Intelligent heating ground management and control method and system based on Internet of Things control

Abstract

The invention provides an intelligent heating ground control method and system based on Internet of Things control, and belongs to the technical field of Internet of Things control. The method comprises the following steps: acquiring a temperature control mode and area monitoring information corresponding to each temperature control node; wherein the temperature control node comprises a heating ground grid such as a ceramic heating ground. Based on the area monitoring information and the temperature control mode, determining a first heating temperature rise time length, and after the time length is smaller than a preset time length, determining whether a heat flow loss abnormal area exists based on the temperature control mode, a node temperature distribution matrix and a preset heat flow direction identification model; if yes, determining a heat flow loss grade according to the heat loss temperature curve and a preset heat loss coefficient matrix, and determining a heat flow compensation power curve according to the heat flow loss grade. And according to the heat flow compensation power curve and a preset grid control rule, generating a dynamic temperature control compensation strategy so as to control the system to dynamically adjust the control parameters of the corresponding heating ground grid and continuously control each temperature control node after the first heating temperature rise duration.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things control technology, and in particular to an intelligent heating floor control method and system based on Internet of Things control. Background Art

[0002] In the development of modern intelligent buildings, indoor floor temperature control systems are widely used in residential, commercial, and industrial settings, particularly in northern and cold climate regions. However, existing floor temperature control systems typically use a uniform heating method with water circulation, which results in slow heating, uneven heat distribution, and difficulty meeting the requirements for precise temperature control and efficient management in complex scenarios.

[0003] At the same time, the unified, fixed-parameter control strategy of the existing ground temperature control system easily leads to unnecessary waste of resources, and is unable to provide targeted heating according to user habits. The dynamic adaptability of temperature control is poor.

[0004] As the market's requirements for intelligence, digitalization, and automation in the construction industry continue to increase, there is an urgent need for a more intelligent and flexible method for controlling heating floors that can adapt to the precise temperature control and efficient management needs in complex scenarios and improve user experience. Summary of the Invention

[0005] The embodiments of the present application provide an intelligent heating floor control method and system based on Internet of Things control, which is used to solve the technical problems of the current ground temperature control system, which is insufficient in intelligence, digitization and automation, difficult to meet the needs of precise temperature control and efficient management in complex scenarios, high cost and poor dynamic adaptability.

[0006] On the one hand, an embodiment of the present application provides an intelligent heating floor control method based on Internet of Things control, the method comprising:

[0007] Obtaining temperature control modes and regional monitoring information corresponding to each temperature control node; wherein the temperature control node includes one or more heating ground grids pre-divided according to the heating area; the temperature control mode includes at least a temperature holding mode, a temperature control following mode, and an energy-saving temperature control mode;

[0008] Determining a corresponding first heating duration based on the current indoor temperature, the node temperature distribution matrix, the indoor temperature change data, and the temperature control mode in the regional monitoring information;

[0009] When the first heating time is shorter than a preset time, determining whether there is an abnormal heat flow loss area based on the temperature control mode, the node temperature distribution matrix, and a preset heat flow direction identification model;

[0010] If so, determining a corresponding heat loss level according to the heat loss temperature curve corresponding to the abnormal heat loss area and a preset heat loss coefficient matrix, and determining a heat loss compensation power curve according to the heat loss level;

[0011] According to the heat flow compensation power curve and the preset grid control rules, a dynamic temperature control compensation strategy is generated to control the control system to dynamically adjust the control parameters of the corresponding heating ground grid after the first heating period, and continuously control each temperature control node according to the dynamically adjusted regional monitoring information.

[0012] On the other hand, an embodiment of the present application further provides an intelligent heating floor control system based on Internet of Things control, the system comprising:

[0013] An acquisition module is configured to acquire temperature control modes and regional monitoring information corresponding to each temperature control node; wherein the temperature control node includes one or more heating ground grids pre-divided according to the heating area; and the temperature control modes include at least a temperature holding mode, a temperature control following mode, and an energy-saving temperature control mode;

[0014] a first determining module, configured to determine a corresponding first heating and temperature rise duration based on the current indoor temperature, the node temperature distribution matrix, the indoor temperature change data, and the temperature control mode in the regional monitoring information;

[0015] a second determining module, configured to determine whether there is an abnormal heat flow loss area based on the temperature control mode, the node temperature distribution matrix, and a preset heat flow direction identification model when the first heating temperature rise time is less than a preset time;

[0016] a third determining module, configured to determine a corresponding heat loss level according to the heat loss temperature curve corresponding to the abnormal heat loss area and a preset heat loss coefficient matrix, so as to determine a heat loss compensation power curve according to the heat loss level;

[0017] A generation module is used to generate a dynamic temperature control compensation strategy based on the heat flow compensation power curve and the preset grid control rules so that the control system dynamically adjusts the control parameters of the corresponding heating ground grid after the first heating time, and continuously controls each of the temperature control nodes according to the dynamically adjusted regional monitoring information.

[0018] Compared with the prior art, this application has the following significant effects:

[0019] Through the above technical solution, the first heating temperature rise duration is determined by combining multi-dimensional data such as the node temperature distribution matrix and regional monitoring information, and the preset heat flow direction identification model is used to accurately locate the abnormal heat flow loss area, thereby achieving personalized and precise temperature control and improving user comfort. At the same time, by monitoring the heat flow loss level and matching the heat flow compensation power curve, dynamic and continuous control of the heating ground is achieved, and temperature control needs are flexibly met under the premise of resource protection. This application also fully considers the temperature control mode, which can meet the diversified temperature control needs of different scenarios, enhance the system's dynamic adaptive ability to the user's personalized temperature control needs, and improve the flexibility and intelligence level of heating ground control. This solves the current technical problems of insufficient intelligence, digitization and automation of the ground temperature control system, making it difficult to meet the needs of precise temperature control and efficient management in complex scenarios, high cost and poor dynamic adaptive ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0021] Figure 1 This is a flow chart of an intelligent heating floor control method based on Internet of Things control in an embodiment of the present application;

[0022] Figure 2 This is a structural diagram of an intelligent heating floor management and control system based on Internet of Things control in an embodiment of the present application. DETAILED DESCRIPTION

[0023] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0024] The embodiments of the present application provide an intelligent heating floor control method and system based on Internet of Things control, which is used to solve the technical problems of the current ground temperature control system, which is insufficient in intelligence, digitization and automation, difficult to meet the needs of precise temperature control and efficient management in complex scenarios, high cost and poor dynamic adaptability.

[0025] The following describes in detail various embodiments of the present application with reference to the accompanying drawings.

[0026] The embodiment of the present application provides an intelligent heating floor control method based on Internet of Things control, such as Figure 1As shown, the method may include steps S101-S105:

[0027] S101, the microcontroller obtains the temperature control mode and regional monitoring information corresponding to each temperature control node.

[0028] The temperature control node includes one or more heating ground grids pre-divided according to the heating area. The temperature control mode includes at least temperature maintenance mode, temperature control follow mode, and energy-saving temperature control mode.

[0029] It should be noted that the execution subject of this application is the microcontroller of the edge device deployed in the user's room. The microcontroller is the execution subject of the intelligent heating floor control method based on Internet of Things control. It is only an example. The execution subject is not limited to the microcontroller of the edge device. In actual use, it can also be a server or a cloud server. For example, the edge device serves as a communication intermediate layer for information collection and transmission, and communicates with the execution subject. The edge device is responsible for collecting indoor related data (such as room temperature, indoor temperature) and heating floor grid data (such as control parameters, node temperature, etc.). The specific calculation and data processing process is executed on the server. The specific execution subject can be selected or set by the user according to the actual use scenario, and this application does not make specific restrictions on this. The edge device can be an intelligent temperature control gateway that is connected to the heating floor grid pre-deployed in the user's room by wire or wireless. Among them, when the edge device is the execution subject, it can be connected to the cloud server, and regularly update its algorithm and upload historical data.

[0030] The heating floor grid of this application can be understood as a heating tile, which is encapsulated with a heating element. The heating floor grid has an interface connected to the power grid, which can be powered on to generate heat for the heating element itself, thereby realizing the heating function. It can be understood that this application is applied to an electric-driven heating floor system, which includes at least multiple heating floor grids and edge devices. Among them, the heating floor grid can have different heating areas, such as a 60*60 square centimeter heating floor grid and a 100*100 square centimeter heating floor grid. Heating floor grids with different heating areas can be used in different installation scenarios, and the specific selection is made by the user.

[0031] In the embodiments of this application, due to different installation locations, such as small-sized buildings, villas, shopping malls, etc., the number of heating ground grids used varies. If data processing is performed on a single heating ground grid, it will waste huge computing resources, fail to reduce the amount of calculation, and improve the data parallel processing capability. This application considers grouping the heating ground grids, thereby clustering them to obtain temperature control nodes. This application provides the following embodiments, specifically including:

[0032] According to each heating ground grid and its location information, a corresponding number of community nodes are constructed. Based on each community node and each community edge weight, and through the preset Louvain algorithm, the modular gain sequence corresponding to each community node is calculated, so as to perform node migration and merging according to the modular gain sequence and the preset migration and merging conditions to construct each first temperature community. Among them, the community edge weight is used to characterize the degree of thermal conduction correlation of the heating ground grid. The first temperature community includes one or more community nodes. Take each first temperature community as a new community node, and construct each second temperature community based on the new community node and the corresponding community edge weight, until the preset migration and merging conditions are not met, and each Nth temperature community is obtained. N is a natural number greater than 1. Take the Nth temperature community as a temperature control node.

[0033] In other words, this application uses a community discovery algorithm, that is, the preset Louvain algorithm, to hierarchically cluster the various heating ground grids installed indoors by the user. Since the Louvain algorithm has low time complexity and is suitable for large-scale networks, it can be used to cluster heating ground grids in different installation locations and quickly generate temperature control nodes.

[0034] Specifically, the present application regards each heating ground grid as an independent community node based on the heating ground grid and its location information, and the heating ground grid can be understood as a tile. Subsequently, the microcontroller uses the community edge weight corresponding to each community node to execute the preset Louvain algorithm, traverse each community node, consider moving it to its adjacent community and calculate the sum of the community edge weights after the move, the sum of the community edge weights connecting the community node and other nodes, the sum of the edge weights of the node vector in the community formed by the current move, and the weight of each community edge, calculate the modular gain corresponding to the preset Louvain algorithm, and determine the modular gains of the community node moving to each adjacent community, and construct the modular gain sequence of the community node. Subsequently, the microcontroller determines the maximum value in each modular gain sequence according to each modular gain sequence, as the community migrated and merged by the community node, traverses each community node, and constructs each first temperature community. Subsequently, the first temperature community of the above cluster is used as the new community node, and the graph is further reconstructed using the preset Louvain algorithm to calculate the modular gains corresponding to the new community nodes. If the change value of the modular gain is less than the preset change threshold after multiple consecutive iterations, the preset migration and merging conditions are not met. At this time, the Nth temperature communities are obtained, and the temperature control nodes are determined.

[0035] In one embodiment of the present application, before calculating the modular gain sequence corresponding to each community node based on each community node and each community edge weight using a preset Louvain algorithm, the community edge weight calculation is further performed, specifically including:

[0036] Based on the preset thermal conductivity coefficient, thermal path information and temperature difference value of each heating ground grid, the first thermal conduction association weight between each heating ground grid is determined. The thermal path information includes at least the grid center spacing and grid edge contact area of ​​the heating ground grid. Based on the thermal response time constant of each heating ground grid, the second thermal conduction association weight is determined. Based on the historical temperature control data, the Pearson correlation coefficient between the historical temperature series of each heating ground grid is calculated to determine the third thermal conduction association weight based on the Pearson correlation coefficient, and construct a dynamic weight matrix. Based on the product and value of each first thermal conduction association weight, each second thermal conduction association weight and the preset static coefficient group, a static weight matrix is ​​generated. Based on the static weight matrix, the dynamic weight matrix and the preset static weight, the corresponding community edge weight matrix is ​​determined. The community edge weight matrix includes the community edge weights between each community node.

[0037] The microcontroller pre-stores the preset thermal conductivity coefficient and thermal path information of each heating ground grid. The temperature value of each heating ground grid can be collected according to the temperature sensor set in the heating ground grid to obtain the temperature difference between the grids. The preset thermal conductivity coefficient is related to the material of the heating ground grid and can be pre-set by expert experience. This application does not make specific restrictions on this. The thermal path information includes the grid center spacing and grid edge contact area between the heating ground grids. These parameters can be set during installation. This application does not make specific restrictions on this. The calculation formula for the first thermal conduction association weight is as follows:

[0038]

[0039] in, is the first heat conduction association weight between heating ground grid i and heating ground grid j; k is the preset heat conduction coefficient; A i,j is the grid edge contact area between heating ground grid i and heating ground grid j; Δx i,j is the grid center distance between heating ground grid i and heating ground grid j; is the absolute value of the temperature difference; T set The target temperature may be a preset default target temperature or a target temperature obtained according to the temperature control mode. The first heat conduction association weight increases with the larger the contact area and the smaller the spacing at the grid edge, and decreases with the larger the temperature deviation.

[0040] Subsequently, the microcontroller will also calculate the second heat conduction correlation weight based on the thermal response time constant between each heating ground grid. The thermal response time constant can be determined by a step response test after installation and can be obtained by a professional through installation testing. The calculation formula is as follows: Among them, τ i is the thermal response time constant of the heating ground grid i, τ j is the thermal response time constant of the heating ground grid j, and ∈ is a preset minimum constant to avoid the denominator being 0.

[0041] Then, the microcontroller combines historical temperature control data, such as the historical temperature control data of the past 24 hours, to determine the historical temperature sequence of the heating ground grid, and calculates the Pearson correlation coefficient between the historical temperature sequences. Combined with the Pearson correlation coefficient of the historical temperature data, the third heat conduction association weight is calculated and constructed as a dynamic weight matrix W1. The third heat conduction association weight The calculation formula is as follows:

[0042]

[0043] in, is the third heat conduction association weight between the heating ground grid i and the heating ground grid j; T i (t) is the historical temperature sequence of the heating ground grid i, t is the time; T j (t) is the historical temperature sequence of the heating ground grid j; corr(T i (t),T j (t)) is the Pearson correlation coefficient between the heating ground grid i and the heating ground grid j.

[0044] The microcontroller will also use the above-mentioned first heat conduction associated weights, second heat conduction associated weights and the preset static coefficient group to perform weighted summation on the first heat conduction associated weights and second heat conduction associated weights to obtain a static weight matrix. Static weight matrix W2 = α·W 1 +β·W 2 , W 1 for The constructed weight matrix, W 2 for The constructed weight matrix, α and β are the static weight coefficients in the preset static coefficient group, for example, α = 0.6, β = 0.4, which can be set by the user according to the actual scenario and are not specifically limited here. The community edge weight matrix W3 = (1-γ)·W1+γ·W2 is then calculated, where γ is the preset static weight. If it is initially set to 0.7, it can be updated based on expert experience, and this application does not make specific restrictions on this. W3 contains the community edge weights between each pair of community nodes.

[0045] In an embodiment of the present application, the user can set the temperature control mode through a user terminal corresponding to a user interaction interface connected to a mobile phone, a computer, or a microcontroller. At the same time, the microcontroller can obtain regional monitoring information through temperature sensors set in the heating ground grid, indoor temperature sensors connected to the network or Bluetooth, outdoor temperature sensors connected to the network or Bluetooth, humidity sensors connected to the network or Bluetooth, etc. The user terminal is not limited to the above-mentioned types of terminals. In actual use, other types of user terminals can also be used to establish a communication connection with the microcontroller to transmit the user's control data or other data for the heating ground grid. This application does not make specific restrictions on this. Among them, the temperature control mode can include temperature holding mode, temperature control following mode, energy-saving temperature control mode, and other temperature control modes. It can be set according to the actual use scenario. This application does not make specific restrictions on this. The temperature holding mode can be understood as the user setting a temperature to keep the indoor temperature at a fixed target temperature; the temperature control following mode can be understood as focusing on heating the area where the user is located while maintaining a fixed target temperature; the energy-saving temperature control mode can be understood as maintaining a minimum comfortable temperature for heating.

[0046] S102, the microcontroller determines a corresponding first heating time based on the current indoor temperature, the node temperature distribution matrix, the indoor temperature change data and the temperature control mode in the regional monitoring information.

[0047] In the embodiment of the present application, based on the current indoor temperature, the node temperature distribution matrix, the indoor temperature change data, and the temperature control mode in the regional monitoring information, the corresponding first heating temperature rise time is determined, specifically including:

[0048] The short-term temperature change rate is calculated based on the indoor temperature change data. The heat loss power is determined based on the outdoor temperature from the user terminal, the current indoor temperature, and the preset heat loss coefficient. The heat capacity calibration coefficient is determined based on the current heating power, the preset grid heating efficiency, the heat loss power, and the short-term temperature change rate, and the calibrated heat capacity value is determined based on the product of the heat capacity calibration coefficient and the preset indoor heat capacity. The corresponding net heating power is determined based on the current heating power, the mode power coefficient corresponding to the temperature control mode, the preset grid heating efficiency, and the heat loss power. The second heating rise time is calculated based on the calibrated heat capacity value, the heating demand value, and the net heating power. The heating demand value is the difference between the target temperature corresponding to the temperature control mode and the current indoor temperature. The indoor temperature distribution correction factor is determined based on the temperature distribution standard deviation obtained from the node temperature distribution matrix, and the low temperature area ratio is determined based on the preset low temperature threshold and the number of low temperature control nodes obtained from the node temperature distribution matrix. The first heating rise time is determined based on the second heating rise time, the temperature distribution correction factor, and the low temperature area ratio.

[0049] In other words, the microcontroller continuously monitors the room temperature of the space where the heating floor grid is located and continuously determines the time it takes for the indoor temperature to reach the set target temperature, i.e., the first heating and heating duration. The microcontroller uses indoor temperature change data, such as the indoor temperature change data for a preset 15-minute monitoring period, to calculate the average temperature change corresponding to that 15-minute period, which serves as the short-term temperature change rate. The heat loss power is then calculated using the outdoor temperature, the current indoor temperature, and a preset heat loss coefficient. The preset heat loss coefficient can be set before installation based on the building materials used and can be set by an expert to reflect the heat loss capacity of the indoor space, but this is not specifically defined here. The heat capacity calibration coefficient is then calculated based on the current heating power of each heating floor grid and the preset grid heating power, thereby obtaining the calibrated heat capacity value corresponding to the indoor space. The preset grid heating efficiency can be set by the manufacturer and is not specifically defined in this application. For different temperature control modes, the heating floor grid also has a corresponding mode power coefficient, which is used to calculate the net heating power. For example, it is 1.2 for temperature maintenance mode and 0.8 for energy-saving temperature control mode. The specific setting is determined by the user based on the actual usage scenario and is not specifically defined here. The theoretical time required to heat the room from the current temperature to the target temperature (the second heating time) is calculated by combining the calibrated heat capacity, heating demand, and net heating power. The node temperature distribution matrix is ​​also used to factor in the indoor temperature distribution factor and the proportion of low-temperature areas. This theoretical time is then adjusted to accurately determine the first heating time.

[0050] The calculation formula for the above calibration heat capacity value is as follows:

[0051]

[0052] Among them, C hc_calib Indicates the calibrated heat capacity value; C hc_initial Indicates the preset indoor heat capacity, which can be the default setting value or the calibrated heat capacity value corresponding to the previous preset monitoring period. It is selected by the user according to the actual usage scenario and is not specifically limited here; P1 is the current heating power, which can be the average heating power of each current heating ground grid; η is the preset grid heating efficiency, which is a preset empirical value, characterizing the actual heat production efficiency of the heating ground grid, such as setting it to 0.95; θ is the preset heat loss coefficient, which can be set by an expert as the initial value or adjusted by the user, and this application does not make specific restrictions on this; T2 is the current indoor temperature in the indoor temperature change data; T1 is the outdoor temperature; θ·(T2-T1) / 1000 corresponds to the heat loss power, and 1000 is used for unit conversion to a unified unit; T3 is the short-term temperature change rate, based on Calculated, T 2,agois the indoor temperature before the preset monitoring period Δt at the current moment, where Δt is in minutes; 60 in the formula is used to convert time from minutes to hours.

[0053] The net heating power is calculated as follows:

[0054]

[0055] Where P2 is the net heating power, k mode is the mode power coefficient; is the heat loss power.

[0056] The calculation formula for the second heating time is as follows:

[0057]

[0058] Among them, t th T is the second heating time; set -T2 is the heating demand value.

[0059] Indoor temperature distribution correction factor k1=1+a·σ T , k1 is the indoor temperature distribution correction factor, a is a preset empirical coefficient, which can be set by experts and is not specifically limited here; σ T The standard deviation of the temperature distribution calculated from the node temperature distribution matrix can be obtained by calculating the average node temperature based on the temperatures of each temperature-controlled node in the node temperature distribution matrix. The standard deviation is then calculated using the temperatures of each temperature-controlled node, the average node temperature, and the standard deviation calculation formula. The low-temperature area percentage can be calculated by counting the number of temperature-controlled nodes in the node temperature distribution matrix that fall below a preset low-temperature threshold and calculating the ratio of this count to the number of temperature-controlled nodes as the low-temperature area percentage. The preset low-temperature threshold can be set by an expert and is not specifically defined here.

[0060] Finally, the first heating time t final Calculation formula: final =t th (k1+k2), where k2 is the penalty factor for low temperature areas, is the proportion of low temperature area, b is a preset empirical coefficient, which can be set by experts, such as 0.5, and is not specifically limited here.

[0061] This approach accurately calculates the duration of the first heating cycle, ensuring that the indoor temperature is reaching the target. By combining physical models with data-driven algorithms, accurate calculations of heating durations are achieved from real-time monitoring data, providing practical control logic for intelligent temperature control systems.

[0062] S103, when the first heating and heating time is less than the preset time, the microcontroller determines whether there is an abnormal heat flow loss area based on the temperature control mode, the node temperature distribution matrix and the preset heat flow direction identification model.

[0063] In the embodiment of the present application, the above-mentioned determination of whether there is an abnormal heat flow loss area based on the temperature control mode, the node temperature distribution matrix and the preset heat flow direction identification model specifically includes:

[0064] According to the temperature control mode, match the first heat flow direction data set in the preset heat flow direction comparison table. Among them, the first heat flow direction data set includes at least one first heat flow loss direction and its corresponding heat loss temperature range vector. Input the node temperature distribution matrix into the preset heat flow direction identification model to determine whether there is a second heat flow loss direction based on the model output result. In the case of determining that there is a second heat flow loss direction, compare each second heat flow loss direction with the first heat flow direction data set to determine the second heat flow loss direction that is consistent with the first heat flow loss direction as the third heat flow loss direction, and eliminate the third heat flow loss direction from each second heat flow loss direction. Match the heat loss temperature vector of the third heat flow loss direction with the corresponding heat loss temperature range vector. Based on the second heat flow loss direction and / or vector matching results, determine whether there is an abnormal heat flow loss area.

[0065] Abnormal heat flow loss areas can be understood as heat flow loss in certain locations when the room is heated and the temperature is raised, and it is not normal heat flow loss such as high temperature in the middle of the room and low temperature near the window or wall. When judging the abnormal heat flow loss area, the microcontroller uses the temperature control mode, the node temperature distribution matrix and the pre-trained heat flow direction recognition model to make a comprehensive judgment. Specifically, the preset heat flow direction comparison table pre-stores the heat flow direction data set corresponding to the room under different temperature control modes, which can be pre-stored in the memory connected to the microcontroller or in the cloud connected to the microcontroller, and is not specifically limited here. The preset heat flow direction recognition model can be a pre-trained machine learning model, which is trained by a user data set constructed by several preset historical data sets and the historical node temperature distribution matrix of the current setting space and the label data of the heat flow loss direction. The preset historical data set can be a data set of the historical node temperature distribution matrix and the label data of the heat flow loss direction of the same household type selected by experts or crawled from the Internet. This application does not make specific limitations on this. After the training is completed, the node temperature distribution matrix can be analyzed by the preset heat flow direction recognition model, and when heat flow loss exists, each second heat flow loss direction can be output.

[0066] The microcontroller compares the second heat loss direction with the first heat loss direction to obtain a third heat loss direction that needs to be further distinguished from the normal heat loss direction. Vector and interval vector matching can be performed by calculating cosine similarity, or by comparing each element in the heat loss temperature vector with each element (interval) in the heat loss temperature interval vector to determine whether the vectors match, thereby establishing conditions that can accurately determine the presence of abnormal heat loss areas.

[0067] The above-mentioned preset duration can be set by experts and updated regularly. For example, as the service life of the heating ground grid increases, the preset duration can be adaptively adjusted to decrease. The specific value can be set according to the actual usage scenario, and this application does not make specific restrictions on this.

[0068] In addition, this application also includes the following embodiments:

[0069] If the first heating duration is greater than or equal to a preset duration, the device updates the first heating duration at a preset interval and accumulates the number of consecutive comparisons where the updated first heating duration is greater than or equal to the preset duration. When the number of consecutive comparisons is determined to be greater than a predetermined value, an abnormality prompt is generated. The abnormality prompt includes an audible signal or a light signal.

[0070] In other words, if the first heating-up duration is not less than the preset duration, indicating that there is still a significant amount of time before the target temperature is reached, the microcontroller can recalculate the first heating-up duration after a preset time interval, reducing unnecessary resource waste caused by real-time monitoring. Furthermore, the preset time interval can become less adaptable as the difference between the first heating-up duration and the preset duration decreases, allowing detection of situations where the first heating-up duration is less than the preset duration at the first instant. The preset time interval can also be a fixed duration, which can be set by an expert or user, and is not specifically limited in this application. The microcontroller can also record the number of consecutive comparisons in which the calculated first heating-up duration is greater than or equal to the preset duration. If the first heating-up duration is calculated three times in a row at the preset time interval and each time is greater than or equal to the preset duration, the number of consecutive comparisons accumulates to three. If the fourth time is less than the preset duration, the number of consecutive comparisons ceases to accumulate; otherwise, it continues to accumulate. If the number of consecutive comparisons exceeds a predetermined value, an abnormality alert message is generated, alerting the user to any indoor events that may affect reaching the target temperature. The predetermined value can be set by an expert, and is not specifically limited in this application. An event can be understood as a user opening a window, door, or other ventilation method, resulting in the inability to reach the target temperature at normal operating power. The abnormality prompt information can be an audible signal, such as prompting "Is there a door or window open?", or a light signal such as a red warning light turning on as an abnormality prompt information. The abnormality prompt information can also be sent as text to the user terminal, and this application does not specifically limit this.

[0071] S104, when the microcontroller determines that there is an abnormal heat loss area, the microcontroller determines the corresponding heat loss level according to the heat loss temperature curve corresponding to the abnormal heat loss area and the preset heat loss coefficient matrix, and determines the heat loss compensation power curve according to the heat loss level.

[0072] In the embodiment of the present application, the corresponding heat loss level is determined based on the heat loss temperature curve corresponding to the abnormal heat loss area and the preset heat loss coefficient matrix, specifically including:

[0073] When a second heat flow loss direction exists and / or the vector matching result is a mismatch, it is determined that an abnormal heat flow loss area exists. A heat loss temperature curve is generated according to the node temperature distribution matrix and the heat flow loss direction. A temperature gradient sequence is calculated based on the heat loss temperature curve. The temperature gradient sequence includes the temperature gradient values ​​of each temperature control node along the heat flow loss direction. Based on the temperature gradient sequence and the heat loss coefficient of each temperature control node in the preset heat loss coefficient matrix, the heat loss contribution corresponding to each temperature control node along the heat flow loss direction is calculated. The preset heat loss coefficient matrix includes the heat loss coefficient of each grid area corresponding to the preset building floor plan. The heat flow loss level is determined based on each heat loss contribution and the preset level judgment conditions. The preset level judgment conditions include at least judgment conditions corresponding to no abnormality, low risk, medium risk, and high risk, respectively. The judgment conditions are obtained based on a combination of a preset low risk threshold, a preset medium risk threshold, and a preset high risk threshold.

[0074] That is, if there is a second heat loss direction and / or the vector matching result does not match, then it is determined that there is an abnormal heat loss area, and the area formed by the temperature control nodes corresponding to the second heat loss direction and / or the third heat loss direction is regarded as the abnormal heat loss area. Subsequently, the microcontroller will use the position coordinate as the horizontal coordinate and the temperature value as the vertical coordinate to establish a heat loss temperature curve according to the heat loss direction, and calculate the temperature gradient value at the same time, such as is the temperature gradient value at position i (i is a natural number). According to the temperature gradient sequence formed by the temperature gradient value and the heat loss coefficient of each temperature control node mentioned above, the heat loss contribution and its mean are calculated. The heat loss coefficient here is a separate value corresponding to different temperature control nodes, which is used to reflect the thermal conductivity of the position. It is not the preset heat loss coefficient mentioned above. The preset heat loss coefficient corresponds to the complete indoor space, and the heat loss coefficient corresponds to a single heating ground grid or temperature control node. The two are not consistent. The preset heat loss coefficient matrix can be set by users or experts according to the preset building plan map. For example, if the heating ground grid or wall in a certain area is damaged, the thermal conductivity may deteriorate, making the heat loss coefficient greater than other areas.

[0075] The microcontroller calculates the product of the absolute value of each temperature gradient value in the temperature gradient sequence and the heat loss coefficient corresponding to the corresponding position, and obtains the heat loss contribution of each node along the heat flow history direction. n , n is the number of temperature control nodes along the heat flow history direction, and the preset low risk threshold Q in the preset level judgment condition low , preset medium risk threshold Q mid And preset high risk threshold Q high , determine the level of heat loss:

[0076] If all Q n ≤Q low , no abnormality at this time;

[0077] If there is a first preset number of Q n Satisfy Q low ≤Q n ≤Q mid , low risk at this time;

[0078] If there are a second predetermined number of consecutive positions of Q n Satisfy Q n >Q mid , at this time the risk is medium;

[0079] If there is Q n >Q high , high risk at this time.

[0080] The above-mentioned first preset number and second preset number can be set by the user or expert, and the user can adjust and modify them by himself, and this application does not make any specific restrictions on this.

[0081] After obtaining the heat loss level, the heat loss compensation power curve is determined according to the heat loss level, specifically including:

[0082] The corresponding curve type is determined based on the heat loss level and the preset power compensation rules. The curve type can be at least one of the following: linear compensation power curve, piecewise linear compensation power curve, exponential compensation power curve, and pulsed step compensation power curve. Based on the curve type and the curve correction parameters from the expert system, the corresponding heat flow compensation power curve is generated.

[0083] In other words, different heat loss levels can have different curve types of compensation power curves, so as to adaptively adjust the heating ground grid to overcome different levels of abnormal heat loss. For example, if the user slightly opens the window, it is judged as low risk. The microcontroller will use a piecewise linear compensation power curve and obtain the curve correction coefficient set by the expert system to generate a heat flow compensation power curve, such as:

[0084]

[0085] Wherein, P(t) is the compensation power obtained from the heat flow compensation power curve, 1.2, 1.5, and 1 are curve correction coefficients, P0 is the basic heating power, [0, t1) is the preheating stage, [t1, t2) is the strengthening stage, and [t2, ∞) is the maintenance stage. For example, t1 is set to 10 minutes, which is specifically set by an expert, and t2 is set to 30 minutes. The specific curve type can also be updated by experts during actual use, and this application does not specifically limit this.

[0086] S105, the microcontroller generates a dynamic temperature control compensation strategy based on the heat flux compensation power curve and the preset grid control rules to control the control system to dynamically adjust the control parameters of the corresponding heating ground grid after the first heating and heating period, and continuously control each temperature control node based on the dynamically adjusted regional monitoring information.

[0087] In the embodiment of the present application, based on the heat flux compensation power curve and the preset grid control rules, a temperature control compensation strategy is generated to dynamically adjust the control parameters of the corresponding heating ground grid after the first heating temperature rise period. Specifically, it includes:

[0088] The heat flux compensation power curve is modified according to preset grid control rules to determine the adaptive compensation power curve corresponding to each heating ground grid to be controlled. The preset grid control rules include at least modifying the initial power of the curve based on the grid's basic attributes. These basic grid attributes include at least a heat loss coefficient and a priority weight. Based on each adaptive compensation power curve, a control parameter sequence for each heating ground grid within a preset compensation period is determined. This generates a dynamic temperature control compensation strategy and, after the first heating period, the control system dynamically adjusts the control parameters of the corresponding heating ground grid.

[0089] The above-mentioned preset grid control rules can be understood as further correcting the heat flux compensation power calculated by the above-mentioned heat flux compensation power curve according to the heat loss coefficient and priority weight, such as calculating the product value of the heat loss coefficient and the heat flux compensation power to generate an adaptive compensation power curve for a single heating ground grid, and adjusting the control parameters of the heating ground grid according to the adaptive compensation power curve in the single heating ground grid and reaching the power maintenance condition, and then dynamically adjusting each heating ground grid according to the adaptive compensation power curve in accordance with the priority weight. Since the adaptive compensation power curve is a curve with time as the horizontal axis and the supplementary power as the vertical axis, different compensation powers correspond to different control parameters at each moment, and the microcontroller will generate a control parameter sequence as a dynamic temperature control compensation strategy. After the above-mentioned first heating time, the dynamic temperature control compensation strategy is used to dynamically adjust the heating ground, continuously control the heating ground, and stabilize the indoor temperature at the target temperature.

[0090] Through the above technical solution, the first heating temperature rise duration is determined by combining multi-dimensional data such as the node temperature distribution matrix and regional monitoring information, and the preset heat flow direction identification model is used to accurately locate the abnormal heat flow loss area, thereby achieving personalized and precise temperature control and improving user comfort. At the same time, by monitoring the heat flow loss level and matching the heat flow compensation power curve, dynamic and continuous control of the heating ground is achieved, and temperature control needs are flexibly met under the premise of resource protection. This application also fully considers the temperature control mode, which can meet the diversified temperature control needs of different scenarios, enhance the system's dynamic adaptive ability to the user's personalized temperature control needs, and improve the flexibility and intelligence level of heating ground control. This solves the current technical problems of insufficient intelligence, digitization and automation of the ground temperature control system, making it difficult to meet the needs of precise temperature control and efficient management in complex scenarios, high cost and poor dynamic adaptive ability.

[0091] Figure 2 A schematic diagram of the structure of an intelligent heating floor control system based on Internet of Things control provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the intelligent heating floor control system 200 based on Internet of Things control includes:

[0092] The acquisition module 201 is used to obtain the temperature control mode and regional monitoring information corresponding to each temperature control node. Among them, the temperature control node includes one or more heating ground grids pre-divided according to the heating area. The temperature control mode includes at least a temperature maintenance mode, a temperature control follow mode, and an energy-saving temperature control mode. The first determination module 202 is used to determine the corresponding first heating temperature rise time based on the current indoor temperature, the node temperature distribution matrix, the indoor temperature change data and the temperature control mode in the regional monitoring information. The second determination module 203 is used to determine whether there is an abnormal heat flow loss area based on the temperature control mode, the node temperature distribution matrix and the preset heat flow direction identification model when the first heating temperature rise time is less than the preset time. The third determination module 204 is used to determine the corresponding heat flow loss level according to the heat loss temperature curve corresponding to the abnormal heat flow loss area and the preset heat loss coefficient matrix, so as to determine the heat flow compensation power curve according to the heat flow loss level. Generation module 205 is used to generate a dynamic temperature control compensation strategy based on the heat flow compensation power curve and the preset grid control rules so that the control system dynamically adjusts the control parameters of the corresponding heating ground grid after the first heating period, and continuously controls each temperature control node based on the dynamically adjusted regional monitoring information.

[0093] The various embodiments in this application are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment.

[0094] The system and method provided in the embodiments of the present application correspond one to one. Therefore, the system also has similar beneficial technical effects to its corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system will not be repeated here.

[0095] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0096] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. An intelligent heating floor control method based on Internet of Things control, characterized in that: The method comprises: Obtaining temperature control modes and regional monitoring information corresponding to each temperature control node; wherein the temperature control node includes one or more heating ground grids pre-divided according to the heating area; the temperature control mode includes at least a temperature holding mode, a temperature control following mode, and an energy-saving temperature control mode; Determining a corresponding first heating duration based on the current indoor temperature, the node temperature distribution matrix, the indoor temperature change data, and the temperature control mode in the regional monitoring information; When the first heating time is shorter than a preset time, determining whether there is an abnormal heat flow loss area based on the temperature control mode, the node temperature distribution matrix and a preset heat flow direction identification model; If so, determining a corresponding heat loss level according to the heat loss temperature curve corresponding to the abnormal heat loss area and a preset heat loss coefficient matrix, and determining a heat loss compensation power curve according to the heat loss level; According to the heat flow compensation power curve and the preset grid control rules, a dynamic temperature control compensation strategy is generated to control the control system to dynamically adjust the control parameters of the corresponding heating ground grid after the first heating period, and continuously control each temperature control node according to the dynamically adjusted regional monitoring information.

2. The intelligent heating floor control method based on Internet of Things control according to claim 1 is characterized in that: The method further comprises: Constructing a corresponding number of community nodes according to each of the heating ground grids and their location information; Based on the weights of the community nodes and the community edges, and using a preset Louvain algorithm, a modular gain sequence corresponding to each of the community nodes is calculated, and node migration and merging are performed according to the modular gain sequence and preset migration and merging conditions to construct each first temperature community; wherein the community edge weight is used to represent the degree of heat conduction correlation of the heat-generating ground grid; and the first temperature community includes one or more community nodes; Each first temperature community is used as a new community node, and each second temperature community is constructed based on the new community node and the corresponding community edge weight, until the preset migration and merging conditions are no longer met, thereby obtaining each Nth temperature community; N is a natural number greater than 1; The Nth temperature community is used as the temperature control node.

3. The intelligent heating floor control method based on Internet of Things control according to claim 2 is characterized in that: Before calculating the modular gain sequences corresponding to the community nodes based on the community nodes and the community edge weights using a preset Louvain algorithm, the method further includes: Determining a first heat conduction association weight between each pair of the heating ground grids based on a preset heat conduction coefficient, heat conduction path information, and temperature difference value of each heating ground grid; wherein the heat conduction path information includes at least a grid center spacing and a grid edge contact area of ​​the heating ground grids; determining a second heat conduction association weight based on a thermal response time constant of each of the heat-generating ground grids; Calculating the Pearson correlation coefficient between each pair of historical temperature sequences of the heating ground grids based on the historical temperature control data, determining a third heat conduction correlation weight based on the Pearson correlation coefficient, and constructing a dynamic weight matrix; generating a static weight matrix according to the product sum of each of the first heat conduction associated weights, each of the second heat conduction associated weights, and a preset static coefficient group; A corresponding community edge weight matrix is ​​determined according to the static weight matrix, the dynamic weight matrix, and the preset static weights; the community edge weight matrix includes the community edge weights between each of the community nodes.

4. The intelligent heating floor control method based on Internet of Things control according to claim 1 is characterized in that: Determining a corresponding first heating duration based on the current indoor temperature, the node temperature distribution matrix, the indoor temperature change data, and the temperature control mode in the regional monitoring information specifically includes: Calculating a short-term temperature change rate based on the indoor temperature change data; Determining heat loss power based on the outdoor temperature from the user terminal, the current indoor temperature, and a preset heat loss coefficient; Determining a heat capacity calibration coefficient based on the current heating power, the preset grid heating efficiency, the heat loss power, and the short-term temperature change rate, and determining a calibrated heat capacity value based on the product of the heat capacity calibration coefficient and the preset indoor heat capacity; Determining a corresponding net heating power according to the current heating power, the mode power coefficient corresponding to the temperature control mode, the preset grid heating efficiency, and the heat loss power; Calculating a second heating time according to the calibrated heat capacity value, the heating demand value, and the net heating power; the heating demand value is the difference between the target temperature corresponding to the temperature control mode and the current indoor temperature; Determine the indoor temperature distribution correction factor based on the temperature distribution standard deviation obtained from the node temperature distribution matrix, and determine the low temperature area ratio based on the preset low temperature threshold and the number of low temperature control nodes obtained from the node temperature distribution matrix; The first heating temperature rise time is determined based on the second heating temperature rise time, the temperature distribution correction factor and the low temperature area ratio.

5. The intelligent heating floor control method based on Internet of Things control according to claim 1 is characterized in that: Based on the temperature control mode, the node temperature distribution matrix, and the preset heat flow direction identification model, determining whether there is an abnormal heat flow loss area specifically includes: According to the temperature control mode, a first heat flow direction data set is matched in a preset heat flow direction comparison table; wherein the first heat flow direction data set includes at least a first heat flow loss direction and its corresponding heat loss temperature interval vector; Inputting the node temperature distribution matrix into the preset heat flow direction identification model to determine whether there is a second heat flow loss direction according to the model output result; If so, comparing each of the second heat flow loss directions with the first heat flow direction data set to determine the second heat flow loss direction that is consistent with the first heat flow loss direction as the third heat flow loss direction, and removing the third heat flow loss direction from each of the second heat flow loss directions; Matching the heat loss temperature vector of the third heat loss direction with the corresponding heat loss temperature interval vector; Determine whether the abnormal heat loss area exists according to the second heat loss direction and / or vector matching result.

6. The intelligent heating floor control method based on Internet of Things control according to claim 1 is characterized in that: The method further comprises: When the first heating time is greater than or equal to the preset time, the first heating time is updated at preset time intervals, and the number of consecutive comparisons in which the updated first heating time is greater than or equal to the preset time is accumulated; When it is determined that the number of consecutive comparisons is greater than a predetermined value, an abnormal prompt message is generated; the abnormal prompt message includes a sound signal and a light signal.

7. The intelligent heating floor control method based on Internet of Things control according to claim 5 is characterized in that: Determine the corresponding heat loss level based on the heat loss temperature curve corresponding to the abnormal heat loss area and the preset heat loss coefficient matrix, specifically including: When the second heat flow loss direction exists and / or the vector matching result is mismatched, determining that the heat flow loss abnormal area exists, and generating the heat loss temperature curve according to the heat flow loss direction based on the node temperature distribution matrix; Calculating a temperature gradient sequence according to the heat loss temperature curve; wherein the temperature gradient sequence includes the temperature gradient value of each temperature control node along the heat loss direction; Calculating the heat loss contribution corresponding to each temperature control node along the heat flow loss direction based on the temperature gradient sequence and the heat loss coefficient of each temperature control node in the preset heat loss coefficient matrix; wherein the preset heat loss coefficient matrix includes the heat loss coefficient of each grid area corresponding to the preset building floor plan map; The heat loss level is determined based on the heat loss contribution and the preset level judgment conditions; wherein the preset level judgment conditions include at least judgment conditions corresponding to no abnormality, low risk, medium risk and high risk respectively; the judgment conditions are obtained based on a combination of a preset low risk threshold, a preset medium risk threshold and a preset high risk threshold.

8. The intelligent heating floor control method based on Internet of Things control according to claim 1 is characterized in that: Determining a heat flow compensation power curve according to the heat flow loss level specifically includes: Determining a corresponding curve type according to the heat loss level and the preset power compensation rule; the curve type is at least one of the following: a linear compensation power curve, a piecewise linear compensation power curve, an exponential compensation power curve, and a pulsed step compensation power curve; The corresponding heat flow compensation power curve is generated according to the curve type and the curve correction parameters from the expert system.

9. The intelligent heating floor control method based on Internet of Things control according to claim 1 is characterized in that: According to the heat flux compensation power curve and the preset grid control rules, a temperature control compensation strategy is generated to control the control system to dynamically adjust the control parameters of the corresponding heating ground grid after the first heating temperature rise time, specifically including: The heat flux compensation power curve is modified according to the preset grid control rule to determine the adaptive compensation power curve corresponding to each heating ground grid to be controlled; wherein the preset grid control rule at least includes modifying the initial power of the curve based on the basic attributes of the grid; the basic attributes of the grid at least include a heat loss coefficient and a priority weight; According to each of the adaptive compensation power curves, a control parameter sequence for each of the heating ground grids within a preset compensation period is determined to generate the dynamic temperature control compensation strategy and control the system to dynamically adjust the control parameters of the corresponding heating ground grid after the first heating time.

10. An intelligent heating floor control system based on Internet of Things control, characterized by: The system comprises: An acquisition module is configured to acquire temperature control modes and regional monitoring information corresponding to each temperature control node; wherein the temperature control node includes one or more heating ground grids pre-divided according to the heating area; and the temperature control modes include at least a temperature holding mode, a temperature control following mode, and an energy-saving temperature control mode; A first determination module is configured to determine a corresponding first heating and temperature rise duration based on the current indoor temperature, the node temperature distribution matrix, the indoor temperature change data, and the temperature control mode in the regional monitoring information; a second determining module, configured to determine whether there is an abnormal heat flow loss area based on the temperature control mode, the node temperature distribution matrix, and a preset heat flow direction identification model when the first heating temperature rise time is less than a preset time; a third determining module, configured to determine, based on the heat loss temperature curve corresponding to the abnormal heat loss region and a preset heat loss coefficient matrix, a corresponding heat loss level, and determine a heat loss compensation power curve according to the heat loss level; A generation module is used to generate a dynamic temperature control compensation strategy based on the heat flow compensation power curve and the preset grid control rules so that the control system dynamically adjusts the control parameters of the corresponding heating ground grid after the first heating temperature rise period, and continuously controls each of the temperature control nodes based on the dynamically adjusted regional monitoring information.

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