Secondary network unbalance detection method, system and equipment for heat supply system

Abstract

The invention provides a secondary network unbalance detection method, system and equipment for a heat supply system, and belongs to the technical field of urban heat supply system digital control. The method comprises the steps that heat supply parameter information corresponding to each preset unbalance adjustment level is obtained; and according to the heat supply parameter information, determining a plurality of heat supply unit consumption values corresponding to the corresponding preset unbalance adjustment hierarchy, and generating a heat supply unit consumption set. And determining a first unbalance detection coefficient and a second unbalance detection coefficient based on the heat supply unit consumption set, the air temperature value from the temperature acquisition equipment and a preset unbalance coefficient algorithm group, and determining an unbalance detection evaluation score according to a pre-trained unbalance detection evaluation model. And on the basis of a comparison result of the unbalance detection evaluation score and a preset unbalance detection evaluation threshold value, whether the corresponding preset unbalance adjustment level meets secondary network unbalance or not is determined, so that unbalance adjustment prompt information is generated when the secondary network unbalance condition is met, and the unbalance adjustment prompt information is sent to the heat supply management terminal.

Description

Technical Field

[0001] This application relates to the field of digital control technology for urban heating systems, and in particular to a method, system and equipment for detecting secondary network imbalance in heating systems. Background Technology

[0002] Urban large-scale pipe network heating systems typically consist of a primary network and a secondary network. The primary network refers to the water supply network from the heat source plant through the main pipeline to the community heat exchange station, while the secondary network refers to the water supply network from the heat station to each unit building. The two water networks exchange heat through specialized equipment.

[0003] Factors such as valve opening between different building units or users, and the physical distribution of heating units can cause some or all users to experience insufficient heating (under-supply) or excessive heating (over-supply). Under the condition of a fixed heat exchange station consumption, based on the collected and uploaded building and household meter data, the aim is to achieve a uniform distribution of room temperature in each household by adjusting the valve opening of each building unit and household (excluding self-set room temperature settings via thermostats). This process is called secondary network balancing regulation.

[0004] When optimizing and adjusting secondary network parameters, it is necessary to first predict the imbalance state of the secondary network in order to obtain the adjustment range and direction of parameters for each unit building or user. However, many existing household valves and unit building valves have not achieved fully automated adjustment, making it difficult for heating managers to adjust the valves of each household at a high frequency in response to temperature changes.

[0005] Therefore, there is an urgent need for a technical solution that can accurately detect and regulate the imbalance of the secondary network in the heating system. Summary of the Invention

[0006] This application provides a method, system, and device for detecting imbalance in the secondary network of a heating system, which addresses the current technical problem of lacking accurate imbalance detection and control schemes for the secondary network in a heating system.

[0007] In a first aspect, embodiments of this application provide a method for detecting secondary network imbalance in a heating system, the method comprising: Obtain heating parameter information corresponding to each preset imbalance adjustment level; wherein, the preset imbalance adjustment level is divided based on the control range of the heating adjustment valve; the heating parameter information includes at least household meter parameters and heating area area; Based on the heating parameter information, determine multiple heating unit consumption values ​​corresponding to the preset imbalance adjustment level, and generate a heating unit consumption set. Based on the heating unit consumption set, the air temperature value from the temperature acquisition device, and the preset imbalance coefficient algorithm set, the first imbalance detection coefficient and the second imbalance detection coefficient are determined, and the imbalance detection evaluation score is determined according to the pre-trained imbalance detection evaluation model. Based on the comparison between the imbalance detection and evaluation score and the preset imbalance detection and evaluation threshold, it is determined whether the corresponding preset imbalance adjustment level meets the secondary network imbalance, so as to generate an imbalance adjustment prompt message when the secondary network imbalance condition is met and send it to the heating management terminal.

[0008] In one implementation of this application, based on the heating parameter information, multiple heating unit consumption values ​​corresponding to the preset imbalance adjustment level are determined, and a heating unit consumption set is generated, specifically including: Based on the meter parameters of each household at the corresponding preset imbalance adjustment level and the preset heating calculation formula, calculate the corresponding heating. Based on the ratio of the heating heat to the area of ​​the matching heating zone, each heating unit consumption value is determined, and each heating unit consumption value is added to the heating unit consumption set.

[0009] In one implementation of this application, a first imbalance detection coefficient and a second imbalance detection coefficient are determined based on the heating unit consumption set, the air temperature value from the temperature acquisition device, and a preset imbalance coefficient algorithm set, specifically including: Based on the set of heating unit consumption of the corresponding preset imbalance adjustment level, calculate the standard deviation of heating unit consumption of the corresponding level, and normalize the temperature value from the temperature acquisition device to obtain the normalized temperature value. The standard deviation of the tiered heating unit consumption and the normalized temperature value are input into the preset imbalance coefficient algorithm group to calculate the Spearman correlation coefficient between the standard deviation of the tiered heating unit consumption and the normalized temperature value, which is the first imbalance detection coefficient. The maximum mutual information coefficient between the standard deviation of the tiered heating unit consumption and the normalized temperature value is calculated, which is the second imbalance detection coefficient.

[0010] In one implementation of this application, before determining the first imbalance detection coefficient and the second imbalance detection coefficient based on the heating unit consumption set, the air temperature value from the temperature acquisition device, and the preset imbalance coefficient algorithm set, the method further includes: Determine the set of unit consumption impact characteristic parameters corresponding to each of the preset imbalance adjustment levels; wherein, the set of unit consumption impact characteristic parameters includes at least the heating area of ​​multiple level object modules, the insulation coefficient of multiple level object modules, and the user density of multiple level object modules; Based on the set of characteristic parameters affecting unit consumption and the preset first weighting coefficient formula, the first weighting coefficient corresponding to each level of object module is determined; The corresponding heating unit consumption values ​​are corrected according to the first weighting coefficient to obtain the corrected heating unit consumption set. Based on the historical temperature response data corresponding to each of the hierarchical object modules, the corresponding temperature sensitivity coefficient is determined, and the normalized temperature value is corrected according to the temperature sensitivity coefficient to obtain the corrected normalized temperature value, so as to determine the first imbalance detection coefficient and the second imbalance detection coefficient based on the preset imbalance coefficient algorithm group.

[0011] In one implementation of this application, before determining whether the corresponding preset imbalance adjustment level satisfies the secondary network imbalance based on the comparison result between the imbalance detection evaluation score and the preset imbalance detection evaluation threshold, the method further includes: Based on the thermal insulation coefficient and historical imbalance frequency of each of the object modules of the corresponding preset imbalance adjustment level, a second weighting coefficient corresponding to the preset benchmark threshold is determined; The corrected preset imbalance detection and evaluation threshold is obtained by multiplying the second weighting coefficient with the preset benchmark threshold.

[0012] In one implementation of this application, before determining the first imbalance detection coefficient and the second imbalance detection coefficient based on the heating unit consumption set, the air temperature value from the temperature acquisition device, and the preset imbalance coefficient algorithm set, the method further includes: Determine the set of valve adjustment amplitudes corresponding to each of the preset imbalance adjustment levels; Based on the set of valve adjustment ranges and the preset range threshold comparison table, determine whether the corresponding preset imbalance adjustment level meets the level interference condition; If so, based on the preset interference quantification model, determine the interference weight of the preset imbalance adjustment level that satisfies the level interference conditions; The corresponding heating unit consumption values ​​are corrected according to the interference weights, and the corrected heating unit consumption values ​​are added to the heating unit consumption set.

[0013] In one implementation of this application, the control scope covers three levels of objects in the secondary network of the heating system, including the household level, unit level, and building level; the household meter parameters include at least water flow rate, inlet water temperature, return water temperature, and heating duration.

[0014] In one implementation of this application, the imbalance detection evaluation model is an LSTM model obtained by inputting several sample data of detection coefficients labeled with imbalance detection evaluation scores, and training the model with the Euclidean distance between the output imbalance detection evaluation score and the imbalance detection evaluation score label as the loss function.

[0015] Secondly, embodiments of this application provide a secondary network imbalance detection system for a heating system, the system comprising: The acquisition module is used to acquire heating parameter information corresponding to each preset imbalance adjustment level; wherein, the preset imbalance adjustment level is divided based on the control range of the heating adjustment valve; the heating parameter information includes at least household meter parameters and heating area area; The first determining module is used to determine multiple heating unit consumption values ​​corresponding to the preset imbalance adjustment level based on the heating parameter information, and generate a heating unit consumption set. The second determining module is used to determine the first imbalance detection coefficient and the second imbalance detection coefficient based on the heating unit consumption set, the air temperature value from the temperature acquisition device and the preset imbalance coefficient algorithm group, and to determine the imbalance detection evaluation score according to the pre-trained imbalance detection evaluation model. The third determining module is used to determine whether the corresponding preset imbalance adjustment level meets the secondary network imbalance based on the comparison result between the imbalance detection and evaluation score and the preset imbalance detection and evaluation threshold. When the secondary network imbalance condition is met, an imbalance adjustment prompt message is generated and sent to the heating management terminal.

[0016] Thirdly, embodiments of this application provide a secondary network imbalance detection device for a heating system, the device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a secondary network imbalance detection method for a heating system as described above.

[0017] Compared with the prior art, the significant advantages of this application are as follows: This application, through the aforementioned scheme, calculates the unit heating consumption at different control levels of heating regulating valves and calculates imbalance detection coefficients in two dimensions, combined with temperature, to obtain an imbalance detection assessment score that incorporates the influence of temperature factors, thereby enabling secondary network imbalance detection. This method effectively eliminates the interference of short-term temperature fluctuations on imbalance detection, is unaffected by sudden temperature rises / falls, and can more realistically and stably reflect the inherent imbalance state caused by internal system factors such as uneven valve opening and hydraulic imbalance, providing a reliable basis for precise regulation. This application achieves accurate imbalance detection of the secondary network in the heating system and is used for secondary network imbalance control. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic flowchart of a method for detecting secondary network imbalance in a heating system according to an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a secondary network imbalance detection system for a heating system according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a secondary network imbalance detection device for a heating system according to an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] When optimizing and adjusting secondary network parameters, it is necessary to first predict the imbalance state of the secondary network in order to obtain the adjustment range and direction of parameters for each unit building or user. However, many existing household valves and unit building valves have not achieved fully automated adjustment, making it difficult for heating managers to adjust the valves of each household at a high frequency in response to temperature changes.

[0021] Based on this, the present application provides a method, system and equipment for detecting imbalance in the secondary network of a heating system, in order to solve the technical problem of the lack of accurate imbalance detection and control scheme for the secondary network in the heating system.

[0022] The various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0023] This application provides a method for detecting secondary network imbalance in a heating system, such as... Figure 1 As shown, the method may include steps S101-S104: S101, the server obtains the heating parameter information corresponding to each preset imbalance adjustment level.

[0024] The preset imbalance adjustment levels are based on the control range of the heating regulating valves. Heating parameter information includes at least household meter parameters and the area of ​​the heating zone.

[0025] It should be noted that the server, as the executing entity for the secondary network imbalance detection method in the heating system, is merely an example, and the executing entity is not limited to the server. This application does not make any specific limitations on this.

[0026] The aforementioned control scope covers three levels of objects in the secondary network of the heating system, including the household level, unit level, and building level. Household meter parameters must include at least water flow rate, inlet water temperature, return water temperature, and heating duration.

[0027] The preset imbalance adjustment levels can include a first level corresponding to the household level, a second level corresponding to the unit level, and a third level corresponding to the building level. Heating regulation valves can adjust the heating supply to different levels respectively. The imbalance prediction mechanism is the same for different preset imbalance adjustment levels in this application. Heating parameter information can be sensors deployed in the secondary network, such as flow sensors and temperature sensors. The type, number, and location of the sensors can be set according to the actual usage scenario and are not specifically limited here. The heating area can be pre-stored in the server. The heating area area is specific to different preset imbalance adjustment levels, such as: the total heating area of ​​Building 1, the total heating area of ​​Unit 1 in Building 1, and the total heating area of ​​all 301 households in Building 1.

[0028] S102, the server determines multiple heating unit consumption values ​​corresponding to the preset imbalance adjustment level based on the heating parameter information, and generates a set of heating unit consumption values.

[0029] In this embodiment of the application, the above-mentioned determination of multiple heating unit consumption values ​​corresponding to the preset imbalance adjustment level based on heating parameter information, and the generation of a heating unit consumption set, specifically includes: Based on the meter parameters of each household at the corresponding preset imbalance adjustment level and the preset heating heat calculation formula, the corresponding heating heat is calculated. According to the ratio of the heating heat to the area of ​​the matching heating area, the heating unit consumption value is determined and added to the heating unit consumption set.

[0030] In other words, the server has a preset formula for calculating heating capacity, which can be used to calculate heating capacity. In this application, taking the area corresponding to the secondary network as a community and the building level as the preset imbalance adjustment level as an example, the meter parameters corresponding to the building are input into the following preset heating capacity calculation formula: .

[0031] in, For the first The heating capacity of the building, For specific heat capacity, The density of water, For the first Water flow rate of the building For the first The water temperature entering the building, For the first The return water temperature of the building For the first Heating schedule for the building.

[0032] Subsequently, this application calculates the heating unit consumption value by determining the ratio of the heating heat supplied to the area of ​​the matching heating zone. The calculation formula is as follows: .

[0033] in, For the first The unit heating consumption of the building, For the first The area of ​​the heating zone of the building.

[0034] Subsequently, this application adds the heating unit consumption value of each building to the heating unit consumption set of the preset imbalance adjustment level. For example, if the heating unit consumption set is { }, This represents the total number of buildings.

[0035] S103, the server determines the first imbalance detection coefficient and the second imbalance detection coefficient based on the heating unit consumption set, the air temperature value from the temperature acquisition device and the preset imbalance coefficient algorithm group, and determines the imbalance detection evaluation score according to the pre-trained imbalance detection evaluation model.

[0036] In this embodiment of the application, the above-mentioned determination of the first imbalance detection coefficient and the second imbalance detection coefficient based on the heating unit consumption set, the air temperature value from the temperature acquisition device, and the preset imbalance coefficient algorithm group specifically includes: Based on the set of heating consumption per unit for the corresponding preset imbalance adjustment levels, the standard deviation of heating consumption per unit for each level is calculated. The air temperature values ​​from the temperature acquisition equipment are then normalized to obtain normalized air temperature values. The standard deviation of heating consumption per unit for each level and the normalized air temperature values ​​are input into a preset imbalance coefficient algorithm group to calculate the Spearman correlation coefficient between the standard deviation of heating consumption per unit for each level and the normalized air temperature value, which serves as the first imbalance detection coefficient. The maximum mutual information coefficient between the standard deviation of heating consumption per unit for each level and the normalized air temperature value is then calculated, serving as the second imbalance detection coefficient.

[0037] In other words, this application further calculates the standard deviation of heating unit consumption at each level by using the set of heating unit consumption obtained through the above steps. Taking the building as the preset imbalance adjustment level as an example, the average unit consumption is calculated. The standard deviation of the unit consumption for tiered heating was then further calculated. Simultaneously, the server normalizes the temperature values ​​collected by temperature acquisition devices deployed in the secondary network control area to obtain dimensionless normalized temperature values. Then, it uses a preset imbalance coefficient algorithm group to calculate the first and second imbalance detection coefficients.

[0038] Specifically, the preset imbalance coefficient algorithm group includes the Spearman correlation coefficient algorithm and the maximum mutual information coefficient algorithm. Among them, the Spearman correlation coefficient algorithm is as follows: .

[0039] in, The Spearman correlation coefficient. This represents the standard deviation of tiered heating unit consumption obtained according to the time series. Indicates N collections The average value obtained Indicates the corresponding Normalized temperature value corresponding to the time, This represents the average of N normalized temperature values ​​collected. The specific value of N can be set according to the actual application scenario and is not specifically limited here.

[0040] Maximum mutual information coefficient algorithm: .

[0041] in, Represents the maximum mutual information coefficient. This represents the mutual information between the standard deviation of heating unit consumption at different levels and the normalized temperature value. Refers to specific mesh parameters, Due to grid resolution limitations, among which, , It can be set based on expert experience in actual use scenarios, and no specific limitations are made here.

[0042] This application employs two parameters, the Spearman correlation coefficient and the maximum mutual information coefficient, as imbalance detection coefficients to perform subsequent secondary network imbalance detection. The Spearman correlation coefficient accurately reflects the monotonic trend correlation between unit consumption fluctuations and temperature fluctuations, while the maximum mutual information coefficient quantifies the full-dimensional dependence between unit consumption and temperature, capturing the nonlinear / implicit correlation between unit consumption fluctuations and temperature fluctuations. Using these two parameters for imbalance detection helps to accurately determine whether the imbalance of the secondary network system is related to temperature.

[0043] Furthermore, in this embodiment of the application, before determining the first imbalance detection coefficient and the second imbalance detection coefficient based on the heating unit consumption set, the air temperature value from the temperature acquisition device, and the preset imbalance coefficient algorithm group, the method further includes: A set of characteristic parameters for the unit consumption impact corresponding to each preset imbalance adjustment level is determined. This set includes at least the heating area, insulation coefficient, and user density of multiple level object modules. Based on the set of characteristic parameters and a preset first weighting coefficient formula, a first weighting coefficient is determined for each level object module. The corresponding heating unit consumption values ​​are then corrected using these first weighting coefficients to obtain a corrected set of heating unit consumption values. Based on historical temperature response data for each level object module, a corresponding temperature sensitivity coefficient is determined. This coefficient is then used to correct the corresponding normalized temperature values, resulting in corrected normalized temperature values. These corrected values ​​are then used to determine the first and second imbalance detection coefficients based on a preset imbalance coefficient algorithm set.

[0044] In other words, the server can retrieve the set of unit consumption impact characteristic parameters for each preset imbalance adjustment level. This set of parameter settings can be pre-stored on the server or in a database electrically connected to the server; no specific limitation is made here. The set of unit consumption impact characteristic parameters includes the heating area, insulation coefficient, and user density corresponding to each level object module in the preset imbalance adjustment level. A level object module can be understood as the smallest unit constituting the preset imbalance adjustment level; for example, the level object module for a building-level preset imbalance adjustment level is the building, the level object module for a unit-level preset imbalance adjustment level is a unit, and so on. Based on the set of unit consumption impact characteristic parameters and the preset first weighting coefficient formula, the first weighting coefficient can be calculated. The preset first weighting coefficient formula is as follows: .

[0045] in, For the first The first weighting coefficient of each hierarchical object module Indicates the first The thermal insulation coefficient of each hierarchical object module. No. User density of hierarchical object modules in each hierarchy. , The first weighting coefficient can be understood as the level of the object module with better insulation, higher user density, and area closer to the regional average heating area. The larger the first weighting coefficient, the higher the collinear weight of its unit consumption fluctuation on the imbalance.

[0046] Subsequently, using the first weighting coefficients obtained above, the product of each first weighting coefficient and the corresponding heating unit consumption value is calculated, such as... This involves correcting the heating unit consumption value to obtain the corrected heating unit consumption value. Then, a set of corrected heating unit consumption is constructed using each corrected heating unit consumption value.

[0047] Furthermore, this application can also obtain historical temperature response data corresponding to each level of object module, which includes historical unit consumption changes and temperature change data. Through calculation... The temperature sensitivity coefficient is obtained. For the first Temperature sensitivity coefficient of each hierarchical object module This represents the historical change in unit consumption. This represents the corresponding temperature change. Subsequently, the normalized temperature value is corrected. , This is the corrected normalized temperature value. The temperature value before correction is the normalized temperature value.

[0048] The server will use the revised set of heating unit consumption and normalized temperature values ​​to execute step S103 to determine the first imbalance detection coefficient and the second imbalance detection coefficient. This will eliminate interference from the inherent characteristics of the hierarchical object module and obtain accurate first and second imbalance detection coefficients.

[0049] Furthermore, in another embodiment of this application, before determining the first imbalance detection coefficient and the second imbalance detection coefficient based on the heating unit consumption set, the air temperature value from the temperature acquisition device, and the preset imbalance coefficient algorithm set, the method further includes: Determine the valve adjustment amplitude set corresponding to each preset imbalance adjustment level. Based on the valve adjustment amplitude set and the preset amplitude threshold lookup table, determine whether the corresponding preset imbalance adjustment level meets the level interference condition. If the corresponding preset imbalance adjustment level meets the level interference condition, determine the interference weight of the preset imbalance adjustment level that meets the level interference condition based on the preset interference quantification model. Adjust the corresponding heating unit consumption values ​​according to the interference weights, and add the adjusted heating unit consumption values ​​to the heating unit consumption set.

[0050] In other words, this application can also determine the set of valve adjustment amplitudes corresponding to each preset imbalance adjustment level in real time. This application has a preset amplitude threshold lookup table for different preset imbalance adjustment levels. This preset amplitude threshold lookup table stores the results of satisfying the level interference conditions corresponding to different valve adjustment amplitudes and different combinations of valve adjustment amplitudes. For example, if the valve adjustment amplitude at the resident level is greater than 10%, it is determined that the level interference condition is satisfied. When the level interference condition is satisfied, the server calls the preset interference quantification model to calculate the interference weight. Among them, the calculation method in the preset interference quantification model for resident level affecting unit level and unit level affecting building level is as follows: ; .

[0051] in, The interference weight of residents to the unit, This refers to the number of households within the unit. The total number of units, For the adjustment range of the valves for residents, To adjust the rationality factor for residents, To adjust the time decay factor; The interference weight of the unit to the building. This refers to the number of units within the building. This refers to the adjustment range of the unit valve. The number of residents within a unit, the total number of units, the resident adjustment rationality factor, the adjustment time decay factor, and the number of units in the building can be preset by the user based on the actual scenario and historical data; no specific limitations are imposed here. The resident valve adjustment range and the unit valve adjustment range can be input by the user. The total floor-level interference weight of residents in the building is... .

[0052] Regarding the interference weights from higher levels to lower levels, the default calculation method in the interference quantization model is as follows: ; .

[0053] in, This indicates the interference weight of the building to the unit. Indicates the adjustment range of the building valve. This indicates the rationality factor for building-level adjustments. This represents the first transmission attenuation coefficient, which is positively correlated with the distance from the unit to the floor valve. This indicates the interference weight of the unit to the residents. This represents the second transmission attenuation coefficient, which is positively correlated with the distance from the resident to the unit valve. The above parameters can be set by the user according to the actual usage scenario, and are not specifically limited here.

[0054] The interference weights obtained from the above calculations are used to correct the heating consumption value of the preset imbalance adjustment level, such as by... This corrects the heating unit consumption value at the building level. Furthermore, this application can also perform a secondary correction on the corrected heating unit consumption value after removing interference from the inherent characteristics of the hierarchical object module, such as... The exemplary formula includes The interference weight can be replaced by any of the above-mentioned interference weights. Users can also choose any correction method to correct the heating unit consumption value in actual use, or they can choose two correction methods to correct the heating unit consumption value. This application does not make specific limitations in this regard.

[0055] The above scheme can effectively avoid the conductive interference caused by imbalance between different levels, so that the imbalance detection is only related to a preset imbalance adjustment level, thus completing accurate imbalance detection.

[0056] Furthermore, this application outputs an imbalance detection score by using a pre-trained imbalance detection evaluation model. This imbalance detection evaluation model is a Long Short-Term Memory (LSTM) network model that is trained by taking input sample data of detection coefficients labeled with imbalance detection evaluation scores and training it with the Euclidean distance between the output imbalance detection evaluation score and the imbalance detection evaluation score label as the loss function.

[0057] In this application, the standard deviation of the tiered heating unit consumption calculated above can be used. Spearman correlation coefficient and maximum mutual information coefficient The input is fed into the LSTM model and processed to obtain the imbalance detection evaluation score.

[0058] S104, the server determines whether the corresponding preset imbalance adjustment level meets the secondary network imbalance based on the comparison result between the imbalance detection and evaluation score and the preset imbalance detection and evaluation threshold. When the secondary network imbalance condition is met, an imbalance adjustment prompt message is generated and sent to the heating management terminal.

[0059] In this embodiment of the application, before determining whether the corresponding preset imbalance adjustment level satisfies the secondary network imbalance based on the comparison result between the imbalance detection evaluation score and the preset imbalance detection evaluation threshold, the method further includes: Based on the thermal insulation coefficient and historical imbalance frequency of each object module in the corresponding preset imbalance adjustment level, a second weighting coefficient corresponding to the preset benchmark threshold is determined. The corrected preset imbalance detection and evaluation threshold is obtained by multiplying the second weighting coefficient by the preset benchmark threshold.

[0060] Specifically, the server can obtain the second weighting coefficient through the insulation coefficient of the hierarchical object module and the historical imbalance frequency. Compare it with a preset baseline threshold. The product of these values ​​is used as the preset imbalance detection and evaluation threshold. This threshold indicates that better insulation results in a smaller threshold coefficient and a smaller allowable fluctuation range. The higher the threshold, the larger the threshold coefficient, thus reducing the risk of misjudgment.

[0061] The imbalance detection and assessment score is compared with the preset imbalance detection and assessment threshold. If the score is greater than the threshold, it indicates a secondary network heating imbalance not caused by temporary temperature fluctuations. In this case, an imbalance adjustment prompt is generated and sent to the heating management terminal. If the score is less than the threshold, it indicates either a secondary network heating imbalance caused by temporary temperature fluctuations or no imbalance at all. In this case, no imbalance adjustment prompt is executed. The imbalance adjustment prompt can be text or light, instructing the user to adjust the secondary network imbalance. The specific information can be set by the user according to the actual usage scenario and is not specifically limited here. For example, the imbalance adjustment prompt could be "Building X has a secondary network imbalance."

[0062] The aforementioned heating management terminal can be a heating manager's mobile phone, computer, or other device, and no specific limitation is made here.

[0063] This application, through the aforementioned scheme, calculates the unit heating consumption at different control levels of heating regulating valves and calculates imbalance detection coefficients in two dimensions, combined with temperature, to obtain an imbalance detection assessment score that incorporates the influence of temperature factors, thereby enabling secondary network imbalance detection. This method effectively eliminates the interference of short-term temperature fluctuations on imbalance detection, is unaffected by sudden temperature rises / falls, and can more realistically and stably reflect the inherent imbalance state caused by internal system factors such as uneven valve opening and hydraulic imbalance, providing a reliable basis for precise regulation. This application achieves accurate imbalance detection of the secondary network in the heating system and is used for secondary network imbalance control.

[0064] Figure 2 This application provides a schematic diagram of the structure of a secondary network imbalance detection system for a heating system, as shown in the embodiment of the present application. Figure 2 As shown, the secondary network imbalance detection system 200 for a heating system includes: The acquisition module 201 is used to acquire heating parameter information corresponding to each preset imbalance adjustment level. The preset imbalance adjustment levels are defined based on the control range of the heating regulating valves. The heating parameter information includes at least household meter parameters and the heating area. The first determination module 202 is used to determine multiple heating unit consumption values ​​corresponding to the corresponding preset imbalance adjustment level based on the heating parameter information, and generate a heating unit consumption set. The second determination module 203 is used to determine the first imbalance detection coefficient and the second imbalance detection coefficient based on the heating unit consumption set, the air temperature value from the temperature acquisition device, and the preset imbalance coefficient algorithm group, and to determine the imbalance detection evaluation score according to the pre-trained imbalance detection evaluation model. The third determination module 204 is used to determine whether the corresponding preset imbalance adjustment level meets the secondary network imbalance based on the comparison result between the imbalance detection evaluation score and the preset imbalance detection evaluation threshold, so as to generate an imbalance adjustment prompt message when the secondary network imbalance condition is met, and send it to the heating management terminal.

[0065] Figure 3 A schematic diagram of a secondary network imbalance detection device for a heating system provided in this application embodiment is shown below. Figure 3 As shown, the device includes: At least one processor; and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: The system acquires heating parameter information corresponding to each preset imbalance adjustment level. The preset imbalance adjustment levels are defined based on the control range of the heating regulating valves. Heating parameter information includes at least household meter parameters and the area of ​​the heating zone. Based on the heating parameter information, multiple heating unit consumption values ​​corresponding to the respective preset imbalance adjustment levels are determined, and a heating unit consumption set is generated. Based on the heating unit consumption set, air temperature values ​​from temperature acquisition equipment, and a preset imbalance coefficient algorithm set, a first imbalance detection coefficient and a second imbalance detection coefficient are determined. An imbalance detection evaluation score is then determined based on a pre-trained imbalance detection evaluation model. Based on the comparison between the imbalance detection evaluation score and the preset imbalance detection evaluation threshold, it is determined whether the corresponding preset imbalance adjustment level meets the secondary network imbalance criteria. If the secondary network imbalance condition is met, an imbalance adjustment prompt message is generated and sent to the heating management terminal.

[0066] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system and device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0067] The systems, devices, and methods provided in this application are one-to-one correspondences. Therefore, the systems and devices also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and devices will not be repeated here.

[0068] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0069] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for detecting imbalance in a secondary network of a heating system, characterized in that, The method includes: Obtain heating parameter information corresponding to each preset imbalance adjustment level; wherein, the preset imbalance adjustment level is divided based on the control range of the heating adjustment valve; the heating parameter information includes at least household meter parameters and heating area area; Based on the heating parameter information, determine multiple heating unit consumption values ​​corresponding to the preset imbalance adjustment level, and generate a heating unit consumption set. Based on the heating unit consumption set, the air temperature value from the temperature acquisition device, and the preset imbalance coefficient algorithm set, the first imbalance detection coefficient and the second imbalance detection coefficient are determined, and the imbalance detection evaluation score is determined according to the pre-trained imbalance detection evaluation model. Based on the comparison between the imbalance detection and evaluation score and the preset imbalance detection and evaluation threshold, it is determined whether the corresponding preset imbalance adjustment level meets the secondary network imbalance, so as to generate an imbalance adjustment prompt message when the secondary network imbalance condition is met and send it to the heating management terminal.

2. The method for detecting secondary network imbalance in a heating system according to claim 1, characterized in that, Based on the heating parameter information, multiple heating unit consumption values ​​corresponding to the preset imbalance adjustment level are determined, and a heating unit consumption set is generated, specifically including: Based on the meter parameters of each household at the corresponding preset imbalance adjustment level and the preset heating calculation formula, calculate the corresponding heating. Based on the ratio of the heating heat to the area of ​​the matching heating zone, each heating unit consumption value is determined, and each heating unit consumption value is added to the heating unit consumption set.

3. The method for detecting secondary network imbalance in a heating system according to claim 2, characterized in that, Based on the aforementioned heating unit consumption set, air temperature values ​​from temperature acquisition devices, and a preset imbalance coefficient algorithm set, a first imbalance detection coefficient and a second imbalance detection coefficient are determined, specifically including: Based on the set of heating unit consumption of the corresponding preset imbalance adjustment level, calculate the standard deviation of heating unit consumption of the corresponding level, and normalize the temperature value from the temperature acquisition device to obtain the normalized temperature value. The standard deviation of the tiered heating unit consumption and the normalized temperature value are input into the preset imbalance coefficient algorithm group to calculate the Spearman correlation coefficient between the standard deviation of the tiered heating unit consumption and the normalized temperature value, which is the first imbalance detection coefficient. The maximum mutual information coefficient between the standard deviation of the tiered heating unit consumption and the normalized temperature value is calculated, which is the second imbalance detection coefficient.

4. The method for detecting secondary network imbalance in a heating system according to claim 3, characterized in that, Before determining the first imbalance detection coefficient and the second imbalance detection coefficient based on the heating unit consumption set, the air temperature value from the temperature acquisition device, and the preset imbalance coefficient algorithm group, the method further includes: Determine the set of unit consumption impact characteristic parameters corresponding to each of the preset imbalance adjustment levels; wherein, the set of unit consumption impact characteristic parameters includes at least the heating area of ​​multiple level object modules, the insulation coefficient of multiple level object modules, and the user density of multiple level object modules; Based on the set of characteristic parameters affecting unit consumption and the preset first weighting coefficient formula, the first weighting coefficient corresponding to each level of object module is determined; The corresponding heating unit consumption values ​​are corrected according to the first weighting coefficient to obtain the corrected heating unit consumption set. Based on the historical temperature response data corresponding to each of the hierarchical object modules, the corresponding temperature sensitivity coefficient is determined, and the normalized temperature value is corrected according to the temperature sensitivity coefficient to obtain the corrected normalized temperature value, so as to determine the first imbalance detection coefficient and the second imbalance detection coefficient based on the preset imbalance coefficient algorithm group.

5. The method for detecting secondary network imbalance in a heating system according to claim 4, characterized in that, Before determining whether the corresponding preset imbalance adjustment level satisfies the secondary network imbalance based on the comparison result between the imbalance detection evaluation score and the preset imbalance detection evaluation threshold, the method further includes: Based on the thermal insulation coefficient and historical imbalance frequency of each of the object modules of the corresponding preset imbalance adjustment level, a second weighting coefficient corresponding to the preset benchmark threshold is determined; The corrected preset imbalance detection and evaluation threshold is obtained by multiplying the second weighting coefficient with the preset benchmark threshold.

6. A method for detecting secondary network imbalance in a heating system according to any one of claims 1-5, characterized in that, Before determining the first imbalance detection coefficient and the second imbalance detection coefficient based on the heating unit consumption set, the air temperature value from the temperature acquisition device, and the preset imbalance coefficient algorithm group, the method further includes: Determine the set of valve adjustment amplitudes corresponding to each of the preset imbalance adjustment levels; Based on the set of valve adjustment ranges and the preset range threshold comparison table, determine whether the corresponding preset imbalance adjustment level meets the level interference condition; If so, based on the preset interference quantification model, determine the interference weight of the preset imbalance adjustment level that satisfies the level interference conditions; The corresponding heating unit consumption values ​​are corrected according to the interference weights, and the corrected heating unit consumption values ​​are added to the heating unit consumption set.

7. The method for detecting secondary network imbalance in a heating system according to claim 1, characterized in that, The control scope covers the three levels of objects in the secondary network of the heating system, including the household level, unit level and building level; the household meter parameters include at least water flow rate, inlet water temperature, return water temperature and heating duration.

8. The method for detecting secondary network imbalance in a heating system according to claim 1, characterized in that, The imbalance detection and evaluation model is obtained by taking input several sample data of detection coefficients labeled with imbalance detection and evaluation scores, and training the model using the Euclidean distance between the output imbalance detection and evaluation scores and the labels of the imbalance detection and evaluation scores as the loss function.

9. A secondary network imbalance detection system for a heating system, characterized in that, The system includes: The acquisition module is used to acquire heating parameter information corresponding to each preset imbalance adjustment level; wherein, the preset imbalance adjustment level is divided based on the control range of the heating adjustment valve; the heating parameter information includes at least household meter parameters and heating area area; The first determining module is used to determine multiple heating unit consumption values ​​corresponding to the preset imbalance adjustment level based on the heating parameter information, and generate a heating unit consumption set. The second determining module is used to determine the first imbalance detection coefficient and the second imbalance detection coefficient based on the heating unit consumption set, the air temperature value from the temperature acquisition device and the preset imbalance coefficient algorithm group, and to determine the imbalance detection evaluation score according to the pre-trained imbalance detection evaluation model. The third determining module is used to determine whether the corresponding preset imbalance adjustment level meets the secondary network imbalance based on the comparison result between the imbalance detection and evaluation score and the preset imbalance detection and evaluation threshold. When the secondary network imbalance condition is met, an imbalance adjustment prompt message is generated and sent to the heating management terminal.

10. A secondary network imbalance detection device for a heating system, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform a method for detecting secondary network imbalance in a heating system as described in any one of claims 1-8.

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