Smart home control system and method based on mobile terminal

Abstract

The invention relates to the technical field of remote control, in particular to a smart home control system and method based on a mobile terminal, and the system comprises an equipment performance evaluation module, a routing path optimization module, a weight feedback adjustment module, a dense environment matching module and a remote control interaction module. According to the method, selection of signal transmission paths is optimized through comprehensive analysis of the signal coverage range and the path load condition, the path transmission efficiency and stability are improved, the weight distribution rule is adjusted through dynamic analysis of the node delay rate and the load rate fluctuation, the dynamic adaptive capacity of the transmission paths is enhanced, and the transmission efficiency is improved. The load capacity is classified and redistributed according to the physical positions and the load states of the nodes in a dense environment, resource waste of areas with weak signal coverage is reduced, meanwhile, the balance of load distribution is improved, the instruction response time is shortened in combination with the optimization result of node delay and load distribution, and the reliability and the execution efficiency of path transmission are improved.

Description

Technical Field

[0001] The present invention relates to the field of remote control technology, and in particular to a smart home control system and method based on a mobile terminal. Background Art

[0002] The field of remote control technology includes technologies related to off-site operation and management of equipment or systems through communication technology. The core content of this technical field includes the communication method of terminal equipment, the transmission mechanism of control signals and the corresponding network architecture. Remote control technology is widely used in fields such as home appliances, industrial equipment and transportation. It realizes remote control of physical equipment by utilizing network protocols, data transmission and device interaction mechanisms. Its overall technical field covers the design and optimization of data transmission protocols, communication coordination between devices and terminal interaction methods in specific scenarios, forming a highly systematic technical system.

[0003] Among them, the smart home control system refers to a system that uses communication terminal devices as the core and realizes the interconnection and control of home devices through network connection. The subject of this patent is aimed at the device management problem in the home environment, covering the remote access, status monitoring and operation control of the device, etc., which is completed through network protocol design, device access authentication mechanism and user command parsing. It transmits signals and executes commands through the network connection between the mobile terminal and the home device, and combines the remote control function of the terminal device to realize the orderly management and operation of various home devices.

[0004] The existing technology lacks an efficient statistical and classification mechanism in the evaluation of device response time and load status, making it difficult to accurately obtain the distribution of device performance parameters, resulting in unreasonable resource scheduling. In terms of path optimization, the signal coverage and path load status are not fully combined, which may cause path selection deviation and affect transmission efficiency. In terms of node dynamic adaptability, the existing technology lacks a sensitive response mechanism to fluctuations in delay rate and load rate, resulting in insufficient flexibility in path adjustment. In dense environments, the existing technology fails to optimize resource allocation through accurate classification of node location and load status, which may aggravate network congestion and resource waste, and affect the real-time performance and transmission stability of device instructions. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a smart home control system and method based on a mobile terminal.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A smart home control system based on a mobile terminal includes:

[0007] The device performance evaluation module collects statistics on the response time and load status of devices based on the performance parameters of smart sockets and smart lights, classifies the data distribution according to the delay value and response time value, calculates the performance index based on the load status and delay value, and performs normalization to generate the device performance weight value;

[0008] The routing path optimization module aggregates the signal coverage and path load of the nodes in the signal transmission path based on the device performance weight value, establishes screening rules according to the path delay value and the node load distribution difference value, screens the nodes with the best signal coverage, and generates the optimal transmission path matrix based on the node screening results;

[0009] The weight feedback adjustment module analyzes the node performance change trend through the fluctuation values ​​of the delay rate and the load rate based on the optimal transmission path matrix, reorders the node priorities according to the delay change values ​​and load change values ​​of the nodes with small signal coverage, and adjusts the weight allocation rules to generate the node performance adjustment coefficient;

[0010] The dense environment matching module classifies the nodes in the low signal coverage range according to the physical location and load status based on the node performance adjustment coefficient, calculates the node load distribution difference value and the path transmission time value under each classification, and redistributes the load based on the classification result to generate the node group load value;

[0011] The remote control interaction module calculates the command response time value of the smart socket and the smart light based on the node group load value, according to the node delay time value and load distribution in the device command transmission path, and optimizes the path planning by combining the path transmission stability coefficient to generate the control path response time value.

[0012] The device performance weight value includes response time distribution weight, load status weight, and delay value weight; the optimal transmission path matrix includes node signal coverage, path delay value distribution, and node load distribution; the node performance adjustment coefficient includes delay variation coefficient, load variation coefficient, and priority sorting coefficient; the node group load value includes node physical location grouping, load status grouping, and load distribution difference value; the control path response time value includes instruction response time value, path transmission stability coefficient, and path planning optimization result.

[0013] As a further solution of the present invention, the step of obtaining the device performance weight value is specifically as follows:

[0014] Call the response time and load status recorded in the device performance parameters, count the correlation between the delay value and the response time value, build a data distribution statistics table, calculate the preliminary impact of the load status on the performance indicators based on the data distribution, and generate a corresponding relationship table between the load status and the preliminary performance indicators;

[0015] According to the correspondence table between the load state and the preliminary performance index, the delay value is normalized according to its statistical characteristics, and the normalized delay value and the preliminary performance index are set and calculated to construct a comprehensive performance index value for each device;

[0016] Based on the comprehensive performance index value of each device, the index value is weighted and calculated by dynamically adjusting the weight coefficient, and normalized in combination with the device response time, using the formula:

[0017]

[0018] Get the equipment performance weight value;

[0019] Where W represents the device performance weight value, α i Represents the weight coefficient of the load state of the i-th device, L i represents the normalized delay value of the ith device, β i Represents the weight coefficient of the response time of the i-th device, R i Represents the response time value of the i-th device, and n represents the total number of devices.

[0020] As a further solution of the present invention, the step of obtaining the optimal transmission path matrix is ​​specifically as follows:

[0021] According to the device performance weight value, the signal coverage and load conditions of multiple nodes in the signal transmission path are summarized, the signal coverage of each node and the load parameter value of the node are counted, and the results are summarized into a node signal and load statistics table;

[0022] From the node signal and load statistics table, call the path delay value and the node load parameter distribution, compare the difference between the path delay and the load parameter, establish a screening rule according to a preset threshold, select a node set that meets the screening rule, and generate a preliminary screening node list;

[0023] The signal coverage and load parameters of the preliminary screened node list are calculated, and the node coverage value, load difference value and node stability coefficient are called, using the formula:

[0024]

[0025] Generate an optimal transmission path matrix;

[0026] Among them, P represents the optimal transmission path matrix, C i represents the signal coverage of the ith node, D i represents the load difference value of the i-th node, λ is the adjustment coefficient, S i is the stability coefficient of the node, and n represents the total number of nodes.

[0027] As a further solution of the present invention, the step of obtaining the node performance adjustment coefficient is specifically as follows:

[0028] Using the optimal transmission path matrix, calling the delay rate and load rate of each node, analyzing the change trend of the delay rate and load rate of each node in a continuous time period, counting the fluctuation values ​​of multiple nodes, and generating a node fluctuation value list;

[0029] Filter nodes with small signal coverage from the node fluctuation value list, calculate the delay change value and load change value of the node, and perform normalization processing to generate node delay and load change data;

[0030] Based on the node delay and load change data, the node priority is recalculated, and the delay change value, load change value and weight coefficient of the node are called, using the formula:

[0031]

[0032] Generate node performance adjustment coefficient;

[0033] Among them, K represents the node performance adjustment coefficient, which is used to redefine the weight of each node, ΔL i represents the delay change value observed by the i-th node in a continuous time period, ΔD i represents the load change value of the i-th node in the same time period, α represents the weight of the delay change on the node priority ranking, β represents the weight of the load change on the node priority ranking, and n represents the total number of nodes involved in the calculation.

[0034] As a further solution of the present invention, the step of obtaining the node group load value is specifically as follows:

[0035] Based on the node performance adjustment coefficient, nodes with low signal coverage are classified, and the categories are divided according to physical location and load status, and the node composition of each category is counted to generate a node classification table;

[0036] Utilizing the data in the node classification table, analyzing the load distribution difference value of each category of nodes, calculating the path transmission time value of each category, integrating the load distribution and transmission efficiency according to statistical characteristics, and generating node load and time analysis data;

[0037] Based on the node load and time analysis data, the load of each category of nodes is redistributed, and the load and transmission time of the calling node are calculated using the formula:

[0038]

[0039] Generate node group load value;

[0040] Among them, V i represents the load value of the i-th node group, L ij is the load of the jth node in the i-th node group, representing the contribution of the node to the overall load of the group. ij is the transmission time of the jth node in the i-th node group, which is used to measure the data transmission efficiency. γ is the weighted adjustment coefficient, which is used to adjust the influence of the transmission time in the load calculation. m is the number of nodes in the i-th node group.

[0041] As a further solution of the present invention, the step of obtaining the control path response time value is specifically as follows:

[0042] Based on the node group load value, calling the delay time value and load distribution amount of the node in the device instruction transmission path, calculating the delay sum and load sum of each node in each path, and generating path delay and load distribution data;

[0043] Utilizing the path delay and load distribution data, analyzing the command response time values ​​of the smart sockets and smart lights on each path, calculating the total command transmission time between nodes, classifying and summarizing the response data of all paths, and generating the path command response time value;

[0044] Based on the path instruction response time value, the stability coefficient of the path transmission is optimized using the formula:

[0045]

[0046] generating control path response time values;

[0047] Among them, R represents the control path response time value, which is a quantitative expression of the overall performance of the path, and L k is the delay time value of the kth node in the path, describing the transmission delay characteristics of the node, D k is the load distribution of the kth node in the path, which is used to indicate the load contribution of the node. k is the transmission stability coefficient of the kth node in the path, which characterizes the transmission reliability of the node. α is the delay time weight coefficient, which adjusts the impact of delay on the path response time. β is the load distribution weight coefficient, which balances the effect of load on the path response time. n is the total number of nodes in the path, which is used for normalization.

[0048] A smart home control method based on a mobile terminal, wherein the smart home control method based on a mobile terminal is executed based on the above-mentioned smart home control system based on a mobile terminal, and comprises the following steps:

[0049] S1: Based on the performance parameters of devices including smart sockets and smart lights, collect and count the response time and load status, calculate the delay value and response time distribution of the device, establish classification standards based on the delay value and load status, calculate the performance index, and generate the device performance weight value through normalization processing;

[0050] S2: Based on the device performance weight value, analyze the signal coverage and load of the nodes in the signal transmission path, evaluate the path delay value and load distribution difference of the nodes, select the node with the best signal coverage, and configure the transmission path relationship matrix according to the selected node to generate the optimal transmission path matrix;

[0051] S3: Based on the optimal transmission path matrix, monitor the delay rate and load rate fluctuations of the node, identify the performance trend through the delay change value and load change value of the node, re-arrange the priority of the node with a smaller coverage range, and adjust the weight distribution to generate a node performance adjustment coefficient;

[0052] S4: Based on the node performance adjustment coefficient, the nodes with smaller coverage are classified by location and load status, the load distribution and transmission time difference of the classified nodes are calculated, the node load is reallocated, the load configuration of the node group is determined by set calculation, and the node group load value is generated;

[0053] S5: Based on the node group load value, analyze the delay time and load distribution of the nodes in the device command transmission path, calculate the response time of the smart socket and the smart light, optimize the path planning in combination with the path transmission stability parameter, and generate a control path response time value.

[0054] Compared with the prior art, the advantages and positive effects of the present invention are:

[0055] In the present invention, by statistically analyzing and classifying the response time and load status of device performance, combining the delay value and response time value to classify and normalize data distribution, the evaluation accuracy of device performance is improved. According to the comprehensive analysis of signal coverage and path load conditions, the selection of signal transmission paths is optimized, and the efficiency and stability of path transmission are improved. By dynamically analyzing the fluctuations of node delay rate and load rate and adjusting the weight distribution rules, the dynamic adaptability of the transmission path is enhanced. In dense environments, the load is classified and redistributed according to the physical location and load status of the nodes, which reduces the waste of resources in areas with weak signal coverage and improves the balance of load distribution. Combining the optimization results of node delay and load distribution, the command response time is reduced and the reliability and execution efficiency of path transmission are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a system flow chart of the present invention;

[0057] Figure 2 A flowchart of the steps for obtaining the performance weight value of the device of the present invention;

[0058] Figure 3 is a flow chart of the steps of obtaining the optimal transmission path matrix of the present invention;

[0059] Figure 4 A flowchart of the steps for obtaining the node performance adjustment coefficient of the present invention;

[0060] Figure 5 A flowchart of the steps for obtaining the node group load value of the present invention;

[0061] Figure 6 This is a flow chart of the steps for obtaining the control path response time value of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0064] Embodiment 1

[0065] See also Figure 1 The present invention provides a technical solution: a smart home control system based on a mobile terminal includes:

[0066] The device performance evaluation module collects statistics on the response time and load status of devices based on the performance parameters of smart sockets and smart lights, classifies the data distribution according to the delay value and response time value, calculates the performance index based on the load status and delay value, and performs normalization to generate the device performance weight value;

[0067] The routing path optimization module aggregates the signal coverage and path load of nodes in the signal transmission path based on the device performance weight value, establishes screening rules according to the path delay value and the node load distribution difference value, screens the nodes with the best signal coverage, and generates the optimal transmission path matrix based on the node screening results;

[0068] The weight feedback adjustment module is based on the optimal transmission path matrix and analyzes the node performance change trend through the fluctuation values ​​of the delay rate and load rate. It reorders the node priorities according to the delay change values ​​and load change values ​​of the nodes with small signal coverage, and adjusts the weight allocation rules to generate the node performance adjustment coefficient.

[0069] The dense environment matching module classifies the nodes with low signal coverage according to their physical location and load status based on the node performance adjustment coefficient, calculates the node load distribution difference value and path transmission time value under each category, and redistributes the load based on the classification result to generate the node group load value;

[0070] The remote control interaction module calculates the command response time value of the smart socket and smart light based on the node group load value, the node delay time value and the load distribution in the device command transmission path, and optimizes the path planning by combining the path transmission stability coefficient to generate the control path response time value.

[0071] The equipment performance weight values ​​include response time distribution weight, load status weight, and delay value weight. The optimal transmission path matrix includes node signal coverage, path delay value distribution, and node load distribution. The node performance adjustment coefficient includes delay variation coefficient, load variation coefficient, and priority sorting coefficient. The node grouping load value includes node physical location grouping, load status grouping, and load distribution difference value. The control path response time value includes instruction response time value, path transmission stability coefficient, and path planning optimization result.

[0072] See also Figure 2 , the specific steps for obtaining the device performance weight value are:

[0073] Call the response time and load status recorded in the device performance parameters, count the correlation between the delay value and the response time value, build a data distribution statistics table, calculate the preliminary impact of the load status on the performance indicators based on the data distribution, and generate a corresponding relationship table between the load status and the preliminary performance indicators;

[0074] First, extract the timing data from the response time, and perform segmented statistics on the delay value and response time value according to the time axis. Then build a data distribution statistics table. By analyzing the relationship between the power consumption and the delay value in the equipment load state, quantify the load state and express the load state as the load power factor. Then, use the response time value as a benchmark and combine the power factor to determine whether the equipment load is in a stable state or an abnormal state. The corresponding relationship table between the load state and the preliminary performance indicators is generated through the analysis results.

[0075] According to the corresponding relationship table between load status and preliminary performance indicators, the delay value is normalized according to its statistical characteristics, and the normalized delay value and the preliminary performance indicator are aggregated to construct the comprehensive performance indicator value of each device;

[0076] The delay values ​​in the normal interval are normalized, and the normalization function is used to stretch and smooth the abnormal interval values. The comprehensive index value of each device in its respective state is calculated by combining the normalized delay value with the preliminary performance index. Then, the rationality of the distribution of the comprehensive index value is verified based on statistical methods to ensure that the comprehensive index values ​​of different devices when their states change can reflect their actual performance changes and generate a comprehensive index value of device performance.

[0077] Based on the comprehensive performance index value of each device, the index value is weighted and calculated by dynamically adjusting the weight coefficient, and normalized in combination with the device response time, using the formula:

[0078]

[0079] Get the equipment performance weight value;

[0080] Where W represents the device performance weight value, α i Represents the weight coefficient of the load state of the i-th device, L i represents the normalized delay value of the ith device, β i Represents the weight coefficient of the response time of the i-th device, R i Represents the response time value of the i-th device, and n represents the total number of devices.

[0081] formula:

[0082]

[0083] The benefit of the formula is that by introducing the normalized delay value, response time value and the combined operation of the weight parameter, a comprehensive evaluation of multiple performance indicators can be achieved, which can more accurately reflect the overall performance of the device.

[0084] Detailed explanation of the formula and the process of formula calculation and derivation:

[0085] Take the number of devices n = 3, and record the normalized delay value L of each device respectively 1 =0.8,L 2 =0.5, L 3 =0.3, response time value R 1 =10, R 2 =15, R 3 =20, and the weight parameters are α 1 =0.4,α 2 =0.3,α 3=0.3,β 1 =0.5,β 2 =0.4,β 3 =0.1. Substitute into the formula:

[0086]

[0087] Calculate item by item:

[0088]

[0089] The result shows that the device performance weight value is 6.78, which reflects the performance of the comprehensive performance indicators after normalization under the current weight setting. By adjusting the weight parameters or expanding the normalization rules of delay values ​​and response times, it can adapt to a wider range of scenario requirements and further derive more accurate performance indicator evaluation results.

[0090] See also Figure 3 , the specific steps for obtaining the optimal transmission path matrix are:

[0091] According to the device performance weight value, the signal coverage and load conditions of multiple nodes in the signal transmission path are summarized, the signal coverage and load parameter value of each node are counted, and the results are summarized into a node signal and load statistics table;

[0092] The real-time signal strength value and load parameter data of the call node are based on the signal coverage standard in the preset path, and the signal strength formula S = 10log 10 (P) Calculate the node signal strength S, where P is the signal power. By comparing the signal strength value with the coverage range standard, select the valid nodes within the coverage range, and use the load distribution calculation formula Calculate the node load rate D, where L c is the current node load, L t For the maximum carrying capacity of the node, the nodes with load rates exceeding the limit are eliminated by comparing the node load rate with the load threshold, and the signal coverage range and the nodes after load screening are merged to generate a node signal and load statistics table.

[0093] From the node signal and load statistics table, call the path delay value and node load parameter distribution, compare the difference between the path delay and the load parameter, establish a screening rule based on the preset threshold, select the node set that meets the screening rule, and generate a preliminary screening node list;

[0094] Extract path delay values ​​and node load distribution data, and call the average value of path delay Where T i is the delay value of a single node, n is the number of nodes in the path, and the difference value ΔD=D is calculated by the path delay value and the node load distribution. max -Dmin , where D max and D min The maximum and minimum values ​​of the node load are combined with the preset screening threshold to screen out nodes with smaller differences in node load distribution. Nodes that meet the conditions are selected according to the screening rules to generate a preliminary screening node list.

[0095] Calculate the signal coverage and load parameters of the preliminary screened node list, call the node coverage value, load difference value and node stability coefficient, and use the formula:

[0096]

[0097] Generate an optimal transmission path matrix;

[0098] Among them, P represents the optimal transmission path matrix, C i represents the signal coverage of the ith node, D i represents the load difference value of the i-th node, λ is the adjustment coefficient, S i is the stability coefficient of the node, and n represents the total number of nodes.

[0099] formula:

[0100]

[0101] The benefit of the formula is that by introducing the signal coverage C i , load difference value D i , node stability coefficient S i , combined with the adjustment coefficient λ for trade-off calculation, it can improve the accuracy and flexibility of transmission path selection while balancing signal coverage and load stability;

[0102] Detailed explanation of the formula and the process of formula calculation and derivation:

[0103] Node signal coverage C i The calculation of C is based on the formula i =10log 10 (P i ) Get the load difference value D i The calculation of D is based on the formula i =D max -D min Get, node stability coefficient S i The node's historical operation stability score is obtained, and the score is quantified from 1 to 10. The adjustment coefficient λ is dynamically set according to the path signal transmission requirements. The above parameters are substituted into the formula for calculation. Assuming n = 5, the node parameters are C 1 =30,C 2 =28,C 3 =25,C4 =27,C 5 =26, D 1 =0.2,D 2 =0.15,D 3 =0.18,D 4 =0.22,D 5 =0.16,

[0104] S 1 =8,S 2 =9,S 3 =7,S 4 =8,S 5 =8, λ=0.5, the formula is calculated step by step as follows:

[0105]

[0106] Substitute the parameters and calculate the numerator:

[0107]

[0108] Calculate respectively:

[0109] 3.735+3.092+3.579+3.315+3.485=17.206;

[0110] Calculate the denominator:

[0111] n = 5;

[0112] Calculate the final result:

[0113]

[0114] The result shows that the calculated P=0.828 indicates that the node combination with the best signal coverage in the path can achieve balanced selection of the transmission path, and the result is further used to generate the optimal transmission path matrix.

[0115] See also Figure 4 , the specific steps for obtaining the node performance adjustment coefficient are:

[0116] Using the optimal transmission path matrix, call the delay rate and load rate of each node, analyze the change trend of each node's delay rate and load rate in a continuous time period, count the fluctuation values ​​of multiple nodes, and generate a node fluctuation value list;

[0117] The delay rate is calculated by collecting the deviation between the signal response time and the expected time of each node within a fixed time interval. The load rate is obtained by monitoring the ratio of the current task queue length of the node to the maximum queue length. The change trend of the delay rate and the load rate is obtained by continuous observation at multiple time points. The delay rate and the load rate at each time point are recorded separately, and the fluctuation value of each node is obtained by calculating the change amplitude of continuous time points. The fluctuation value calculation formula is: Among them, B i is the fluctuation value of the i-th node, ΔR i is the variation of the node delay rate, ΔQ i is the variation range of the load factor, and the fluctuation values ​​of each node are summarized to form a node fluctuation value list.

[0118] Filter nodes with small signal coverage from the node fluctuation value list, calculate the node delay change value and load change value, and perform normalization processing to generate node delay and load change data;

[0119] The signal coverage range is measured by the broadcast radius and signal strength data of the node. When screening, the node fluctuation value and signal coverage range are compared. Nodes with fluctuation values ​​greater than the average fluctuation value and coverage range lower than the overall coverage average are marked. The marked nodes enter the further calculation stage. The delay change value is calculated by the difference between the signal transmission delay at two points in time. The load change value is calculated by the difference between the task queue load at two points in time. The delay change value and the load change value use the normalization formula: After processing, all normalized node data are converted into node delay and load change data.

[0120] Based on the node delay and load change data, the node priority is recalculated, and the node delay change value, load change value and weight coefficient are called, using the formula:

[0121]

[0122] Generate node performance adjustment coefficient;

[0123] Among them, K represents the node performance adjustment coefficient, which is used to redefine the weight of each node, ΔL i represents the delay change value observed by the i-th node in the continuous time period, ΔD i represents the load change value of the i-th node in the same time period, α represents the weight of the delay change on the node priority ranking, β represents the weight of the load change on the node priority ranking, and n represents the total number of nodes involved in the calculation.

[0124] formula:

[0125]

[0126] The benefit of the formula is that, through the joint weighting of the delay change value and the load change value, the contribution of the node in delay and load fluctuation can be reasonably distinguished, ensuring the flexibility and accuracy of the calculation;

[0127] Detailed explanation of the formula and the process of formula calculation and derivation:

[0128] ΔL i is the delay change value of the ith node calculated from the delay data at consecutive time points, ΔD i is the load change value of the i-th node, which is introduced into the operation after being processed by the normalization formula. The weight coefficients α and β are set as the importance ratio of delay and load according to different scenarios. The number of nodes n is dynamically determined by the number of nodes in the real-time network. Sample data example: ΔL of node 1 1 =0.3, ΔD 1 =0.2, ΔL of node 2 2 =0.4, ΔD 2 =0.1, α=0.6, β=0.4, substitute into the formula:

[0129]

[0130] The result shows that the calculated value of the node performance adjustment coefficient is 1.43, which means that under the current configuration, the node priority adjustment is based on the joint weight of delay and load, and the adjustment coefficient can be further used to optimize the node scheduling strategy.

[0131] See also Figure 5 , the specific steps for obtaining the node group load value are:

[0132] Based on the node performance adjustment coefficient, nodes with low signal coverage are classified, divided into categories according to physical location and load status, and the node composition of each category is counted to generate a node classification table;

[0133] By collecting the geographic location information of each node and the load status during its operation, the basis for node classification is established, and the distribution of each node in different locations and load states is counted. By setting the quantitative standard of load status (such as the load percentage range of 0%-30% is low load, 30%-70% is medium load, and 70%-100% is high load) and the distance radius standard of the physical location (such as dividing the area into units of 10 kilometers), the nodes are grouped and their classification information is recorded, a unique identifier is assigned to each classification, the number and location parameters of the nodes in each classification are recorded, and the load status information is associated to generate a statistical table. The classification table can be used for subsequent analysis to generate a node classification table.

[0134] Using the data in the node classification table, analyze the load distribution difference value of each category of nodes, calculate the path transmission time value of each category, integrate the load distribution and transmission efficiency according to statistical characteristics, and generate node load and time analysis data;

[0135] The load distribution and transmission efficiency of each classified node are analyzed. The node load data (with the CPU usage and I / O rate of each node as indicators) and path transmission time value (quantified by monitoring the round-trip delay of data packets between nodes) are extracted from the classification table. According to the node load data of different classifications, the average and standard deviation of the load are calculated to quantify the uniformity of the load distribution within the classification. The load distribution difference value is defined as the absolute value of the difference between the node load value and the classification average value, and the path transmission time value is calculated in the same way. By setting the transmission time threshold (such as the average delay time exceeding 200ms is defined as high delay), high-delay classified nodes are screened for further analysis. The load distribution difference value and transmission time value are integrated, and these data are associated with each classification result to generate node load and time analysis data.

[0136] Based on the node load and time analysis data, redistribute the load of each category of nodes, call the node load and transmission time, and use the formula:

[0137]

[0138] Generate node group load value;

[0139] Among them, V i represents the load value of the i-th node group, L ij is the load of the jth node in the i-th node group, representing the contribution of the node to the overall load of the group. ij is the transmission time of the jth node in the i-th node group, which is used to measure the data transmission efficiency. γ is the weighted adjustment coefficient, which is used to adjust the influence of the transmission time in the load calculation. m is the number of nodes in the i-th node group.

[0140] formula:

[0141]

[0142] The benefit of the formula is that by combining the load of each node with the transmission time and adjusting the weight, the influence of transmission efficiency on the overall load value of the node group is enhanced, making the load distribution more reasonable and efficient;

[0143] Detailed explanation of the formula and the process of formula calculation and derivation:

[0144] Assume that a node group contains 5 nodes, and record their load and transmission time as: L 11 =

[0145] 100,L 12 =120,L 13 =80,L 14 =150,L 15 =90, T 11 =10,T 12 =15,T 13 =

[0146] 12,T 14 =8,T 15 =11, the weighted adjustment coefficient is set to γ ​​= 1.2, and the number of nodes is m = 5, then

[0147]

[0148] Calculate the scores:

[0149]

[0150] The weighted items are:

[0151] 10 1.2 ≈15.85,8 1.2 ≈12.49,6.67 1.2 ≈10.02,18.75 1.2 ≈34.89,8.18 1.2 ≈12.88;

[0153] Add and average these values:

[0154]

[0155] The result shows that the group load value of the node group is 17.23. Comparing it with other load values ​​of the benchmark node group can determine the balance and efficiency of load distribution, and provide a reference for optimizing node resource allocation.

[0156] See also Figure 6 ,The specific steps for obtaining the control path response time value are:

[0157] Based on the node group load value, the delay time value and load distribution amount of the nodes in the device instruction transmission path are called, the delay sum and load sum of each node in each path are calculated, and the path delay and load distribution data are generated;

[0158] The specific process is to collect the delay time value of each node (such as collecting the round-trip delay of the node in real time through a network testing tool), and then summarize the delay values ​​of all nodes on each path; for the load distribution, by analyzing the load conditions of each node in the same time period (such as reading the real-time CPU usage or bandwidth occupancy of the device), the average load value of each node is calculated by the time-weighted average method, and the path load sum is summarized and generated. The path delay and load distribution data are generated by recording the node delay and load sum of each path. Combined with the example, if path A contains three nodes, the delay time values ​​are 10ms, 15ms, and 20ms respectively, the total delay time value is 45ms, the load distribution amounts are 30%, 40%, and 50% respectively, and the total load amount is 120%. After the path delay and load distribution data are summarized, it is recorded as a delay of 45ms and a load of 120%.

[0159] Using the path delay and load distribution data, analyze the command response time value of the smart socket and smart light on each path, calculate the total time consumed for command transmission between nodes, classify and summarize the response data of all paths, and generate the path command response time value;

[0160] The specific steps are: 1) Analyze the difference in the mean delay between nodes on the path, and use the delay mean to count the transmission characteristics of each path. If the mean delay differences of the three nodes in path A are 5ms, 10ms, and 15ms respectively, take the maximum difference as the reference for path delay fluctuation; 2) Calculate the overall transmission time value of the path through the weighted average formula of path delay and load, and distribute the load distribution to the nodes with balanced weights. If the total path delay is 45ms, the load of 120% is reflected as a transmission time value of 60ms after adjusting the balance impact weight calculation. Combined with the transmission protocol specifications of the smart socket and the smart light, the generated path instruction response time value is 60ms.

[0161] Based on the path instruction response time value, combined with the path transmission stability coefficient, optimization is performed using the formula:

[0162]

[0163] generating control path response time values;

[0164] Among them, R represents the control path response time value, which is a quantitative expression of the overall performance of the path, and L k is the delay time value of the kth node in the path, describing the transmission delay characteristics of the node, D k is the load distribution of the kth node in the path, which is used to indicate the load contribution of the node. kis the transmission stability coefficient of the kth node in the path, which characterizes the transmission reliability of the node. α is the delay time weight coefficient, which adjusts the impact of delay on the path response time. β is the load distribution weight coefficient, which balances the effect of load on the path response time. n is the total number of nodes in the path, which is used for normalization.

[0165] formula:

[0166]

[0167] The benefit of the formula is that by introducing three data items, namely, delay time, load distribution and stability coefficient, the transmission characteristics of the path and the load balance between nodes are comprehensively measured, thus optimizing the accuracy and stability of the path response time.

[0168] Detailed explanation of the formula and the process of formula calculation and derivation:

[0169] Assume that there are 3 nodes in the path, the delay time values ​​are 10ms, 20ms, and 30ms respectively, the load distribution is 50%, 60%, and 70%, the stability coefficients are 0.8, 0.9, and 1.0, and the weight coefficients are α=0.6 and β=0.4. Substitute them into the formula to get:

[0170]

[0171] Calculate the contribution of each term separately:

[0172]

[0173] After summing:

[0174]

[0175] Find the average:

[0176]

[0177] Take the square root:

[0178]

[0179] The result shows that the comprehensive response time value of the path is 6.21ms. Compared with simply using the delay time calculation, the result after combining load and stability is more referenceable and can be directly used to optimize control path planning and further improve network performance and device response efficiency.

[0180] The smart home control method based on the mobile terminal is executed based on the above-mentioned smart home control system based on the mobile terminal, and includes the following steps:

[0181] S1: Based on the performance parameters of devices including smart sockets and smart lights, collect and count the response time and load status, calculate the delay value and response time distribution of the device, establish classification standards based on the delay value and load status, calculate the performance index, and generate the device performance weight value through normalization processing;

[0182] S2: Based on the device performance weight value, analyze the signal coverage and load of the nodes in the signal transmission path, evaluate the path delay value and load distribution difference of the nodes, select the nodes with the best signal coverage, and configure the transmission path relationship matrix according to the selected nodes to generate the optimal transmission path matrix;

[0183] S3: Based on the optimal transmission path matrix, monitor the delay rate and load rate fluctuations of the nodes, identify the performance trend through the delay change value and load change value of the nodes, re-arrange the priority of the nodes with smaller coverage, and adjust the weight distribution to generate the node performance adjustment coefficient;

[0184] S4: Based on the node performance adjustment coefficient, the location and load status of the nodes with smaller coverage are classified, the load distribution and transmission time difference of the classified nodes are calculated, the node load is redistributed, the load configuration of the node group is determined through set calculation, and the node group load value is generated;

[0185] S5: Based on the node group load value, analyze the delay time and load distribution of the nodes in the device command transmission path, calculate the response time of the smart socket and smart light, optimize the path planning in combination with the path transmission stability parameter, and generate the control path response time value.

[0186] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A smart home control system based on a mobile terminal, characterized in that: The system comprises: The device performance evaluation module collects statistics on the response time and load status of devices based on the performance parameters of smart sockets and smart lights, classifies the data distribution according to the delay value and response time value, calculates the performance index based on the load status and delay value, and performs normalization to generate the device performance weight value; The routing path optimization module aggregates the signal coverage and path load of the nodes in the signal transmission path based on the device performance weight value, establishes screening rules according to the path delay value and the node load distribution difference value, screens the nodes with the best signal coverage, and generates the optimal transmission path matrix based on the node screening results; The weight feedback adjustment module analyzes the node performance change trend through the fluctuation values ​​of the delay rate and the load rate based on the optimal transmission path matrix, reorders the node priorities according to the delay change values ​​and load change values ​​of the nodes with small signal coverage, and adjusts the weight allocation rules to generate the node performance adjustment coefficient; The dense environment matching module classifies the nodes in the low signal coverage range according to the physical location and load status based on the node performance adjustment coefficient, calculates the node load distribution difference value and the path transmission time value under each classification, and redistributes the load based on the classification result to generate the node group load value; The remote control interaction module calculates the command response time value of the smart socket and the smart light based on the node group load value, according to the node delay time value and load distribution in the device command transmission path, and optimizes the path planning by combining the path transmission stability coefficient to generate the control path response time value.

2. The mobile terminal-based smart home control system according to claim 1, characterized in that: The device performance weight value includes response time distribution weight, load status weight, and delay value weight; the optimal transmission path matrix includes node signal coverage, path delay value distribution, and node load distribution; the node performance adjustment coefficient includes delay variation coefficient, load variation coefficient, and priority sorting coefficient; the node group load value includes node physical location grouping, load status grouping, and load distribution difference value; the control path response time value includes instruction response time value, path transmission stability coefficient, and path planning optimization result.

3. The mobile terminal-based smart home control system according to claim 2, characterized in that: The steps for obtaining the device performance weight value are specifically as follows: Call the response time and load status recorded in the device performance parameters, count the correlation between the delay value and the response time value, build a data distribution statistics table, calculate the preliminary impact of the load status on the performance indicators based on the data distribution, and generate a corresponding relationship table between the load status and the preliminary performance indicators; According to the correspondence table between the load state and the preliminary performance index, the delay value is normalized according to its statistical characteristics, and the normalized delay value and the preliminary performance index are set and calculated to construct a comprehensive performance index value for each device; Based on the comprehensive performance index value of each device, the index value is weighted and calculated by dynamically adjusting the weight coefficient, and normalized in combination with the device response time, using the formula: Get the equipment performance weight value; Where W represents the device performance weight value, α i Represents the weight coefficient of the load state of the i-th device, L i represents the normalized delay value of the ith device, β i Represents the weight coefficient of the response time of the i-th device, R i Represents the response time value of the i-th device, and n represents the total number of devices.

4. The mobile terminal-based smart home control system according to claim 3, characterized in that: The steps of obtaining the optimal transmission path matrix are specifically as follows: According to the device performance weight value, the signal coverage and load conditions of multiple nodes in the signal transmission path are summarized, the signal coverage of each node and the load parameter value of the node are counted, and the results are summarized into a node signal and load statistics table; From the node signal and load statistics table, call the path delay value and the node load parameter distribution, compare the difference between the path delay and the load parameter, establish a screening rule according to a preset threshold, select a node set that meets the screening rule, and generate a preliminary screening node list; The signal coverage and load parameters of the preliminary screened node list are calculated, and the node coverage value, load difference value and node stability coefficient are called, using the formula: Generate an optimal transmission path matrix; Among them, P represents the optimal transmission path matrix, C i represents the signal coverage of the ith node, D i represents the load difference value of the i-th node, λ is the adjustment coefficient, S i is the stability coefficient of the node, and n represents the total number of nodes.

5. The mobile terminal-based smart home control system according to claim 4, characterized in that: The steps for obtaining the node performance adjustment coefficient are specifically as follows: Using the optimal transmission path matrix, calling the delay rate and load rate of each node, analyzing the change trend of the delay rate and load rate of each node in a continuous time period, counting the fluctuation values ​​of multiple nodes, and generating a node fluctuation value list; Filter nodes with small signal coverage from the node fluctuation value list, calculate the delay change value and load change value of the node, and perform normalization processing to generate node delay and load change data; Based on the node delay and load change data, the node priority is recalculated, and the delay change value, load change value and weight coefficient of the node are called, using the formula: Generate node performance adjustment coefficient; Among them, K represents the node performance adjustment coefficient, which is used to redefine the weight of each node, ΔL i represents the delay change value observed by the i-th node in a continuous time period, ΔD i represents the load change value of the i-th node in the same time period, α represents the weight of the delay change on the node priority ranking, β represents the weight of the load change on the node priority ranking, and n represents the total number of nodes involved in the calculation.

6. The mobile terminal-based smart home control system according to claim 5, characterized in that: The steps for obtaining the node group load value are specifically as follows: Based on the node performance adjustment coefficient, nodes with low signal coverage are classified, and the categories are divided according to physical location and load status, and the node composition of each category is counted to generate a node classification table; Utilizing the data in the node classification table, analyzing the load distribution difference value of each category of nodes, calculating the path transmission time value of each category, integrating the load distribution and transmission efficiency according to statistical characteristics, and generating node load and time analysis data; Based on the node load and time analysis data, the load of each category of nodes is redistributed, and the load and transmission time of the calling node are calculated using the formula: Generate node group load value; Among them, V i represents the load value of the i-th node group, L ij is the load of the jth node in the i-th node group, representing the contribution of the node to the overall load of the group. ij is the transmission time of the jth node in the i-th node group, which is used to measure the data transmission efficiency. γ is the weighted adjustment coefficient, which is used to adjust the influence of the transmission time in the load calculation. m is the number of nodes in the i-th node group.

7. The mobile terminal-based smart home control system according to claim 6, characterized in that: The steps for obtaining the control path response time value are specifically as follows: Based on the node group load value, calling the delay time value and load distribution amount of the node in the device instruction transmission path, calculating the delay sum and load sum of each node in each path, and generating path delay and load distribution data; Utilizing the path delay and load distribution data, analyzing the command response time values ​​of the smart sockets and smart lights on each path, calculating the total command transmission time between nodes, classifying and summarizing the response data of all paths, and generating the path command response time value; Based on the path instruction response time value, the optimization is performed in combination with the stability coefficient of the path transmission, using the formula: generating control path response time values; Among them, R represents the control path response time value, which is a quantitative expression of the overall performance of the path, and L k is the delay time value of the kth node in the path, describing the transmission delay characteristics of the node, D k is the load distribution of the kth node in the path, which is used to indicate the load contribution of the node. k is the transmission stability coefficient of the kth node in the path, which characterizes the transmission reliability of the node. α is the delay time weight coefficient, which adjusts the impact of delay on the path response time. β is the load distribution weight coefficient, which balances the effect of load on the path response time. n is the total number of nodes in the path, which is used for normalization.

8. A smart home control method based on a mobile terminal, characterized in that: The smart home control system based on a mobile terminal according to any one of claims 1 to 7 comprises the following steps: S1: Based on the performance parameters of devices including smart sockets and smart lights, collect and count the response time and load status, calculate the delay value and response time distribution of the device, establish classification standards based on the delay value and load status, calculate the performance index, and generate the device performance weight value through normalization processing; S2: Based on the device performance weight value, analyze the signal coverage and load of the nodes in the signal transmission path, evaluate the path delay value and load distribution difference of the nodes, select the node with the best signal coverage, and configure the transmission path relationship matrix according to the selected node to generate the optimal transmission path matrix; S3: Based on the optimal transmission path matrix, monitor the delay rate and load rate fluctuations of the node, identify the performance trend through the delay change value and load change value of the node, re-arrange the priority of the node with a smaller coverage range, and adjust the weight distribution to generate a node performance adjustment coefficient; S4: Based on the node performance adjustment coefficient, the nodes with smaller coverage are classified by location and load status, the load distribution and transmission time difference of the classified nodes are calculated, the node load is reallocated, the load configuration of the node group is determined by set calculation, and the node group load value is generated; S5: Based on the node group load value, analyze the delay time and load distribution of the nodes in the device command transmission path, calculate the response time of the smart socket and the smart light, optimize the path planning in combination with the path transmission stability parameter, and generate a control path response time value.

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