Optimization method and system for precise room temperature control

Abstract

The embodiment of the present invention relates to the field of air conditioning control technology, and discloses an optimization method for precise control of room temperature, including: obtaining terminal control information of each spatial area of ​​a target building stored in a pipeline database; obtaining target temperature information set for each spatial area in the target building, and controlling the operation status information of each device and terminal control module in the air conditioning unit according to the target control parameter; determining the regional status information of each spatial area in the target building; matching the regional status information with the target regional status, and if the match is inconsistent, determining the regional status information and the target regional status information to determine the adjustment parameters of the air conditioning unit, and controlling and adjusting the air conditioning unit of the target building according to the dynamic adjustment parameters. The embodiment of the present invention can accurately control the temperature and ensure that the temperature of each spatial area is maintained within the target range set by the user, thereby greatly improving the user's comfort.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning control, and in particular to an optimization method and system for precise control of room temperature. Background Art

[0002] In the existing air conditioning technology field, most air conditioning systems adopt an integrated adjustment strategy. This strategy means that the air conditioning system can usually only adjust the temperature, humidity and other environmental parameters of the entire building or a larger space area in a unified manner. However, in actual applications, the needs of users in different spaces may also vary.

[0003] Existing air conditioning systems also lack the ability to perceive changes in the space environment in real time. They can usually only be adjusted according to preset programs or manual operations by users, and cannot be adjusted intelligently and adaptively according to factors such as the actual temperature, humidity, and personnel activities in the space. This lack of perception makes the air conditioning system unable to respond to changes in the space environment in a timely manner, thereby further reducing its adjustment effect and energy efficiency. Therefore, designing a solution that can perform control optimization and adjustment for different spaces has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0004] In view of the above-mentioned defects, an embodiment of the present invention discloses an optimization method for precise control of room temperature, which can realize dynamic temperature adjustment of each room and provide a more comfortable environment.

[0005] The first aspect of the embodiment of the present invention discloses an optimization method for precise control of room temperature, including:

[0006] Acquire terminal control information of each spatial area of ​​the target building stored in the pipeline database, wherein the terminal control information includes fan information, valve opening information, floor information, room number information and pipeline point information;

[0007] Obtain target temperature information set for each space area in the target building, determine target control parameters for each space area in the target building according to the target temperature information set for each space area and the pre-configured initial operating parameters of the air-conditioning unit, and control the operating status information of each device in the air-conditioning unit and the terminal control module according to the target control parameters;

[0008] Determine the regional status information of each spatial area in the target building by means of sensor components arranged in each spatial area;

[0009] The regional status information is matched with the target regional status. If the match is inconsistent, the regional status information and the target regional status information are determined to determine the adjustment parameters of the air-conditioning unit, and the air-conditioning unit of the target building is controlled, adjusted and optimized according to the dynamic adjustment parameters.

[0010] As an optional implementation, in the first aspect of the embodiment of the present invention, the regional state information includes a temperature adjustment curve, and the target regional state includes a target adjustment curve; the determining the regional state information of each spatial area in the target building by the sensor components arranged in each spatial area includes:

[0011] The temperature sensor at the return air outlet of each space area is used to obtain the temperature sensor signal of the corresponding space area within the set time period, and the corresponding temperature adjustment curve is generated according to the temperature sensor signal of the corresponding space area within the set time period, and the temperature adjustment curve is used to characterize the state of temperature drop within the set time range;

[0012] The matching of the region state information with the target region state, and if the matches are inconsistent, determining the state difference between the region state information and the target region state information, includes:

[0013] The temperature adjustment curve is recorded as the first alignment sequence of length m, the target adjustment curve is recorded as the second alignment sequence of length n, and an initial distance matrix D of m*n is created. ij , where the initial distance matrix D ij Used to represent the distance between the i-th point in the first alignment sequence and the j-th point in the second alignment sequence;

[0014] Create a distance matrix D with the initial ij Cumulative distance matrix C of the same size ij , the cumulative distance matrix C ij Used to store the cumulative minimum distance from the starting position to the current position;

[0015] For the cumulative distance matrix C ij At each position in the , select the element with the smallest cumulative distance from the adjacent position and add the distance D of the current position ij As the cumulative distance of the current position; when the cumulative distance matrix C is filled ij After that, the end point element C mn That is, the dynamic regularization distance between the temperature adjustment curve and the target adjustment curve;

[0016] The calculated dynamic regularization distance is compared with the preset dynamic distance threshold to determine the similarity between the temperature adjustment curve and the target adjustment curve. If the calculated dynamic regularization distance is greater than the set dynamic distance threshold, the two are inconsistently matched, and the distance difference between the two is determined based on the dynamic regularization distance.

[0017] As an optional implementation, in the first aspect of the embodiment of the present invention, when the cumulative distance matrix C is filled ij After that, the end point element C mn That is, the dynamic regularization distance between the temperature adjustment curve and the target adjustment curve also includes:

[0018] Creating an optimal storage list, wherein the optimal storage list is used to store data points on the optimal path;

[0019] From the cumulative distance matrix C ij The end point element C mn Start backtracking to the starting element C 00 ,During the backtracking process, the adjacent position with the smallest cumulative distance is selected according to the current position and added to the path list;

[0020] When tracing back to the starting element C 00 When , the optimal storage list includes all data points on the optimal path; an optimal path curve is generated according to all data points on the optimal path, and if the curvature of the optimal path curve exceeds a set value, it is determined that there is a large difference between the temperature adjustment curve and the target adjustment curve;

[0021] Generate temperature adjustment parameters for the terminal control system based on the comparison results.

[0022] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the optimization method further includes:

[0023] Acquire infrared image video information captured by an infrared camera, extract key frames in the infrared image video information, and stitch the key frames in the infrared image video information in sequence to obtain infrared stitching image information;

[0024] Inputting the infrared stitching image information into a pre-built infrared behavior model for identification to determine the number of people and the behavior of people in the corresponding infrared image information;

[0025] The physical sensation range of the environment in which the corresponding personnel are located is determined based on the personnel quantity information and the personnel behavior information. If the physical sensation range of the environment in which the corresponding personnel are located is a comfort range, the various devices in the air-conditioning unit will not be adjusted. If the comfort range of the environment in which the corresponding personnel are located is a hotter range, the ambient temperature of the corresponding space area will be controlled to be lowered. If the comfort range of the environment in which the corresponding personnel are located is a colder range, the temperature of the corresponding space area will be controlled to be increased.

[0026] As an optional implementation, in the first aspect of the embodiment of the present invention, the infrared behavior model includes an input layer, a convolution layer, a first pooling layer, an improved residual block, a second pooling layer and a fully connected layer, wherein the convolution layer is a multi-branch homogeneous convolution layer, and an attention extraction module is set in the improved residual block to realize the extraction of channel features and spatial features of the input features; the infrared behavior model is constructed by the following steps:

[0027] The spliced ​​infrared training images are divided into a training set and a test set according to a set ratio, and the number of training sets is expanded by data enhancement; the set ratio is 7:3;

[0028] Set the relevant hyperparameters of the infrared behavior initial model and initialize the network weights; pass the training set into the infrared behavior initial model and perform forward propagation;

[0029] During the model training process, the network loss function is calculated according to the loss function formula, and then the back propagation algorithm is used to update the network weights and bias parameters to minimize the loss function. When the loss value tends to be stable, it is determined that the network model has reached a convergence state; the loss function formula is:

[0030] Among them, p(x) represents the true category of sample x, q(x) represents the predicted category of sample x obtained by SofMax classifier, and finally the back propagation algorithm is used to update the weights to minimize the loss function;

[0031] If the model meets the training requirements, the training of the network model is completed, and the optimal parameters of the network model are saved to obtain the infrared behavior model;

[0032] After inputting the infrared stitching image information into the pre-built infrared behavior model for identification to determine the number of people and the behavior information of people in the corresponding infrared image information, the method further includes:

[0033] The personnel behavior information is matched with the set behavior strategy, and if the match is consistent, the temperature is adjusted according to the matching result.

[0034] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the optimization method further includes:

[0035] When it is detected that load adjustment is required, the number of people in each space area in the corresponding time period is obtained through infrared cameras set up in each space area;

[0036] Obtain the temperature demand information of each space area and the space number information of the infrared camera in the corresponding time period;

[0037] The regional state value of each spatial area in the corresponding time period is determined according to the number of people in each spatial area in the corresponding time period, the temperature demand information and the pre-designed regional value calculation formula; wherein the regional value calculation formula is:

[0038]

[0039] Among them, k1 and k2 are influence coefficients, and k1+k2=1, R(t) represents the number of people in the space area at time t, S is the area occupied by the space interval, f(t) represents the temperature demand at time t, and y is the area state value;

[0040] Comparing the regional status value of each spatial area with the preset adjustment interval, if the regional status value is between the first adjustment areas, the maximum set temperature is used as the adjustment temperature parameter to control and adjust the air conditioning unit;

[0041] If the zone state value is between the second adjustment zones, the product of the maximum set temperature and the temperature coefficient is compared with the current set temperature. If the product of the maximum set temperature and the temperature coefficient is greater than the current set temperature, the product of the maximum set temperature and the temperature coefficient is used as the adjustment temperature parameter to adjust the control of the air-conditioning unit. If the product of the maximum set temperature and the temperature coefficient is not greater than the current set temperature, the current set temperature is used as the adjustment temperature parameter to adjust the control of the air-conditioning unit.

[0042] If the zone status value is in the third adjustment interval, the current set temperature is used as the adjustment temperature parameter to perform control adjustment of the air-conditioning unit.

[0043] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the optimization method further includes:

[0044] Obtain the personnel movement information in the corresponding time period in the spatial area, and calculate the activity value of each person according to the personnel movement information and the activity analysis formula, wherein the activity analysis formula is:

[0045]

[0046] Wherein, ρ is the moving distance ratio, L is the moving distance in the corresponding time period, T is the duration of the corresponding time period, vt is the instantaneous speed at time t in the corresponding time period, vk is the standard moving speed, dt is the time integral, and SK is the standard moving distance;

[0047] The movement analysis difference is obtained by comparing each personnel activity value with a preset movement analysis threshold;

[0048] The movement analysis difference and the average temperature value in the spatial area are input into the constructed recurrent neural network model to obtain the temperature value of the spatial area when the absolute value of the movement analysis difference is the minimum, and the temperature value of the spatial area when the absolute value of the movement analysis difference is the minimum is determined as the suitable temperature for the corresponding person in the corresponding time period;

[0049] The temperature of the corresponding space area is adjusted according to the suitable temperature for the corresponding personnel in the corresponding time period.

[0050] A second aspect of an embodiment of the present invention discloses an optimization system for precise control of room temperature, comprising:

[0051] The first acquisition module is used to acquire the terminal control information of each space area of ​​the target building stored in the pipeline database, wherein the terminal control information includes fan information, valve opening information, floor information, room number information and pipeline point information;

[0052] The second acquisition module is used to obtain the target temperature information set for each space area in the target building, determine the target control parameters for each space area in the target building according to the target temperature information set for each space area and the pre-configured initial operation parameters of the air-conditioning unit, and control the operation status information of each device in the air-conditioning unit and the terminal control module according to the target control parameters;

[0053] Determination module: used to determine the regional status information of each spatial area in the target building through the sensor components arranged in each spatial area;

[0054] Matching module: used to match the regional status information with the target regional status. If the match is inconsistent, the regional status information and the target regional status information are determined to determine the adjustment parameters of the air-conditioning unit, and the air-conditioning unit of the target building is controlled, adjusted and optimized according to the dynamic adjustment parameters.

[0055] A third aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the optimization method for precise control of room temperature disclosed in the first aspect of the embodiment of the present invention.

[0056] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the optimization method for precise control of room temperature disclosed in the first aspect of an embodiment of the present invention.

[0057] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0058] The optimization method for precise room temperature control in the embodiment of the present invention can timely detect and correct temperature deviations by real-time monitoring of the regional status information (such as actual temperature, humidity, etc.) of each spatial area and matching it with the target regional status. This dynamic adjustment mechanism can avoid unnecessary energy consumption and improve the operating efficiency of the air-conditioning system. Accurate temperature control can ensure that the temperature of each spatial area is maintained within the target range set by the user, thereby greatly improving the user's comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 It is a flow chart of an optimization method for precise control of room temperature disclosed in an embodiment of the present invention;

[0061] Figure 2 It is a schematic diagram of the process of comparing and matching cooling curves disclosed in an embodiment of the present invention;

[0062] Figure 3 It is a schematic diagram of the infrared recognition process disclosed in the embodiment of the present invention;

[0063] Figure 4 It is a schematic diagram of the structure of the infrared behavior model disclosed in the embodiment of the present invention;

[0064] Figure 5 is a schematic diagram of a load adjustment process disclosed in an embodiment of the present invention;

[0065] Figure 6 is a schematic diagram of a temperature adjustment process disclosed in an embodiment of the present invention; Figure 7 It is a structural schematic diagram of a medium-wave infrared curing control system provided by an embodiment of the present invention;

[0066] Figure 8 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0069] Existing air conditioning systems also lack the ability to perceive changes in the spatial environment in real time. They can usually only be adjusted according to preset programs or manual operations of users, and cannot be adjusted intelligently and adaptively according to factors such as the actual temperature, humidity, and personnel activities in the space. This lack of perception makes it impossible for the air conditioning system to respond to changes in the spatial environment in a timely manner, thereby further reducing its adjustment effect and energy efficiency. Based on this, the embodiment of the present invention discloses an optimization method, system, electronic device and storage medium for precise control of room temperature, which can detect and correct temperature deviations in a timely manner by monitoring the regional status information (such as actual temperature, humidity, etc.) of each spatial area in real time and matching it with the target area state. This dynamic adjustment mechanism can avoid unnecessary energy consumption and improve the operating efficiency of the air conditioning system. Accurate temperature control can ensure that the temperature of each spatial area is maintained within the target range set by the user, thereby greatly improving the user's comfort.

[0070] Embodiment 1

[0071] See also Figure 1 , Figure 1It is a flow chart of an optimization method for precise control of room temperature disclosed in an embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and / or hardware, and the execution subject can receive relevant information by wired or / and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices. For example Figure 1 As shown, the optimization method based on precise control of room temperature includes the following steps:

[0072] S101: Acquire terminal control information of each space area of ​​the target building stored in the pipeline database, wherein the terminal control information includes fan information, valve opening information, floor information, room number information and pipeline point information;

[0073] S102: Obtain target temperature information set for each space area in the target building, determine target control parameters for each space area in the target building according to the target temperature information set for each space area and the pre-configured initial operating parameters of the air-conditioning unit, and control the operating status information of each device in the air-conditioning unit and the terminal control module according to the target control parameters;

[0074] S103: Determine the regional status information of each spatial area in the target building through the sensor components arranged in each spatial area;

[0075] S104: Matching the area status information with the target area status, if the match is inconsistent, determining the adjustment parameters of the air-conditioning unit by comparing the area status information with the target area status information, and controlling and adjusting the air-conditioning unit of the target building according to the dynamic adjustment parameters.

[0076] By acquiring the terminal control information (such as fan information, valve opening information, etc.) of each spatial area, the embodiment of the present invention can more carefully understand and control the temperature adjustment equipment in each area, which provides basic data support for realizing accurate control of room temperature.

[0077] Based on the target temperature information set for each space area and the initial operating parameters of the air conditioning unit, the target control parameters for each area can be calculated and set. This customized control strategy based on target temperature can significantly improve the accuracy of temperature control.

[0078] When the regional status information is inconsistent with the target status, the embodiment of the present invention determines the adjustment parameters according to the difference and adjusts the operating status of the air-conditioning unit in real time, which helps to reduce energy waste and achieve energy conservation and emission reduction. The real-time monitoring and dynamic adjustment mechanism can also quickly respond to environmental changes or changes in user needs, ensuring that the operation of the air-conditioning system always meets the user's expectations.

[0079] More preferably, the regional status information includes a temperature adjustment curve, and the target regional status includes a target adjustment curve; and the step of determining the regional status information of each spatial region in the target building by means of the sensor components arranged in each spatial region includes:

[0080] The temperature sensor at the return air outlet of each space area is used to obtain the temperature sensor signal of the corresponding space area within the set time period, and the corresponding temperature adjustment curve is generated according to the temperature sensor signal of the corresponding space area within the set time period, and the temperature adjustment curve is used to characterize the state of temperature drop within the set time range;

[0081] like Figure 2 As shown, the matching of the region state information with the target region state, and if the matching is inconsistent, determining the state difference between the region state information and the target region state information, includes:

[0082] S1041: Record the temperature adjustment curve as a first comparison sequence with a length of m, record the target adjustment curve as a second comparison sequence with a length of n, and create an initial distance matrix D of m*n. ij , where the initial distance matrix D ij Used to represent the distance between the i-th point in the first alignment sequence and the j-th point in the second alignment sequence;

[0083] S1042: Create a distance matrix D from the initial ij Cumulative distance matrix C of the same size ij , the cumulative distance matrix C ij Used to store the cumulative minimum distance from the starting position to the current position;

[0084] S1043: For the cumulative distance matrix C ij At each position in the , select the element with the smallest cumulative distance from the adjacent position and add the distance D of the current position ij As the cumulative distance of the current position; when the cumulative distance matrix C is filled ij After that, the end point element C mn That is, the dynamic regularization distance between the temperature adjustment curve and the target adjustment curve;

[0085] S1044: Compare the calculated dynamic regularization distance with the preset dynamic distance threshold to determine the similarity between the temperature adjustment curve and the target adjustment curve. If the calculated dynamic regularization distance is greater than the preset dynamic distance threshold, the two are inconsistently matched, and the distance difference between the two is determined based on the dynamic regularization distance. The embodiment of the present invention introduces a temperature adjustment curve and a target adjustment curve, which not only considers the temperature state at a single time point, but also considers the temperature change trend over time. This curve-based matching method can better reflect the actual situation of temperature control than simple single-point matching, thereby improving the matching accuracy.

[0086] By using the above algorithm to calculate the dynamic regularization distance between the temperature adjustment curve and the target adjustment curve, the expansion and contraction and bending problems of the two curves on the time axis can be effectively handled. This algorithm is particularly suitable for processing time series data that are not synchronized or have inconsistent speeds, and improves the flexibility and accuracy of matching.

[0087] When determining the state difference between the regional state information and the target regional state information, the similarity between the two can be accurately quantified by calculating the dynamic regularization distance and comparing it with the preset dynamic distance threshold. When the dynamic regularization distance is greater than the threshold, it means that there is a large difference between the current temperature control state and the target state. At this time, the specific difference size can be determined according to the dynamic regularization distance to provide a basis for subsequent adjustments. Based on accurate state difference information, more refined adjustment strategies can be formulated. For example, according to the size and direction of the difference, the output power, fan speed, valve opening and other parameters of the air-conditioning unit can be adjusted to achieve faster and more accurate temperature control. This data-driven adjustment strategy is more scientific and effective than traditional empirical adjustments.

[0088] Through precise temperature control and dynamic adjustment strategies, the temperature of each space area can be maintained within the user's desired range, thereby improving user comfort and satisfaction. In addition, due to the more refined adjustment strategy, unnecessary energy waste can be reduced, operating costs can be reduced, and user experience can be further improved.

[0089] More preferably, when the cumulative distance matrix C is filled ij After that, the end point element C mn That is, the dynamic regularization distance between the temperature adjustment curve and the target adjustment curve also includes:

[0090] Creating an optimal storage list, wherein the optimal storage list is used to store data points on the optimal path;

[0091] From the cumulative distance matrix C ij The end point element C mn Start backtracking to the starting element C 00,During the backtracking process, the adjacent position with the smallest cumulative distance is selected according to the current position and added to the path list;

[0092] When tracing back to the starting element C 00 When , the optimal storage list includes all data points on the optimal path; an optimal path curve is generated according to all data points on the optimal path, and if the curvature of the optimal path curve exceeds a set value, it is determined that there is a large difference between the temperature adjustment curve and the target adjustment curve;

[0093] Generate temperature adjustment parameters for the terminal control system based on the comparison results.

[0094] The embodiment of the present invention can clearly track the best matching path between the temperature adjustment curve and the target adjustment curve by creating an optimal storage list to store the optimal path data points in the dynamic regularization algorithm. An optimal path curve is generated based on all data points on the optimal path, and its curvature is evaluated. If the curvature exceeds the set value, it indicates that there is a large mismatch or deviation between the temperature adjustment curve and the target adjustment curve. After determining the state difference between the temperature adjustment curve and the target adjustment curve and the curvature of the optimal path curve, more accurate temperature adjustment parameters can be generated for the terminal control system based on this information. These parameters can more accurately reflect the difference between the current temperature state and the target state, and guide the system to make corresponding adjustments to achieve faster and more accurate temperature control.

[0095] By accurately matching the temperature adjustment curve with the target adjustment curve and generating temperature adjustment parameters based on the optimal path, the response speed and stability of the system can be significantly improved. The system can more quickly identify temperature deviations and take corresponding adjustment measures to keep the indoor temperature within the set range and improve user comfort and satisfaction. Accurate temperature control and dynamic adjustment strategies help reduce unnecessary energy waste. By optimizing adjustment parameters and paths, it can be ensured that the system meets user needs while minimizing energy consumption and operating costs, achieving sustainable green development.

[0096] When making a specific comparison, you can compare the various spatial areas of the same cold water pipeline on a floor, or you can compare the curves obtained at different time periods at the same location. What are the benefits of this? Comparing the various spatial areas of the same cold water pipeline on a floor can ensure that these areas obtain similar temperature control effects under the same conditions. This helps to reduce temperature fluctuations between different spatial areas and improve the uniformity of overall temperature control, thereby enhancing user comfort and satisfaction.

[0097] By comparing the temperature adjustment curves of different spatial areas of the same cold water pipeline, it is easier to identify possible problems in the system, such as pipeline blockage, valve failure, or terminal equipment performance degradation. These problems may cause inaccurate or inconsistent temperature control. Timely detection and resolution of these problems can ensure stable operation of the system.

[0098] By comparing the curves obtained at different time periods at the same location, we can understand the trend and law of temperature changes over time. This helps to formulate more reasonable time period temperature control strategies, such as adjusting the output power, fan speed or valve opening of the air conditioning unit in different time periods to adapt to different temperature requirements and external environmental conditions. By comparing the temperature adjustment curves of different time periods, we can identify which time periods are redundant or unnecessary, thereby optimizing the adjustment strategy and reducing unnecessary energy waste.

[0099] Specifically, the room near the main pipe is very cold, while the cold water cannot reach the room at the end, resulting in insufficient cooling capacity. When implementing the system, the return air temperature is monitored on the equipment, and the platform records the direction of the chilled water pipes in different rooms. For example, room 901 is close to the main pipe, and room 920 is at the end of the main pipe; room 1501 is close to the main pipe, and room 1520 is at the end of the main pipe. When the air conditioners are all turned on, the platform determines the cooling rate of the room based on the cooling curve. If it is found that room 901 cools down significantly faster than room 920, the opening of valves such as 901 and 902 will be appropriately reduced, for example, adjusted to 50%, so that the cold water can reach room 920.

[0100] More preferably, Figure 3 and Figure 4 As shown, the optimization method further includes:

[0101] S105a: acquiring infrared image video information captured by an infrared camera, extracting key frames in the infrared image video information, and stitching the key frames in the infrared image video information in sequence to obtain infrared stitching image information;

[0102] S105b: inputting the infrared stitching image information into a pre-built infrared behavior model for identification to determine the number of people and the behavior of people in the corresponding infrared image information;

[0103] S105c: Determine the somatosensory range of the environment in which the corresponding personnel are located based on the personnel quantity information and personnel behavior information. If the somatosensory range of the environment in which the corresponding personnel are located is a comfort range, then the various devices in the air-conditioning unit are not adjusted. If the comfort range of the environment in which the corresponding personnel are located is a hotter range, then control is performed to lower the ambient temperature of the corresponding space area. If the comfort range of the environment in which the corresponding personnel are located is a colder range, then control is performed to increase the temperature of the corresponding space area.

[0104] The embodiment of the present invention uses infrared image video information obtained by the infrared camera, and the system can perceive the distribution and dynamic behavior of people in the indoor environment in real time. This non-contact monitoring method not only improves the convenience of data acquisition, but also avoids the discomfort or interference that may be caused by traditional sensors.

[0105] By extracting key frames from infrared image video information and performing image stitching, the system can generate complete infrared stitching image information, thereby more accurately reflecting the temperature distribution of the indoor environment. Combined with the pre-built infrared behavior model, the system can identify the number of people and behavior information, and then adjust the operating parameters of the air-conditioning unit according to the actual needs and comfort of the people. This temperature control method based on human behavior and comfort is more accurate and humane than traditional fixed temperature settings or time-based temperature control.

[0106] Adjusting the operating parameters of the air conditioning unit based on the number of people and behavior information can avoid unnecessary energy waste. For example, when there are fewer people in the room or the activities are relatively quiet, the system can reduce the output power of the air conditioning unit or shut down some equipment to save energy. When there are more people in the room or the activities are more intense, the system can increase the output power of the air conditioning unit or adjust the air supply volume and air supply temperature to meet the comfort needs of the people. This on-demand adjustment method helps to improve energy efficiency and reduce operating costs.

[0107] By real-time monitoring and adjusting the temperature of the indoor environment, the system can ensure that people work and live in a comfortable environment. This temperature control method based on human behavior and comfort not only improves the user's comfort, but also enhances the user's trust and satisfaction with the system. At the same time, the system's intelligence and automation have also been improved, providing users with a more convenient and intelligent use experience.

[0108] More preferably, the infrared behavior model includes an input layer, a convolution layer, a first pooling layer, an improved residual block, a second pooling layer and a fully connected layer, wherein the convolution layer is a multi-branch homogeneous convolution layer, and an attention extraction module is set in the improved residual block to realize the extraction of channel features and spatial features of the input features; the infrared behavior model is constructed by the following steps:

[0109] The spliced ​​infrared training images are divided into a training set and a test set according to a set ratio, and the number of training sets is expanded by data enhancement; the set ratio is 7:3;

[0110] Set the relevant hyperparameters of the infrared behavior initial model and initialize the network weights; pass the training set into the infrared behavior initial model and perform forward propagation;

[0111] During the model training process, the network loss function is calculated according to the loss function formula, and then the back propagation algorithm is used to update the network weights and bias parameters to minimize the loss function. When the loss value tends to be stable, it is determined that the network model has reached a convergence state; the loss function formula is:

[0112] Among them, p(x) represents the true category of sample x, q(x) represents the predicted category of sample x obtained by SofMax classifier, and finally the back propagation algorithm is used to update the weights to minimize the loss function;

[0113] If the model meets the training requirements, the training of the network model is completed, and the optimal parameters of the network model are saved to obtain the infrared behavior model;

[0114] After inputting the infrared stitching image information into the pre-built infrared behavior model for identification to determine the number of people and the behavior information of people in the corresponding infrared image information, the method further includes:

[0115] The personnel behavior information is matched with the set behavior strategy, and if the match is consistent, the temperature is adjusted according to the matching result.

[0116] During the specific implementation, the user's behavior actions are obtained through a camera; the temperature parameters of the corresponding area are determined based on the user's behavior actions, for example, it can be identified whether the user feels that the environment is cold; there can be two actions, the first is that the user has no clothes, hugs himself, and curls up; the user's behavior intention, the user's behavior intention is used to characterize the user's feelings about the environment, and second, the corresponding user has clothes and puts on clothes; each user has different feelings about the environment; here, dressing and undressing can also be used as an indicator to determine the user's behavior.

[0117] Specifically, based on the identified user behavior actions, the user's behavior intention is further analyzed. For example, when the user is without clothes, hugging himself, and curling up, it may indicate that the user feels that the environment is cold; when the user puts on clothes, it may indicate that the user feels that the environment is suitable or slightly cool but still within an acceptable range. The user's behavior intention is converted into a representation of the environmental feeling. This can be achieved by defining a series of behavior intention labels (such as "cold", "comfortable", "hot", etc.), and associating the identified user behavior actions with these labels.

[0118] Determine the temperature parameters of the corresponding area based on the user's representation of the environment. For example, if a large number of users are identified to feel that the environment is cold (such as hugging themselves, curling up), the temperature parameters of the area can be adjusted higher; if the user is identified to put on clothes, it may be necessary to adjust the temperature parameters or maintain the current temperature according to the specific situation. Develop a temperature adjustment strategy, including the adjustment range and speed. This can be considered comprehensively based on factors such as the intensity of the user's behavioral intention, the floor space of the space area, and the temperature requirements of other users. The determined temperature parameters are passed to the air conditioning unit control system, and the temperature of the space area is accurately controlled by adjusting the output power, air supply volume and other parameters of the air conditioning unit.

[0119] The infrared behavior model of the embodiment of the present invention adopts a multi-branch homogeneous convolution layer and an improved residual block, combined with an attention extraction module, which can more effectively extract the channel features and spatial features of the input features. This design enhances the model's ability to recognize the behavior and number of people in infrared images, and improves the accuracy and robustness of recognition.

[0120] By dividing the spliced ​​infrared training images into training and test sets in a ratio of 7:3, and using data enhancement to expand the number of training sets, the model can learn more diverse features and improve generalization capabilities. At the same time, the loss function formula and back propagation algorithm are used to optimize network parameters, so that the model gradually converges during the training process and achieves optimal performance.

[0121] After identifying the number of people and behavior information in the infrared image information, the model matches this information with the set behavior strategy. If the match is consistent, the room temperature is automatically adjusted according to the matching result. This intelligent temperature adjustment method not only improves the user's comfort, but also reduces energy waste and achieves the goal of energy conservation and emission reduction. Through intelligent temperature adjustment, the system can adjust the operating parameters of the air-conditioning unit in real time according to the actual needs and comfort of the personnel. This temperature control method based on personnel behavior and comfort is more humane and improves the user experience and satisfaction.

[0122] During the specific implementation, the overall operating parameters can be increased within a certain period of time to make the temperature in the corresponding area higher. Because this period of time is relatively active, the overall temperature perception of people will rise. For example, in the summer, when you just enter the office or just finish exercising, the temperature will be more obvious. At this time, cooling is needed. It can be determined by identifying the amount of heat dissipated by the person. The infrared camera can not only identify the user's behavior, but also the user's heat dissipation, and determine the user's behavior through heat dissipation.

[0123] During the specific implementation, each air-conditioning unit is associated with a spatial area, the physical information input by the user is received, and each space is personalized. Because everyone's feelings are different, the above-mentioned personalized configuration method is used to provide more diverse data parameters.

[0124] like Figure 5 As shown, more preferably, the optimization method further includes:

[0125] S106a: When it is detected that load adjustment is required, the number of people in each space area in a corresponding time period is obtained through infrared cameras set in each space area;

[0126] S106b: Acquire temperature requirement information of each space area and space number information of the infrared camera in a corresponding time period;

[0127] S106c: Determine the regional state value of each spatial area in the corresponding time period according to the number of people in each spatial area in the corresponding time period, temperature demand information and a pre-designed regional value calculation formula; wherein the regional value calculation formula is:

[0128]

[0129] Among them, k1 and k2 are influence coefficients, and k1+k2=1, R(t) represents the number of people in the space area at time t, S is the area occupied by the space interval, f(t) represents the temperature demand at time t, and y is the area state value;

[0130] S106d: comparing the regional status value of each spatial area with the preset adjustment interval, and if the regional status value is within the first adjustment area, using the maximum set temperature as the adjustment temperature parameter to control and adjust the air conditioning unit;

[0131] S106e: If the zone state value is between the second adjustment zones, the product of the maximum set temperature and the temperature coefficient is compared with the current set temperature. If the product of the maximum set temperature and the temperature coefficient is greater than the current set temperature, the product of the maximum set temperature and the temperature coefficient is used as an adjustment temperature parameter to adjust the control of the air-conditioning unit. If the product of the maximum set temperature and the temperature coefficient is not greater than the current set temperature, the current set temperature is used as an adjustment temperature parameter to adjust the control of the air-conditioning unit.

[0132] S106f: If the zone status value is in the third adjustment interval, the current set temperature is used as the adjustment temperature parameter to perform control adjustment of the air-conditioning unit.

[0133] The embodiment of the present invention uses an infrared camera to obtain the number of people in each space area in real time, and combined with the temperature demand information and space number information, the system can more accurately understand the load demand of each space area. This refined load adjustment method helps to avoid excessive or insufficient cooling / heating, thereby improving energy efficiency.

[0134] The system can dynamically adjust the temperature parameters by comparing the regional status values ​​with the pre-set adjustment intervals. This dynamic adjustment method can more flexibly adapt to the load requirements of different spatial areas and different time periods, and improve the accuracy and response speed of temperature control. By comprehensively considering the number of people and temperature requirements to determine the adjustment temperature parameters, the system can ensure that people work and live in a comfortable environment. This temperature control method based on human behavior and comfort not only improves the user's comfort level, but also enhances the user's trust and satisfaction with the system.

[0135] The system can adjust the operating parameters of the air conditioning unit according to the status values ​​of different areas, thereby avoiding unnecessary energy waste. For example, in a space with fewer people or lower temperature requirements, the system can reduce the output power of the air conditioning unit or shut down some equipment to save energy. This on-demand adjustment method helps to improve energy efficiency and reduce operating costs.

[0136] In the embodiment of the present invention, the central air conditioning system control area in the building can be divided into n control areas according to the characteristics of the user's electricity consumption behavior (such as peak hours, electricity consumption habits, etc.) or the building structure (such as room size, orientation, floor location, etc.). Each control area has independent temperature control and regulation capabilities, which can be flexibly adjusted according to actual needs.

[0137] When a demand response event (such as a peak in grid load, energy shortage, etc.) occurs, each control area is divided into response levels according to preset strategies and algorithms. Response levels are usually divided into three categories: strong response areas, weak response areas, and non-response areas. Strong response areas: areas where energy consumption needs to be reduced first in demand response events. Weak response areas: areas where energy consumption can be appropriately reduced in demand response events, but have a lower priority than strong response areas. Non-response areas: areas where energy consumption is not reduced or the reduction is small in demand response events. The regional value is a comprehensive indicator used to measure the response capability and priority of each control area in a demand response event. The range of regional values ​​is usually set according to actual needs.

[0138] In the embodiment of the present invention, the temperature coefficient is an important parameter in the adjustment formula of the set temperature of the control area, which reflects the impact of the change of the regional value on the adjustment range of the set temperature during the demand response event.

[0139] More preferably, Figure 6 As shown, the optimization method further includes:

[0140] S107a: Obtaining personnel movement information in a corresponding time period in a spatial area, and calculating each personnel activity value according to the personnel movement information and an activity analysis formula, wherein the activity analysis formula is:

[0141]

[0142] Wherein, ρ is the moving distance ratio, L is the moving distance in the corresponding time period, T is the duration of the corresponding time period, vt is the instantaneous speed at time t in the corresponding time period, vk is the standard moving speed, dt is the time integral, and SK is the standard moving distance;

[0143] S107b: Obtaining a movement analysis difference value by comparing each personnel activity value with a preset movement analysis threshold value;

[0144] S107c: inputting the movement analysis difference and the average temperature value in the spatial area into the constructed recurrent neural network model to obtain the temperature value of the spatial area when the absolute value of the movement analysis difference is the minimum, and determining the temperature value of the spatial area when the absolute value of the movement analysis difference is the minimum as the suitable temperature for the corresponding person in the corresponding time period;

[0145] S107d: Adjust the temperature of the corresponding space area according to the suitable temperature for the corresponding personnel in the corresponding time period.

[0146] The embodiment of the present invention introduces an activity analysis formula, and the optimization method can calculate the activity value (AC) of each person based on the person's movement information (such as movement distance, movement speed, etc.). This calculation method not only takes into account the movement distance of the person, but also combines the change of movement speed, thereby more comprehensively reflecting the activity status of the person in the spatial area.

[0147] The embodiment of the present invention can obtain a movement analysis difference by comparing the calculated personnel activity value with a preset movement analysis threshold. This step helps to identify the difference between personnel activities and preset standards, thereby providing a basis for subsequent temperature adjustment. The movement analysis difference and the average temperature value in the spatial area are input into the recurrent neural network model, and the temperature value of the spatial area when the absolute value of the movement analysis difference is the smallest can be predicted. This prediction result reflects the most suitable temperature for the corresponding personnel activity level within a given time period.

[0148] The embodiment of the present invention determines the appropriate temperature for the corresponding person in the corresponding time period based on the output result of the recurrent neural network model, that is, the temperature value of the spatial area when the absolute value of the mobile analysis difference is the smallest. This step realizes the intelligent temperature adjustment, and can automatically adjust the temperature of the spatial area according to the change of the activity state of the person. Through this intelligent temperature adjustment method, not only can the comfort of the personnel be improved, but also energy can be saved to a certain extent, achieving the goal of energy saving and emission reduction.

[0149] The recurrent convolutional neural network in the embodiment of the present invention is constructed by the following steps:

[0150] Obtain the mobility analysis difference and the average temperature value of the spatial area during the historical monitoring period, the mobility analysis difference and the temperature value of the spatial area after temperature adjustment, and construct a recurrent neural network model whose input is the mobility analysis difference and the temperature value of the spatial area during the historical monitoring period, and whose output is the temperature value of the spatial area when the absolute value of the mobility analysis difference is the minimum;

[0151] The extracted mobility analysis differences and spatial area temperature values ​​during the historical monitoring period, and the mobility analysis differences and spatial area temperature values ​​after temperature adjustment are divided into 70% parameter training sets and 30% parameter test sets; the 70% parameter training set is input into the recurrent neural network model for training to obtain an initial recurrent neural network model; the 30% parameter test set is used to test the initial recurrent neural network model, and the initial recurrent neural network model with the highest judgment accuracy of the spatial area temperature value when the absolute value of the preset mobility analysis difference is minimized is output as the recurrent neural network model until the corresponding training conditions are met.

[0152] The optimization method for precise room temperature control in the embodiment of the present invention can timely detect and correct temperature deviations by real-time monitoring of the regional status information (such as actual temperature, humidity, etc.) of each spatial area and matching it with the target regional status. This dynamic adjustment mechanism can avoid unnecessary energy consumption and improve the operating efficiency of the air-conditioning system. Accurate temperature control can ensure that the temperature of each spatial area is maintained within the target range set by the user, thereby greatly improving the user's comfort.

[0153] Embodiment 2

[0154] See also Figure 7 , Figure 7 Schematic diagram of the structure of the medium-wave infrared curing control system disclosed in the embodiment of the present invention. Figure 7 As shown, the medium-wave infrared curing control system may include:

[0155] The first acquisition module 21 is used to acquire the terminal control information of each space area of ​​the target building stored in the pipeline database, wherein the terminal control information includes fan information, valve opening information, floor information, room number information and pipeline point information;

[0156] The second acquisition module 22 is used to acquire the target temperature information set for each space area in the target building, determine the target control parameters for each space area in the target building according to the target temperature information set for each space area and the pre-configured initial operation parameters of the air-conditioning unit, and control the operation status information of each device in the air-conditioning unit and the terminal control module according to the target control parameters;

[0157] Determination module 23: used to determine the regional status information of each spatial area in the target building through the sensor components arranged in each spatial area;

[0158] Matching module 24: used to match the area status information with the target area status. If the match is inconsistent, the area status information and the target area status information are determined to determine the adjustment parameters of the air-conditioning unit, and the air-conditioning unit of the target building is controlled and adjusted and optimized according to the dynamic adjustment parameters.

[0159] The optimization method for precise room temperature control in the embodiment of the present invention can timely detect and correct temperature deviations by real-time monitoring of the regional status information (such as actual temperature, humidity, etc.) of each spatial area and matching it with the target regional status. This dynamic adjustment mechanism can avoid unnecessary energy consumption and improve the operating efficiency of the air-conditioning system. Accurate temperature control can ensure that the temperature of each spatial area is maintained within the target range set by the user, thereby greatly improving the user's comfort.

[0160] Embodiment 3

[0161] See also Figure 8 , Figure 8 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may be a computer, a server, etc. Of course, in certain circumstances, it may also be a smart device such as a mobile phone, a tablet computer, a monitoring terminal, and an image acquisition device with processing functions. Figure 8 As shown, the electronic device may include:

[0162] A memory 510 storing executable program codes;

[0163] a processor 520 coupled to the memory 510;

[0164] The processor 520 calls the executable program code stored in the memory 510 to execute part or all of the steps in the optimization method for precise control of room temperature in the first embodiment.

[0165] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute part or all of the steps in the optimization method for precise control of room temperature in embodiment 1.

[0166] The embodiment of the present invention further discloses a computer program product, wherein when the computer program product is run on a computer, the computer is enabled to execute part or all of the steps in the optimization method for precise control of room temperature in the first embodiment.

[0167] An embodiment of the present invention further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the optimization method for precise control of room temperature in embodiment one.

[0168] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0169] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0170] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0171] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the method described in each embodiment of the present invention.

[0172] In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0173] A person of ordinary skill in the art can understand that some or all of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0174] The above is a detailed introduction to the optimization method, system, electronic device and storage medium for precise control of room temperature disclosed in the embodiments of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An optimization method for precise control of room temperature, characterized in that: include: Acquire terminal control information of each spatial area of ​​the target building stored in the pipeline database, wherein the terminal control information includes fan information, valve opening information, floor information, room number information and pipeline point information; Obtain target temperature information set for each space area in the target building, determine target control parameters for each space area in the target building according to the target temperature information set for each space area and the pre-configured initial operating parameters of the air-conditioning unit, and control the operating status information of each device in the air-conditioning unit and the terminal control module according to the target control parameters; Determine the regional status information of each spatial area in the target building by means of sensor components arranged in each spatial area; Matching the regional status information with the target regional status, and if the match is inconsistent, determining the regional status information and the target regional status information to determine the adjustment parameters of the air-conditioning unit, and controlling and adjusting the air-conditioning unit of the target building according to the adjustment parameters; The optimization method further includes: Acquire infrared image video information captured by an infrared camera, extract key frames in the infrared image video information, and stitch the key frames in the infrared image video information in sequence to obtain infrared stitching image information; Inputting the infrared stitching image information into a pre-built infrared behavior model for identification to determine the number of people and the behavior of people in the corresponding infrared image information; The body temperature range of the environment in which the corresponding personnel are located is determined based on the personnel quantity information and the personnel behavior information. If the body temperature range of the environment in which the corresponding personnel are located is a comfort range, the various devices in the air-conditioning unit will not be adjusted. If the comfort range of the environment in which the corresponding personnel are located is a hotter range, the ambient temperature of the corresponding space area will be controlled to be lowered. If the comfort range of the environment in which the corresponding personnel are located is a colder range, the temperature of the corresponding space area will be controlled to be increased.

2. The optimization method for precise control of room temperature according to claim 1, characterized in that: The regional state information includes a temperature adjustment curve, and the target regional state includes a target adjustment curve; the regional state information of each spatial area in the target building is determined by the sensor components arranged in each spatial area, including: The temperature sensor at the return air outlet of each space area is used to obtain the temperature sensor signal of the corresponding space area within the set time period, and the corresponding temperature adjustment curve is generated according to the temperature sensor signal of the corresponding space area within the set time period, and the temperature adjustment curve is used to characterize the state of temperature drop within the set time range; The matching of the region state information with the target region state, and if the matches are inconsistent, determining the state difference between the region state information and the target region state information, includes: The temperature adjustment curve is recorded as the first alignment sequence of length m, the target adjustment curve is recorded as the second alignment sequence of length n, and an initial distance matrix D of m*n is created. ij , where the initial distance matrix D ij Used to represent the distance between the i-th point in the first alignment sequence and the j-th point in the second alignment sequence; Create a distance matrix D with the initial ij Cumulative distance matrix C of the same size ij , the cumulative distance matrix C ij Used to store the cumulative minimum distance from the starting position to the current position; For the cumulative distance matrix C ij At each position in the , select the element with the smallest cumulative distance from the adjacent position and add the distance D of the current position ij As the cumulative distance of the current position; when the cumulative distance matrix C is filled ij After that, the end point element C mn That is, the dynamic regularization distance between the temperature adjustment curve and the target adjustment curve; The calculated dynamic regularization distance is compared with the preset dynamic distance threshold to determine the similarity between the temperature adjustment curve and the target adjustment curve. If the calculated dynamic regularization distance is greater than the set dynamic distance threshold, the two are inconsistently matched, and the distance difference between the two is determined based on the dynamic regularization distance.

3. The optimization method for precise control of room temperature according to claim 2, characterized in that: When the cumulative distance matrix C is filled ij After that, the end point element C mn That is, the dynamic regularization distance between the temperature adjustment curve and the target adjustment curve also includes: Creating an optimal storage list, wherein the optimal storage list is used to store data points on the optimal path; From the cumulative distance matrix C ij The end point element C mn Start backtracking to the starting element C 00 ,During the backtracking process, the adjacent position with the smallest cumulative distance is selected according to the current position and added to the path list; When tracing back to the starting element C 00 When , the optimal storage list includes all data points on the optimal path; an optimal path curve is generated according to all data points on the optimal path, and if the curvature of the optimal path curve exceeds a set value, it is determined that there is a large difference between the temperature adjustment curve and the target adjustment curve; Generate temperature adjustment parameters for the terminal control system based on the comparison results.

4. The optimization method for precise control of room temperature according to claim 1, characterized in that: The infrared behavior model includes an input layer, a convolution layer, a first pooling layer, an improved residual block, a second pooling layer and a fully connected layer, wherein the convolution layer is a multi-branch homogeneous convolution layer, and an attention extraction module is set in the improved residual block to realize the extraction of channel features and spatial features of input features; the infrared behavior model is constructed by the following steps: The spliced ​​infrared training images are divided into a training set and a test set according to a set ratio, and the number of training sets is expanded by data enhancement; the set ratio is 7:3; Set the relevant hyperparameters of the infrared behavior initial model and initialize the network weights; pass the training set into the infrared behavior initial model and perform forward propagation; During the model training process, the network loss function is calculated according to the loss function formula, and then the back propagation algorithm is used to update the network weights and bias parameters to minimize the loss function. When the loss value tends to be stable, it is determined that the network model has reached a convergence state; the loss function formula is: Among them, p(x) represents the true category of sample x, q(x) represents the predicted category of sample x obtained by SofMax classifier, and finally the back propagation algorithm is used to update the weights to minimize the loss function; If the model meets the training requirements, the training of the network model is completed, and the optimal parameters of the network model are saved to obtain the infrared behavior model; After inputting the infrared stitching image information into the pre-built infrared behavior model for identification to determine the number of people and the behavior information of people in the corresponding infrared image information, the method further includes: The personnel behavior information is matched with the set behavior strategy, and if the match is consistent, the temperature is adjusted according to the matching result.

5. The optimization method for precise control of room temperature according to claim 1, characterized in that: The optimization method further includes: When it is detected that load adjustment is required, the number of people in each space area in the corresponding time period is obtained through infrared cameras set up in each space area; Obtain the temperature demand information of each space area and the space number information of the infrared camera in the corresponding time period; The regional state value of each spatial area in the corresponding time period is determined according to the number of people in each spatial area in the corresponding time period, the temperature demand information and the pre-designed regional value calculation formula; wherein the regional value calculation formula is: Among them, k1 and k2 are influence coefficients, and k1+k2=1, R(t) represents the number of people in the space area at time t, S is the area occupied by the space interval, f(t) represents the temperature demand at time t, and y is the area state value; Comparing the regional status value of each spatial area with the preset adjustment interval, if the regional status value is between the first adjustment areas, the maximum set temperature is used as the adjustment temperature parameter to control and adjust the air conditioning unit; If the zone state value is between the second adjustment zones, the product of the maximum set temperature and the temperature coefficient is compared with the current set temperature. If the product of the maximum set temperature and the temperature coefficient is greater than the current set temperature, the product of the maximum set temperature and the temperature coefficient is used as the adjustment temperature parameter to adjust the control of the air-conditioning unit. If the product of the maximum set temperature and the temperature coefficient is not greater than the current set temperature, the current set temperature is used as the adjustment temperature parameter to adjust the control of the air-conditioning unit. If the zone status value is in the third adjustment interval, the current set temperature is used as the adjustment temperature parameter to perform control adjustment of the air-conditioning unit.

6. The optimization method for precise control of room temperature according to claim 1, characterized in that: The optimization method further includes: Obtain the personnel movement information in the corresponding time period in the spatial area, and calculate the activity value of each person according to the personnel movement information and the activity analysis formula, wherein the activity analysis formula is: Among them, ρ is the moving distance ratio, L is the moving distance in the corresponding time period, T is the length of the corresponding time period, vt is the instantaneous speed at time t in the corresponding time period, vk is the standard moving speed, dt is the time integral, and SK is the standard moving distance; The movement analysis difference is obtained by comparing each personnel activity value with a preset movement analysis threshold; The movement analysis difference and the average temperature value in the spatial area are input into the constructed recurrent neural network model to obtain the spatial area temperature value when the absolute value of the movement analysis difference is the minimum, and the spatial area temperature value when the absolute value of the movement analysis difference is the minimum is determined as the suitable temperature for the corresponding person in the corresponding time period; The temperature of the corresponding space area is adjusted according to the suitable temperature for the corresponding personnel in the corresponding time period.

7. An optimization system for precise control of room temperature, characterized in that: include: The first acquisition module is used to acquire the terminal control information of each space area of ​​the target building stored in the pipeline database, wherein the terminal control information includes fan information, valve opening information, floor information, room number information and pipeline point information; The second acquisition module is used to obtain the target temperature information set for each space area in the target building, determine the target control parameters for each space area in the target building according to the target temperature information set for each space area and the pre-configured initial operation parameters of the air-conditioning unit, and control the operation status information of each device in the air-conditioning unit and the terminal control module according to the target control parameters; Determination module: used to determine the regional status information of each spatial area in the target building through the sensor components arranged in each spatial area; Matching module: used to match the regional status information with the target regional status, and if the match is inconsistent, determine the regional status information and the target regional status information to determine the adjustment parameters of the air-conditioning unit, and control the air-conditioning unit of the target building according to the adjustment parameters. The optimization system further comprises: Acquire infrared image video information captured by an infrared camera, extract key frames in the infrared image video information, and stitch the key frames in the infrared image video information in sequence to obtain infrared stitching image information; Inputting the infrared stitching image information into a pre-built infrared behavior model for identification to determine the number of people and the behavior of people in the corresponding infrared image information; The body temperature range of the environment in which the corresponding personnel are located is determined based on the personnel quantity information and the personnel behavior information. If the body temperature range of the environment in which the corresponding personnel are located is a comfort range, the various devices in the air-conditioning unit will not be adjusted. If the comfort range of the environment in which the corresponding personnel are located is a hotter range, the ambient temperature of the corresponding space area will be controlled to be lowered. If the comfort range of the environment in which the corresponding personnel are located is a colder range, the temperature of the corresponding space area will be controlled to be increased.

8. An electronic device, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the optimization method for precise control of room temperature as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program enables a computer to execute the optimization method for precise control of room temperature according to any one of claims 1 to 6.

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