulating water pump, f d Indicates the optimal operating frequency of the circulating water pump, pH indicates the acidity and alkalinity of the water, PH dIndicates neutral pH value, H indicates water hardness, H d Indicates standard water hardness, L indicates boiler operating load, L d Indicates the rated operating load of the boiler, ΔP d Indicates the theoretical pipe network pressure difference, T s Indicates the water supply temperature, T r Indicates the return water temperature, ΔT d Indicates the expected network temperature difference, P s Indicates water supply pressure, P r represents the return water pressure, and a, b, c2, d, e, g, and h are weight coefficients.
[0045] Optionally, the weight coefficient satisfies the following formula: a+b+c²+d+e+g+h=1 Among them, a represents the first weight coefficient, b represents the second weight coefficient, c2 represents the third weight coefficient, d represents the fourth weight coefficient, e represents the fifth weight coefficient, g represents the sixth weight coefficient, and h represents the seventh weight coefficient.
[0046] Optionally, the diagnosis management module 204 is further configured to construct clusters based on the probabilities corresponding to the fault points; Target fault clusters and edge anomalies are identified in the clustering clusters, and fault handling strategies are generated based on the target fault clusters and edge anomalies.
[0047] In the description provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems may also be used in conjunction with the examples of the present invention. Based on the above description, it is apparent that the structure required for constructing such systems is well understood. In addition, the present invention is not directed to any specific programming language. It should be understood that various programming languages may be utilized to implement the present invention described herein, and the description of specific languages above is provided for the purpose of disclosing preferred embodiments of the present invention.
[0048] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.
[0049] Those skilled in the art will appreciate that the modules, units, or components of the devices in the examples disclosed herein may be arranged in the device described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or further divided into multiple submodules.
[0050] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition they can be divided into multiple submodules or subunits or subassemblies. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification and all processes or units of any method or device disclosed in this manner can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification can be replaced by an alternative feature providing the same, equivalent or similar purpose.
[0051] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions described. Thus, a processor having the necessary instructions for implementing the method or method element forms a device for implementing the method or method element. Furthermore, the elements described herein of the device embodiments are examples of devices for implementing the functions performed by the elements for the purpose of implementing the invention.
[0052] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved and are not intended to imply that the objects so described must have a given order in time, space, ranking, or in any other manner.
[0053] Although the present invention has been described with respect to a limited number of embodiments, it will be apparent to those skilled in the art, having benefit of the foregoing description, that other embodiments are contemplated within the scope of the invention thus described. Furthermore, it should be noted that the language used in this specification has been selected primarily for readability and didactic purposes, rather than for the purpose of explaining or defining the subject matter of the present invention. Consequently, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the accompanying description. The disclosure of the present invention is intended to be illustrative and not restrictive of the scope of the invention, which is defined by the accompanying description.
Claims
1. A method for intelligent diagnosis and management of regional boiler room cluster pipe networks, characterized in that: The following steps are involved: Monitor the operating status of the heating pipe network of the regional boiler room cluster through sensors and obtain operating status data; Preprocessing the operating status data to obtain preprocessed operating status data; Based on the pre-processed operating status data, obtaining the probability corresponding to the fault point of the heating pipe network of the regional boiler room cluster; A fault handling strategy is generated according to the probability corresponding to the fault point, so as to perform intelligent diagnosis and management of the regional boiler room cluster pipe network based on the fault handling strategy.
2. The regional boiler room cluster pipe network intelligent diagnosis and management method according to claim 1 is characterized in that: The obtaining, based on the pre-processed operating status data, a probability corresponding to a fault point of the heating pipe network of the regional boiler room cluster, includes: Acquire thermal parameters, power parameters, and water quality and equipment operating parameters, wherein the thermal parameters include the supply water temperature, return water temperature, specific heat capacity, and hot water flow in each branch pipe network; the power parameters include the operating frequency, supply water pressure, and return water pressure of the circulating water pump; the water quality and equipment operating parameters include water pH, water hardness, and boiler operating load; based on the thermal parameters, the power parameters, and the water quality and equipment operating parameters, calculate the probability corresponding to the fault point of the heating pipe network of the regional boiler room cluster.
3. The intelligent diagnosis and management method for regional boiler room cluster pipe network according to claim 2 is characterized in that: The formula for calculating the probability P corresponding to the failure point of the heating pipe network of the regional boiler room cluster is as follows: Where c1 represents the specific heat capacity, Q represents the hot water flow in each branch pipe network, and Q d represents the theoretical heat flow, ΔT represents the temperature difference of the pipe network, f represents the operating frequency of the circulating water pump, f d Indicates the optimal operating frequency of the circulating water pump, pH indicates the acidity and alkalinity of the water, PH d Indicates neutral pH value, H indicates water hardness, H d Indicates standard water hardness, L indicates boiler operating load, L d Indicates the rated operating load of the boiler, ΔP d Indicates the theoretical pipe network pressure difference, T s Indicates the water supply temperature, T r Indicates the return water temperature, ΔT d Indicates the expected network temperature difference, P s Indicates water supply pressure, P r represents the return water pressure, and a, b, c2, d, e, g, and h are weight coefficients.
4. The intelligent diagnosis and management method for regional boiler room cluster pipe network according to claim 3 is characterized in that: The weight coefficient satisfies the following formula: a+b+c²+d+e+g+h=1 Among them, a represents the first weight coefficient, b represents the second weight coefficient, c2 represents the third weight coefficient, d represents the fourth weight coefficient, e represents the fifth weight coefficient, g represents the sixth weight coefficient, and h represents the seventh weight coefficient.
5. The method for intelligent diagnosis and management of regional boiler room cluster pipe networks according to claim 1 is characterized in that: Generating a fault handling strategy according to the probability corresponding to the fault point includes: Constructing clusters based on the probabilities corresponding to the fault points; A target fault cluster and an edge abnormal point are identified in the cluster, and the fault handling strategy is generated based on the target fault cluster and the edge abnormal point.
6. An intelligent diagnosis and management device for a regional boiler room cluster pipe network, characterized in that: include: A first acquisition module is used to monitor the operating status of the heating pipe network of the regional boiler room cluster through sensors and obtain operating status data; A data processing module, configured to pre-process the operating status data to obtain pre-processed operating status data; A second acquisition module is configured to acquire, based on the pre-processed operating status data, the probability corresponding to the failure point of the heating pipe network of the regional boiler room cluster; The diagnosis management module is used to generate a fault handling strategy according to the probability corresponding to the fault point, so as to perform intelligent diagnosis management of the regional boiler room cluster pipe network based on the fault handling strategy.
7. The intelligent diagnosis and management device for regional boiler room cluster pipe network according to claim 6 is characterized in that: The second acquisition module is further configured to: Acquire thermal parameters, power parameters, and water quality and equipment operating parameters, wherein the thermal parameters include the supply water temperature, return water temperature, specific heat capacity, and hot water flow in each branch pipe network; the power parameters include the operating frequency, supply water pressure, and return water pressure of the circulating water pump; the water quality and equipment operating parameters include water pH, water hardness, and boiler operating load; based on the thermal parameters, the power parameters, and the water quality and equipment operating parameters, calculate the probability corresponding to the fault point of the heating pipe network of the regional boiler room cluster.
8. The intelligent diagnosis and management device for regional boiler room cluster pipe network according to claim 7 is characterized in that: The formula for calculating the probability P corresponding to the failure point of the heating pipe network of the regional boiler room cluster is as follows: Where c1 represents the specific heat capacity, Q represents the hot water flow in each branch pipe network, and Q d represents the theoretical heat flow, ΔT represents the temperature difference of the pipe network, f represents the operating frequency of the circulating water pump, f d Indicates the optimal operating frequency of the circulating water pump, pH indicates the acidity and alkalinity of the water, PH d Indicates neutral pH value, H indicates water hardness, H d Indicates standard water hardness, L indicates boiler operating load, L d Indicates the rated operating load of the boiler, ΔP d Indicates the theoretical pipe network pressure difference, T s Indicates the water supply temperature, T r Indicates the return water temperature, ΔT d Indicates the expected network temperature difference, P s Indicates water supply pressure, P r represents the return water pressure, and a, b, c2, d, e, g, and h are weight coefficients.
9. The regional boiler room cluster pipe network intelligent diagnosis and management device according to claim 8, characterized in that: The weight coefficient satisfies the following formula: a+b+c²+d+e+g+h=1 Among them, a represents the first weight coefficient, b represents the second weight coefficient, c2 represents the third weight coefficient, d represents the fourth weight coefficient, e represents the fifth weight coefficient, g represents the sixth weight coefficient, and h represents the seventh weight coefficient.
10. The intelligent diagnosis and management device for regional boiler room cluster pipe network according to claim 6, characterized in that: The diagnostic management module is also used for: Constructing clusters based on the probabilities corresponding to the fault points; A target fault cluster and an edge abnormal point are identified in the cluster, and the fault handling strategy is generated based on the target fault cluster and the edge abnormal point.