Detection system based on data collection of central heating network

Abstract

The present invention relates to the technical field of heating pipe network detection, and in particular to a detection system based on centralized heating pipe network data acquisition, comprising: a heating demand acquisition module, a data acquisition module, a heating analysis module and an abnormality analysis module, wherein the heating demand acquisition module determines the heating type and heating standard of a building area, the data acquisition module acquires relevant data of the pipe network in real time, the heating analysis module determines the heating performance fluctuation tendency parameter in combination with the data and the heating standard, and the abnormality analysis module uses different analysis methods to judge whether the system has abnormalities according to the heating fluctuation tendency category, evaluates the heating efficiency by calculating the ratio of a preset to actual energy consumption, or determines whether there is a pressure abnormality by analyzing the pipe network pressure and water quality data; the present invention improves the operation efficiency and safety of the heating pipe network, reduces computing power consumption by reducing unnecessary data detection, and provides a scientific basis for energy conservation and pipe network maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of heating pipe network detection, and in particular to a detection system based on centralized heating pipe network data collection. Background Art

[0002] Modern technologies mainly use sensor monitoring, remote monitoring systems, and data analysis and prediction methods in the data monitoring of heating pipe networks. Sensor monitoring collects data in real time by installing temperature and pressure sensors at key locations, and transmits it to the central control system via wired or wireless means. The remote monitoring system uses the Internet of Things technology to achieve remote transmission of data over the Internet, making it easy to monitor the status of the pipe network anytime and anywhere. Data analysis and prediction uses technologies such as machine learning to process large amounts of sensor data, predict faults, and make optimization suggestions. However, the application of these technologies has inconveniences such as high cost, high technical complexity, and information security risks, such as the need for large initial investments, professional technicians to carry out design and maintenance, and the need for additional security measures to protect data privacy and integrity.

[0003] Chinese patent publication number: CN113657019A, discloses a heating pipe network early warning system, including: a data acquisition module, a data processing module, a digital modeling module, a remote monitoring system and an intelligent early warning system; the data acquisition module is used to collect first operating information of the heating pipe network; the data processing module is used to receive and process the first operating information; the remote monitoring system is used to monitor the operating status of the heating pipe network online, obtain second operating information, and transmit the processed first operating information and second operating information to the intelligent early warning system; the digital modeling module is used to establish a digital model of the heating pipe network; the intelligent early warning system uses the digital model to perform deep fusion processing on the second operating information and the first operating information, and adopts the least squares support vector machine algorithm to judge the damage and leakage level and future state of the heating pipe network and then issue an early warning; it can be seen that the heating pipe network early warning system has the following problems: real-time collection and analysis of data of the heating pipe network, resulting in unnecessary waste of computing power and unable to determine whether the heating pipe network has low energy utilization rate. Summary of the invention

[0004] To this end, the present invention provides a detection system based on centralized heating network data collection, so as to overcome the problem that real-time data collection and analysis of the heating network in the prior art causes unnecessary waste of computing power and cannot determine whether the heating network has low energy utilization rate.

[0005] To achieve the above object, the present invention provides a detection system based on centralized heating network data collection, comprising:

[0006] A heating demand collection module is used to determine the heating type of each building area and the corresponding heating standard of each building area;

[0007] A data acquisition module, which is connected to the heating demand acquisition module, and includes a water temperature acquisition unit for acquiring the water supply temperature and return water temperature of each building area, an information acquisition unit for acquiring the energy consumption data of the heating pipe network, and a pipe network detection unit for detecting water pollution data and heating pipe pressure;

[0008] A heating analysis module, which is connected to the data acquisition module and the heating demand acquisition module respectively, and is used to determine the heating performance fluctuation tendency parameter of the heating network according to the water temperature of each building area combined with the heating standard, so as to determine the heating fluctuation tendency category of the heating network;

[0009] An abnormality analysis module, which is respectively connected to the data acquisition module, the data acquisition module and the heat supply analysis module, is used to determine whether the data acquisition is abnormal according to the heat supply fluctuation tendency category of the heat supply network, including:

[0010] Calculate the preset energy consumption according to the heating standard corresponding to the building area, and determine the thermal energy efficiency of the heating network in combination with the energy consumption data to determine whether the heating network has abnormal heating efficiency;

[0011] Or, the heating pipe pressure and water pollution data are obtained, and the pressure abnormality tendency parameter is determined to determine whether the heating pipe network has an abnormality.

[0012] Further, the heating type of each building area determined by the heating demand collection module includes a continuous heating type and;

[0013] The heating standard corresponding to each of the building areas is the heating thermal index corresponding to the building type.

[0014] Furthermore, the heating analysis module determines the heating performance fluctuation tendency parameter of the heating network according to the water temperature of each building area in combination with the heating standard. The heating performance fluctuation parameter is determined according to formula (1).

[0015]

[0016] In formula (1), K is the heating performance fluctuation parameter, n1 is the total number of building areas of peak heating type, n2 is the total number of building areas of stable heating type, ti0 is the water supply temperature of the i-th peak heating building area, ti is the return water temperature of the i-th peak heating building area, tj0 is the water supply temperature of the j-th stable heating building area, tj is the return water temperature of the j-th stable heating building area, △t i0 is the preset peak heating type water temperature difference, △tj0 is the preset stable heating type water temperature difference, α is the number of peak times, and α is 0 or 0.5.

[0017] Furthermore, the heat supply analysis module determines the heat supply fluctuation tendency category of the heat supply network according to the heat supply performance fluctuation tendency parameter of the heat supply network, including:

[0018] If the heating performance fluctuation parameter is greater than or equal to the standard performance fluctuation parameter, the heating fluctuation tendency category of the heating network is determined to be a stable heating tendency category;

[0019] If the heating performance fluctuation parameter is less than the standard performance fluctuation parameter, the heating fluctuation tendency category of the heating network is determined to be a fluctuating heating tendency category.

[0020] Furthermore, the abnormality analysis module determines whether data collection is abnormal according to the heat supply fluctuation tendency category of the heat supply network, including:

[0021] If the heating fluctuation tendency category of the heating network is a stable heating tendency category, the analysis method is to calculate the preset energy consumption according to the heating standard corresponding to the building area, and determine the thermal energy efficiency of the heating network in combination with the energy consumption data to determine whether the heating network has abnormal heating efficiency;

[0022] If the heating fluctuation tendency category of the heating network is the fluctuating heating tendency category, the analysis method is to obtain the heating pipe pressure and water pollution data, determine the pressure abnormality tendency parameter to determine whether the heating network has an abnormality.

[0023] Furthermore, the abnormal analysis module calculates the preset energy consumption according to the heating standard and heating time corresponding to the building area; determines the actual energy consumption according to the energy consumption data and the energy type;

[0024] Wherein, the preset energy consumption is less than the actual energy consumption.

[0025] Further, the abnormality analysis module determines the thermal energy efficiency according to the ratio of the preset energy consumption to the actual energy consumption;

[0026] If the thermal energy efficiency is greater than or equal to the standard thermal energy efficiency, it is determined that the heating network has no abnormal heating efficiency;

[0027] If the thermal energy efficiency is less than the standard thermal energy efficiency, it is determined that the heating efficiency of the heating network is abnormal.

[0028] Furthermore, the pressure abnormality tendency parameter is determined according to formula (2):

[0029]

[0030] In formula (2), Q is the pressure anomaly tendency parameter, n0 is the total number of building areas, Fa is the heating pipe pressure of the a-th building area, Fa0 is the reference heating pipe pressure of the a-th building area, β is the water quality influence coefficient, and β takes values ​​of 0, 0.2, and 0.5.

[0031] Furthermore, the abnormality analysis module determines whether the heating network is abnormal according to the pressure abnormality tendency parameter, including:

[0032] If the pressure abnormal tendency parameter is greater than or equal to the preset abnormal tendency parameter, it is determined that the heating pipe network is abnormal;

[0033] If the pressure abnormal tendency parameter is less than the preset abnormal tendency parameter, it is determined that no abnormality occurs in the heating pipe network.

[0034] Furthermore, the abnormality analysis module is also used to determine the cause of the abnormality by adjusting the pressure valve of the heating pipe network when an abnormality occurs in the heating pipe network.

[0035] Compared with the prior art, the beneficial effect of the present invention lies in that the system determines the heating type and heating standard of the building area through the heating demand collection module, and uses the data collection module to obtain key parameters such as water supply temperature, return water temperature, energy consumption and pipe network status in real time. The heating analysis module combines these data and heating standards to evaluate the heating performance and determine the parameters of the heating performance fluctuation tendency. The abnormal analysis module uses different analysis methods to determine whether there is an abnormality in the system according to the category of heating fluctuation tendency, and evaluates the heating efficiency by calculating the ratio of preset and actual energy consumption, or identifies pressure abnormalities by analyzing pipe network pressure and water quality data. The present invention improves the operating efficiency and safety of the heating pipe network, reduces computing power consumption by reducing unnecessary data detection, and provides a scientific basis for energy conservation and pipe network maintenance.

[0036] Furthermore, in the present invention, by considering that different building types have different heating demands at different time periods each day, building types are divided, thereby providing a data basis for saving heating energy for the energy supply end of the heating network while ensuring the heating demand.

[0037] Furthermore, in the present invention, considering that the difference between the return water temperature and the supply water temperature can most intuitively reflect the heating performance of the heating system, a large difference indicates that the heating effect of the heating system on the current building area is lower than expected. Therefore, the heating stability of the heating network to the covered area is characterized according to the return water temperature and the supply water temperature of each area, and then, the analysis method of whether the heat pipes of the heating network in each area have abnormalities is adaptively selected to facilitate determining the cause of the abnormality and issuing an early warning. Then, under the premise of ensuring stable heating, the computing power consumption caused by continuous detection of all data of the heating network is reduced, and at the same time, a basis is provided for ensuring energy utilization.

[0038] Furthermore, in the present invention, taking into account the actual situation, there is a situation where the heating effect is stable, but the heating energy consumed, such as coal, is too much, resulting in unnecessary energy waste. Therefore, in view of the above situation, the thermal energy efficiency is determined to determine whether the heating energy is excessively used, and a data basis is provided for the heating system to adjust the heating parameters and energy usage, thereby reducing unnecessary energy usage while ensuring the stability of the heating network function, so as to achieve the effect of energy saving and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a structural schematic diagram of a detection system based on centralized heating network data collection according to an embodiment of the present invention;

[0040] Figure 2 A logic diagram for determining the heat supply fluctuation tendency category of a heat supply network according to an embodiment of the present invention;

[0041] Figure 3 A logic diagram for determining whether a heating efficiency abnormality occurs in a heating network according to an embodiment of the present invention;

[0042] Figure 4 This is a logic diagram for determining whether an abnormality occurs in a heating network based on an abnormal pressure tendency parameter according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0045] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0046] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] See also Figure 1 As shown, it is a structural schematic diagram of a detection system based on centralized heating network data collection according to an embodiment of the present invention; the present invention provides a detection system based on centralized heating network data collection, including:

[0048] A heating demand collection module is used to determine the heating type of each building area and the corresponding heating standard of each building area;

[0049] A data acquisition module, which is connected to the heating demand acquisition module, and includes a water temperature acquisition unit for acquiring the water supply temperature and return water temperature of each building area, an information acquisition unit for acquiring the energy consumption data of the heating pipe network, and a pipe network detection unit for detecting water pollution data and heating pipe pressure;

[0050] A heating analysis module, which is connected to the data acquisition module and the heating demand acquisition module respectively, and is used to determine the heating performance fluctuation tendency parameter of the heating network according to the water temperature of each building area combined with the heating standard, so as to determine the heating fluctuation tendency category of the heating network;

[0051] An abnormality analysis module, which is respectively connected to the data acquisition module, the data acquisition module and the heat supply analysis module, is used to determine whether the data acquisition is abnormal according to the heat supply fluctuation tendency category of the heat supply network, including:

[0052] Calculate the preset energy consumption according to the heating standard corresponding to the building area, and determine the thermal energy efficiency of the heating network in combination with the energy consumption data to determine whether the heating network has abnormal heating efficiency;

[0053] Or, the heating pipe pressure and water pollution data are obtained, and the pressure abnormality tendency parameter is determined to determine whether the heating pipe network has an abnormality.

[0054] During implementation, the water temperature collection unit can be directly obtained through any temperature detection device, the heating pipe pressure detection unit can be directly obtained through the control end of the heating pipe network, the water pollution data is the bacteria concentration and metal ion concentration in the water, the metal ions are calcium ions, copper ions, iron ions, lead ions and magnesium ions; the water pollution data can be obtained through any existing detection equipment.

[0055] The system determines the heating type and heating standard of the building area through the heating demand collection module, and uses the data collection module to obtain key parameters such as water supply temperature, return water temperature, energy consumption and pipe network status in real time. The heating analysis module combines these data and heating standards to evaluate the heating performance and determine the parameters of the heating performance fluctuation tendency. The abnormal analysis module uses different analysis methods to determine whether there is an abnormality in the system according to the category of heating fluctuation tendency, evaluates the heating efficiency by calculating the ratio of preset and actual energy consumption, or identifies pressure abnormalities by analyzing pipe network pressure and water quality data. The present invention improves the operating efficiency and safety of the heating pipe network, reduces computing power consumption by reducing unnecessary data detection, and provides a scientific basis for energy conservation and pipe network maintenance.

[0056] The present invention does not limit the specific structures of the heating demand acquisition module, the heating analysis module and the abnormality analysis module, which can be composed of logic components, and the logic components include a field programmable processor, a computer and a microprocessor in the computer.

[0057] Specifically, the heating demand collection module determines that the heating type of each building area includes a continuous heating type and;

[0058] The heating standard corresponding to each of the building areas is the heating thermal index corresponding to the building type.

[0059] During implementation, the types of buildings covered by the heating pipeline network include residential, commercial, office and medical types. For buildings corresponding to commercial and office types, such as office buildings, shopping malls and schools, there may be a phenomenon of no human activities for any long period of time in a day. The heating type of this building area is peak heating type. On the contrary, for places such as residential areas and hospitals where people are active all day long, continuous and stable heating is required throughout the day. The heating type of this building area is continuous heating type.

[0060] In the present invention, by considering that different building types have different heating demands at different time periods every day, building types are divided, and while ensuring the heating demand, a data basis for saving heating energy is provided for the energy supply end of the heating network.

[0061] See also Figure 2 As shown, it is a logic diagram for determining the heating fluctuation tendency category of the heating network in an embodiment of the present invention. The heating analysis module determines the heating performance fluctuation tendency parameter of the heating network according to the water temperature of each building area combined with the heating standard. The heating performance fluctuation parameter is determined according to formula (1).

[0062]

[0063] In formula (1), K is the heating performance fluctuation parameter, n1 is the total number of building areas of peak heating type, n2 is the total number of building areas of stable heating type, ti0 is the water supply temperature of the i-th peak heating building area, ti is the return water temperature of the i-th peak heating building area, tj0 is the water supply temperature of the j-th stable heating building area, tj is the return water temperature of the j-th stable heating building area, △t i0 is the preset peak heating type water temperature difference, △tj0 is the preset stable heating type water temperature difference, α is the number of peak times, and α is 0 or 0.5.

[0064] i, j are integers greater than 0.

[0065] In implementation, the total number of building areas of various types covered by the heating network can be directly obtained based on the building areas corresponding to the design of the heating network; α is determined according to the heating time period. Within 6:00-10:00 every day, α is 0.5, and the rest of the day α is 0;

[0066] The preset peak heating type water temperature difference and the preset stable heating type water temperature difference are determined according to the building type. Generally, the preset peak heating type water temperature difference is 20°~25°, and the preset stable heating type water temperature difference is 15°~20°

[0067] Specifically, the heat supply analysis module determines the heat supply fluctuation tendency category of the heat supply network according to the heat supply performance fluctuation tendency parameter of the heat supply network, including:

[0068] If the heating performance fluctuation parameter is greater than or equal to the standard performance fluctuation parameter, the heating fluctuation tendency category of the heating network is determined to be a stable heating tendency category;

[0069] If the heating performance fluctuation parameter is less than the standard performance fluctuation parameter, the heating fluctuation tendency category of the heating network is determined to be a fluctuating heating tendency category.

[0070] The standard performance fluctuation parameter is selected in the interval [0.8,0.9].

[0071] In the present invention, it is considered that the difference between the return water temperature and the supply water temperature can most intuitively reflect the heating performance of the heating system. A large difference indicates that the heating effect of the heating system on the current building area is lower than expected. Therefore, the heating stability of the heating network to the covered area is characterized according to the return water temperature and the supply water temperature of each area. Then, an analysis method is adaptively selected to determine whether there is an abnormality in the heat pipes of the heating network in each area, which is convenient for determining the cause of the abnormality and issuing an early warning. Then, under the premise of ensuring stable heating, the computing power consumption caused by continuous detection of all data of the heating network is reduced, and at the same time, a basis is provided for ensuring energy utilization.

[0072] Specifically, the abnormality analysis module determines whether data collection is abnormal according to the heat supply fluctuation tendency category of the heat supply network, including:

[0073] If the heating fluctuation tendency category of the heating network is a stable heating tendency category, the analysis method is to calculate the preset energy consumption according to the heating standard corresponding to the building area, and determine the thermal energy efficiency of the heating network in combination with the energy consumption data to determine whether the heating network has abnormal heating efficiency;

[0074] If the heating fluctuation tendency category of the heating network is the fluctuating heating tendency category, the analysis method is to obtain the heating pipe pressure and water pollution data, determine the pressure abnormality tendency parameter to determine whether the heating network has an abnormality.

[0075] See also Figure 3 As shown, it is a logic diagram for determining whether the heating efficiency of the heating network is abnormal according to an embodiment of the present invention, wherein the abnormality analysis module calculates the preset energy consumption according to the heating standard and heating time corresponding to the building area; and determines the actual energy consumption according to the energy consumption data and the energy type;

[0076] Wherein, the preset energy consumption is less than the actual energy consumption.

[0077] In implementation, the heating standards corresponding to each building area can be obtained according to the urban heating and heating specifications in the national standard. The unit of building heating heat index in the urban heating and heating specifications is W / m 2 , preset energy consumption = building heating heat index × total building heating area × heating time × total building area, actual energy consumption = energy type corresponding calorific value × energy usage;

[0078] Generally, the energy source is coal.

[0079] The building heating thermal indicators of various building types in the urban heating specifications are shown in Table 1.

[0080] Table 1

[0081]

[0082] Specifically, the abnormality analysis module determines the thermal energy efficiency according to the ratio of the preset energy consumption to the actual energy consumption;

[0083] If the thermal energy efficiency is greater than or equal to the standard thermal energy efficiency, it is determined that the heating network has no abnormal heating efficiency;

[0084] If the thermal energy efficiency is less than the standard thermal energy efficiency, it is determined that the heating efficiency of the heating network is abnormal.

[0085] The standard thermal efficiency is selected in the interval [0.8,0.9].

[0086] In the present invention, taking into account the actual situation, there is a situation where the heating effect is stable, but the heating energy consumed, such as coal, is too much, resulting in unnecessary energy waste. Therefore, in view of the above situation, the thermal energy efficiency is determined to determine whether the heating energy is used excessively, and a data basis is provided for the heating system to adjust the heating parameters and energy usage, thereby reducing unnecessary energy usage while ensuring the stability of the heating network function, so as to achieve the effect of energy saving and environmental protection.

[0087] See also Figure 4 As shown, it is a logic diagram of determining whether an abnormality occurs in the heating network according to the pressure abnormality tendency parameter according to an embodiment of the present invention. The pressure abnormality tendency parameter is determined according to formula (2):

[0088]

[0089] In formula (2), Q is the pressure anomaly tendency parameter, n0 is the total number of building areas, Fa is the heating pipe pressure of the a-th building area, Fa0 is the reference heating pipe pressure of the a-th building area, β is the water quality influence coefficient, and β takes values ​​of 0, 0.2, and 0.5.

[0090] In implementation, a is an integer greater than 0, n0=n1+n2;

[0091] The reference heating pipe pressure of each building area is determined based on the average value of abnormal heating pipe pressures under the same outdoor temperature conditions in historical records.

[0092] If the bacterial content is less than 0.1 cfu / ml and the total metal ion content is less than 40 mg / l, β is taken as 0;

[0093] If either the bacterial content or the metal ion content does not meet the requirements, the β value is 0.2;

[0094] If the bacterial content or the metal ion content does not meet the requirements, the β value is 0.5.

[0095] Specifically, the abnormality analysis module determines whether the heating network is abnormal according to the pressure abnormality tendency parameter, including:

[0096] If the pressure abnormal tendency parameter is greater than or equal to the preset abnormal tendency parameter, it is determined that the heating pipe network is abnormal;

[0097] If the pressure abnormal tendency parameter is less than the preset abnormal tendency parameter, it is determined that no abnormality occurs in the heating pipe network.

[0098] The preset abnormal tendency parameter is selected in the interval [0.3,0.4].

[0099] Specifically, the abnormality analysis module is also used to determine the cause of the abnormality by adjusting the pressure valve of the heating pipe network when an abnormality occurs in the heating pipe network.

[0100] It is understandable that excessive pipe network pressure may cause the return water temperature to be higher than expected, affecting the heating effect, or the water may absorb heat in a shorter time, resulting in low water supply temperature. In addition, it may also cause greater pressure on the pipeline, increasing the risk of pipeline leakage and damage.

[0101] During implementation, it is considered that water pollution may also cause blockage in a pipe somewhere in the network, resulting in excessive pressure in the pipe. If the pressure does not change significantly after adjusting the pressure valve, it can be determined that there is a blockage and the water quality needs to be improved. The improvement process is an existing technology and is not specifically limited here.

[0102] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A detection system based on data collection of a central heating network, characterized in that: include: A heating demand collection module is used to determine the heating type of each building area and the corresponding heating standard of each building area; The heating type of each building area determined by the heating demand collection module includes a continuous heating type and a peak heating type; The heating standard corresponding to each of the building areas is the heating heat index corresponding to the building type; A data acquisition module, which is connected to the heating demand acquisition module, and includes a water temperature acquisition unit for acquiring the water supply temperature and return water temperature of each building area, an information acquisition unit for acquiring the energy consumption data of the heating pipe network, and a pipe network detection unit for detecting water pollution data and heating pipe pressure; A heating analysis module, which is connected to the data acquisition module and the heating demand acquisition module respectively, and is used to determine the heating performance fluctuation tendency parameter of the heating network according to the water temperature of each building area combined with the heating standard, so as to determine the heating fluctuation tendency category of the heating network; The heating performance fluctuation parameter is determined according to formula (1): In formula (1), K is the heating performance fluctuation parameter, n1 is the total number of building areas of peak heating type, n2 is the total number of building areas of continuous heating type, ti0 is the water supply temperature of the i-th peak heating building area, ti is the return water temperature of the i-th peak heating building area, tj0 is the water supply temperature of the j-th stable heating building area, tj is the return water temperature of the j-th stable heating building area, △ti0 is the preset peak heating type water temperature difference, △tj0 is the preset stable heating type water temperature difference, and α is 0 or 0.5; If the heating performance fluctuation parameter is greater than or equal to the standard performance fluctuation parameter, the heating fluctuation tendency category of the heating network is determined to be a stable heating tendency category; If the heating performance fluctuation parameter is less than the standard performance fluctuation parameter, determining that the heating fluctuation tendency category of the heating network is a fluctuating heating tendency category; An abnormality analysis module, which is respectively connected to the data acquisition module, the data acquisition module and the heat supply analysis module, and is used to determine whether data acquisition is abnormal according to the heat supply fluctuation tendency category of the heat supply network, including: If the heating fluctuation tendency category of the heating network is a stable heating tendency category, the preset energy consumption is calculated according to the heating standard corresponding to the building area, and the thermal energy efficiency of the heating network is determined in combination with the energy consumption data to determine whether the heating efficiency of the heating network is abnormal.

2. The detection system based on centralized heating network data collection according to claim 1 is characterized in that: The abnormality analysis module determines whether data collection is abnormal according to the heat supply fluctuation tendency category of the heat supply network, and further includes: If the heating fluctuation tendency category of the heating network is the fluctuating heating tendency category, the heating pipe pressure and water pollution data are obtained, and whether the heating network is abnormal is determined according to the pressure abnormality tendency parameter, and the pressure abnormality tendency parameter is determined according to formula (2), In formula (2), Q is the pressure anomaly tendency parameter, n0 is the total number of building areas, Fa is the heating pipe pressure of the a-th building area, Fa0 is the reference heating pipe pressure of the a-th building area, β is the water quality influence coefficient, and β takes values ​​of 0, 0.2, and 0.

5.

3. The detection system based on data collection of the central heating network according to claim 1 is characterized in that: The abnormal analysis module calculates the preset energy consumption according to the heating standard and heating time corresponding to the building area; and determines the actual energy consumption according to the energy consumption data and the energy type; Wherein, the preset energy consumption is less than the actual energy consumption.

4. The detection system based on centralized heating network data collection according to claim 3 is characterized in that: The abnormality analysis module determines the thermal energy efficiency according to the ratio of the preset energy consumption to the actual energy consumption; If the thermal energy efficiency is greater than or equal to the standard thermal energy efficiency, it is determined that the heating network has no abnormal heating efficiency; If the thermal energy efficiency is less than the standard thermal energy efficiency, it is determined that the heating efficiency of the heating network is abnormal.

5. The detection system based on data collection of the central heating network according to claim 2 is characterized in that: The abnormality analysis module determines whether the heating pipe network is abnormal according to the pressure abnormality tendency parameter, including: If the pressure abnormal tendency parameter is greater than or equal to the preset abnormal tendency parameter, it is determined that the heating pipe network is abnormal; If the pressure abnormal tendency parameter is less than the preset abnormal tendency parameter, it is determined that no abnormality occurs in the heating pipe network.

6. The detection system based on data collection of the central heating network according to claim 5 is characterized in that: The abnormality analysis module is also used to determine the cause of the abnormality by adjusting the pressure valve of the heating pipe network when an abnormality occurs in the heating pipe network.

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