Z-wave based intelligent control method and system for COB mosquito repellent lamp

Abstract

The application discloses a COB mosquito-repelling lamp intelligent control method and system based on Z-Wave, belongs to the technical field of intelligent home equipment control, and comprises the following steps: acquiring a multi-dimensional indoor environment parameter set, constructing a fuzzy control matrix model, outputting a mosquito-repelling grade index value, judging a mosquito-repelling risk grade, adopting a rolling scheduling algorithm to perform trend identification and adaptive optimization on multi-period data, inputting the mosquito-repelling risk grade into a Z-Wave communication control engine, combining a current linkage equipment state to generate a control instruction set, and realizing dynamic control on the COB mosquito-repelling lamp. The method has the advantages of accurate response, high energy saving and efficiency, rhythm-friendly, linkage intelligence and the like, and is suitable for intelligent mosquito-repelling lighting system deployment in multiple scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart home device control, in particular to a COB mosquito-repelling lamp intelligent control method and system based on Z-Wave. BACKGROUND

[0002] With people's increasing emphasis on the comfort and health of living environment, mosquito control in indoor environment has become a focus of attention. In recent years, COB mosquito-repelling technology based on specific waveband (such as 585-595nm) spectrum has gradually attracted attention due to its low blue light, eye protection, high color rendering, and significant mosquito-repelling effect. For example, by using indoor golden light mosquito-repelling technology (peak wavelength of 585-595nm), the R1-R6 photoreceptor cells of mosquito compound eyes are activated by specific spectrum, and the expression of carbon dioxide chemotaxis receptors is inhibited, thereby triggering the avoidance behavior. At the same time, the golden warm light with a color temperature of 1800K is designed to create a comfortable and warm atmosphere, and has high color rendering index (CRI), high color purity, and high luminous efficiency, RG0 has no blue light hazard, which helps to protect vision health, rhythm friendly, and does not affect the human natural biological clock.

[0003] However, the existing COB mosquito-repelling lamp generally uses fixed time or manual switch control, and cannot dynamically respond to environmental changes such as circadian rhythm, human activity, room temperature change, window opening and closing state, etc., which easily causes low mosquito-repelling efficiency and high energy consumption. At the same time, the existing smart home system mostly uses Wi-Fi or Bluetooth protocol, which has the problems of large delay, poor stability, and high power consumption in coordinating control between devices, and it is difficult to meet the comprehensive needs of efficient mosquito-repelling and intelligent energy saving.

[0004] Z-Wave is a low-power, low-delay short-range wireless communication protocol suitable for multi-device linkage control in home automation systems, which has high stability and compatibility, and is particularly suitable for intelligent lighting control systems with high requirements for response speed and reliability. Therefore, how to apply Z-Wave communication technology to COB spectrum mosquito-repelling lamp to realize the intelligent control method of on-demand control, environment perception, and energy-saving linkage has become a technical problem to be solved in the field. SUMMARY

[0005] The purpose of the present application is to provide a COB mosquito-repelling lamp intelligent control method and system based on Z-Wave to solve the problems in the background art.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme: a COB mosquito-repelling lamp intelligent control method based on Z-Wave, comprising:

[0007] Obtaining a set of indoor multi-dimensional environment parameters, including environmental physical parameters, behavior state parameters, structural boundary parameters, and time sequence parameters;

[0008] A fuzzy control matrix model is constructed based on the indoor multi-dimensional environment parameter set, which is used for grading judgment of the mosquito repellent risk level, including three levels of low risk, medium risk and high risk.

[0009] The risk level result is input into a Z-Wave communication control engine, and a control instruction set is generated in combination with the device node state associated with the COB mosquito repellent lamp in the current Z-Wave network;

[0010] The mosquito repellent lamp dynamically responds according to the control instruction set, and periodically feeds back the running state and environmental changes to the master node, and the master node updates the control strategy adaptively through a rolling scheduling algorithm;

[0011] When the risk level is in a high risk state for two consecutive periods and the night rhythm index is greater than a set threshold, a cross-node Z-Wave linkage strategy is started to control the air conditioner, curtain and door and window sensor to enter the anti-mosquito mode to suppress mosquito gathering sources.

[0012] Preferably, the environmental physical parameters include room temperature, humidity and light intensity; the behavior state parameters include human activity state and activity frequency; the structure boundary parameters include window opening and closing state and door lock switch state; and the timing parameters include current time and sunshine rhythm mapping value.

[0013] Preferably, the fuzzy control matrix model is constructed based on the indoor multi-dimensional environment parameter set, including:

[0014] Membership functions are respectively established for each sub-parameter in the obtained indoor multi-dimensional environment parameter set;

[0015] The membership values of all sub-parameters are input into a fuzzy rule reasoning system, and a fuzzy control rule library is established by using a fuzzy control algorithm;

[0016] A mosquito repellent level index value is generated according to the fuzzy reasoning result.

[0017] Preferably, the grading judgment of the mosquito repellent risk level includes:

[0018] A mosquito repellent risk influence factor set is established according to the indoor multi-dimensional environment parameter set, including temperature factor T1, humidity factor T2, human activity factor T3, structure boundary factor T4 and rhythm factor T5; initial weight values W1 to W5 are set for each factor to form a mosquito repellent risk calculation weight model;

[0019] The real-time membership value corresponding to each factor is weighted with its weight to generate a comprehensive mosquito repellent risk index R;

[0020] The risk level is divided according to the size of R value, wherein:

[0021] When R is less than or equal to 0.35, the mosquito repellent risk level is low risk;

[0022] When R is greater than 0.35 and less than or equal to 0.7, the mosquito repellent risk level is medium risk;

[0023] When R is greater than 0.7, the mosquito repellent risk level is high risk.

[0024] Preferably, the risk level result is input into the Z-Wave communication control engine, comprising:

[0025] The mosquito repellent risk level result is input into the Z-Wave communication control engine, and according to the interval in which the current mosquito repellent risk level is located among low risk, medium risk and high risk, the corresponding initial control strategy is determined through a level mapping table;

[0026] The state information of the node device currently linked with the COB mosquito repellent lamp is acquired in real time, and the result is screened and filtered according to the node priority and the control conflict table, to build an effective linkage node set;

[0027] Based on the effective linkage node set, the control instruction set is generated according to the linkage logic rules between the mosquito repellent risk level and the node state.

[0028] Preferably, the control instruction set comprises: light start / stop, spectrum excitation intensity adjustment, or working cycle and pulse rhythm control.

[0029] Preferably, the control strategy is adaptively updated by the master node through a rolling scheduling algorithm, comprising:

[0030] A control strategy dynamic history window maintained by the master node is built, to store the mosquito repellent risk level result, the COB mosquito repellent lamp execution state, the linkage device response condition and the environmental parameter change curve in the last n cycles;

[0031] Trend analysis and discrete change detection are performed on the multi-cycle data in the history window, and local mean smoothing and weighted difference algorithm are used to identify abnormal patterns, including risk level continuous rise or fall, linkage device response consistency decline or environmental parameter fluctuation amplitude exceeding a preset standard threshold;

[0032] According to the identification result, a strategy adjustment scheme matching the current trend is selected and loaded, including: increasing or decreasing the spectrum intensity and periodic pulse frequency of the COB lamp, or dynamically adjusting the weight parameters of each risk factor.

[0033] Preferably, when the risk level is in high risk state for two consecutive cycles and the night rhythm index is greater than a set threshold, comprising:

[0034] The master node continuously monitors the mosquito repellent risk level of two control periods, and compares the night rhythm index Rc with the set threshold Rt, when the mosquito repellent risk level of the first period and the second period is high risk and the current rhythm index Rc is greater than the preset threshold Rt, the cross-node mosquito prevention linkage preparation state is entered;

[0035] The preset mosquito prevention mode control strategy template is read, and the Z-Wave device linkage interface is called to retrieve the real-time running state of the current air conditioner, curtain and electric door and window sensor linkage device node;

[0036] If any linkage device is in the mosquito easy-to-enter state, the corresponding control instruction enters the mosquito prevention linkage mode.

[0037] The application also provides a Z-Wave-based COB mosquito repellent lamp intelligent control system, comprising:

[0038] The parameter acquisition module acquires a set of indoor multi-dimensional environment parameters, including environmental physical parameters, behavior state parameters, structural boundary parameters and timing parameters;

[0039] The risk level division module: based on the set of indoor multi-dimensional environment parameters, a fuzzy control matrix model is constructed for grading judgment of the mosquito repellent risk level, including three levels of low risk, medium risk and high risk;

[0040] The control instruction generation module: the risk level result is input into the Z-Wave communication control engine, and the device node state associated with the COB mosquito repellent lamp in the current Z-Wave network is combined to generate a control instruction set;

[0041] The response feedback module: the mosquito repellent lamp dynamically responds according to the control instruction set, and periodically feeds back the running state and environmental changes to the master node, and the master node updates the control strategy by the rolling scheduling algorithm;

[0042] The device linkage module: when the risk level is in the high risk state for two consecutive periods and the night rhythm index is greater than the set threshold, the cross-node Z-Wave linkage strategy is started, and the air conditioner, curtain and door and window sensor are controlled to enter the mosquito prevention mode to suppress the mosquito aggregation source.

[0043] In the above technical solution, the application provides technical effects and advantages:

[0044] 1. The application introduces a fuzzy control mechanism based on indoor multi-dimensional environmental parameters and a mosquito risk level evaluation model, realizing the fine and intelligent control of COB mosquito repellent lamps, breaking through the limitations of traditional mosquito repellent lamps relying on timing or single induction. The system can not only dynamically identify the current space mosquito repellent demand, but also adaptively optimize the control strategy combined with the rhythm factor and environmental fluctuation trend, especially suitable for residential and office scenes with high requirements for health, comfort and energy saving.

[0045] 2. The application further constructs a multi-node linkage mechanism through the Z-Wave communication protocol. When the mosquito risk level continuously rises and enters the high sensitivity stage of the night rhythm, it can actively mobilize air conditioners, curtains, doors and windows and other intelligent devices to enter the "anti-mosquito mode" together, and build a collaborative defense system centered on COB mosquito repellent lamps. This strategy has the characteristics of early warning, closed loop and linkage, not only improves the stability and control depth of the overall system, but also effectively reduces the mosquito gathering source, enhances the human rhythm friendliness, and embodies the engineering generalizability of the application in the smart home scene. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0047] Figure 1 The method flowchart of the present application.

[0048] Figure 2 The system module flowchart of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0050] Embodiment 1, please refer to Figure 1 The COB mosquito repellent lamp intelligent control method based on Z-Wave described in this embodiment includes:

[0051] Obtain a set of indoor multi-dimensional environmental parameters, including environmental physical parameters, behavior state parameters, structure boundary parameters and timing parameters;

[0052] A fuzzy control matrix model is constructed based on the indoor multi-dimensional environment parameter set, which is used for hierarchical judgment of the mosquito repellent risk level, including three levels of low risk, medium risk and high risk.

[0053] The risk level result is input into a Z-Wave communication control engine, and a control instruction set is generated in combination with the device node state associated with the COB mosquito repellent lamp in the current Z-Wave network.

[0054] The mosquito repellent lamp dynamically responds to the control instruction set and periodically feeds back the running state and environmental changes to the master node, and the master node updates the control strategy adaptively through a rolling scheduling algorithm.

[0055] When the risk level is in the high risk state for two consecutive periods and the night rhythm index is greater than the set threshold, a cross-node Z-Wave linkage strategy is started to control the air conditioner, curtain and door and window sensor to enter the anti-mosquito mode to suppress the mosquito gathering source.

[0056] In the present application, obtaining the indoor multi-dimensional environment parameter set is the basic link of the entire intelligent control system, aiming to comprehensively and finely perceive the indoor environment.

[0057] The environmental physical parameters refer to the basic indoor climate data that affect the activity level of mosquitoes and the comfort of human body, mainly including indoor temperature, humidity and light intensity.

[0058] Indoor temperature (T): The current real-time temperature data is obtained by a digital temperature and humidity sensor installed in the room. This parameter directly affects the activity frequency and life cycle of mosquitoes, and is also a key input variable for adjusting the working state of the mosquito repellent lamp (such as light intensity and on-time).

[0059] Relative humidity (H): The level of humidity has a significant impact on the breeding and flying ability of mosquitoes, especially under high humidity conditions, mosquitoes tend to be active. The system obtains this data in real time through the humidity module in the environmental perception unit and compares and analyzes it with historical data.

[0060] Light intensity (L): The current indoor light intensity is measured by a light sensor (such as a photoresistor or a digital light module). This parameter is used to determine whether it is in a period of sufficient natural light, thereby assisting in determining whether the COB mosquito repellent light source needs to be activated to avoid unnecessary energy consumption.

[0061] The behavior state parameter is mainly used to evaluate whether there is human activity in the current space, as well as the frequency and intensity of human activity, so as to judge whether there is a need for mosquito repellent in the space.

[0062] Human activity state (M): Detect whether there is a human motion signal in the space through an infrared sensor (PIR) or a millimeter wave radar module. If a human activity signal is detected, it is considered that the space is in the "someone is active" state, and has the demand for mosquito repellent.

[0063] Activity frequency (F): The system counts the number of human activity triggers per unit time, and calculates the activity frequency by setting a statistical time window (such as every 30 minutes or every hour). The higher the activity frequency, the greater the intensity of space use, and the higher the requirement for mosquito repellent effect.

[0064] This type of parameter is continuously tracked by introducing a time sliding window algorithm, generating stable behavior judgment indicators in multiple cycles, further enhancing the timeliness and accuracy of strategy development.

[0065] Structural boundary parameters are used to identify the key node state of the building structure that affects the entry and exit of mosquitoes, mainly including the opening and closing of physical channels such as doors and windows.

[0066] Window opening and closing state (W): Determine whether it is currently open or closed through a magnetic induction switch installed on the window. In the open state, outdoor mosquitoes are more likely to enter, and the mosquito repellent priority should be correspondingly improved.

[0067] Door lock switch state (D): The system obtains the opening and closing state of the current door lock through a smart door lock or door magnetic module, especially for doors leading to balconies, courtyards and other areas. This parameter, together with the window state, forms the "structural leakage risk" index, which is used to evaluate the exposure of the space.

[0068] In the present application, the structural boundary parameters and the above-mentioned behavior parameters form a joint model, which can identify "no one + doors and windows always open" or "someone + closed environment" and other typical scenarios, thereby finely adjusting the mosquito repellent strategy.

[0069] Timing parameters refer to time dimension reference information, including current time and mapping relationship with natural sunlight rules, used to build a rhythm control model.

[0070] Current time (t): The system extracts the current system time from the control host, accurate to the minute level, as the basis for all time-related judgments.

[0071] Sunlight rhythm mapping value (S): The system has a built-in annual sunlight duration database based on geographic location settings, which automatically maps the corresponding circadian rhythm parameters based on the current date, including sunrise, sunset time, and total daylight duration. By comparing the current time with this database, the system can determine whether it is currently in the "low sunlight period" (i.e. night or early morning), when the demand for mosquito repellent is usually higher.

[0072] To enhance adaptability, the system introduces a rhythm index Rc, which represents the relative position of the current time in the 24-hour day-light cycle. The value of the rhythm index ranges from 0 to 1, and the closer the value is to 1, the closer it is to midnight, and the demand for mosquito repellent intensity should be correspondingly increased.

[0073] For example, if the current time is 22:00 at night and the sunset time of the day is 18:30, the rhythm index can be represented as: (current time-sunset time) / (sunset time-sunset time). This index will be an important reference parameter for the spectral control curve, achieving circadian rhythm matching.

[0074] In the present application, the intelligent control core of the mosquito repellent lamp is to dynamically determine the mosquito repellent risk level according to the indoor multi-dimensional environmental parameters, and to intelligently adjust the COB spectral output accordingly.

[0075] Among them, a fuzzy control matrix model is constructed based on the indoor multi-dimensional environmental parameter set. This step is used to normalize the parameters input from different sources, and a "mosquito repellent level index value" is deduced by means of fuzzy logic reasoning, which can be used as a key input variable for subsequent COB mosquito repellent spectral control and Z-Wave linkage control. The process includes:

[0076] Membership functions are established for each sub-parameter in the indoor multi-dimensional environmental parameter set to represent the contribution of each parameter to the mosquito repellent risk. Specifically, it includes:

[0077] For temperature, humidity, and light intensity, triangular membership functions are used to model and divide them into "low", "medium", and "high" levels.

[0078] For human activity frequency (such as the number of activity triggers per unit time), a trapezoidal membership function is used to model and divide it into "still", "light activity", and "frequent activity".

[0079] For the window opening and closing state and the door lock state, which are two types of structural boundary parameters, they are assigned a membership degree of 0 or 1 according to their Boolean variables (open or closed).

[0080] The rhythm factor (i.e. rhythm index, representing the position of the current time in the night cycle) is processed using a Gaussian membership function to reflect the curve influence of the rhythm factor on mosquito repellent demand.

[0081] The setting of this membership function can be optimized through experimental data and empirical rules to ensure the consistency and rationality of the response of each input variable in the fuzzy system.

[0082] The membership degree values of all sub-parameters are input into the fuzzy rule inference system, and a "multiple input and one output" fuzzy control logic is used to establish a control rule base. The rule base can be constructed using an empirical rule design method, and an example of the rule is as follows:

[0083] If the temperature is high, the humidity is high, the human activity is frequent, the window is open, and the rhythm index is high, the mosquito repellent level is high.

[0084] If the temperature is medium, the humidity is medium, the human activity is slight, the window is closed, and the rhythm index is medium, the mosquito repellent level is medium.

[0085] If the temperature is low, the humidity is low, there is no human activity, the door and window are closed, and the rhythm index is low, the mosquito repellent level is low.

[0086] The system internally uses a fuzzy reasoning engine (such as Mamdani reasoning mechanism) to execute the above rules and aggregate the membership values of each input dimension, outputting a continuous variable representing the mosquito repellent level index value of the current comprehensive mosquito repellent demand.

[0087] The fuzzy reasoning result is a floating-point value between 0 and 1, representing the mosquito repellent demand intensity. This value is not only used to control the output intensity of the COB lamp, but also serves as the basis input for the subsequent risk classification module, improving the consistency and response accuracy of the overall system.

[0088] To enable the system to quickly and accurately identify the urgency of mosquito repellent demand in different scenarios, the present application further constructs a mosquito repellent risk level evaluation model based on the above mosquito repellent level index value, classifying the mosquito repellent demand into three grades: low risk, medium risk, and high risk.

[0089] This process includes the following three steps:

[0090] The system extracts the current environmental data and organizes it into five core factors:

[0091] Temperature factor (T1); humidity factor (T2); human activity factor (T3); structure boundary factor (T4, including window and door lock status); rhythm factor (T5);

[0092] An initial weight value W1 to W5 is preset for each factor, representing its influence weight on mosquito activity and mosquito repellent demand. The weight setting can be dynamically adjusted according to different seasons or usage habits, for example, in the high temperature stage of summer, the weights of T1 and T2 will be significantly increased.

[0093] The current membership value of each factor is weighted and summed with the corresponding weight value, and then normalized to obtain the comprehensive mosquito repellent risk index R, R has a value range of 0 to 1. The calculation logic expressed in words is:

[0094] The comprehensive risk index R is equal to the membership of the temperature factor multiplied by W1, plus the membership of the humidity factor multiplied by W2, plus the membership of the human activity factor multiplied by W3, plus the membership of the structure boundary factor multiplied by W4, plus the membership of the rhythm factor multiplied by W5, and finally normalizing the sum. That is, R=(T1xW1+T2xW2+T3xW3+T4xW4+T5xW5)÷(W1+W2+W3+W4+W5);

[0095] The system divides the mosquito repellent risk level according to the R value, and the specific method is as follows:

[0096] When R is less than or equal to 0.35, it indicates that the current mosquito repellent risk level is “low risk”, at this time the COB lamp can be maintained closed or in the lowest power state;

[0097] When R is greater than 0.35 and less than or equal to 0.7, it is determined to be “medium risk”, and the COB lamp can be turned on and run at medium spectrum intensity;

[0098] When R is greater than 0.7, it is determined to be “high risk”, and the system enters the active mosquito repellent mode, and the linkage strategy with other Z-Wave devices is started, such as closing the curtains, closing the doors and windows, starting the air conditioner external circulation, etc.

[0099] In the present application, in order to realize the intelligentization, multi-dimensional perception and collaborative response control of the mosquito repellent system, a technical scheme is proposed, which takes the mosquito repellent risk level as the decision basis, and drives the Z-Wave communication control engine to generate a multi-node control instruction set.

[0100] The mosquito repellent risk level is a comprehensive evaluation index obtained by fusing the fuzzy control matrix and the multi-dimensional environmental parameters in the early stage, and its level is divided into three levels of low risk, medium risk and high risk, corresponding to different control strategy levels. In this step, the system transmits the level result into the Z-Wave communication control engine, and performs initial control parameter matching according to the built-in level-control strategy mapping table.

[0101] Specifically, the Z-Wave communication control engine is internally provided with a level-strategy mapping table Smap, which presets the initial strategy template corresponding to different risk levels. The typical corresponding relationship is as follows:

[0102] When the mosquito repellent risk level is low risk, the corresponding strategy in the level-strategy mapping table Smap is to close the COB mosquito repellent lamp, or maintain the lowest spectrum intensity output, and only perform periodic monitoring;

[0103] When the risk level is medium risk, the system will call the medium-level strategy template, activate the COB lamp and set it to medium spectrum intensity (such as wavelength control at 589±2nm), and the pulse period is extended to every 5 minutes;

[0104] When the risk level is high, the high-level strategy template is called, the maximum spectral intensity output (the wave band is near 595nm) is started, the pulse frequency is increased (such as once every 2 minutes) and the cross-node linkage control is activated.

[0105] The above strategy template is only an initial control model, and subsequent dynamic fine-tuning will still be combined with device state and real-time feedback.

[0106] After the control strategy is initially determined, the system will call the device state synchronization module built in the Z-Wave network to obtain real-time state information of all device nodes that have linkage control relationship with the COB mosquito-repellent lamp. The linkage devices usually include:

[0107] Intelligent air conditioner controller (for adjusting indoor air flow and temperature);

[0108] Electric window curtain system (for controlling natural light and window shading);

[0109] Door and window magnetic sensor (for detecting the opening state of the physical boundary);

[0110] Fresh air system or air purification equipment (for assisting environmental improvement);

[0111] Feedback control engine in the master control node, etc.

[0112] Each device node has a unique address and device function identifier in the Z-Wave network, and its state information includes whether it is online, the current function running state, whether it supports remote control, the current response delay, etc. The system will input the above state information into the node priority and conflict rule table P_map for screening processing.

[0113] The rule table sets the following screening logic:

[0114] If the device is offline, it is excluded from the current control instruction;

[0115] If the device has a high priority (such as an air conditioner), it will try to remain even if some of the states are abnormal;

[0116] If there is a control conflict between two devices (such as air conditioner cooling and window ventilation), the optimal node path is selected according to the risk level;

[0117] If the device has continuously refused control instructions for more than three times in the recent period, the node will be temporarily frozen to participate in linkage.

[0118] The finally constructed device subset is called the effective linkage node set Neffective, which only includes nodes that are currently available, have normal responses, and have no control conflicts. This node set will be the target control object for the next control instruction generation.

[0119] The system generates a set of refined control instruction set Pcmd according to the risk level, the initial strategy template and the Neffective node set, which contains three types of core control contents:

[0120] Light control instructions: start or turn off the COB mosquito repellent lamp; adjust the spectral excitation intensity output by the COB chip (such as from the standard mode to the high mosquito repellent mode); control the output mode of the mosquito repellent lamp (continuous lighting or periodic pulse) and set the pulse period (unit: minutes); adjust the spectral working time window (for example, set the working time from 20:00 to 6:00 the next day);

[0121] Linkage device control instructions: send "turn off air supply" or "switch to internal circulation" instructions to the intelligent air conditioner; send "immediately close" or "keep light blocking mode" instructions to the curtain controller; send state locking requests or trigger mosquito prevention prompt sounds to the door and window sensor system; if the risk level is high, the Z-Wave group broadcast instruction may be started to enforce the linkage strategy;

[0122] Strategy execution monitoring and feedback mechanism instructions: set the response timeout threshold for each node (for example, if there is no response within 10 seconds, it is marked as abnormal); enable the failure retry strategy: each instruction can be sent up to 3 times; enable the running state feedback mechanism to prompt the nodes to report the execution situation periodically for the use of the master node scheduling.

[0123] The control instruction set Pcmd is encoded by the Z-Wave communication control engine into a frame structure conforming to the Z-Wave protocol and is sent to each device node through multicast (or specified unicast). The mosquito repellent lamp and other devices enter the corresponding state immediately after receiving the instructions and execute the control tasks.

[0124] In the present application, in order to achieve the goals of intelligent, dynamic and high-robustness system operation, while the COB mosquito repellent lamp executes the control instructions, a set of running state feedback mechanism and master node rolling scheduling update mechanism are established, so as to realize the closed-loop optimization and long-term adaptive adjustment of the mosquito repellent strategy, and avoid the invalidation of the control strategy due to environmental changes.

[0125] When the system completes multi-dimensional environmental parameter collection, fuzzy control reasoning and mosquito repellent risk level evaluation, the generated control instruction set will be sent to the COB mosquito repellent lamp device through the Z-Wave communication protocol. The control instruction set includes but is not limited to the following contents:

[0126] Lamp on / off state instructions;

[0127] COB spectral excitation wavelength segment (such as 585-595 nm) and light intensity level;

[0128] Light output mode (such as continuous light, periodic pulse output);

[0129] Working cycle length and starting time point;

[0130] Linkage logic trigger identification with other Z-Wave nodes.

[0131] After receiving the control instruction set, the COB mosquito repellent lamp responds in real time according to the instruction content, and records the working state information of itself in the running process, including the current output light intensity, the current working mode, the cumulative running time, the temperature rise change and the like. At the same time, the environmental perception module built-in the mosquito repellent lamp will also detect the surrounding temperature, humidity and light change regularly to assist subsequent strategy revision.

[0132] The system sets the feedback period parameter Tf, which represents that the mosquito repellent lamp needs to send a comprehensive feedback data packet to the master node every Tf minutes. The data packet at least includes:

[0133] The current running state of the mosquito repellent lamp (on / off, brightness level, mosquito repellent wave band);

[0134] The environmental physical parameter snapshot (temperature, humidity, light);

[0135] The current period instruction execution (whether to successfully respond, whether to interrupt);

[0136] The linkage result of the related Z-Wave node (such as whether the curtain has been closed, whether the air conditioner has executed the command or not).

[0137] The feedback data is uploaded to the master node through the Z-Wave low-power channel, and serves as an important reference basis for subsequent strategy scheduling decisions.

[0138] In order to avoid the problems such as "strategy obsolescence", "response lag" or "high energy consumption" of the control strategy in complex environment, the rolling scheduling algorithm (Rolling Scheduling Algorithm) is adopted in the application, and the master node periodically dynamically evaluates and adaptively updates the control strategy.

[0139] The scheduling mechanism includes:

[0140] The master node internally maintains a strategy history data window Hwin, which stores the key running data of the last n control periods (for example, n=10). The window is updated in a "first-in, first-out" manner, and after completing a feedback period, the window is slid forward by one unit, automatically removes the earliest historical data and adds the data of the latest period.

[0141] The contents recorded in the window include:

[0142] The mosquito repellent risk level (low, medium, high) in each period;

[0143] COB mosquito repellent lamp execution status (instruction execution success rate, opening time, energy consumption data);

[0144] Response of linked devices (response delay, refusal to execute, conflict detection, etc.);

[0145] Current environmental parameter snapshot (temperature, humidity, light) and its trend.

[0146] This data structure provides a continuous analysis basis for control strategy optimization, which helps to identify the "macro trend" of the system running in multiple periods.

[0147] After obtaining the data in the historical window, the system analyzes the data through the built-in trend recognition algorithm, which specifically includes:

[0148] Local mean smoothing: sliding average of risk level curve to eliminate random noise in periodic fluctuations;

[0149] Weighted difference algorithm: Calculate the weighted difference value between the current period and the previous period, and the weight can be set as a time decay function, so that recent data has a greater impact;

[0150] Environmental anomaly pattern recognition: If consecutive multiple periods of risk level continue to rise, or temperature and humidity change suddenly and dramatically (exceeding the preset threshold Te), it will be determined as environmental disturbance anomaly;

[0151] Device response consistency analysis: If some Z-Wave devices refuse to execute the linkage instruction in multiple periods, or the response time is significantly delayed, the system will identify it as a control link exception.

[0152] When any of the above conditions are met, the master node enters the strategy adjustment preparation state and waits for the next strategy load.

[0153] After identifying the trend pattern or abnormal signal, the master node will select a control strategy that matches the current situation from the preset strategy library, or generate a new set of strategy parameters according to the strategy rule engine. Possible strategy adjustment schemes include but are not limited to:

[0154] Adjust the COB mosquito light spectrum output intensity: such as increasing the light intensity or extending the pulse light period;

[0155] Modify the spectrum frequency rhythm: introduce a micro-rhythm disturbance mechanism to match the rhythm index change;

[0156] Dynamically adjust the risk factor weight: For example, increase the humidity factor weight under continuous high humidity and high temperature conditions to improve the response priority;

[0157] Temporarily freeze part of the linked devices: such as detecting that the curtain refuses to execute multiple times, temporarily closing the linkage channel to avoid resource waste and system congestion.

[0158] The updated strategy will replace the current active strategy template in real time and be stored in the strategy version record table, realizing the evolutionary management of control logic. Subsequently, the master node broadcasts the new strategy summary information to the associated nodes through Z-Wave, ensuring that all devices operate based on the latest strategy.

[0159] In the present application, in order to achieve the goal of multi-device collaboration and active prevention of mosquito aggregation, the system designs a cross-node Z-Wave linkage strategy mechanism based on mosquito repellent risk level trend identification and night rhythm perception, which is used to intelligently coordinate devices such as air conditioners, curtains, and door and window sensors, and quickly respond and enter the "anti-mosquito mode" in specific high-risk situations.

[0160] The master node monitors the mosquito repellent risk level using a rolling window mechanism, continuously recording in units of periods. Assuming that the control period unit is T (for example, every 10 minutes), the master node updates the current risk level evaluation value at the end of each period and retains the results of the previous period.

[0161] Let the current period number be n and the previous period be n-1. The system will determine whether the following two conditions are met simultaneously:

[0162] The mosquito repellent risk levels of period n and period n-1 are both high risk;

[0163] The current rhythm index Rc is greater than the system set threshold Rt.

[0164] Among them, the rhythm index Rc is the system's quantitative expression of the position of the current time in the night cycle, ranging from 0 to 1. The closer to 1, the closer the current time to midnight, the higher the mosquito activity, and the stronger the human rest demand.

[0165] The calculation of the rhythm index can be based on the sunrise time Tsunrise and the sunset time Tsunset, and standardized by the current time Tcurrent, expressed as follows:

[0166] Rhythm index Rc = (Tcurrent - Tsunset) ÷ (Tsunrise_next - Tsunset);

[0167] For example, when the sunset time is 18:30 and the next day's sunrise time is 05:30, if the current time is 23:30, the rhythm index Rc is about 0.5, and if it is 03:00, Rc can reach 0.9.

[0168] The threshold Rt is the preset rhythm sensitivity threshold, for example, set to 0.85, indicating that the system considers the deep night stage to be the most sensitive to mosquito repellent control.

[0169] When both cycle risk levels are high risk, and Rc > Rt, the system enters the cross-node mosquito prevention linkage preparation state.

[0170] In the linkage preparation state, the master node will call the Z-Wave device linkage interface module to obtain the status of external devices that have a linkage relationship with the COB mosquito-repellent lamp one by one. Key devices include but are not limited to:

[0171] Air conditioner controller: Detect its current operating mode (blowing, cooling, internal circulation, or off);

[0172] Electric window curtain system: Determine whether it is in the open, half-open, or fully closed state;

[0173] Electric door and window sensor or magnetic control switch: Determine whether the door and window are currently open and the security lock status.

[0174] Device status information is returned to the master node through the status synchronization mechanism of the Z-Wave protocol and is locally parsed and standardized. The status value of each device is abstracted as "mosquito susceptibility level" in the system, i.e., whether its current state can constitute a channel for mosquitoes to enter.

[0175] The system has the following judgment logic built-in:

[0176] If the air conditioner is currently in external blowing or fresh air mode, it is considered high susceptibility;

[0177] If the curtain is not fully closed or in a state other than light blocking mode, it is considered medium susceptibility;

[0178] If any door or window is open or the security lock is not locked, it is considered high susceptibility;

[0179] If multiple devices have susceptibility at the same time, the system determines that there is a risk of mosquito aggregation.

[0180] When any device is in the above susceptible state, the system will enter the linkage execution state and start the cross-node mosquito prevention control strategy.

[0181] After meeting the trigger conditions, the master node generates a multi-device linkage instruction set according to the pre-set mosquito prevention mode control strategy template and distributes the instruction set to related devices through the Z-Wave communication protocol.

[0182] The control strategy includes the following typical operation contents:

[0183] Air conditioner control strategy: If the air conditioner is in external circulation blowing, automatically switch to internal circulation; If the air conditioner is in the open state and the blowing direction is towards the window area, you can adjust the low wind speed or pause the blowing; If the user allows, you can directly turn off the air conditioner to block the air flow channel;

[0184] Curtain control strategy: automatically trigger the full-closed command of the curtain; if the curtain supports shading mode, activate the "night shading" profile; additional visual reminders such as LED status indicators can be added.

[0185] Door and window control strategy: send a closing request to the door and window sensor; if the door and window are electric, issue an automatic closing execution instruction; if they are magnetic or mechanical sensors, send a state reminder or prompt sound feedback.

[0186] State monitoring and feedback mechanism: all linked nodes must return a "command received" or "execution successful" state within a set time window (e.g., 10 seconds); if the device fails to respond, the master node will trigger a retry mechanism (up to 3 times) and record the exception; after the execution of the linkage strategy is completed, the master strategy execution record is updated, forming a complete response loop.

[0187] All control instructions are propagated in the network in multicast mode in the format of Z-Wave network protocol frames, ensuring that all target devices can be controlled simultaneously, with low response delay and high control consistency.

[0188] Example 2, please refer to Figure 2 The Z-Wave-based COB mosquito-repelling lamp intelligent control system described in this embodiment includes:

[0189] Parameter acquisition module: acquire a set of indoor multi-dimensional environmental parameters, including environmental physical parameters, behavior state parameters, structural boundary parameters, and timing parameters;

[0190] Risk level division module: construct a fuzzy control matrix model based on the set of indoor multi-dimensional environmental parameters, used for grading judgment of mosquito-repelling risk levels, including three levels of low risk, medium risk, and high risk;

[0191] Control instruction generation module: input the risk level result into the Z-Wave communication control engine, and generate a set of control instructions in combination with the current state of the device nodes associated with the COB mosquito-repelling lamp in the Z-Wave network;

[0192] Response feedback module: the mosquito-repelling lamp dynamically responds according to the set of control instructions and periodically feeds back the running state and environmental changes to the master node, which updates the control strategy through a rolling scheduling algorithm.

[0193] Device linkage module: when the risk level is in a high-risk state for two consecutive periods and the night rhythm index is greater than a set threshold, start the cross-node Z-Wave linkage strategy to control the air conditioner, curtain, and door and window sensor to enter the anti-mosquito mode to suppress mosquito gathering sources.

[0194] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application.

Claims

1. A COB mosquito-repellent lamp intelligent control method based on Z-Wave, characterized in that: The application comprises the following steps: acquiring a set of indoor multi-dimensional environment parameters, including environment physical parameters, behavior state parameters, structure boundary parameters, and timing parameters; constructing a fuzzy control matrix model based on the set of indoor multi-dimensional environment parameters, for hierarchical judgment of mosquito repellent risk levels, including three levels of low risk, medium risk, and high risk; inputting the risk level results into a Z-Wave communication control engine, combining the device node states associated with the COB mosquito repellent lamp in the current Z-Wave network, to generate a control instruction set; the inputting of the risk level results into the Z-Wave communication control engine comprises: inputting the mosquito repellent risk level results into the Z-Wave communication control engine, and determining the corresponding initial control strategy through a level mapping table according to the interval in which the current mosquito repellent risk level is located among the low risk, medium risk, and high risk; real-time acquisition of the node device state information currently associated with the COB mosquito repellent lamp, and filtering and screening of the results according to the node priority and control conflict table to construct an effective linkage node set; based on the effective linkage node set, the control instruction set is generated according to the linkage logic rules between the mosquito repellent risk level and the node state; the control instruction set includes: light start / stop, spectral excitation intensity adjustment, or working cycle and pulse rhythm control; the mosquito repellent lamp dynamically responds to the control instruction set and periodically feeds back the running state and environmental changes to the master node, which updates the control strategy adaptively through a rolling scheduling algorithm; when the risk level is in the high risk state for two consecutive periods and the night rhythm index is greater than a set threshold, a cross-node Z-Wave linkage strategy is started to control the air conditioner, curtain, and door and window sensor to enter the anti-mosquito mode to suppress mosquito gathering sources; the calculation method of the night rhythm index is: night rhythm index = (current time - sunset time) / (next sunrise time - sunset time).

2. The Z-Wave-based COB mosquito-repelling lamp intelligent control method according to claim 1, characterized in that: The environment physical parameters include room temperature, humidity, and light intensity; the behavior state parameters include human activity state and activity frequency; the structure boundary parameters include window opening and closing state and door lock opening and closing state; the timing parameters include current time and solar rhythm mapping value, i.e., a full-year solar duration database set based on geographical location, which automatically maps the corresponding diurnal rhythm parameters including sunrise, sunset time, and full-day lighting duration according to the current date.

3. The Z-Wave based COB mosquito repellent lamp intelligent control method according to claim 1, characterized in that: The fuzzy control matrix model is constructed based on the set of indoor multi-dimensional environment parameters, including: establishing a membership function for each sub-parameter in the acquired set of indoor multi-dimensional environment parameters; inputting the membership values of all sub-parameters into a fuzzy rule reasoning system, using a fuzzy control algorithm to establish a fuzzy control rule library; generating a mosquito repellent level index value according to the fuzzy reasoning results.

4. The Z-Wave based COB mosquito repellent lamp intelligent control method according to claim 3, characterized in that: The hierarchical judgment of mosquito repellent risk levels comprises: establishing a mosquito repellent risk influence factor set according to the set of indoor multi-dimensional environment parameters, including temperature factor T1, humidity factor T2, human activity factor T3, structure boundary factor T4, and night rhythm index T5; setting initial weight values W1 to W5 for each factor to form a mosquito repellent risk calculation weight model; The real-time membership value corresponding to each factor is weighted with its weight to generate a comprehensive mosquito repellent risk index R; According to the size of R value, the risk level is divided, wherein: When R is less than or equal to 0.35, the mosquito repellent risk level is low risk; When R is greater than 0.35 and less than or equal to 0.7, the mosquito repellent risk level is medium risk; When R is greater than 0.7, the mosquito repellent risk level is high risk.

5. The Z-Wave based COB mosquito repellent lamp intelligent control method according to claim 1, characterized in that: The adaptive updating of the control strategy by the master node through the rolling scheduling algorithm includes: A control strategy dynamic history window maintained by the master node is constructed to store the mosquito repellent risk level results, COB mosquito repellent lamp execution state, linkage device response, and environmental parameter change curve in the last n cycles; Trend analysis and discrete change detection are performed on the multi-cycle data in the history window, and local mean smoothing and weighted difference algorithm is used to identify abnormal patterns, including risk level continuous rise or fall, linkage device response consistency decline, or environmental parameter fluctuation amplitude exceeding the preset standard threshold; According to the identification result, a strategy adjustment scheme matching the current trend is selected and loaded, including: increasing or decreasing the COB light spectrum intensity and periodic pulse frequency, or dynamically adjusting the weight parameters of each risk factor.

6. The Z-Wave-based COB mosquito-repelling lamp intelligent control method according to claim 5, characterized in that: When the risk level is in the high risk state for two consecutive cycles and the night rhythm index is greater than the set threshold, it includes: The master node continuously monitors the mosquito repellent risk level of two control cycles, and compares the night rhythm index Rc with the set threshold Rt. When the mosquito repellent risk level of the first cycle and the second cycle is high risk and the current night rhythm index Rc is greater than the preset threshold Rt, the cross-node mosquito prevention linkage preparation state is entered; A preset mosquito prevention mode control strategy template is read, and a Z-Wave device linkage interface is called to retrieve the real-time running state of the current air conditioner, curtain, and electric door and window sensor linkage device nodes; If any linkage device is in the mosquito easy-to-enter state, the corresponding control instruction enters the mosquito prevention linkage mode.

7. A Z-Wave based COB mosquito repellent lamp intelligent control system for implementing the Z-Wave based COB mosquito repellent lamp intelligent control method according to any one of claims 1-6, characterized in that: It includes: Parameter acquisition module: acquire indoor multi-dimensional environmental parameter set, including environmental physical parameters, behavior state parameters, structure boundary parameters, and timing parameters; Risk level division module: construct a fuzzy control matrix model based on the indoor multi-dimensional environmental parameter set for grading judgment of mosquito repellent risk level, including low risk, medium risk, and high risk; Control instruction generation module: input the risk level result into the Z-Wave communication control engine, combine the device node state associated with the COB mosquito repellent lamp in the current Z-Wave network, and generate a control instruction set; Response feedback module: the mosquito repellent lamp dynamically responds according to the control instruction set and periodically feeds back the running state and environmental changes to the master node, which adaptively updates the control strategy through the rolling scheduling algorithm; Device linkage module: when the risk level is in the high risk state for two consecutive cycles and the night rhythm index is greater than the set threshold, the cross-node Z-Wave linkage strategy is started to control the air conditioner, curtain, and door and window sensor to enter the mosquito prevention mode to suppress mosquito aggregation sources.

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