Street lamp intelligent control system based on Z-Wave

Abstract

The invention discloses a street lamp intelligent control system based on Z-Wave, and relates to the technical field of street lamp intelligent control. The system comprises an intelligent acquisition module, an intelligent analysis module and an execution module. An existing street lamp intelligent control system based on Z-Wave still has some defects such as insufficient environmental adaptability, inaccurate sensor data processing and relatively low energy utilization efficiency, an intelligent analysis module is used for trend prediction and complexity calculation, accurate adaptation and dynamic adjustment of ambient light are realized, and the energy utilization efficiency is improved. The energy-saving performance and the response capability of the system are improved; multi-level data analysis and KDZ calculation are carried out to effectively distinguish specific scenes such as footstep sound and speech sound, so that the reliability and response sensitivity of data processing are greatly improved; through the technology of adjusting the angles of the multiple illumination sensors and the power generation panel by the execution module, the solar energy utilization rate is remarkably improved, and green and low-carbon development is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of street lamp intelligent control, and in particular to a street lamp intelligent control system based on Z-Wave. Background Art

[0002] Z-Wave is a wireless communication technology widely used in the field of Internet of Things. Due to its low power consumption, high reliability and strong networking capabilities, it is widely used in smart homes, industrial control and smart cities. In smart street light control, the high compatibility and stability of the Z-Wave protocol make it an important technical means to achieve remote control, smart dimming and energy consumption optimization;

[0003] However, the existing Z-Wave-based street light intelligent control system still has some shortcomings. For example: Insufficient environmental adaptability: Traditional systems have poor adaptability to changes in ambient light and periodic characteristics, which can easily cause insufficient light or excessive brightness waste; Inaccurate sensor data processing: The existing system has low recognition accuracy for human activities and sound characteristics, and is easily affected by background noise and interference; Low energy efficiency: The existing system does not make full use of renewable energy, especially in solar energy collection. Summary of the invention

[0004] In order to solve the technical problems raised by the above background technology, the present invention is proposed. An embodiment of the present invention provides a street lamp intelligent control system based on Z-Wave.

[0005] The object of the present invention can be achieved by the following technical solutions: a street lamp intelligent control system based on Z-Wave, including an intelligent analysis module and an execution module;

[0006] The intelligent analysis module is used to analyze the light sensing data and motion data, specifically:

[0007] Analyze the light perception data: obtain the light intensity in the measurement environment, display the light intensity on the time axis, obtain the historical trend of the light intensity, and analyze it: smooth the historical trend of the light intensity to obtain the smoothing factor, light intensity and the generated light-on signal and assign a fixed value L1; analyze whether the light intensity increases or decreases over time to obtain the change pattern of the light intensity over time; then convert the change of light intensity over time into a frequency domain signal to calculate the representation value of the light intensity signal in the frequency domain. If the trend prediction shows that the light intensity is about to decrease, generate a fixed light-on value K1; otherwise, generate a fixed light-off value G1; then perform a comprehensive calculation of the fixed value L1 assigned to the light-on signal, the fixed light-on value K1, and the fixed light-off value G1 to obtain the light-on standby coefficient KDJ;

[0008] Analyze the motion data: When the detection generates the light-on standby execution instruction, the detection and recognition area is monitored by the human body sensor and the sound sensor to obtain the final recognition coefficient RTZ; the sound sensor collects data to identify the walking sound and speaking sound of the person to obtain the walking sound assignment ZL and the speaking assignment SH; the human body final recognition coefficient RTZ is calculated with the walking sound assignment ZL and the speaking assignment SH to obtain the light-on value KDZ; if the light-on value KDZ is higher than the set light-on threshold, a light-on instruction is generated;

[0009] The execution module is used to track users in the detection and identification area and the smart street light power generation panel to track the strongest light source. Specifically, to track users in the detection and identification area: the human body sensor and sound sensor detect the user's current position and obtain the direction of movement; the sound direction and intensity are obtained through sound source positioning technology; the data obtained by the human body sensor and the sound sensor are integrated to generate the user's accurate position information; to track strong light sources: multiple light sensors measure the light intensity in all directions in real time, and the position sensor records the direction angle of the current smart street light power generation panel; and then compare the light intensity values ​​detected by each light sensor to obtain the direction of the strongest light source.

[0010] As a preferred embodiment of the present invention, it also includes an intelligent acquisition module;

[0011] The intelligent acquisition module collects data through Z-Wave. The specific process is as follows:

[0012] The smart street light body is running, and the smart street light is turned on manually by the user; when the smart street light is turned on, the light sensor and motion sensor installed in the smart acquisition module are started and self-checked to confirm that the sensor and Z-Wave communication module are operating normally, and the acquisition module is connected to the Z-Wave communication module to establish a home LAN; the gateway assigns an address to the smart acquisition module and establishes communication; the smart acquisition module collects light data through the light sensor and collects motion data in the courtyard through the motion sensor.

[0013] As a preferred embodiment of the present invention, the specific process of smoothing the historical trend of light intensity to obtain the smoothing factor, the light intensity and the generated light-on signal and assigning a fixed value L1 is as follows:

[0014] By formula: Output the smoothed light intensity value at time t, where I(t) is the original light intensity data at a certain time t, and N is the window size; output the smoothing factor α according to the formula: Is(t)=αI(t)+(1-α)Is(t-1), and the smoothing factor α is between 0 and 1; if the light intensity I(t) is less than the threshold set by the user, generate an initial light-on signal 1, if the smoothing factor α is between 0 and 1, generate an initial light-on signal 2, when both the initial light-on signal 1 and the initial light-on signal 2 are generated at the same time, generate a light-on signal 1; assign a fixed value to the generated light-on signal, marked as L1.

[0015] As a preferred embodiment of the present invention, the specific process of analyzing whether the light intensity increases or decreases over time to obtain the change pattern of the light intensity over time is:

[0016] If the light intensity changes linearly over time, the trend slope a of the light intensity is output through the formula: I(t) = a·t+b, and b is the intercept, which indicates the light intensity when t = 0; the slope a and intercept b are calculated as follows: Output the specific values ​​of a and b, N1 is the number of data points, ∑ is the sum of all data points; if a>0, the light intensity increases over time, indicating that the light source is gradually getting brighter; if a<0, the light intensity decreases over time, indicating sunset or the light source is gradually weakening; if a=0, the light intensity remains relatively stable; if the historical trend calculated is more complicated, the formula is: I(t)=a0+a1t+a2t 2 +…+a n t n The change pattern of light intensity over time is obtained; a0 is a constant term, which represents the basic level of light intensity, the light intensity when time t = 0; a1 is the coefficient of the first-order term at time t; a2, a3, ..., a n They are all coefficients of the quadratic, cubic, ..., nth-order terms of time.

[0017] As a preferred embodiment of the present invention, the specific process of converting the change of light intensity over time into a frequency domain signal to calculate and obtain the representation value of the light intensity signal in the frequency domain is:

[0018] The periodic component of light intensity is obtained through Fourier transform, which is calculated by the formula: The output light intensity signal is represented by the frequency domain value X(k), where I(n) is the value of the light intensity at the jth time point, N3 is the corresponding total number of data points, and k is the frequency index.

[0019] As a preferred implementation of the present invention, the specific process of monitoring the detection and recognition area to obtain the final recognition coefficient RTZ is:

[0020] The human body sensor obtains its own background noise B, obtains the sensor running in an inactive environment for a fixed period of time, calculates the mean and standard deviation of multiple groups of collected signals, and uses the formula: Get the mean value of the collected signal, M is the number of sampling points, IR(n w ) is the infrared signal value collected each time, and w = 1, 2, ..., M; then perform denoising, and obtain the human body recognition coefficient RT(n) by the formula: RT(n) = IR(n)-BZ, IR(n) is the original infrared signal detected by the sensor at the current moment; by setting a fixed time interval to collect signals in an inactive environment, and update the background noise B, the updated background noise value B1 is obtained by the formula: B2 = β·B0+(1-β)·B1, B0 is the background noise value recorded before, B1 is the average background noise collected at the current moment, β is the preset weight factor, which determines the update speed; further occasional interference analysis of the human body recognition coefficient RT(n): by using a sliding average filter: Output the smoothed signal value - RTo(n), P is the sliding window size, u = 1, 2, ..., P; then process the scene sensitive to high-frequency noise, using Gaussian filtering: The output smoothed signal value is RTg(n), where G(u,σ) is the Gaussian weight.

[0021] As a preferred embodiment of the present invention, the execution module is also used to receive a light-on instruction to control the switch of the street lamp. When the light-on instruction is detected and generated, the street lamp is controlled to be turned on, and the on time is set by the user. If the light-on instruction is detected and generated again during the lighting period of the street lamp, the duration of the previous street lamp lighting period is eliminated and the on time is recorded again.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. The present invention uses a Z-Wave-based street light intelligent control system to accurately control the opening and closing of street lights by real-time collection and analysis of light and motion data, combined with trend prediction and complexity calculation. The system fully considers the changes in ambient light and periodic characteristics, realizes energy-saving, efficient, and responsive intelligent lighting, while improving courtyard safety and user experience, providing a reliable and flexible solution for smart lighting.

[0024] 2. The present invention realizes accurate recognition of human activities and sound characteristics through multi-level data analysis of human sensors and sound sensors. Dynamic updating of background noise, removal of interference and efficient extraction of feature parameters (such as RTZ, MFCCs) ensure the reliability and robustness of data processing; combined with the calculation of the light-on value (KDZ), it can intelligently distinguish footsteps from speaking sounds and respond quickly, providing adaptive lighting control for different activity scenes. This method improves the sensitivity and adaptability of the system, significantly enhances the user experience, while reducing energy waste and promoting the efficient and safe development of smart lighting.

[0025] 3. The present invention improves the response efficiency and energy utilization of smart street lamps by implementing precise user and strong light source tracking technology in the execution module. Real-time tracking of user position and movement direction enables dynamic rotation and brightness adjustment of lighting lamps to ensure that lighting always covers users; the angle of the power generation panel is adjusted by multiple light sensors and servo motors to maximize the utilization of solar energy. This intelligent control not only improves user experience and safety, but also significantly enhances energy collection efficiency, providing strong support for green energy saving and smart lighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present invention.

[0027] Figure 1 It is a principle block diagram of the present invention;

[0028] Figure 2 The mechanism of the present invention is shown in FIG. Figure 1 ;

[0029] Figure 3 The mechanism of the present invention is shown in FIG. Figure 2 . DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present invention.

[0031] As shown in the present invention and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list, and the method or device may also include other steps or elements.

[0032] Although the present invention has made various references to certain modules in the system according to an embodiment of the present invention, any number of different modules can be used and run on a user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0033] The present invention uses a flow chart to illustrate the operations performed by the system according to an embodiment of the present invention. It should be understood that the preceding or following operations are not necessarily performed precisely in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0034] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0035] See also Figure 1-3 As shown, a street lamp intelligent control system based on Z-Wave includes: an intelligent acquisition module, an intelligent analysis module and an execution module;

[0036] The intelligent acquisition module collects data through Z-Wave. The specific process is as follows:

[0037] The intelligent control system of street lights is operated through the intelligent street light body, and the intelligent street lights are turned on manually by the user. When the intelligent street lights are turned on, the light sensor and motion sensor in the intelligent acquisition module are started and self-checked to confirm that the light sensor, motion sensor and Z-Wave communication module are operating normally. Then the user is guided by the instructions to connect the acquisition module to the Z-Wave communication module to establish a home LAN. The gateway assigns an address to the intelligent acquisition module and establishes communication with it to ensure that the data transmission path is unobstructed.

[0038] The intelligent acquisition module collects light-sensing data through light sensors and collects motion data in the courtyard through motion sensors. Its light-sensing data includes the light intensity in the measured environment and the speed of change of light intensity over time.

[0039] The intelligent analysis module is used to analyze light sensing data and motion data, specifically:

[0040] Analyze the light perception data: obtain the light intensity in the measurement environment, display the obtained light intensity on the time axis to obtain the historical trend of the light intensity, and analyze it:

[0041] The historical trend of light intensity is smoothed by the formula: Output the smoothed light intensity value at time t, where I(t) is the original light intensity data at a certain time t, and N is the window size (the number of moments considered when calculating the average value). It should be noted that if N=5, it means calculating the average light value of the current moment and the four moments before it. The larger the N value is, the more obvious the smoothing effect is, but short-term changes will also be ignored. Then give a greater weight to the calculated smoothed light intensity value to capture recent trends, and output the smoothing factor α through the formula: Is(t)=αI(t)+(1-α)Is(t-1), and the smoothing factor α is between 0 and 1 (a larger smoothing factor α reflects recent changes in light intensity).

[0042] If the light intensity I(t) is less than the threshold set by the user, an initial light-on signal 1 is generated. If the smoothing factor α is between 0 and 1, an initial light-on signal 2 is generated. When both initial light-on signal 1 and initial light-on signal 2 are generated at the same time, a light-on signal 1 is generated. The generated light-on signal is assigned a fixed value, labeled L1, and 0 <L1;

[0043] Then conduct a trend analysis to see if the light intensity is increasing or decreasing over time:

[0044] If the light intensity changes linearly over time, the trend slope a of the light intensity (indicating the speed of change of light over time) is output through the formula: I(t) = a·t+b, where b is the intercept, indicating the light intensity when t = 0; then by calculating the specific values ​​of the slope a and the intercept b, the formula is: Output the specific values ​​of a and b, where N1 is the number of data points, and ∑ means adding all data points; if a>0, the light intensity increases over time, indicating that the light source is gradually getting brighter; if a<0, the light intensity decreases over time, indicating sunset or the light source is gradually weakening; if a=0, the light intensity remains relatively stable; if the calculated historical trend is more complicated (complexity, by calculating the fluctuation amplitude and frequency of the historical trend of light intensity, specifically: through the formula: Standard deviation of output light intensity historical trend σ GQ ,in, is the average value of the machine light intensity, N2 is the number of corresponding data points, the larger the standard deviation, the more drastic the change in light intensity and the higher the complexity; the standard deviation σ GQCompared with the preset complexity coefficient threshold, if the standard deviation σ GQ If it is greater than the preset complexity coefficient threshold, it is complex), then the formula is: I(t) = a0+a1t+a2t 2 +…+a n t n The change pattern of light intensity over time is obtained; where a0 is a constant term, which represents the basic level of light intensity, that is, the light intensity when time t=0, and describes an initial value in the light change; a1 is the coefficient of the first-order term at time t, which represents the linear change trend of light intensity over time. Its positive or negative value can tell us whether the light is gradually increasing (positive value) or decreasing (negative value); a2, a3, ..., a n They are the coefficients of higher-order terms such as quadratic and cubic terms of time; they describe nonlinear trends in light changes, if the light intensity changes with time in a curve, such as gradually getting brighter and then darker (such as the light pattern at sunrise and sunset);

[0045] Then, the change of light intensity over time is converted into a frequency domain signal, and the periodic component of light intensity is obtained through Fourier transform, such as the regular daily cycle phenomenon of sunrise and sunset; specifically, the formula is: Output the light intensity signal in the frequency domain as X(k), where I(n) is the value of the light intensity at the jth time point, N3 is the total number of corresponding data points, and k is the frequency index, representing different frequency components. It should be noted that: through Fourier transform, the main frequency components of the signal are identified to understand whether the light intensity has periodic changes (such as changes in brightness within a day); if the intensity corresponding to a certain frequency component is found to be particularly high, it means that the light intensity has significant changes in the corresponding period, such as the daily sunrise and sunset pattern;

[0046] If the detected trend forecast shows that the light intensity is about to decrease, a fixed light-on value is generated, marked as K1; conversely, if the detected trend forecast shows that the light intensity is about to increase, a fixed light-off value is generated, marked as G1; and K1 and G1 cannot be generated at the same time, 0 <K1<G1;

[0047] Finally, L1 is calculated comprehensively with K1 and G1, using the formula: Obtaining a light-on standby coefficient KDJ, wherein L', G', and K' are respective corresponding preset weights; comparing the light-on standby coefficient KDJ with its corresponding preset threshold, and if the light-on standby coefficient KDJ is greater than the corresponding preset threshold, generating a light-on standby execution instruction;

[0048] Analysis of sports data:

[0049] See also Figure 2As shown, the intelligent street lamp body is rotatably connected with the street lamp power generation rotating joint 1; the street lamp power generation rotating joint 1 is rotatably connected with the street lamp power generation rotating joint 2, the street lamp power generation rotating joint 3 and the street lamp lighting pillar; the street lamp lighting pillar is rotatably connected with the lighting lamp; the intelligent street lamp solar power generation panel 1 is fixedly connected with the street lamp power generation rotating joint 3; the intelligent street lamp solar power generation panel 2 is fixedly connected with the street lamp power generation rotating joint 2; the intelligent street lamp is also equipped with a human body sensor and a sound sensor;

[0050] When the detection generates the light-on standby execution instruction, the detection and recognition area is monitored by the human body sensor and sound sensor. Specifically:

[0051] The human body sensor obtains its own background noise B (background noise refers to the static signal generated by the sensor due to environmental thermal radiation or sensor hardware characteristics when there is no human activity). The sensor is run in an inactive environment for a fixed period of time (such as 10 seconds), and the mean and standard deviation of multiple groups of collected signals are calculated using the formula: Get the mean value of the collected signal, M is the number of sampling points, IR(n w ) is the infrared signal value collected each time, and w = 1, 2, ..., M; then perform denoising, and obtain the human body recognition coefficient - RT (n) through the formula: RT (n) = IR (n) - BZ, IR (n) is the original infrared signal detected by the sensor at the current moment,

[0052] In long-term operation, the background noise changes due to ambient temperature or equipment aging, and the background noise B needs to be dynamically updated. By setting a fixed time interval to collect signals in an inactive environment and update the background noise B,

[0053] The updated background noise value B1 is obtained by the formula: B2 = β·B0+(1-β)·B1, where B0 is the previously recorded background noise value, B1 is the average background noise value collected at the current moment, and β is a preset weight factor that determines the update speed (larger β: the update is smoother, suitable for a more stable background environment; smaller β: more sensitive to changes, suitable for a dynamically fluctuating environment);

[0054] Further analysis of the accidental interference (such as accidental electromagnetic interference or hardware jitter) of the human recognition coefficient RT (n): remove it by using a sliding average filter: Output the smoothed signal value - RTo(n), P is the sliding window size (usually 3 to 5, depending on the sensor response speed), u = 1, 2, ..., P; then process the scene sensitive to high-frequency noise by using Gaussian filtering: Output the smoothed signal value RTg(n), G(u,σ) is the Gaussian weight, the weight decreases as u increases, and k is the window range;

[0055] The human body recognition coefficient obtained by human body sensor recognition is subjected to denoising and occasional interference processing to finally obtain the final human body recognition coefficient RTZ.

[0056] The sound sensor collects data for detection and identifies the sound of people walking and talking:

[0057] Convert the collected ADC (analog-to-digital converter) into a digital signal; then convert the complex original sound signal into numerical features, which include time domain features and frequency domain features; analyze the time domain features: through the formula: Output the intensity E of the sound signal, x(b) is the sound signal value of the b-th sampling point; Z is the total number of sampling points of the signal, and b = 1, 2, ..., Z; then through the formula: Output zero crossing rate GLS, where It is an indicator function, which takes the value of 1 when the condition is met, otherwise it takes the value of 0. It should be noted that the zero-crossing rate is used to measure the number of times the signal crosses the zero value. Sounds with high zero-crossing rates are usually high-frequency, such as sharp speaking sounds; sounds with low zero-crossing rates are generally low-frequency, such as footsteps.

[0058] Analyze frequency domain features: Use fast Fourier transform to convert the sound signal from time domain to frequency domain, analyze its frequency components, and then use Mel filter bank to simulate the sensitivity of human ears to different frequencies, map the frequency domain data to Mel scale, and finally, perform logarithmic and discrete cosine transform to generate compact MFCC features (MFCCs are important indicators for distinguishing between voices, footsteps and other sound types);

[0059] The obtained features are distinguished, and the recognition intervals of the corresponding features are preset, including the footstep interval and the speech interval; if the obtained features belong to the footstep interval, it is the footstep; if the obtained features belong to the speech interval, it is the speech;

[0060] When walking sounds and talking sounds are recognized, a walking light-on command and a talking light-on command are generated respectively; the generated walking light-on command and the talking light-on command are assigned values ​​of ZL and SH respectively, where ZL and SH are custom set values, and 0 <SH<ZL。

[0061] The final human body recognition coefficient RTZ is calculated with ZL and SH, and the light-on value KDZ is output through the formula: KDZ = RTZ×τ1+ZL×τ2+SH×τ3, where τ1, τ2 and τ3 are the corresponding preset weights; if the light-on value KDZ is higher than the set light-on threshold, a light-on command is generated.

[0062] The execution module is used to receive a light-on command to control the switch of the street lamp. When the light-on command is detected and generated, the street lamp is controlled to be turned on, and the on time is set by the user. If the light-on command is detected and generated again during the lighting period of the street lamp, the duration of the previous street lamp lighting period is eliminated and the on time is recorded again.

[0063] The execution module is also used to track users in the detection and identification area, and the smart street light power generation panel tracks the strongest light source, specifically:

[0064] Tracking users in the detection and identification area: Detect the user's current position and obtain the direction of movement through human body sensors and sound sensors, and then obtain the direction and intensity of the sound through sound source positioning technology; fuse the data obtained by the human body sensor and sound sensor to generate the user's accurate position information; then control the built-in motor of the stepper to drive the street lamp lighting pillar and the lighting lamp to rotate and move to the closest distance to the user's accurate position, and obtain the ambient brightness through the light sensor to control the brightness of the lighting lamp;

[0065] Tracking of strong light sources: multiple light sensors measure the light intensity in all directions in real time, and the position sensor records the direction angle of the current smart street light power generation panel; then compare the light intensity values ​​detected by each light sensor to get the direction of the strongest light source. If the current power generation panel direction is θ0 and the direction of the strongest light source is θ1, then the direction error Δθ is Δθ=θ1-θ0; then the execution module uses the direction error Δθ to control the servo motor to rotate the street light power generation rotating joint 1, street light power generation rotating joint 2 and street light power generation rotating joint 3 to adjust the angles of the smart street light power generation panel 1 and the smart street light power generation panel 2.

[0066] In addition, those skilled in the art will appreciate that various aspects of the present invention may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product, or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present invention may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present invention may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0067] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined as such herein.

[0068] The above is an explanation of the present invention and should not be considered as a limitation thereof. Although several exemplary embodiments of the present invention have been described, it will be readily appreciated by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined in the claims. It should be understood that the above is an explanation of the present invention and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. A street lamp intelligent control system based on Z-Wave, including an intelligent analysis module and an execution module; characterized in that: The intelligent analysis module is used to analyze the light sensing data and motion data, specifically: Analyze the light perception data: obtain the light intensity in the measurement environment, display the light intensity on the time axis, obtain the historical trend of the light intensity, and analyze it: smooth the historical trend of the light intensity to obtain the smoothing factor, light intensity, and the generated light-on signal and assign a fixed value L1; Analyze whether the light intensity is increasing or decreasing over time to obtain the change pattern of the light intensity over time; then convert the change of the light intensity over time into a frequency domain signal to calculate the representation value of the light intensity signal in the frequency domain. If the trend prediction shows that the light intensity is about to decrease, generate a fixed light-on value K1; otherwise, generate a fixed light-off value G1; Then, the fixed value L1 assigned to the light-on signal is comprehensively calculated with the fixed light-on value K1 and the fixed light-off value G1 to obtain the light-on standby coefficient KDJ; Analyze motion data: When the detection generates the light-on standby execution instruction, the detection and recognition area is monitored by the human body sensor and sound sensor to obtain the final recognition coefficient RTZ; The sound sensor collects data to identify the walking sound and speaking sound of a person to obtain the walking sound assignment ZL and the speaking assignment SH; the final human body recognition coefficient RTZ is calculated with the walking sound assignment ZL and the speaking assignment SH to obtain the light-on value KDZ; if the light-on value KDZ is higher than the set light-on threshold, a light-on instruction is generated; The execution module is used to track users in the detection and identification area and the intelligent street light power generation board tracks the strongest light source. Specifically, the user in the detection and identification area is tracked: the human body sensor and the sound sensor detect the user's current position and obtain the movement direction; Obtain the direction and intensity of sound through sound source localization technology; Fuse the data obtained by human body sensors and sound sensors to generate accurate location information of users; Tracking of strong light sources: multiple light sensors measure the light intensity in all directions in real time, and the position sensor records the direction angle of the current smart street light power generation panel; then compare the light intensity values ​​detected by each light sensor to obtain the direction of the strongest light source.

2. According to claim 1, a Z-Wave based street lamp intelligent control system is characterized in that: It also includes intelligent acquisition modules; The intelligent acquisition module collects data through Z-Wave. The specific process is as follows: The smart street light is running, and the user turns it on manually. When the smart street light is turned on, the light sensor and motion sensor installed in the smart acquisition module are started, and self-check is performed to confirm that the sensor and Z-Wave communication module are operating normally. The acquisition module is connected to the Z-Wave communication module to establish a home LAN. The gateway assigns an address to the intelligent collection module and establishes communication; the intelligent collection module collects light data through the light sensor and collects motion data in the courtyard through the motion sensor.

3. According to claim 1, a Z-Wave based street lamp intelligent control system is characterized in that: The specific process of smoothing the historical trend of the light intensity to obtain the smoothing factor, the light intensity and the generated light-on signal to assign a fixed value L1 is as follows: By formula: Output the smoothed light intensity value at time t, where I(t) is the original light intensity data at a certain time t, and N is the window size; output the smoothing factor α according to the formula: Is(t)=αI(t)+(1-α)Is(t-1), and the smoothing factor α is between 0 and 1; if the light intensity I(t) is less than the threshold set by the user, generate an initial light-on signal 1, if the smoothing factor α is between 0 and 1, generate an initial light-on signal 2, when both the initial light-on signal 1 and the initial light-on signal 2 are generated at the same time, generate a light-on signal 1; assign a fixed value to the generated light-on signal, marked as L1.

4. The Z-Wave-based street lamp intelligent control system according to claim 3, characterized in that: The specific process of analyzing whether the light intensity increases or decreases over time to obtain the change pattern of the light intensity over time is: If the light intensity changes linearly over time, the trend slope a of the light intensity is output through the formula: I(t) = a·t+b, and b is the intercept, which indicates the light intensity when t = 0; the slope a and intercept b are calculated as follows: Output the specific values ​​of a and b, N1 is the number of data points, ∑ is the sum of all data points; if a>0, the light intensity increases over time, indicating that the light source is gradually getting brighter; if a<0, the light intensity decreases over time, indicating sunset or the light source is gradually weakening; if a=0, the light intensity remains relatively stable; if the historical trend calculated is more complicated, the formula is: I(t)=a0+a1t+a2t 2 +…+a n t n The change pattern of light intensity over time is obtained; a0 is a constant term, which represents the basic level of light intensity, the light intensity when time t = 0; a1 is the coefficient of the first-order term at time t; a2, a3, ..., a n They are all coefficients of the quadratic, cubic, ..., nth-order terms of time.

5. The Z-Wave-based street lamp intelligent control system according to claim 4, characterized in that: The specific process of converting the change of light intensity over time into a frequency domain signal and calculating the value of the light intensity signal in the frequency domain is as follows: The periodic component of light intensity is obtained through Fourier transform, which is calculated by the formula: The output light intensity signal is represented by the frequency domain value X(k), where I(n) is the value of the light intensity at the jth time point, N3 is the corresponding total number of data points, and k is the frequency index.

6. The Z-Wave-based street lamp intelligent control system according to claim 1, characterized in that: The specific process of monitoring the detection and recognition area to obtain the final recognition coefficient RTZ is: The human body sensor obtains its own background noise B, obtains the sensor running in an inactive environment for a fixed period of time, calculates the mean and standard deviation of multiple groups of collected signals, and uses the formula: Get the mean value of the collected signal, M is the number of sampling points, IR(n w ) is the infrared signal value collected each time, and w = 1, 2, ..., M; then perform denoising, and obtain the human recognition coefficient RT(n) by the formula: RT(n) = IR(n)-BZ, IR(n) is the original infrared signal detected by the sensor at the current moment; collect the signal in an inactive environment by setting a fixed time interval, and update the background noise B, and obtain the updated background noise value B1 by the formula: B2 = β·B0+(1-β)·B1, B0 is the background noise value recorded before, B1 is the background noise average collected at the current moment, and β is a preset weight factor that determines the update speed; Further analysis of occasional interference of the human recognition coefficient RT(n): by using a sliding average filter: Output the smoothed signal value - RTo(n), P is the sliding window size, u = 1, 2, ..., P; then process the scene sensitive to high-frequency noise, using Gaussian filtering: The output smoothed signal value is RTg(n), where G(u,σ) is the Gaussian weight.

7. The Z-Wave-based street lamp intelligent control system according to claim 1, characterized in that: The execution module is also used to receive a light-on instruction to control the switch of the street lamp. When the light-on instruction is detected and generated, the street lamp is controlled to be turned on, and the on time is set by the user. If the light-on instruction is detected and generated again during the lighting period of the street lamp, the duration of the previous street lamp lighting period is eliminated and the on time is recorded again.

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