CN114004339B - Urban lighting system adjusting method and device based on width learning and storage medium - Google Patents
Urban lighting system adjusting method and device based on width learning and storage medium Download PDFInfo
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Abstract
The invention discloses a width learning-based urban lighting system adjusting method, a device and a storage medium, wherein the method comprises the steps of acquiring traffic flow, a vehicle speed value and environment brightness of a road surface below an intelligent street lamp as initial data through sensors arranged on the street lamp; the initial data is combined to serve as a training expansion value of the fuzzy neural network after feature extraction and node enhancement in BLS width learning; the method comprises the steps of constructing a FNN fuzzy neural network, optimizing a learning algorithm part of the fuzzy neural network by applying a particle swarm algorithm PSO in an offline learning stage of the FNN fuzzy neural network, obtaining an optimal initial solution of the network, obtaining an optimized improved neural network, and inputting a training expansion value to train the improved neural network. The intelligent control of the street lamp brightness in the urban lighting system is realized by the urban lighting system adjusting method.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, an apparatus, and a storage medium for adjusting an urban lighting system based on width learning.
Background
In recent years, intelligent street lamps are rapidly developed along with infrastructure, but the control algorithm of the existing intelligent street lamps cannot fully honor the functional concept in the concept of intelligent cities, namely the so-called intelligent street lamps do not really fall to the ground. Especially in the aspect of a software control system, two modes are still mainly adopted in the current urban road lighting control system to realize the automatic and energy-saving requirements of street lamp control, and firstly, different street lamp switch control strategies are adopted; and secondly, selecting different remote control communication modes of the street lamp.
The switching control strategies of the street lamp are mainly three at present: manual control, timing control and illumination control. The manual control is that the person on duty in the center of the lighting system is responsible for switching on and off the street lamp or adjusting the brightness of the street lamp according to the road condition. The method is suitable for the scene of insufficient infrastructure capability of the information matching, and can save the construction cost of an information control system to replace the local lower labor cost. However, in the last decade, major cities have been phased out, and manual control methods are currently mainly represented by manual intervention. The timing control firstly sets reasonable timing control rules according to different dates and road states of cities, then adjusts the time control switch corresponding to the street lamp according to the rules, including clock calibration and the on-off time of the dates, the setting of the timing control switch can be realized through methods such as on-site manual or intelligent power supply, gateway and the like, and compared with the method of manual control, the timing control device has higher accuracy and stronger degree of automation, and is convenient for unified informatization management. However, the simple automatic control has no sensing capability at the edge, so that people still need to assist to complete the sensing function to meet the urban road lighting requirements of special situations when dealing with sudden conditions such as heavy rain, sand storm, daily complete food, daily partial food and the like. The illumination control is to connect or directly integrate the power module of the street lamp with the light controller and lead out the photosensitive probe to sense the external luminosity change. The controller is typically set with a fixed illumination threshold, with illumination on when natural illumination is below the threshold illumination and illumination off when natural illumination is above the threshold. However, the perception of single-point luminosity is greatly limited, such as cloudy weather like thundercloud, and the external illumination intensity may have unstable fluctuation, and the threshold judgment type control method may cause frequent occurrence of a controller to cause the street lamp to flash, so that the service life of the system is influenced and poor social experience is caused; in addition, sensor aging, dust accumulation or nearby clutter can also lead to inaccurate control. In addition, all the three main flow switch control strategies cannot meet the fine control requirements of the smart city on the urban lighting system Fa, for example, the road of the lighting system cannot be controlled to flexibly adjust to the changes of the road conditions such as the traffic flow and the vehicle speed of the area where the lighting system is located.
Disclosure of Invention
The invention provides an urban lighting system adjusting method based on width learning, which aims at the defects in the prior art and comprises the following steps:
s1, acquiring traffic flow, a vehicle speed value and ambient brightness of a road surface below an intelligent street lamp through each sensor arranged on the street lamp, and taking the traffic flow, the vehicle speed value and the ambient brightness as initial data;
S2, combining initial data acquired by a sensor as training expansion values of a fuzzy neural network after feature extraction and node enhancement in BLS width learning;
S3, constructing a FNN fuzzy neural network, wherein the output value of the FNN fuzzy neural network is street lamp brightness, optimizing a learning algorithm part of the fuzzy neural network by applying a particle swarm algorithm PSO in an offline learning stage of the FNN fuzzy neural network, acquiring an optimal initial solution of the network, obtaining an optimized improved neural network, and inputting a training expansion value to train the improved neural network.
Preferably, the FNN fuzzy neural network comprises an input layer, a fuzzy rule calculation layer and an output layer, wherein the input layer is connected with an input parameter x j, and the number of nodes is the same as the dimension of an input vector;
The fuzzy layer uses membership function to fuzzify the input value to obtain a fuzzy membership value, wherein the membership function is that Respectively the center and the width of the membership function, n is the fuzzy subset number, and k is the input parameter number;
The fuzzy rule calculation layer carries out fuzzy calculation on each fuzzy membership degree, and a fuzzy operator is adopted as a continuous multiplication operator: k is the number of input parameters;
The output layer calculates and obtains the output value y i of the fuzzy neural network, wherein The weight coefficient of the corresponding subscript of x 0…xk in the fuzzy neural network in the i-th subset.
Preferably, in the step S3, the optimization of the learning algorithm portion of the network by using the particle swarm optimization PSO specifically includes:
Error calculation is carried out: Wherein y d is the expected output of the network, y c is the actual output of the network, and e is the error between the expected output and the actual output;
coefficient correction is performed: wherein/> For the neural network coefficient, alpha is the network learning rate, x j is the network input parameter, omega i is the membership product of the input parameters;
parameter correction is carried out: Beta is another network learning rate,/> The center and width of the membership functions, respectively.
Preferably, the step S2 specifically includes:
Constructing a BLS neural network, wherein the BLS neural network comprises an input layer, a middle layer and an output layer, and the detected traffic flow and the detected vehicle speed value are used as input values (x 1,x2) of the input layer;
Calculating feature nodes and enhancement nodes of a BLS neural network middle layer, wherein the feature nodes finish feature extraction through a feature extraction function, the feature extraction function is Z=phi (x W 1+B1),W1 is a randomly generated weight value used for connecting an input layer and the feature nodes, B 1 is a threshold value of the feature extraction function, and the enhancement nodes are generated by the feature nodes through an enhancement node function, wherein the enhancement node function is H=phi (Z W 2+B2),W2 is a randomly generated weight value used for connecting the enhancement nodes and the feature nodes, and B 2 is a threshold value of the enhancement node function);
And after the initial data acquired by the sensors are subjected to feature extraction and node enhancement by the BLS neural network, merging the initial data to be used as training expansion values of the fuzzy neural network, wherein the initial data is the vehicle flow and the vehicle speed values of the road surface below the intelligent road lamp acquired by each sensor arranged on the intelligent road lamp.
The invention also discloses an urban lighting system adjusting device based on width learning, which comprises: the sensing module is used for acquiring traffic flow, speed value and environment brightness of the road surface below the street lamp; the control module is used for merging the real-time parameters acquired by the sensor into training expansion values of the fuzzy neural network after feature extraction and node enhancement in BLS width learning; constructing a FNN fuzzy neural network, wherein the output value of the FNN fuzzy neural network is street lamp brightness, optimizing a learning algorithm part of the fuzzy neural network by applying a particle swarm algorithm PSO in an offline learning stage of the FNN fuzzy neural network, acquiring an optimal initial solution of the network and obtaining an optimized improved neural network, and inputting a training expansion value to train the improved neural network; and the lighting module is used for adjusting the brightness of the road lamp according to the output value of the control module after the real-time parameters obtained by the sensing module enter the neural network after training.
Preferably, the FNN fuzzy neural network constructed by the control module comprises an input layer, a fuzzy rule calculation layer and an output layer, wherein the input layer is connected with an input parameter x j, and the number of nodes is the same as the dimension of an input vector; the fuzzy layer uses membership function to fuzzify the input value to obtain a fuzzy membership value, wherein the membership function is that Respectively the center and the width of the membership function, n is the fuzzy subset number, and k is the input parameter number; the fuzzy rule calculation layer carries out fuzzy calculation on each fuzzy membership degree, and a fuzzy operator is adopted as a continuous multiplication operator: k is the number of input parameters; the output layer calculates and obtains the output value y i of the fuzzy neural network, wherein/> The weight coefficient of the corresponding subscript of x 0…xk in the fuzzy neural network in the i-th subset.
Preferably, the control module optimizes a learning algorithm part of the network by adopting a particle swarm optimization PSO, and specifically includes: error calculation is carried out: Wherein y d is the expected output of the network, y c is the actual output of the network, and e is the error between the expected output and the actual output; coefficient correction is performed: /(I) Wherein the method comprises the steps of For the neural network coefficient, alpha is the network learning rate, x j is the network input parameter, omega i is the membership product of the input parameters; parameter correction is carried out: /(I) Beta is another network learning rate,/>The center and width of the membership functions, respectively.
Preferably, the control module enhances real-time parameters acquired by the sensor through feature extraction and nodes in BLS width learning, and specifically includes: constructing a BLS neural network, wherein the BLS neural network comprises an input layer, a middle layer and an output layer, and the detected traffic flow and the detected vehicle speed value are used as input values (x 1,x2) of the input layer; calculating feature nodes and enhancement nodes of a BLS neural network middle layer, wherein the feature nodes finish feature extraction through a feature extraction function, the feature extraction function is Z=phi (x W 1+B1),W1 is a randomly generated weight value used for connecting an input layer and the feature nodes, B 1 is a threshold value of the feature extraction function, the enhancement nodes are generated by the feature nodes through the enhancement node function, the enhancement node function is H=phi (Z W 2+B2),W2 is a randomly generated weight value used for connecting the enhancement nodes and the feature nodes, B 2 is a threshold value of the enhancement node function, and after feature extraction and node enhancement of initial data acquired by a sensor are carried out on the BLS neural network, the initial data are combined to serve as training expansion values of a fuzzy neural network, and the initial data are used for each sensor arranged on an intelligent street lamp to acquire traffic flow and a vehicle speed value of a road surface below the street lamp.
The invention also discloses an urban lighting system regulating device based on width learning, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of any one of the methods when executing the computer program.
The invention also discloses a computer readable storage medium storing a computer program which when executed by a processor implements the steps of any of the methods described above.
The invention discloses a method and a device for adjusting an urban lighting system based on width learning, which aim at the characteristics that the acquired data of the existing intelligent street lamp sensor is unstructured or semi-structured, and has ambiguity and flexibility in feature extraction, adopt a fuzzy neural network FNN to control, and aim at the problem that the optimal solution is difficult to obtain in the actual dimming process of the fuzzy neural network, adopt a particle swarm algorithm PSO to optimize the super-parameters in the FNN, avoid the problem of non-convergence of fitting, and adopt a width learning BLS to perform feature extraction work, thereby overcoming the problem of insufficient data of dimming samples or data abnormality frequently encountered in the actual process to a certain extent. The method adopting the combination of the BLS and the PSO-FNN is equivalent to replacing the artificial features or the simple feature layers of the FNN by the enhanced node output of the BLS, achieves the effects of high model training efficiency and less network parameters, and can better solve the problem of inaccurate feature extraction in the method of multi-intelligent street lamp prediction, especially based on edge calculation.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
Fig. 1 is a schematic flow chart of steps of an urban lighting system adjusting method based on width learning according to an embodiment of the invention.
Fig. 2 is a schematic diagram of a width learning structure according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of a FNN fuzzy neural network based on a T-S model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, unless expressly stated or limited otherwise, a first feature "above" or "below" a second feature may include both the first and second features being in direct contact, as well as the first and second features not being in direct contact but being in contact with each other through additional features therebetween. Moreover, a first feature being "above," "over" and "on" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly under and obliquely below the second feature, or simply means that the first feature is less level than the second feature.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like in the description and in the claims, are not used for any order, quantity, or importance, but are used for distinguishing between different elements. Likewise, the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one.
The intelligent street lamp control in the urban lighting system needs to solve the problem that a fine dimming strategy tracks the perceived change of the edge environment, and in the traditional street lamp control strategy, the government often takes measures to turn on and off the street lamps according to a single factor, such as uniformly and regularly. The street lamp switch is controlled according to the same time all the year round, and the street lamp control measures are not intelligent and humanized enough, for example, the street lamps at different placement positions have different illumination requirements, such as the street lamps close to the crossroads, the street lamps close to the cameras, the street lamps close to the luxury road sections, the street lamps close to the curves and junctions and the like, and can be specially treated. The intelligent street lamp control system is a multivariable control system, and factors affecting street lamp control are various as follows: temperature, humidity, illumination, PM2.5, noise, rainfall, traffic flow, holidays, etc. The control problem of the multivariable system is always a research important and difficult problem in the control field. If the conventional industrial control method is adopted, the problem of the switching strategy of the street lamp is difficult to be solved after various conditions are considered at the same time.
The FNN network structure error after the width learning fusion is smaller, so that the energy-saving optimization control can be better completed. Meanwhile, according to the energy-saving ratio formula, the energy consumption of the intelligent street lamp control system is greatly reduced compared with that of the intelligent street lamp control system in a traditional mode. As shown in fig. 1, the urban lighting system adjusting method based on width learning disclosed in the embodiment specifically includes the following steps:
Step S1, acquiring the traffic flow, the speed value and the ambient brightness of the road surface below the intelligent street lamp through each sensor arranged on the intelligent street lamp as initial data.
And S2, combining initial data acquired by the sensor as training expansion values of the fuzzy neural network after feature extraction and node enhancement in BLS width learning.
Specifically, width learning (read LEARNING SYSTEM, BLS) is used as a simple novel fast incremental learning neural network, based on a single hidden layer forward network RVFL (Random Vector Functional Link Network), original input is firstly subjected to sparse mapping feature learning through feature nodes, then enhanced features are obtained through nonlinear expansion of enhanced nodes, and two feature expressions are connected in parallel and serve as final total input to be sent to an output layer for classification and identification, so that important features can be learned from training data, and high fitting is achieved on the training data. The method can well solve the problems of inaccurate precision and the like caused by insufficient data and abnormal data. Considering that the fuzzy neural network model can already prescribe a fuzzy rule of the fuzzy neural network model, the fuzzy neural network model has good generalization capability, and the BLS has the characteristics of learning important features from training data and achieving high fitting on the training data, the data is input into a BLS layer through fusion width learning to further extract important feature adding nodes to expand a network structure.
As shown in fig. 2, the width learning is a planar neural network developed from a function-link neural network, and includes an input layer, an intermediate layer and an output layer. The middle layer comprises two different nodes, namely a characteristic node and an enhancement node. The feature node completes feature extraction through a phi () feature extraction function, then an enhancement node is generated from the feature node through an epsilon () function, and finally the two nodes together form a middle layer. In this embodiment, the step S2 may specifically include the following.
In step S21, a BLS neural network is constructed, where the BLS neural network includes an input layer, an intermediate layer, and an output layer, and the detected traffic flow and the detected vehicle speed value are used as input values (x 1,x2) of the input layer.
Step S22, calculating feature nodes and enhancement nodes of a BLS neural network middle layer, wherein feature nodes finish feature extraction through feature extraction functions, the feature extraction functions are Z=phi (x W 1+B1),W1 is a randomly generated weight value used for connecting an input layer and the feature nodes, B 1 is a threshold value of the feature extraction functions, enhancement nodes are generated by the feature nodes through enhancement node functions, the enhancement node functions are H=epsilon (Z W 2+B2),W2 is a randomly generated weight value used for connecting the enhancement nodes and the feature nodes, and B 2 is a threshold value of the enhancement node functions).
And S23, after the initial data acquired by the sensors are extracted through the characteristics of the BLS neural network and enhanced through the nodes, merging the initial data to be used as a training expansion value of the fuzzy neural network, wherein the initial data is a vehicle flow and a vehicle speed value of a road surface below the intelligent road lamp acquired by each sensor arranged on the intelligent road lamp. The data acquired by the sensor are subjected to feature extraction and reinforcement node generation in width learning, then are combined and serve as input of a fuzzy neural network, and then the model is trained.
The embodiment realizes the optimized output to obtain higher precision by enlarging the width of the input node. The model has strong nonlinear fitting capability, does not need too much data to describe the mapping relation between the input and the output of the system, and fits the function. The system combines the fuzzy neural network with the width learning system, has simple structure, high model training efficiency and less network parameters, and can better solve the problem of inaccurate feature extraction in the method of multi-intelligent street lamp prediction.
And S3, constructing a FNN fuzzy neural network, wherein the output value of the FNN fuzzy neural network is street lamp brightness, optimizing a learning algorithm part of the fuzzy neural network by applying a particle swarm algorithm PSO in an offline learning stage of the FNN fuzzy neural network, acquiring an optimal initial solution of the network, obtaining an optimized improved neural network, and inputting a training expansion value to train the improved neural network.
In this embodiment, the FNN fuzzy neural network based on the T-S model is divided into an input layer, a fuzzy rule calculation layer and an output layer, wherein the input layer is connected with the input parameter x j, and the number of nodes is the same as the dimension of the input vector.
By combining with the urban road illumination design standard, the intelligent dimming system adopts a fuzzy neural network controller, and the accuracy of dimming value output and the degree of automation of the system are improved by utilizing the learning capability of the neural network and the fuzzy reasoning capability of fuzzy control. As shown in fig. 3, the T-S model system has strong self-adaptation performance, and not only can be automatically updated, but also can continuously correct the membership function of the fuzzy subset.
Is provided withIs a fuzzy set of fuzzy systems,/>Is a fuzzy system parameter; y j is the output derived from the fuzzy rule, the input part is fuzzy and the output part is deterministic, the fuzzy inference means that the output is a linear combination of inputs.
Firstly, calculating the membership degree of each input variable xj according to a fuzzy rule: namely, the fuzzy layer uses a membership function to fuzzify an input value to obtain a fuzzy membership value, wherein the membership function is The center and the width of the membership function are respectively, n is the fuzzy subset number, and k is the input parameter number.
The fuzzy rule calculation layer carries out fuzzy calculation on each fuzzy membership degree, and a fuzzy operator is adopted as a continuous multiplication operator: k is the number of input parameters.
The output layer calculates and obtains the output value y i of the fuzzy neural network according to the result of the steps, wherein The weight coefficient of the corresponding subscript of x 0…xk in the fuzzy neural network in the i-th subset.
In the step S3, the learning algorithm portion of the network is optimized by using the particle swarm algorithm PSO. Specifically, although the fuzzy system and the neural network have many advantages, the problem is that the fuzzy neural network adopts a forward error transmission mode, the error is calculated by adopting a gradient method in the forward error transmission mode, the method possibly involves solving and utilizing a higher-order derivative in a multi-optimization process, the fuzzy neural network is difficult to realize optimal solution calculation, and the convergence of the whole network is possibly deteriorated when the parameter selection of the fuzzy neural network is improper. Specifically, in this embodiment, the PSO algorithm is used to optimize the learning algorithm portion of the network, where the learning algorithm of the fuzzy neural network algorithm is divided into the following three steps:
Error calculation is carried out: Where y d is the network desired output, y c is the network actual output, and e is the error between the desired output and the actual output.
Coefficient correction is performed: wherein/> Alpha is the network learning rate, the network input parameters and omega i is the membership product of the input parameters. The model learning process requires optimization of the coefficients of each x j,/>Meaning is equivalent to the coefficient/>, for the input x j in the ith subsetT corresponds to a count of the number of updates, and has values 1,2,3,4 … ….
Parameter correction is carried out: Beta is another network learning rate,/> The center and width of the membership functions, respectively. /(I)And/>I.e. representing their t-th optimization revision update. The parameter beta is the same as alpha, and is the network learning rate with different values.
The urban lighting system adjusting method based on width learning disclosed by the embodiment is used for controlling the FNN by adopting the fuzzy neural network aiming at the characteristics that the acquired data of the existing intelligent street lamp sensor is unstructured or semi-structured and has ambiguity and flexibility in feature extraction, and optimizing the super-parameters in the FNN by adopting the particle swarm optimization PSO aiming at the problem that the optimal solution is difficult to obtain in the actual dimming process of the fuzzy neural network, so that the problem of non-convergence of fitting is avoided, and meanwhile, the feature extraction work is carried out by adopting the width learning BLS, so that the problem of insufficient data of a dimming sample or data abnormality frequently encountered in the actual process is overcome to a certain extent. The method adopting the combination of the BLS and the PSO-FNN is equivalent to replacing the artificial features or the simple feature layers of the FNN by the enhanced node output of the BLS, achieves the effects of high model training efficiency and less network parameters, and can better solve the problem of inaccurate feature extraction in the method especially based on edge calculation in multi-intelligent street lamp prediction, thereby realizing accurate brightness control of the street lamps at different placement positions according to the parameters such as traffic flow, speed value, ambient brightness and the like of the area where the street lamps are positioned, and achieving the intellectualization and humanization of the regulation control of the urban lighting system.
The invention also discloses an urban lighting system adjusting device based on width learning, which comprises: the sensing module is used for acquiring traffic flow, speed value and environment brightness of the road surface below the street lamp; the control module is used for merging the real-time parameters acquired by the sensor into training expansion values of the fuzzy neural network after feature extraction and node enhancement in BLS width learning; constructing a FNN fuzzy neural network, wherein the output value of the FNN fuzzy neural network is street lamp brightness, optimizing a learning algorithm part of the fuzzy neural network by applying a particle swarm algorithm PSO in an offline learning stage of the FNN fuzzy neural network, acquiring an optimal initial solution of the network and obtaining an optimized improved neural network, and inputting a training expansion value to train the improved neural network; and the lighting module is used for adjusting the brightness of the road lamp according to the output value of the control module after the real-time parameters obtained by the sensing module enter the neural network after training.
Preferably, the FNN fuzzy neural network constructed by the control module comprises an input layer, a fuzzy rule calculation layer and an output layer, wherein the input layer is connected with an input parameter x j, and the number of nodes is the same as the dimension of an input vector; the fuzzy layer uses membership function to fuzzify the input value to obtain a fuzzy membership value, wherein the membership function is that Respectively the center and the width of the membership function, n is the fuzzy subset number, and k is the input parameter number; the fuzzy rule calculation layer carries out fuzzy calculation on each fuzzy membership degree, and a fuzzy operator is adopted as a continuous multiplication operator: /(I)K is the number of input parameters; the output layer calculates and obtains the output value y i of the fuzzy neural network, wherein The weight coefficient of the corresponding subscript of x 0…xk in the fuzzy neural network in the i-th subset.
Preferably, the control module optimizes a learning algorithm part of the network by adopting a particle swarm optimization PSO, and specifically includes: error calculation is carried out: Wherein y d is the expected output of the network, y c is the actual output of the network, and e is the error between the expected output and the actual output; coefficient correction is performed: /(I) Wherein the method comprises the steps of For the neural network coefficient, alpha is the network learning rate, x j is the network input parameter, omega i is the membership product of the input parameters; parameter correction is carried out: /(I) Beta is another network learning rate,/>The center and width of the membership functions, respectively.
Preferably, the control module enhances real-time parameters acquired by the sensor through feature extraction and nodes in BLS width learning, and specifically includes: constructing a BLS neural network, wherein the BLS neural network comprises an input layer, a middle layer and an output layer, and the detected traffic flow and the detected vehicle speed value are used as input values (x 1,x2) of the input layer; calculating feature nodes and enhancement nodes of a BLS neural network middle layer, wherein the feature nodes finish feature extraction through a feature extraction function, the feature extraction function is Z=phi (x W 1+B1),W1 is a randomly generated weight value used for connecting an input layer and the feature nodes, B 1 is a threshold value of the feature extraction function, the enhancement nodes are generated by the feature nodes through the enhancement node function, the enhancement node function is H=phi (Z W 2+B2),W2 is a randomly generated weight value used for connecting the enhancement nodes and the feature nodes, B 2 is a threshold value of the enhancement node function, and after feature extraction and node enhancement of initial data acquired by a sensor are carried out on the BLS neural network, the initial data are combined to serve as training expansion values of a fuzzy neural network, and the initial data are used for each sensor arranged on an intelligent street lamp to acquire traffic flow and a vehicle speed value of a road surface below the street lamp.
It should be noted that, in the present specification, the foregoing embodiments are described in a progressive manner, and each embodiment is mainly described as different from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. The foregoing width learning-based urban lighting system adjusting device disclosed in the embodiments corresponds to the width learning-based urban lighting system adjusting method disclosed in the foregoing embodiments, so that the description is relatively simple, and the relevant points are referred to in the method section.
The invention also provides another urban lighting system adjusting device based on width learning, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the urban lighting system adjusting method based on width learning as described in the embodiments.
The width learning based urban lighting system adjustment device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a width learning based urban lighting system adjustment device and does not constitute a limitation of the width learning based urban lighting system adjustment device apparatus, and may comprise more or less components than illustrated, or may combine certain components, or different components, e.g. the width learning based urban lighting system adjustment device apparatus may further comprise input and output devices, network access devices, buses, etc.
The Processor may be a central processing unit (Central Processing Unit, CPU), other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the width learning based urban lighting system regulator device, and which connects the various parts of the entire width learning based urban lighting system regulator device using various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the width learning based urban lighting system regulator device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like, and the memory may include a high-speed random access memory, and may further include a nonvolatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage device.
The width learning-based urban lighting system adjustment device data management method may be stored in a computer readable storage medium if implemented in the form of a software functional unit and sold or used as a stand alone product. Based on such understanding, the present invention may implement all or part of the above-described embodiment of the method, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of each of the above-described embodiments of the method for adjusting a urban lighting system based on width learning when the computer program is executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
In summary, the foregoing description is only of the preferred embodiments of the present invention, and all equivalent changes and modifications made in accordance with the claims should be construed to fall within the scope of the invention.
Claims (6)
1. An urban lighting system adjusting method based on width learning is characterized by comprising the following steps:
s1, acquiring traffic flow, a vehicle speed value and ambient brightness of a road surface below an intelligent street lamp through each sensor arranged on the street lamp, and taking the traffic flow, the vehicle speed value and the ambient brightness as initial data;
S2, combining initial data acquired by a sensor as training expansion values of a fuzzy neural network after feature extraction and node enhancement in BLS width learning;
S3, constructing a FNN fuzzy neural network, wherein the output value of the FNN fuzzy neural network is street lamp brightness, optimizing a learning algorithm part of the fuzzy neural network by applying a particle swarm algorithm PSO in an offline learning stage of the FNN fuzzy neural network, acquiring an optimal initial solution of the network, obtaining an optimized improved neural network, and inputting a training expansion value to train the improved neural network;
wherein the FNN fuzzy neural network comprises an input layer, a fuzzy rule calculation layer and an output layer, wherein the input layer and the input parameters The number of nodes is the same as the dimension of the input vector; the fuzzy layer uses membership function to fuzzify the input value to obtain a fuzzy membership value, wherein the membership function is that，/>、/>Respectively the center and the width of the membership function, n is the fuzzy subset number, and k is the input parameter number; the fuzzy rule calculation layer carries out fuzzy calculation on each fuzzy membership degree, and a fuzzy operator is adopted as a continuous multiplication operator: /(I)K is the number of input parameters; output layer calculation to obtain output value/>, of fuzzy neural networkWherein， />、/>For i < th > subset/>The weight coefficient of the corresponding subscript in the fuzzy neural network;
The particle swarm optimization PSO is adopted to optimize the learning algorithm part of the network, and the method specifically comprises the following steps:
Error calculation is carried out: Wherein/> For network expected output,/>E is the error of the expected output and the actual output;
coefficient correction is performed: wherein/> ，/>Is a neural network coefficient, alpha is a network learning rate,/>Inputting parameters for the network,/>Membership product for input parameters;
parameter correction is carried out: ，/> Beta is another network learning rate,/> 、/>The center and width of the membership functions, respectively.
2. The method for adjusting an urban lighting system based on width learning according to claim 1, wherein said step S2 comprises:
constructing a BLS neural network, wherein the BLS neural network comprises an input layer, a middle layer and an output layer, and the detected traffic flow and the detected vehicle speed value are used as the input values of the input layer ；
Calculating feature nodes and enhancement nodes of a BLS neural network middle layer, wherein the feature nodes complete feature extraction through feature extraction functions, and the feature extraction functions are as follows ，/>For randomly generated weights for connecting input layers and feature nodes,/>Generating enhancement nodes from the feature nodes through enhancement node functions for the threshold values of the feature extraction functions; the enhanced node function is/> ，/>For randomly generated weights used to connect enhancement nodes and feature nodes,/>A threshold value for the enhanced node function;
And after the initial data acquired by the sensors are subjected to feature extraction and node enhancement by the BLS neural network, merging the initial data to be used as training expansion values of the fuzzy neural network, wherein the initial data is the vehicle flow and the vehicle speed values of the road surface below the intelligent road lamp acquired by each sensor arranged on the intelligent road lamp.
3. An urban lighting system adjustment device based on width learning, comprising:
the sensing module is used for acquiring traffic flow, speed value and environment brightness of the road surface below the street lamp;
The control module is used for merging the real-time parameters acquired by the sensor into training expansion values of the fuzzy neural network after feature extraction and node enhancement in BLS width learning; constructing a FNN fuzzy neural network, wherein the output value of the FNN fuzzy neural network is street lamp brightness, optimizing a learning algorithm part of the fuzzy neural network by applying a particle swarm algorithm PSO in an offline learning stage of the FNN fuzzy neural network, acquiring an optimal initial solution of the network and obtaining an optimized improved neural network, and inputting a training expansion value to train the improved neural network;
The lighting module is used for adjusting the brightness of the road lamp according to the output value of the control module after the real-time parameters obtained by the sensing module enter the neural network after training;
The FNN fuzzy neural network constructed by the control module comprises an input layer, a fuzzy rule calculation layer and an output layer, wherein the input layer and the input parameters The number of nodes is the same as the dimension of the input vector; the fuzzy layer uses membership function to fuzzify the input value to obtain a fuzzy membership value, wherein the membership function is that，/>、/>Respectively the center and the width of the membership function, n is the fuzzy subset number, and k is the input parameter number; the fuzzy rule calculation layer carries out fuzzy calculation on each fuzzy membership degree, and a fuzzy operator is adopted as a continuous multiplication operator: /(I)K is the number of input parameters; output layer calculation to obtain output value/>, of fuzzy neural networkWherein， />、/>For i < th > subset/>The weight coefficient of the corresponding subscript in the fuzzy neural network;
the control module adopts a particle swarm optimization PSO to optimize a learning algorithm part of the network, and specifically comprises the following steps: error calculation is carried out: Wherein/> For network expected output,/>E is the error of the expected output and the actual output; coefficient correction is performed: /(I)Wherein/> ，/>Is a neural network coefficient, alpha is a network learning rate,/>Inputting parameters for the network,/>Membership product for input parameters; parameter correction is carried out: /(I) ，/>Beta is another network learning rate,/>、/>The center and width of the membership functions, respectively.
4. The urban lighting system adjusting device based on width learning according to claim 3, wherein the control module enhances real-time parameters collected by the sensor by feature extraction and nodes in BLS width learning, and specifically comprises:
constructing a BLS neural network, wherein the BLS neural network comprises an input layer, a middle layer and an output layer, and the detected traffic flow and the detected vehicle speed value are used as the input values of the input layer ; Calculating feature nodes and enhancement nodes of a BLS neural network middle layer, wherein feature nodes complete feature extraction through feature extraction functions, and the feature extraction functions are/> ，For randomly generated weights for connecting input layers and feature nodes,/>Generating enhancement nodes from the feature nodes through enhancement node functions for the threshold values of the feature extraction functions; the enhanced node function is/> ，/>For randomly generated weights used to connect enhancement nodes and feature nodes,/>A threshold value for the enhanced node function; and after the initial data acquired by the sensors are subjected to feature extraction and node enhancement by the BLS neural network, merging the initial data to be used as training expansion values of the fuzzy neural network, wherein the initial data is the vehicle flow and the vehicle speed values of the road surface below the intelligent road lamp acquired by each sensor arranged on the intelligent road lamp.
5. An urban lighting system adjustment device based on width learning, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: the processor, when executing the computer program, implements the steps of the method according to any of claims 1-2.
6. A computer-readable storage medium storing a computer program, characterized in that: the computer program implementing the steps of the method according to any of claims 1-2 when executed by a processor.
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