Clothes airing machine stroke control method and system based on load analysis

Abstract

The invention belongs to the technical field of clothes drying machines, and discloses a clothes drying machine stroke control method and system based on load analysis, and the method comprises the steps: collecting the load data of each position of a clothes drying machine, analyzing the load distribution, and calculating the control parameters of the clothes drying machine based on the analysis result; fusing the control parameters and a deep learning algorithm to predict the load state of the clothes airing machine, and setting a load threshold value and a corresponding control strategy; according to the set load threshold value and the control strategy, the clothes airing machine is correspondingly controlled, and airing tasks are distributed; the state and performance of the clothes airing machine are monitored in real time, the monitoring result is used as feedback, and the control strategy is optimized. By collecting and analyzing the load data of each position of the clothes airing machine, the clothes distribution condition can be accurately mastered, so that the airing layout is optimized, uniform stress of the clothes in the airing process is ensured, the problem of poor airing effect or equipment damage caused by uneven load is avoided, and the airing efficiency and the clothes drying uniformity are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of clothes drying machines, and in particular to a clothes drying machine stroke control method and system based on load analysis. Background Art

[0002] As part of the modern smart home, clothes drying racks, often referred to as smart clothes drying racks or electric clothes drying racks, are designed to improve the convenience, efficiency, and health of drying clothes. While retaining the basic functions of a traditional clothes drying rack, they also achieve significant functional improvements through technological innovation and intelligent design. Particularly notable is their travel control function, a core feature that directly determines the vertical reach of the clothes drying rack during its raising and lowering process, specifically the efficiency and stability of the transition from fully retracted to fully extended.

[0003] The travel of a clothes drying machine is affected by many factors, among which load distribution and load-bearing capacity are the most critical factors. Load distribution refers to the distribution of the weight of the clothes on the clothesline, while load-bearing capacity defines the maximum weight that the clothes drying machine can bear while ensuring safety. However, in the travel control process of existing clothes drying machines, if the weight distribution at different positions on the clothesline is uneven, it will lead to changes in load distribution. For example, when clothes are concentrated on one side, it may cause uneven force on the clothes drying machine. This unbalanced state will not only affect the stability of the clothes drying machine, causing shaking or displacement, but may also put additional pressure on its mechanical structure, thereby affecting the accuracy and reliability of travel control and may bring potential safety hazards.

[0004] Therefore, how to provide a clothes drying machine stroke control method and system based on load analysis is a problem that needs to be solved urgently. Summary of the Invention

[0005] The embodiments of the present invention provide a method and system for controlling the travel of a clothes drying machine based on load analysis to solve the above-mentioned technical problems existing in the prior art.

[0006] To provide a basic understanding of some aspects of the disclosed embodiments, the following is a brief summary. This summary is not intended to be an extensive review, identify key or critical elements, or delineate the scope of these embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a method for controlling a clothes drying machine's travel based on load analysis is provided.

[0008] In one embodiment, a method for controlling a clothes drying machine's travel based on load analysis includes: Collect load data at each location of the clothes drying machine, analyze the load distribution, and calculate the clothes drying machine control parameters based on the analysis results; Fusion of control parameters and deep learning algorithms to predict the load status of the clothes drying machine, set the load threshold and corresponding control strategy; According to the set load threshold and control strategy, the clothes drying machine is controlled accordingly and the drying task is assigned; Monitor the status and performance of the clothes drying machine in real time, and use the monitoring results as feedback to optimize the control strategy.

[0009] In one embodiment, collecting load data at each position of the clothes drying machine, analyzing load distribution, and calculating control parameters of the clothes drying machine based on the analysis results includes: Collect the load data of each position of the clothes drying machine, pre-process the load data, and establish a load data set; Based on the load data set, analyze the load differences at different locations and identify the characteristics of the load distribution; Analyze the load distribution characteristics, divide the clothes drying machine into different areas, and calculate the control parameters of each area.

[0010] In one embodiment, analyzing the load differences at different locations based on the load data set and identifying the characteristics of the load distribution includes: Use kernel density estimation analysis algorithm to obtain the density distribution characteristics of the load data set; Analyze the spatial correlation of load data between different locations, combine density distribution characteristics, draw load distribution maps, and identify the load distribution center; Calculate the similarity matrix according to the load distribution center, identify the load data groups with similar load characteristics based on the similarity matrix, and construct the load data subsets at different locations; The spatial correlation of load data subsets at different locations is analyzed, unclassified load data are assigned to corresponding load data subsets, and load distribution characteristics are identified.

[0011] In one embodiment, the formula of the kernel density estimation analysis algorithm is:

[0012] Where g(x) represents the kernel density estimation analysis function; k represents the number of samples in the load data set; x represents the sample data in the load data set; n represents the nth sample data in the load data set; s represents the bandwidth of the dimension in the load data set; p represents the type value of each sample data; D represents the kernel function; and X represents the sample group in the load data set.

[0013] In one embodiment, the formula for the spatial correlation is:

[0014] Where T represents the spatial correlation function; c represents the number of spatial load data; W represents the sum of all elements of the weight matrix; a represents the ath load data; b represents the bth load data; ω ab Represents the spatial relationship between load data a and load data b; z a Indicates the variable value of the a-th load data; z b The variable value representing the bth load data; Indicates the average value of the load data variable.

[0015] In one embodiment, calculating a similarity matrix based on the load distribution center, identifying load data groups with similar load characteristics based on the similarity matrix, and constructing load data subsets at different locations includes: According to the load distribution center, several load data with the highest similarity are selected as reference points; Based on the reference points, the similarity matrix of the load data set is calculated; Using the similarity matrix, we identify load data groups with similar load characteristics. Starting from the reference point of each load data group, the skeleton is constructed along the direction of highest similarity and decreasing density to obtain load data subsets at different positions.

[0016] In one embodiment, analyzing the load distribution characteristics, dividing the clothes drying machine into different areas, and calculating the control parameters of each area includes: Analyze the load distribution characteristics, determine the division boundaries of the clothes drying machine, and divide the clothes drying machine into different areas based on the division boundaries; Design a travel control strategy based on the load distribution characteristics of each area and the functional requirements of the clothes drying machine; Obtain the load data of each area, the status of the clothes drying machine, and user instructions, and calculate the control parameters of each area of ​​the clothes drying machine in combination with the travel control strategy.

[0017] In one embodiment, the control parameters include: target position, moving speed and dwell time.

[0018] In one embodiment, the integration of control parameters and deep learning algorithms to predict the load state in the clothes drying machine travel control model, set the load threshold and the corresponding control strategy include: Integrate the control parameters of each region with the deep learning algorithm to build a recurrent neural network model; Apply the distribution characteristics of load data to train the recurrent neural network model; Use the trained recurrent neural network model to predict the load status of the clothes drying machine and set the load threshold based on the prediction results; Combine the set load threshold with the control parameters of each area to formulate the corresponding control strategy.

[0019] According to a second aspect of an embodiment of the present invention, a clothes drying machine travel control system based on load analysis is provided.

[0020] In one embodiment, the load analysis-based clothes drying machine travel control system includes: A data collection and analysis module is used to collect load data at various locations of the clothes drying machine, analyze the load distribution, and calculate the control parameters of the clothes drying machine based on the analysis results; The prediction and strategy setting module is used to integrate control parameters and deep learning algorithms to predict the load status of the clothes drying machine, set the load threshold and the corresponding control strategy; The control execution and task allocation module is used to perform corresponding control on the clothes drying machine and allocate drying tasks according to the set load threshold and control strategy; The real-time monitoring and feedback optimization module is used to monitor the status and performance of the clothes drying machine in real time, and use the monitoring results as feedback to optimize the control strategy.

[0021] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0022] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0023] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0024] In one embodiment, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0025] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: 1. By collecting and analyzing load data at various positions of the clothes drying machine, the present invention can accurately grasp the distribution of clothes, thereby optimizing the drying layout, ensuring that the clothes are evenly stressed during the drying process, avoiding poor drying effects or equipment damage caused by uneven load, and significantly improving drying efficiency and clothes drying uniformity.

[0026] 2. By integrating control parameters with deep learning algorithms, the present invention enables the clothes drying machine to intelligently predict the load status and automatically adjust control parameters such as lifting height and telescopic length according to the real-time load conditions. This not only reduces manual intervention, but also achieves efficient energy utilization and reduces energy consumption costs.

[0027] 3. By setting a load threshold and corresponding control strategy, the present invention can effectively prevent malfunctions or safety accidents caused by overload operation of the clothes drying machine. When it is detected that the load exceeds the preset load threshold, the system can automatically take corresponding measures, such as suspending lifting and lowering, issuing an alarm, etc., to ensure the stability of the equipment operation and the safety of users.

[0028] 4. The present invention monitors the status and performance of the clothes drying machine in real time and uses the monitoring results as feedback to continuously optimize the control strategy, making the operation of the clothes drying machine more convenient and intelligent. Users do not need to frequently manually adjust the drying position or worry about equipment failure, thereby improving the overall user experience.

[0029] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0031] Figure 1 is a flow chart showing a method for controlling a clothes drying machine's travel based on load analysis according to an exemplary embodiment; Figure 2 is a principle block diagram of a clothes drying machine travel control system based on load analysis according to an exemplary embodiment; Figure 3 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0032] The following description and accompanying drawings sufficiently illustrate the specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims, including all available equivalents thereof. Herein, the terms "first," "second," and the like are used solely to distinguish one element from another and do not require or imply any actual relationship or order between these elements. In practice, the first element can also be referred to as the second element, and vice versa. Furthermore, the terms "comprise," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a structure, device, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such structure, device, or apparatus. Without further limitation, an element defined by the phrase "comprising a..." does not preclude the presence of other identical elements in the structure, device, or apparatus comprising the element. The various embodiments herein are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Similar or identical parts between the various embodiments can be referenced to each other.

[0033] The terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like used herein to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are intended only to facilitate the description of this document and simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the present invention. In the description herein, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, they can be mechanical or electrical connections, or they can be internal connections between two elements, they can be directly connected, or they can be indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0034] As used herein, unless otherwise specified, the term "plurality" means two or more.

[0035] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0036] In this article, the term "and / or" is used to describe the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0037] It should be understood that, although the various steps in the flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0038] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software so that the processor can call and execute the operations corresponding to the above modules.

[0039] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0040] Figure 1 An embodiment of a method for controlling a clothes drying machine's travel based on load analysis according to the present invention is shown.

[0041] In this optional embodiment, the method for controlling the travel of a clothes drying machine based on load analysis includes: Step S101: Collect load data at each position of the clothes drying machine, analyze the load distribution, and calculate the control parameters of the clothes drying machine based on the analysis results.

[0042] It should be noted that load data includes weight data, position data, and time-related data. Weight data refers to the weight of clothes at various locations and is obtained by installing weight sensors on key support points or clothes drying poles on the clothes drying rack. Position data combines data from position sensors and weight / pressure sensors to understand which areas are more heavily loaded and which are relatively lightly loaded. Time-related data, for example, monitors the weight loss of clothes during the drying process, which can reflect the drying speed of clothes and help adjust the drying strategy.

[0043] In this optional embodiment, collecting load data at each position of the clothes drying machine, analyzing the load distribution, and calculating the control parameters of the clothes drying machine based on the analysis results includes: Load data from each location of the clothes drying machine is collected and preprocessed to establish a load dataset. Based on the load dataset, the load differences at different locations are analyzed to identify the characteristics of the load distribution. The load distribution characteristics are analyzed to divide the clothes drying machine into different areas, and the control parameters of each area are calculated.

[0044] In this optional embodiment, analyzing the load differences at different locations based on the load data set and identifying the characteristics of the load distribution includes: The kernel density estimation analysis algorithm is used to obtain the density distribution characteristics of the load data set; the spatial correlation of load data between different locations is analyzed, and the load distribution diagram is drawn in combination with the density distribution characteristics, and the load distribution center is identified; the similarity matrix is ​​calculated according to the load distribution center, and based on the similarity matrix, load data groups with similar load characteristics are identified, and load data subsets at different locations are constructed; the spatial correlation of load data subsets at different locations is analyzed, and the unclassified load data are assigned to the corresponding load data subsets to identify the load distribution characteristics.

[0045] In this optional embodiment, the formula of the kernel density estimation analysis algorithm is:

[0046] Where g(x) represents the kernel density estimation analysis function; k represents the number of samples in the load data set; x represents the sample data in the load data set; n represents the nth sample data in the load data set; s represents the bandwidth of the dimension in the load data set; p represents the type value of each sample data; D represents the kernel function; and X represents the sample group in the load data set.

[0047] In this optional embodiment, the formula for the spatial correlation is:

[0048] Where T represents the spatial correlation function; c represents the number of spatial load data; W represents the sum of all elements of the weight matrix; a represents the ath load data; b represents the bth load data; ω ab Represents the spatial relationship between load data a and load data b; z a Indicates the variable value of the a-th load data; z b The variable value representing the bth load data; Indicates the average value of the load data variable.

[0049] In this optional embodiment, calculating a similarity matrix based on the load distribution center, identifying load data groups with similar load characteristics based on the similarity matrix, and constructing load data subsets at different locations includes: According to the load distribution center, several load data with the highest similarity are selected as reference points. Based on the reference points, the similarity matrix of the load data set is calculated. The similarity matrix is ​​used to identify load data groups with similar load characteristics. Starting from the reference point of each load data group, a skeleton is constructed along the direction of highest similarity and decreasing density to obtain load data subsets at different locations.

[0050] In this optional embodiment, analyzing the load distribution characteristics, dividing the clothes drying machine into different areas, and calculating the control parameters of each area includes: Analyze the load distribution characteristics, determine the division boundaries of the clothes dryer, and divide the clothes dryer into different areas based on the division boundaries; design a travel control strategy based on the load distribution characteristics of each area and the functional requirements of the clothes dryer; obtain the load data, clothes dryer status and user instructions of each area, and calculate the control parameters of each area of ​​the clothes dryer based on the travel control strategy.

[0051] In this optional embodiment, the control parameters include: target position, moving speed and dwell time.

[0052] It should be noted that the load distribution characteristics include: Regional load differences: Different areas may show significant load differences due to different weight data (number, type, or weight of clothes hung). For example, some areas may be full of heavy clothes, while other areas may be relatively light or empty.

[0053] Load concentration: The load may not be evenly distributed across all areas of the clothes dryer, but may be concentrated in certain areas or hot spots.

[0054] Load change trend: As the drying process progresses, the weight of the clothes may gradually decrease, causing changes in the load distribution; in addition, users may add or remove clothes, which will also cause dynamic changes in the load.

[0055] Spatial distribution pattern: The load distribution on the clothes drying machine may follow a certain spatial pattern, such as uniform distribution, non-uniform distribution, layered distribution, etc. These patterns may be related to the design of the clothes drying machine, the user's usage habits or the type of clothes being dried.

[0056] Based on regional load differences, load concentration, load change trends and spatial distribution patterns, clothes drying machines are divided into load density areas, functional requirement areas, user preference areas and physical structure areas.

[0057] Step S103: Fusing control parameters with a deep learning algorithm to predict the load state of the clothes drying machine, and setting a load threshold and a corresponding control strategy.

[0058] In this optional embodiment, the integration of control parameters and deep learning algorithms to predict the load state in the clothes drying machine travel control model, set the load threshold and the corresponding control strategy include: The control parameters of each area are integrated with the deep learning algorithm to construct a recurrent neural network model. The distribution characteristics of the load data are used to train the recurrent neural network model. The trained recurrent neural network model is used to predict the load status of the clothes drying machine, and the load threshold is set based on the prediction results. The set load threshold is combined with the control parameters of each area to formulate a corresponding control strategy.

[0059] It's important to note that the control strategy includes load state prediction, load threshold setting, zone control parameter adjustment, and user command response. For example, when the clothes dryer's load reaches or exceeds the load threshold, the corresponding control strategy is triggered to ensure stable operation and efficient drying. In heavily loaded areas, the machine's movement speed is reduced and the dwell time is extended to ensure sufficient drying of clothes. In lightly loaded areas, the movement speed can be increased and the dwell time shortened to improve drying efficiency.

[0060] Step S105: According to the set load threshold and control strategy, corresponding control is performed on the clothes drying machine and a drying task is assigned.

[0061] It should be noted that the load conditions of the clothes drying rod and the drying space are monitored in real time through the built-in sensors of the clothes drying machine (such as weight sensors, pressure sensors, etc.), and the monitored load data is compared with the set load threshold to determine the current load status; when the load is detected to be close to or reaches the set load threshold, the clothes drying machine automatically performs control operations, such as: stopping the lowering of the clothes drying rod to prevent exceeding the maximum load-bearing capacity, sounding an alarm or displaying a prompt message to notify the user of the current load status, automatically adjusting the distribution of the drying space, and optimizing the drying effect; based on the current load conditions and the drying requirements set by the user, the clothes drying machine automatically assigns drying tasks. When assigning tasks, factors such as the type, material, and color of the clothes should be taken into consideration to avoid mutual contamination or affecting the drying effect. Different drying areas or levels can be set to classify and dry the clothes according to their characteristics.

[0062] Step S107: monitor the status and performance of the clothes drying machine in real time, and use the monitoring results as feedback to optimize the control strategy.

[0063] It's important to note that a feedback mechanism is established based on monitoring results of the clothes drying machine's status (power supply, operating status, and fault status) and performance (load, air drying, and energy consumption) to continuously optimize the machine's control strategy. For example, the lifting and lowering strategies are adjusted based on load conditions to ensure efficient operation without exceeding the load threshold.

[0064] Figure 2 An embodiment of a clothes drying machine travel control system based on load analysis of the present invention is shown.

[0065] In this optional embodiment, the clothes drying machine travel control system based on load analysis includes: The data collection and analysis module 201 is used to collect load data at each position of the clothes drying machine, analyze the load distribution, and calculate the control parameters of the clothes drying machine based on the analysis results; The prediction and strategy setting module 203 is used to integrate the control parameters and the deep learning algorithm to predict the load state of the clothes drying machine, set the load threshold and the corresponding control strategy; The control execution and task allocation module 205 is used to perform corresponding control on the clothes drying machine and allocate drying tasks according to the set load threshold and control strategy; The real-time monitoring and feedback optimization module 207 is used to monitor the status and performance of the clothes drying machine in real time, and use the monitoring results as feedback to optimize the control strategy.

[0066] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment are implemented.

[0067] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0068] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0069] In addition, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0070] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes in the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0071] The present invention is not limited to the structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for controlling the travel of a clothes drying machine based on load analysis, characterized in that: include: Collect load data at each location of the clothes drying machine, analyze the load distribution, and calculate the clothes drying machine control parameters based on the analysis results; Fusion of control parameters and deep learning algorithms to predict the load status of the clothes drying machine, set the load threshold and corresponding control strategy; According to the set load threshold and control strategy, the clothes drying machine is controlled accordingly and the drying task is assigned; Monitor the status and performance of the clothes drying machine in real time, and use the monitoring results as feedback to optimize the control strategy.

2. The method for controlling the travel of a clothes drying machine based on load analysis according to claim 1, characterized in that: The collecting of load data at each position of the clothes drying machine, analyzing the load distribution, and calculating the control parameters of the clothes drying machine based on the analysis results includes: Collect the load data of each position of the clothes drying machine, pre-process the load data, and establish a load data set; Based on the load data set, analyze the load differences at different locations and identify the characteristics of the load distribution; Analyze the load distribution characteristics, divide the clothes drying machine into different areas, and calculate the control parameters of each area.

3. The method for controlling the travel of a clothes drying machine based on load analysis according to claim 2, characterized in that: The load data set is used to analyze the load differences at different locations and identify the characteristics of the load distribution, including: Use kernel density estimation analysis algorithm to obtain the density distribution characteristics of the load data set; Analyze the spatial correlation of load data between different locations, combine density distribution characteristics, draw load distribution maps, and identify the load distribution center; Calculate the similarity matrix according to the load distribution center, identify the load data groups with similar load characteristics based on the similarity matrix, and construct the load data subsets at different locations; The spatial correlation of load data subsets at different locations is analyzed, unclassified load data are assigned to corresponding load data subsets, and load distribution characteristics are identified.

4. The method for controlling the travel of a clothes drying machine based on load analysis according to claim 3, characterized in that: The formula of the kernel density estimation analysis algorithm is: Where g(x) represents the kernel density estimation analysis function; k represents the number of samples in the load data set; x represents the sample data in the load data set; n represents the nth sample data in the load data set; s represents the bandwidth of the dimension of the payload dataset; p represents the type value of each sample data; D represents the kernel function; X represents the sample group in the payload dataset.

5. The method for controlling the travel of a clothes drying machine based on load analysis according to claim 3, characterized in that: The formula for the spatial correlation is: Where T represents the spatial correlation function; c represents the amount of spatial payload data; W represents the sum of all elements of the weight matrix; a represents the ath load data; b represents the bth load data; ω ab Represents the spatial relationship between load data a and load data b; z a The variable value representing the ath load data; z b The variable value representing the bth load data; Indicates the average value of the load data variable.

6. The method for controlling the travel of a clothes drying machine based on load analysis according to claim 3, characterized in that: The calculating of the similarity matrix according to the load distribution center, identifying the load data groups with similar load characteristics based on the similarity matrix, and constructing the load data subsets at different locations includes: According to the load distribution center, several load data with the highest similarity are selected as reference points; Based on the reference points, the similarity matrix of the load data set is calculated; Using the similarity matrix, we identify load data groups with similar load characteristics. Starting from the reference point of each load data group, the skeleton is constructed along the direction of highest similarity and decreasing density to obtain load data subsets at different positions.

7. The method for controlling the travel of a clothes drying machine based on load analysis according to claim 2, characterized in that: The analysis of load distribution characteristics, dividing the clothes drying machine into different areas, and calculating the control parameters of each area include: Analyze the load distribution characteristics, determine the division boundaries of the clothes drying machine, and divide the clothes drying machine into different areas based on the division boundaries; Design a travel control strategy based on the load distribution characteristics of each area and the functional requirements of the clothes drying machine; Obtain the load data of each area, the status of the clothes drying machine, and user instructions, and calculate the control parameters of each area of ​​the clothes drying machine in combination with the travel control strategy.

8. The method for controlling the travel of a clothes drying machine based on load analysis according to claim 7, characterized in that: The control parameters include: target position, moving speed and dwell time.

9. The method for controlling the travel of a clothes drying machine based on load analysis according to claim 1, characterized in that: The fusion of control parameters and deep learning algorithms to predict the load state in the clothes drying machine travel control model and set the load threshold and corresponding control strategy include: Integrate the control parameters of each region with the deep learning algorithm to build a recurrent neural network model; Apply the distribution characteristics of load data to train the recurrent neural network model; Use the trained recurrent neural network model to predict the load status of the clothes drying machine and set the load threshold based on the prediction results; Combine the set load threshold with the control parameters of each area to formulate the corresponding control strategy.

10. A clothes drying machine travel control system based on load analysis, characterized in that: The system includes: A data collection and analysis module is used to collect load data at various locations of the clothes drying machine, analyze the load distribution, and calculate the control parameters of the clothes drying machine based on the analysis results; The prediction and strategy setting module is used to integrate control parameters and deep learning algorithms to predict the load status of the clothes drying machine, set the load threshold and the corresponding control strategy; The control execution and task allocation module is used to perform corresponding control on the clothes drying machine and allocate drying tasks according to the set load threshold and control strategy; The real-time monitoring and feedback optimization module is used to monitor the status and performance of the clothes drying machine in real time, and use the monitoring results as feedback to optimize the control strategy.

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