Energy consumption regulation and control method and system for plant illumination

Abstract

The invention provides an energy consumption regulation and control method and system for plant illumination, and the method comprises the steps: obtaining a working scene of each independent illumination region of a plant and each first illumination intensity in each independent illumination region, carrying out the preprocessing of each first illumination intensity, and obtaining each first illumination intensity after preprocessing; determining the actual illumination intensity of each independent illumination area; determining an independent illumination area needing to be regulated and controlled based on the working scene and the actual illumination intensity; the actual illumination intensity of the independent illumination area needing to be regulated and controlled is substituted into a pre-established factory building illumination energy consumption optimization objective function, and optimization solution is performed on the factory building illumination energy consumption optimization objective function by using an elite difference variation collaborative search algorithm to obtain optimized illumination power consumption, illumination and comfort information; and the independent lighting area needing to be regulated and controlled is regulated and controlled by using the active-disturbance-rejection controller. According to the technical scheme provided by the invention, the precision and comfort of energy consumption regulation and control are improved, and the illumination energy consumption is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent lighting control, and in particular to an energy consumption control method and system for factory lighting. Background Art

[0002] In modern industrial production, the lighting system of the factory is an indispensable part. It is not only related to the work efficiency and safety of workers, but also directly affects the production cost and the economic benefits of the enterprise. With the development of science and technology, intelligent control of factory lighting has become an important means to improve production efficiency and reduce energy consumption. Reasonable lighting design can improve the visual comfort of workers, reduce visual fatigue, and thus improve work efficiency. In a high-intensity working environment, good lighting conditions are one of the key factors to ensure the safety of workers. With the enhancement of environmental awareness, energy conservation and emission reduction have become part of corporate social responsibility. Through intelligent control of lighting, the brightness can be adjusted according to actual needs to avoid unnecessary energy waste. However, the existing technology has low control accuracy for factory lighting, resulting in poor energy consumption and comfort. Therefore, it is urgent to propose a solution that can improve the accuracy and comfort of energy consumption control and reduce lighting energy consumption. Summary of the invention

[0003] The present application provides an energy consumption control method and system for factory lighting, so as to at least solve the technical problem in the prior art that the control accuracy of factory lighting is low, resulting in poor energy consumption and comfort.

[0004] The first embodiment of the present application provides an energy consumption control method for factory lighting, the method comprising:

[0005] Acquire the working scenes of each independent lighting area of ​​the factory building and each first light intensity in the each independent lighting area, and preprocess the each first light intensity to obtain each preprocessed first light intensity;

[0006] Determine the actual illumination intensity of each independent illumination area according to each preprocessed first illumination intensity in each independent illumination area;

[0007] Determine an independent lighting area to be adjusted based on the working scene and the actual light intensity;

[0008] Substitute the actual light intensity of the independent lighting area to be regulated into the pre-established factory lighting energy consumption optimization objective function, and use the elite differential mutation collaborative search algorithm to optimize and solve the factory lighting energy consumption optimization objective function to obtain the optimized lighting power consumption, illumination and comfort information;

[0009] Based on the optimized lighting power consumption, illumination and comfort information, the independent lighting area to be regulated is regulated by using an anti-disturbance control controller.

[0010] Preferably, n photosensors are installed in each independent lighting area;

[0011] The photosensor is used to collect the first light intensity;

[0012] The working scenes include: working state and maintenance state.

[0013] Furthermore, the preprocessing of the first illumination intensities to obtain the preprocessed first illumination intensities includes:

[0014] The first illumination intensities are processed by using a Z-score method to obtain preprocessed first illumination intensities.

[0015] Furthermore, the calculation formula for the actual illumination intensity of each independent lighting area is as follows:

[0016]

[0017] In the formula, K actual,q is the actual illumination intensity of the qth independent lighting area, K q,n is the first light intensity after preprocessing collected by the nth photosensor in the qth independent lighting area, A q,n is the coverage area corresponding to the nth photosensor in the qth independent lighting area, h is the standard height of the factory building, H q,n is the actual height of the factory building in the coverage area corresponding to the nth photosensitive sensor in the qth independent lighting area, ρ q,n is the reflectivity of the wall and ground in the area corresponding to the nth photosensor in the qth independent lighting area.

[0018] Furthermore, the determining of the independent lighting area to be regulated based on the working scene and the actual light intensity includes:

[0019] Filtering out the independent lighting areas in working state based on the working scenes of the independent lighting areas;

[0020] Determine whether the actual light intensity of each independent lighting area in working state belongs to the preset illumination range. If so, the independent lighting area is determined as an independent lighting area that does not need to be regulated. Otherwise, the independent lighting area is determined as an independent lighting area that needs to be regulated.

[0021] Furthermore, the calculation formula of the factory lighting energy consumption optimization objective function is as follows:

[0022]

[0023] Where Y q is the energy consumption index of the qth independent lighting area, E is the power consumption of factory lighting, Comf q is the comfort index of the qth independent lighting area;

[0024] The calculation formula of the comfort index of the qth independent lighting area is as follows:

[0025] Comf q =-K actual,q 2 +2.5×10 3 K actual,q -1.3125×10 6

[0026] In the formula, Comf q is the comfort index of the qth independent lighting area.

[0027] Furthermore, the collaborative search algorithm using elite differential mutation is used to optimize and solve the objective function of the plant lighting energy consumption optimization to obtain optimized lighting power consumption, illumination and comfort information, including:

[0028] The control parameters of the linear state extended observer and the control parameters of the linear state error feedback in the self-disturbance rejection controller are optimized and adjusted by utilizing the team building stage, team communication stage, reflective learning stage and internal competition stage of the collaborative search algorithm of elite differential mutation until the energy consumption index in the plant lighting energy consumption optimization objective function is less than or equal to the preset energy consumption value;

[0029] The lighting power consumption, illumination and comfort information corresponding to the energy consumption index in the factory lighting energy consumption optimization objective function is less than or equal to the preset energy consumption value as the optimized lighting power consumption, illumination and comfort information.

[0030] The second embodiment of the present application provides an energy consumption control system for factory lighting, including:

[0031] An acquisition module, used to acquire the working scenes of each independent lighting area of ​​the factory building and each first light intensity in each independent lighting area, and preprocess each first light intensity to obtain each preprocessed first light intensity;

[0032] A first determining module, configured to determine the actual illumination intensity of each independent illumination area according to each preprocessed first illumination intensity in each independent illumination area;

[0033] A second determination module, configured to determine an independent lighting area to be regulated based on the working scene and the actual light intensity;

[0034] An optimization module is used to substitute the actual light intensity of the independent lighting area to be regulated into the pre-established plant lighting energy consumption optimization objective function, and optimize and solve the plant lighting energy consumption optimization objective function using the elite differential mutation collaborative search algorithm to obtain optimized lighting power consumption, illumination and comfort information;

[0035] The control module is used to control the independent lighting area to be controlled based on the optimized lighting power consumption, illumination and comfort information and using an anti-disturbance control controller.

[0036] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect is implemented.

[0037] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect of the present application.

[0038] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0039] The present application proposes a method and system for energy consumption control of factory lighting, the method comprising: obtaining the working scene of each independent lighting area of ​​the factory and each first light intensity in the independent lighting area, and preprocessing the first light intensity to obtain each preprocessed first light intensity; determining the actual light intensity of each independent lighting area according to the preprocessed first light intensity in each independent lighting area; determining the independent lighting area to be controlled based on the working scene and the actual light intensity; substituting the actual light intensity of the independent lighting area to be controlled into the pre-established factory lighting energy consumption optimization objective function, and optimizing and solving the factory lighting energy consumption optimization objective function using the elite differential variation collaborative search algorithm to obtain optimized lighting power consumption, illumination and comfort information; based on the optimized lighting power consumption, illumination and comfort information, and using the self-disturbance rejection controller to control the independent lighting area to be controlled. The technical solution proposed in the present application improves the accuracy and comfort of energy consumption control and reduces lighting energy consumption.

[0040] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0042] Figure 1 A flowchart of an energy consumption control method for factory lighting provided according to an embodiment of the present application;

[0043] Figure 2 A schematic diagram of the LADRC structure provided according to an embodiment of the present application;

[0044] Figure 3 A flow chart of CSA algorithm optimization parameters provided according to an embodiment of the present application;

[0045] Figure 4 A detailed flow chart of a method for intelligently controlling energy consumption of factory lighting provided according to an embodiment of the present application;

[0046] Figure 5 The present invention is a structural diagram of an energy consumption control system for factory lighting provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0048] The present application proposes a method and system for energy consumption control of factory lighting, the method comprising: obtaining the working scene of each independent lighting area of ​​the factory and each first light intensity in the independent lighting area, and preprocessing the first light intensity to obtain each preprocessed first light intensity; determining the actual light intensity of each independent lighting area according to the preprocessed first light intensity in each independent lighting area; determining the independent lighting area to be controlled based on the working scene and the actual light intensity; substituting the actual light intensity of the independent lighting area to be controlled into the pre-established factory lighting energy consumption optimization objective function, and optimizing and solving the factory lighting energy consumption optimization objective function using the collaborative search algorithm of elite differential variation to obtain optimized lighting power consumption, illumination and comfort information; based on the optimized lighting power consumption, illumination and comfort information, and using the self-disturbance rejection controller to control the independent lighting area to be controlled. The technical solution proposed in the present application improves the accuracy and comfort of energy consumption control and reduces lighting energy consumption.

[0049] The following describes an energy consumption control method and system for factory lighting according to an embodiment of the present application with reference to the accompanying drawings.

[0050] Embodiment 1

[0051] Figure 1 A flowchart of an energy consumption control method for factory lighting provided according to an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0052] Step 1: Acquire the working scenes of each independent lighting area of ​​the factory building and each first light intensity in the independent lighting area, and preprocess the first light intensities to obtain the preprocessed first light intensities;

[0053] It should be noted that before step 1, it also includes: dividing the factory into areas according to lighting needs, dividing the entire factory into several lighting areas, and the lighting areas are independent of each other, so that separate control of lighting between areas can be achieved. For example, when some areas are not working, the lights can be dimmed or turned off, thereby reducing lighting power consumption.

[0054] The divided areas are set up with working scenarios, and the working scenarios of the areas are set into two categories. The first category is the working state, and the second category is the maintenance state. The two states have inconsistent requirements for light illumination. The system performs real-time control of the lights for the first category. For the second category, the lights are set to a constant light source and remain on, with the illumination maintained at 500lx, to ensure sufficient light source during maintenance.

[0055] The working conditions of each area on that day or for a period of time are selected by the staff based on the actual situation and entered into the system at the front end.

[0056] It should be noted that n photosensitive sensors are installed in each independent lighting area;

[0057] The photosensor is used to collect the first light intensity;

[0058] The working scenes include: working state and maintenance state.

[0059] In the embodiment of the present disclosure, the preprocessing of the first illumination intensities to obtain the preprocessed first illumination intensities includes:

[0060] The first illumination intensities are processed by using a Z-score method to obtain preprocessed first illumination intensities.

[0061] It should be noted that the Z-score method is used to process missing values ​​or abnormal values ​​in the data collected by the photosensitive sensor. The specific calculation formula is as follows:

[0062]

[0063] In the formula, z is the correction value of missing values ​​or abnormal values; x is the true value collected by the current photosensitive sensor; μ is the average value of the data collected by all photosensitive sensors; σ is the standard deviation of the data collected by all photosensitive sensors;

[0064]

[0065] Where, X n is the data collected by the nth photosensor; N is the number of photosensors in an independent area;

[0066] Step 2: determining the actual illumination intensity of each independent illumination area according to each preprocessed first illumination intensity in each independent illumination area;

[0067] In the embodiment of the present disclosure, the calculation formula for the actual illumination intensity of each independent lighting area is as follows:

[0068]

[0069] In the formula, K actual,q is the actual illumination intensity of the qth independent lighting area, K q,n is the first light intensity after preprocessing collected by the nth photosensor in the qth independent lighting area, A q,n is the coverage area corresponding to the nth photosensor in the qth independent lighting area, h is the standard height of the factory building, H q,n is the actual height of the factory building in the coverage area corresponding to the nth photosensitive sensor in the qth independent lighting area, ρ q,n is the reflectivity of the wall and ground in the area corresponding to the nth photosensor in the qth independent lighting area, ρ q,n ∈[0.3~0.9].

[0070] Step 3: Determine the independent lighting area to be adjusted based on the working scene and the actual light intensity;

[0071] In the embodiment of the present disclosure, step 3 specifically includes:

[0072] Filtering out the independent lighting areas in working state based on the working scenes of the independent lighting areas;

[0073] Determine whether the actual light intensity of each independent lighting area in the working state belongs to the preset illumination range. If so, determine the independent lighting area as an independent lighting area that does not need to be regulated. Otherwise, determine the independent lighting area as an independent lighting area that needs to be regulated.

[0074] The preset illumination interval may be [200, 500].

[0075] It should be noted that when the actual illumination of the detected area (Unit of illumination: lx, lux) and is in working state, a control signal will be generated to activate the LADRC of the current area and start to adjust the lamps in the area; when the illumination K of the area is within the set range, that is, K∈[200,500], it means that the natural light intensity of the current area can meet the lighting needs and there is no need to turn on the lights.

[0076] The linear auto-disturbance rejection controller LADRC is used to control the lamps, and each area has its own controller; LADRC converts the light intensity information collected by the sensor into a control signal, and adjusts the brightness or switching the lamp on and off by controlling the power output of the lamp.

[0077] The internal structure of LADRC is mainly composed of the linear extended state observer (LESO), the linear tracking differentiator (LTD) and the linear state error feedback (LSEF), such as Figure 2 shown.

[0078] The linear ESO form of a general n-order controlled object is as follows:

[0079]

[0080] Where, e is the system deviation; z1, z2, … are the state vectors of the linear state extended observer LESO; is the first-order differential of z1,z2,…; β 1, β2… are the parameters that need to be adjusted in LESO, and r is the input signal of the system, that is, the illumination collected by the photosensor.

[0081] The LSEF form of the n-th order controlled object is as follows:

[0082]

[0083] Where z1, z2, … are the state vectors of the linear state extended observer LESO; e1, e2, … are the errors between the outputs of the system state estimation LESO and LTD; v1 is the tracking signal generated by the linear tracking differentiator (LTD); α1, α2, … are the parameters that need to be regulated by the LSEF. u0 is the virtual control quantity calculated by linearly combining the error signal. Specifically, u0 is the error between the transition process signal generated by the linear tracking differentiator (LTD) and its differential signal and the system state estimated by the linear extended state observer (LESO), obtained by linearly combining the gain coefficients of the proportional-differential (PD) controller. This virtual control quantity u0 is used to calculate the final control quantity u(t) to achieve precise control of the system state and disturbance suppression.

[0084] When the lamp needs to be adjusted, the LADRC controller will receive the input signal. First, the differential tracker LTD will extract the input signal containing random noise and its differential signal; the state observer estimates the real-time action value of the disturbance inside and outside the system, and compensates it in the feedback, using the compensation method to eliminate the influence of the disturbance; the state error feedback controller LSEF controls and compensates for the given signal and the differential of the signal obtained by LTD, and the error of the system output and output derivative observed by LESO.

[0085] Step 4: Substitute the actual light intensity of the independent lighting area to be regulated into the pre-established plant lighting energy consumption optimization objective function, and use the elite differential mutation collaborative search algorithm to optimize and solve the plant lighting energy consumption optimization objective function to obtain the optimized lighting power consumption, illumination and comfort information;

[0086] In the embodiment of the present disclosure, the calculation formula of the factory lighting energy consumption optimization objective function is as follows:

[0087]

[0088] Where Y q is the energy consumption index of the qth independent lighting area, E is the power consumption of the factory lighting, Comf q is the comfort index of the qth independent lighting area;

[0089] Among them, the factory lighting power consumption is the sum of the lighting power consumption of all areas, and the calculation formula is as follows:

[0090]

[0091] Where, E is the total power consumption of factory lighting; p q is the total power of the lamps in the qth area; t q is the working time of the lamps in the qth area; Q is the number of areas;

[0092] The calculation formula of the comfort index of the qth independent lighting area is as follows:

[0093] Comf q =-K actual,q 2 +2.5×10 3 K actual,q -1.3125×10 6

[0094] In the formula, Comf q is the comfort index of the qth independent lighting area.

[0095] It should be noted that comfort will be affected by changes in illumination. Too high or too low illumination will reduce comfort. The best illumination for eye protection is 200-500 lux (lx). Therefore, the relationship between comfort and illumination can be regarded as a concave quadratic function, and its expression is:

[0096] Comf q =-K actual,q 2 +2.5×10 3 K actual,q -1.3125×10 6

[0097] The final objective function is weighted sum of the three:

[0098]

[0099] Furthermore, the collaborative search algorithm using elite differential mutation is used to optimize and solve the objective function of the plant lighting energy consumption optimization to obtain optimized lighting power consumption, illumination and comfort information, including:

[0100] The control parameters of the linear state extended observer and the control parameters of the linear state error feedback in the self-disturbance rejection controller are optimized and adjusted by utilizing the team building stage, team communication stage, reflective learning stage and internal competition stage of the collaborative search algorithm of elite differential mutation until the energy consumption index in the plant lighting energy consumption optimization objective function is less than or equal to the preset energy consumption value;

[0101] The lighting power consumption, illumination and comfort information corresponding to the energy consumption index in the factory lighting energy consumption optimization objective function is less than or equal to the preset energy consumption value as the optimized lighting power consumption, illumination and comfort information.

[0102] It should be noted that in order to improve the control performance of the controller, the collaborative search algorithm CSA is used to optimize the parameters β1, β2 and α1, α2 (second-order system) that need to be adjusted by the controller. The CSA optimization process has four stages, namely team building, team communication, reflective learning, and internal competition. Figure 3 As shown; the specific optimization process is as follows:

[0103] Team building phase:

[0104]

[0105] Among them, I and J are different random numbers; is the jth value of the ith solution in the kth period; is a function that generates random numbers uniformly distributed in the range [L,U];

[0106] In the team building phase, a set of solutions is randomly generated using the above formula and the performance is evaluated. A group of solutions with the best performance are selected as the external elite set. The elite set will be continuously updated as the number of iterations increases, and the last four parameters will be selected from the elite set.

[0107] Team communication stage:

[0108]

[0109] in, is the jth value of the i-th solution in the k+1-th cycle; represents the knowledge gained from a randomly selected chairperson from an external elite set; and are the average knowledge obtained from the M global best known solutions and I personal best known solutions found so far, respectively;

[0110] In the team communication phase, all generated solutions learn from the elite set, and the elite set is updated by comparing the fitness values ​​of each solution;

[0111] Reflective learning phase:

[0112]

[0113] in, is the jth value of the i-th reflected solution in the k+1-th period; is the jth value of the i-th solution in the k+1-th cycle; is the jth value of the i-th reflection solution of the individual k+1-th period;

[0114] In the reflection phase, each solution learns in the opposite direction based on its own experience, and updates the elite set when a better solution is obtained than the elite set.

[0115] Internal competition stage:

[0116]

[0117] Among them, F(x) is the fitness value of solution x;

[0118] The internal competition phase protects solutions with strong optimization capabilities and updates the elite set in this way;

[0119] After the above four stages and when the optimization termination condition is reached, the optimal elite set will be obtained. Then the optimal value in the elite set will be used as the parameter value that needs to be adjusted for LADRC and input into the controller.

[0120] The collaborative search algorithm CSA is prone to fall into the local optimal solution, so the elite differential mutation EDV is integrated into the algorithm. By introducing new individuals into the population, the EDV algorithm can explore a wider area of ​​the solution space and avoid falling into the local optimal solution. The specific steps are as follows:

[0121] Randomly generate the initial population:

[0122] P={x1,x2,...,x N}

[0123] Where P is the population; x j (j=1,2,…) is the individual of the population;

[0124] Differential mutation, for each individual x j (i=1,2,…), randomly select three different individuals x a , x b , x c And calculate the difference vector:

[0125] v i =x a +F·(x b -x c )

[0126] In the formula, v i is the difference vector; x a , x b , x c are three individuals randomly selected from P; F is the scaling factor (a constant between 0 and 1);

[0127] Combine crossover, cross the mutated individuals with the target individuals to generate new candidate solutions:

[0128]

[0129] In the formula, u iis a new candidate solution; CR is the crossover probability (a constant between 0 and 1); rand is a random number in the range [0,1];

[0130] Individual selection, if the new candidate solution u i The fitness of the target individual x j , then use u i Replace x j :

[0131]

[0132] In the formula, is the new individual selected; f(·) is the fitness function;

[0133] The optimal new individuals generated by DE are integrated into the elite set of CSA to improve the global search ability of the CSA algorithm and avoid falling into the local optimum.

[0134] Step 5: Based on the optimized lighting power consumption, illumination and comfort information, the independent lighting area to be regulated is regulated by using an anti-disturbance rejection controller.

[0135] The detailed process of the energy consumption control method for factory lighting provided in this embodiment can be as follows: Figure 4 shown.

[0136] In summary, the energy consumption control method for factory lighting proposed in this embodiment has the following advantages: 1. The linear anti-disturbance controller LADRC is combined with the collaborative search algorithm CSA to improve the control accuracy of the system; 2. The elite differential variation EDV is integrated into the CSA to improve the global search capability of the algorithm, avoid the algorithm from falling into the local optimum, and further improve the control effect of the controller; 3. The factory is divided into areas, and the working conditions are set for the areas. According to the actual lighting needs of the areas, more refined control of the regional lamps is achieved; 4. In the objective function, in addition to meeting the illumination requirements and the minimum power consumption requirements, comfort is also introduced to keep the light intensity of the light in the optimal comfortable state, which can improve the work efficiency of the staff; 5. Compared with the traditional factory lighting control method, the present invention can adjust the brightness, darkness, brightness and extinguishing of the lamps in real time, greatly reducing the lighting power consumption and improving the economic benefits.

[0137] Embodiment 2

[0138] Figure 5 The structure diagram of an energy consumption control system for factory lighting provided according to one embodiment of the present application is as follows: Figure 5 As shown, the system comprises:

[0139] The acquisition module 100 is used to acquire the working scene of each independent lighting area of ​​the factory building and each first light intensity in each independent lighting area, and preprocess each first light intensity to obtain each preprocessed first light intensity;

[0140] It should be noted that n photosensitive sensors are installed in each independent lighting area;

[0141] The photosensor is used to collect the first light intensity;

[0142] The working scenes include: working state and maintenance state.

[0143] A first determination module 200, configured to determine the actual illumination intensity of each independent illumination area according to each preprocessed first illumination intensity in each independent illumination area;

[0144] The calculation formula for the actual illumination intensity of each independent lighting area is as follows:

[0145]

[0146] In the formula, K actual,q is the actual illumination intensity of the qth independent lighting area, K q,n is the first light intensity after preprocessing collected by the nth photosensor in the qth independent lighting area, A q,n is the coverage area corresponding to the nth photosensor in the qth independent lighting area, h is the standard height of the factory building, H q,n is the actual height of the factory building in the coverage area corresponding to the nth photosensitive sensor in the qth independent lighting area, ρ q,n is the reflectivity of the wall and ground in the area corresponding to the nth photosensor in the qth independent lighting area.

[0147] A second determination module 300, configured to determine an independent lighting area to be regulated based on the working scene and the actual light intensity;

[0148] The optimization module 400 is used to substitute the actual light intensity of the independent lighting area to be regulated into the pre-established factory lighting energy consumption optimization objective function, and optimize and solve the factory lighting energy consumption optimization objective function by using the elite differential mutation collaborative search algorithm to obtain the optimized lighting power consumption, illumination and comfort information;

[0149] The calculation formula of the factory lighting energy consumption optimization objective function is as follows:

[0150]

[0151] Where Y qis the energy consumption index of the qth independent lighting area, E is the power consumption of factory lighting, Comf q is the comfort index of the qth independent lighting area;

[0152] The calculation formula of the comfort index of the qth independent lighting area is as follows:

[0153] Comf q =-K actual,q 2 +2.5×10 3 K actual,q -1.3125×10 6

[0154] In the formula, Comf q is the comfort index of the qth independent lighting area.

[0155] The control module 500 is used to control the independent lighting area to be controlled based on the optimized lighting power consumption, illumination and comfort information and using an anti-disturbance rejection controller.

[0156] In the embodiment of the present disclosure, the acquisition module 100 is further used for:

[0157] The first illumination intensities are processed by using a Z-score method to obtain preprocessed first illumination intensities.

[0158] In the embodiment of the present disclosure, the second determining module 300 is further configured to:

[0159] Filtering out the independent lighting areas in working state based on the working scenes of the independent lighting areas;

[0160] Determine whether the actual light intensity of each independent lighting area in working state belongs to the preset illumination range. If so, the independent lighting area is determined as an independent lighting area that does not need to be regulated. Otherwise, the independent lighting area is determined as an independent lighting area that needs to be regulated.

[0161] In the embodiment of the present disclosure, the optimization module 400 is further used to:

[0162] The control parameters of the linear state extended observer and the control parameters of the linear state error feedback in the self-disturbance rejection controller are optimized and adjusted by utilizing the team building stage, team communication stage, reflective learning stage and internal competition stage of the collaborative search algorithm of elite differential mutation until the energy consumption index in the plant lighting energy consumption optimization objective function is less than or equal to the preset energy consumption value;

[0163] The lighting power consumption, illumination and comfort information corresponding to the energy consumption index in the factory lighting energy consumption optimization objective function is less than or equal to the preset energy consumption value as the optimized lighting power consumption, illumination and comfort information.

[0164] In summary, the energy consumption control system for factory lighting proposed in this embodiment improves the accuracy and comfort of energy consumption control and reduces lighting energy consumption.

[0165] Embodiment 3

[0166] In order to implement the above embodiments, the present disclosure further proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in Embodiment 1 is implemented.

[0167] Embodiment 4

[0168] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the first embodiment is implemented.

[0169] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0170] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0171] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for controlling energy consumption of factory lighting, characterized in that: The method comprises: Acquire the working scenes of each independent lighting area of ​​the factory building and each first light intensity in the each independent lighting area, and preprocess the each first light intensity to obtain each preprocessed first light intensity; Determine the actual illumination intensity of each independent illumination area according to each preprocessed first illumination intensity in each independent illumination area; Determine an independent lighting area to be adjusted based on the working scene and the actual light intensity; Substitute the actual light intensity of the independent lighting area to be regulated into the pre-established factory lighting energy consumption optimization objective function, and use the elite differential mutation collaborative search algorithm to optimize and solve the factory lighting energy consumption optimization objective function to obtain the optimized lighting power consumption, illumination and comfort information; Based on the optimized lighting power consumption, illumination and comfort information, the independent lighting area to be regulated is regulated by using an anti-disturbance control controller.

2. The method according to claim 1, characterized in that Each of the independent lighting areas is equipped with n photosensitive sensors; The photosensor is used to collect the first light intensity; The working scenes include: working state and maintenance state.

3. The method according to claim 2, characterized in that The preprocessing of the first illumination intensities to obtain the preprocessed first illumination intensities includes: The first illumination intensities are processed by using a Z-score method to obtain preprocessed first illumination intensities.

4. The method according to claim 3, characterized in that The calculation formula for the actual illumination intensity of each independent lighting area is as follows: In the formula, K actual,q is the actual illumination intensity of the qth independent lighting area, K q,n is the first light intensity after preprocessing collected by the nth photosensor in the qth independent lighting area, A q,n is the coverage area corresponding to the nth photosensor in the qth independent lighting area, h is the standard height of the factory building, H q,n is the actual height of the factory building in the coverage area corresponding to the nth photosensitive sensor in the qth independent lighting area, ρ q,n is the reflectivity of the wall and ground in the area corresponding to the nth photosensor in the qth independent lighting area.

5. The method according to claim 3, characterized in that The determining of the independent lighting area to be regulated based on the working scene and the actual light intensity includes: Filtering out the independent lighting areas in working state based on the working scenes of the independent lighting areas; Determine whether the actual light intensity of each independent lighting area in working state belongs to the preset illumination range. If so, the independent lighting area is determined as an independent lighting area that does not need to be regulated. Otherwise, the independent lighting area is determined as an independent lighting area that needs to be regulated.

6. The method according to claim 4, characterized in that The calculation formula of the plant lighting energy consumption optimization objective function is as follows: Where Y q is the energy consumption index of the qth independent lighting area, E is the power consumption of factory lighting, Comf q is the comfort index of the qth independent lighting area; The calculation formula of the comfort index of the qth independent lighting area is as follows: Comf q =-K actual,q 2 +2.5×10 3 K actual,q -1.3125×10 6 In the formula, Comf q is the comfort index of the qth independent lighting area.

7. The method according to claim 6, characterized in that The collaborative search algorithm using elite differential mutation is used to optimize and solve the factory lighting energy consumption optimization objective function to obtain optimized lighting power consumption, illumination and comfort information, including: The control parameters of the linear state extended observer and the control parameters of the linear state error feedback in the self-disturbance rejection controller are optimized and adjusted by utilizing the team building stage, team communication stage, reflective learning stage and internal competition stage of the collaborative search algorithm of elite differential mutation until the energy consumption index in the plant lighting energy consumption optimization objective function is less than or equal to the preset energy consumption value; The lighting power consumption, illumination and comfort information corresponding to the energy consumption index in the factory lighting energy consumption optimization objective function is less than or equal to the preset energy consumption value as the optimized lighting power consumption, illumination and comfort information.

8. An energy consumption control system for factory lighting, characterized in that: The system comprises: An acquisition module, used to acquire the working scenes of each independent lighting area of ​​the factory building and each first light intensity in each independent lighting area, and preprocess each first light intensity to obtain each preprocessed first light intensity; A first determining module, configured to determine the actual illumination intensity of each independent illumination area according to each preprocessed first illumination intensity in each independent illumination area; A second determination module, configured to determine an independent lighting area to be regulated based on the working scene and the actual light intensity; An optimization module is used to substitute the actual light intensity of the independent lighting area to be regulated into the pre-established plant lighting energy consumption optimization objective function, and optimize and solve the plant lighting energy consumption optimization objective function using the elite differential mutation collaborative search algorithm to obtain optimized lighting power consumption, illumination and comfort information; The control module is used to control the independent lighting area to be controlled based on the optimized lighting power consumption, illumination and comfort information and using an anti-disturbance control controller.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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