Street lamp intelligent control system and method based on data analysis

Abstract

The invention discloses an intelligent street lamp control system and method based on data analysis, relates to the technical field of intelligent street lamp control, and aims to solve the problem that street lamp control is insufficient in the intelligent level. Obtaining a standardized street lamp data set; calculating the brightness difference proportion of adjacent street lamps, comparing the brightness difference proportion with a preset threshold value, and generating a compensation instruction when the difference exceeds the preset threshold value; dynamically adjusting the brightness of the street lamp based on the brightness and the distance of the adjacent street lamp; in the street lamp layout in opposite ranking, when the street lamps are abnormal and adjacent street lamps on the same side exist, the street lamps on the same side and on the different sides are linked to supplement light, the compensation radius is calculated with the fault street lamp as the center, the brightness adjustment amplitude of each street lamp participating in compensation is determined according to rules, and the optimal compensation strategy is solved through an intelligent algorithm. And intelligent control of the street lamp is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control of street lamps, in particular to a street lamp intelligent control system and method based on data analysis. BACKGROUND

[0002] At present, the demand for intelligent and refined management in the field of urban street lamp control is increasing, and street lamps, as an important part of urban infrastructure, directly affect the lighting quality, energy consumption and traffic safety.

[0003] The existing street lamp control method mainly adopts manual control or timing control, which has limitations in dealing with complex scenes.

[0004] The prior art in the above has the following defects: first, the control efficiency is low, the number of urban street lamps is large and widely distributed, and the traditional control method is difficult to adjust according to the real-time environment, in the morning and evening peak, weather mutation and other scenes, it is easy to appear insufficient lighting or excessive lighting problem, which not only affects the travel safety but also causes energy waste; in addition, it lacks global collaborative management capability, the brightness difference of adjacent road sections is too large, the energy consumption is unbalanced, and other problems, which cannot comprehensively consider the lighting demand and mutual influence of different road sections, scenes, and is difficult to meet the development demand of urban intelligent lighting. SUMMARY

[0005] The purpose of the present application is to provide a street lamp intelligent control system and method based on data analysis.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme: a street lamp intelligent control method based on data analysis, acquiring data related to street lamps, cleaning and preprocessing the acquired data to obtain a standardized street lamp data set;

[0007] Calculate the brightness difference ratio of adjacent street lamps, compare with the preset threshold value, and generate a compensation instruction when the difference exceeds the set threshold value;

[0008] Based on the brightness and distance of adjacent street lamps, dynamically adjust the brightness of the street lamp;

[0009] In the opposite order of street lamp layout, when the street lamp appears abnormal and there is a same side adjacent street lamp, the same side and opposite side surrounding street lamps are linked to compensate for light, the compensation radius is calculated with the fault street lamp as the center, the brightness adjustment amplitude of each participating compensation street lamp is determined according to the rules, and the optimal compensation strategy is solved by intelligent algorithm.

[0010] Acquiring data related to street lamps, cleaning and preprocessing the acquired data to obtain a standardized street lamp data set, the specific steps include:

[0011] Further, the data related to the street lamp is acquired, including the unique identification code of each street lamp, the real-time luminous brightness parameter, and the physical interval distance information of the adjacent street lamps in the road layout;

[0012] The acquired data related to the street lamp is cleaned and preprocessed, and the data format is standardized, and all data is stored according to a unified database structure;

[0013] The Min-Max normalization algorithm is used to process the acquired data related to the street lamp, and the data is uniformly mapped to the interval [0, 1];

[0014] The cleaned data is composed into a standardized street lamp data set, denoted as DS = {d s1 ,d s2 ,....,d sn}; wherein d s1 ,d s2 ,....,d sn represent the data related to the 1st, 2nd,..., nth street lamp.

[0015] The luminance difference ratio of adjacent street lamps is calculated, and compared with a preset threshold value, and when the difference exceeds the set threshold value, a compensation instruction is generated, and the specific steps include:

[0016] Further, the luminance gradient of the adjacent street lamp node is calculated, and the calculation formula is as follows: ΔL i,i+1 = |L i -L i+1 | / max(L i ,L i+1 ); wherein L i and L i+1 represent the luminance of the i-th and i+1-th street lamp, and ΔL i,i+1 represents the luminance gradient of the i-th and i+1-th street lamp;

[0017] The relationship between the luminance gradient of the adjacent street lamp node and the preset luminance gradient threshold value is compared, and for the luminance gradient of the adjacent street lamp node greater than the preset luminance gradient threshold value, a luminance compensation instruction is generated, and the luminance of the i-th street lamp is adjusted according to the formula f(L i ,L i+1 ,d i,i+1 ), wherein d i,i+1 represents the distance between the i-th street lamp and the i+1-th street lamp.

[0018] Based on the luminance and distance of the adjacent street lamp, the luminance of the street lamp is dynamically adjusted, and the specific steps include:

[0019] Further, the luminance of the i-th street lamp is adjusted, and the calculation formula is as follows:

[0020] When L i Less than L i+1 At that time, according to L i ’ =f(L i ,L i+1 ,d i,i+1 ) = L i +L i+1 / (1+e^(-d i,i+1 / d0))*ΔL i,i+1 *(L i+1 -L i The brightness of the i-th street light is adjusted, where L i ’ d represents the adjusted brightness of the i-th street light. i,i+1 Let di represent the distance between streetlight i and streetlight i+1, d0 represent the standard distance, and Lmin ≤ Li+1. i ’ ≤Lmax, Lmin, and Lmax represent the brightness range specified by the national standard, respectively;

[0021] When L i Greater than or equal to L i+1 At that time, according to f(L) i ,L i+1 ,d i,i+1 ) = L i -L i+1 / (1+e^(-d i,i+1 / d0))*ΔL i,i+1 *(L i+1 -L i Adjust the brightness of the i-th street light.

[0022] In a street light layout arranged opposite each other, when a street light malfunctions and there are adjacent street lights on the same side, it triggers supplementary lighting from surrounding street lights on the same and opposite sides. The compensation radius is calculated with the faulty street light as the center, and the brightness adjustment range of each participating street light is determined according to rules. The optimal compensation strategy is then solved using an intelligent algorithm. The specific steps include:

[0023] Furthermore, within a detection cycle, based on the street light arrangement in the opposite layout, the current street light S is determined. (l,i) Are there adjacent streetlights? For streetlight S (l,i) There is an adjacent street light S (l,i-1) In the case of street light S (l,i) In case of abnormality, adjust the street light S (l,i) The illuminance of the surrounding streetlights thus meets the lighting needs within the illumination range, wherein the streetlight S (l,i) Surrounding streetlights, including streetlight S (l,i-1) S (l,i+1), street lamp S (r,i-1) , street lamp S (r,i) , street lamp S (r,i+1) , S (l,i-1) , S (l,i+1) respectively represent the i-1th and i+1th street lamps on the same side of the street lamp S (l,i) , S (r,i-1) , S (r,i) , S (r,i+1) respectively represent the i-1th, i and i+1th street lamps on the opposite side of the street lamp S (l,i) ;

[0024] When the street lamp S (l,i) fails, a compensation radius R is calculated with the street lamp S (l,i) as the center, and the compensation radius is defined as follows: R = max(d l,i-1 , d l,i+1 ); wherein d l,i-1 and d l,i+1 respectively represent the distances between the i-1th street lamp and the i+1th street lamp on the same side of the street lamp S (l,i) ;

[0025] For the street lamps participating in compensation k ∈ {(l, i + 1), (l, i + 1), (r, i), (r, i + 1)}, a linkage compensation is calculated, and is defined as follows: L k ’ = L k + γ k *(R-d i,k ) / R*(L req -L i ); wherein L req represents the lowest illumination standard of the road, γ k represents the compensation coefficient of the street lamps participating in compensation to the street lamp that fails, and d i,k represents the distance between the ith street lamp and the street lamp participating in compensation.

[0026] By establishing an optimization equation, a Pareto optimal solution set is solved by the NSGA-II algorithm; wherein the optimization equation needs to satisfy: min(α*ΣP k +β*max|ΔL i,i+1 |), s.t. L k ≥ L req , dL / dt ≤ q; wherein α represents the energy consumption weight, β represents the uniformity weight, dL / dt represents the brightness change rate, and q represents the set brightness change rate threshold.

[0027] The application discloses a street lamp intelligent control system based on data analysis, which comprises a data acquisition module, a brightness difference analysis module, a brightness adjustment module and an abnormal compensation module, wherein the data acquisition module is used for acquiring data related to street lamps, cleaning and preprocessing the acquired data, and obtaining a standardized street lamp data set; the brightness difference analysis module is used for calculating the brightness difference proportion of adjacent street lamps, comparing with a preset threshold value, and generating a compensation instruction when the difference exceeds the set threshold value; the brightness adjustment module is used for dynamically adjusting the brightness of street lamps based on the brightness and distance of adjacent street lamps; and the abnormal compensation module is used for, when a street lamp appears abnormal and there is a same-side adjacent street lamp in the opposite-order street lamp layout, compensating the light of the same-side and different-side surrounding street lamps, taking the fault street lamp as the center to calculate a compensation radius, determining the brightness adjustment range of each participating compensation street lamp according to rules, and solving the optimal compensation strategy through an intelligent algorithm.

[0028] Further, the data acquisition module comprises a spatial data acquisition unit, an operating state data acquisition unit and a preprocessing unit, wherein the spatial data acquisition unit is used for acquiring data related to street lamps, including the unique identification code of each street lamp and the physical interval distance information of adjacent street lamps in the road layout; the operating state data acquisition unit is used for acquiring the real-time luminous brightness parameters of street lamps; and the preprocessing unit is used for cleaning and standardizing the collected original data, storing the cleaned and standardized data in a unified database structure, forming a standardized street lamp data set, and connecting the input end of the brightness difference analysis module to the output end of the data acquisition module.

[0029] Further, the brightness difference analysis module comprises a brightness gradient calculation unit, a threshold value comparison unit and a compensation instruction generation unit, wherein the brightness gradient calculation unit is used for calculating the brightness gradient of adjacent street lamp nodes; the threshold value comparison unit is used for comparing the calculated brightness gradient with a preset brightness gradient threshold value, and judging whether the brightness difference exceeds the set range; and the compensation instruction generation unit is used for generating a brightness compensation instruction when the brightness gradient is greater than the preset threshold value, and triggering the brightness adjustment of street lamps, wherein the output end of the brightness gradient calculation unit is connected to the input end of the threshold value comparison unit, the output end of the threshold value comparison unit is connected to the input end of the compensation instruction generation unit, and the output end of the compensation instruction generation unit is connected to the output end of the brightness adjustment module.

[0030] Further, the brightness adjustment module comprises a brightness relationship judging unit, an adjustment calculation unit and a brightness control execution unit, the brightness relationship judging unit is used for judging the size relationship between the current street lamp brightness and the adjacent street lamp brightness, and determining the adjustment direction, the adjustment calculation unit calculates the street lamp brightness adjustment value based on the adjacent street lamp brightness and the spacing parameter, and the brightness control execution unit is used for executing the brightness adjustment instruction, adjusting the street lamp brightness, and limiting the adjusted brightness in the range stipulated by the national standard, the output end of the brightness relationship judging unit is connected with the input end of the adjustment calculation unit, the output end of the adjustment calculation unit is connected with the input end of the brightness control execution unit, and the output end of the brightness control execution unit is connected with the input end of the abnormality compensation module.

[0031] Further, the abnormality compensation module comprises an abnormality detection unit, a compensation range determination unit, a compensation calculation unit and an optimal strategy solving unit, the abnormality detection unit is used for judging whether the current street lamp has an abnormality and whether there is an adjacent street lamp in a detection period, the compensation range determination unit is used for calculating the compensation radius with the fault street lamp as the center, and determining the range of the surrounding street lamps participating in the compensation, the compensation calculation unit is used for calculating the brightness adjustment amplitude of each participating compensation street lamp according to the distance between the participating compensation street lamp and the fault street lamp and the compensation coefficient parameter, and the optimal strategy solving unit is used for solving the Pareto optimal solution set by establishing an optimization equation and using the NSGA-II algorithm, the output end of the abnormality detection unit is connected with the input end of the compensation range determination unit, the output end of the compensation range determination unit is connected with the input end of the compensation calculation unit, and the output end of the compensation calculation unit is connected with the input end of the optimal strategy solving unit.

[0032] By adopting the above technical scheme, the intelligent control of the street lamp is realized.

[0033] In summary, the beneficial technical effects of the present application are as follows:

[0034] 1. The scheme of linking the surrounding street lamps to compensate for light when the street lamp is abnormal in the opposite ordering layout and solving the optimal strategy by the intelligent algorithm is adopted, so that the lighting demand is met when the street lamp is abnormal, and the effects of optimizing the energy consumption and the lighting uniformity are realized.

[0035] 2. The mechanism of dynamically adjusting the street lamp brightness based on the adjacent street lamp brightness and the spacing is adopted, so that the brightness is adjusted according to the brightness relationship and the distance parameter, and the effect of making the adjusted brightness meet the national standard range is produced.

[0036] 3. The method of calculating the adjacent street lamp brightness gradient and comparing with the preset threshold value is adopted, so that the situation that the brightness difference exceeds the range is found in time, the compensation instruction is generated, and the effect of guaranteeing the lighting uniformity is realized. BRIEF DESCRIPTION OF DRAWINGS

[0037] The accompanying drawings 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 principles of the application. In the drawings:

[0038] Fig. 1 is a flow diagram of a street lamp intelligent control method based on data analysis of the application;

[0039] Fig. 2 is a structural diagram of a street lamp intelligent control system based on data analysis of the application. DETAILED DESCRIPTION

[0040] The application will be further described below in conjunction with the drawings.

[0041] The technical solutions in the embodiments of the application will be clearly and completely described below in conjunction with the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0042] Please refer to Figs. 1-2 The application provides a technical solution: a street lamp intelligent control method based on data analysis, obtaining data related to street lamps, cleaning and preprocessing the obtained data to obtain a standardized street lamp data set;

[0043] Calculating the brightness difference ratio of adjacent street lamps, comparing with a preset threshold, and generating a compensation instruction when the difference exceeds the set threshold;

[0044] Based on the brightness and distance of adjacent street lamps, the brightness of the street lamp is dynamically adjusted;

[0045] In the face-ordered street lamp layout, when the street lamp appears abnormal and there is a same-side adjacent street lamp, the same-side and opposite-side surrounding street lamps are linked to compensate for light. The compensation radius is calculated with the faulty street lamp as the center, the brightness adjustment range of each participating compensation street lamp is determined according to the rules, and the optimal compensation strategy is solved by an intelligent algorithm.

[0046] Obtaining data related to street lamps, cleaning and preprocessing the obtained data to obtain a standardized street lamp data set, the specific steps including:

[0047] Further, the data related to the street lamps includes the unique identification code of each street lamp, the real-time light brightness parameter, and the physical interval distance information of the adjacent street lamps in the road layout;

[0048] The obtained data related to the street lamps is cleaned and preprocessed, and the data format is standardized. All data is stored according to a unified database structure;

[0049] The Min-Max normalization algorithm is used to process the obtained data related to the street lamp, and the data is uniformly mapped to the interval [0, 1];

[0050] The cleaned data is composed of a standardized street lamp data set, denoted as DS={d s1 ,d s2 ,....,d sn}; wherein d s1 ,d s2 ,....,d sn represent the related data of the 1st, 2nd,..., nth street lamp.

[0051] Specifically, in a certain city, the road type is a two-way four-lane urban trunk road, the road width is 20 meters, the street lamps are arranged in opposite order, the same side distance is 30 meters, and the vertical distance of the opposite street lamps is 20 meters. The street lamps are numbered, and the street lamp S (l,5) The normal brightness is 20 Lux, and the brightness after failure is reduced to 5 Lux. The spatial data: the street lamp S (l,5) The distance between the same side S (l,5) , S (l,6) is 30 meters, and the vertical distance of the opposite S (r,4) , S (r,5) , S (r,6) is 20 meters.

[0052] Running state: S (l,5) The brightness is 5 Lux, the brightness of the adjacent S (l,5) is 18 Lux, the brightness of S (l,5) is 19 Lux, the brightness of the opposite S (r,4) is 17 Lux, the brightness of S (r,5) is 18 Lux, and the brightness of S (r,6) is 17 Lux.

[0053] The Min-Max normalization algorithm is used to map the brightness data to the interval [0, 1].

[0054] The brightness difference ratio of the adjacent street lamps is calculated, and compared with the preset threshold value. When the difference exceeds the set threshold value, a compensation instruction is generated. The specific steps include:

[0055] Further, the brightness gradient of the adjacent street lamp nodes is calculated, and the calculation formula is as follows: ΔL i,i+1 =|L i -L i+1 | / max(L i ,L i+1 ); wherein L i and L i+1 represent the brightness of the i-th and i+1-th street lamps, and ΔL i,i+1represents the luminance gradient of the i-th and i+1-th street lamps;

[0056] The relationship between the luminance gradient of the adjacent street lamp nodes and the preset luminance gradient threshold is compared, and for the luminance gradient of the adjacent street lamp nodes being greater than the preset luminance gradient threshold, a luminance compensation instruction is generated, and the luminance of the i-th street lamp is adjusted according to the formula f(L i ,L i+1 ,d i,i+1 ), wherein d i,i+1 represents the distance between the i-th street lamp and the i+1-th street lamp.

[0057] Based on the luminance and distance of the adjacent street lamps, the luminance of the street lamp is dynamically adjusted, and the specific steps include:

[0058] Further, the luminance of the i-th street lamp is adjusted, and the calculation formula is as follows:

[0059] When L i is less than L i+1 , the luminance of the i-th street lamp is adjusted according to L i ’ =f(L i ,L i+1 ,d i,i+1 )=L i +L i+1 / (1+e^(-d i,i+1 / d0))*ΔL i,i+1 *(L i+1 -L i ), wherein L i ’ represents the adjusted luminance of the i-th street lamp, d i,i+1 represents the distance between the i-th street lamp and the i+1-th street lamp, d0 represents a standard distance, Lmin≤L i ’ ≤Lmax, Lmin and Lmax respectively represent the luminance range specified in the national standard;

[0060] When L i is greater than or equal to L i+1 , the luminance of the i-th street lamp is adjusted according to f(L i ,L i+1 ,d i,i+1 )=L i -L i+1 / (1+e^(-d i,i+1 / d0))*ΔL i,i+1 *(L i+1 -L i ).

[0061] Specifically, the luminance of the i-th street lamp S (l,4) and the i+1-th street lamp SThe luminance gradient of S (l,5) is 0.72, the preset threshold is 0.3, and the luminance compensation instruction is triggered;

[0062] By judging the relationship between the luminance of the street lamp S (l,4) and the luminance of the street lamp S (l,5) , the adjusted luminance of the street lamp S (l,5) is calculated to be 18.84 Lux, and the adjusted luminance meets the national standard Lmin of 15 Lux and Lmax of 25 Lux.

[0063] In the opposite ordering of the street lamp layout, when the street lamp appears abnormal and there is a same side adjacent street lamp, the surrounding street lamps on the same side and the opposite side are linked to compensate for light, the compensation radius is calculated with the fault street lamp as the center, the luminance adjustment range of each participating compensation street lamp is determined according to the rules, and the optimal compensation strategy is solved through intelligent algorithm, and the specific steps include:

[0064] Further, in a detection period, for the street lamp ordering situation showing opposite ordering layout, it is judged whether there is an adjacent street lamp S (l,i) for the current street lamp S (l,i) , for the case that the street lamp S (l,i-1) has an adjacent street lamp S (l,i) , when the street lamp S (l,i) has abnormal conditions, the illumination intensity of the surrounding street lamps of the street lamp S (l,i) is adjusted to meet the lighting demand in the lighting range, wherein the surrounding street lamps of the street lamp S (l,i-1) include the street lamp S (l,i+1) , the street lamp S (r,i-1) , the street lamp S (r,i) , the street lamp S (r,i+1) , S (l,i-1) , S (l,i+1) respectively represent the i-1th and i+1th street lamps on the same side with the street lamp S (l,i) , S (r,i-1) , S (r,i) , S (r,i+1) respectively represent the i-1th, i and i+1th street lamps on the opposite side with the street lamp S (l,i) ;

[0065] When the street lamp S (l,i) fails, the compensation radius R is calculated with the street lamp S (l,i) as the center, and the compensation radius is defined as follows: R = max(d l,i-1 , d l,i+1 ); wherein d l,i-1 and d l,i+1 respectively represent the distance between the i-1th street lamp and the i+1th street lamp on the same side with the street lamp S (l,i) ;

[0066] For the participating compensation street lamp k∈{(l,i+1),(l,i+1),(r,i),(r,i+1)} to calculate linkage compensation, defined as shown below: L k ’ = L k + γ k *(R-d i,k ) / R*(L req -L i ); wherein, L req represents the road minimum illumination standard, γ k represents the compensation coefficient of the participating compensation street lamp pair to the failed street lamp, d i,k represents the distance between the ith street lamp and the participating compensation street lamp.

[0067] By establishing an optimization equation, the Pareto optimal solution set is solved by NSGA-II algorithm; wherein, the optimization equation needs to meet: min(α*ΣP k +β*max|ΔL i,i+1 |), s.t.L k ≥L req , dL / dt≤q; wherein, α represents the energy consumption weight, β represents the uniformity weight, dL / dt represents the brightness change rate, q represents the set brightness change rate threshold.

[0068] Specifically, the brightness of the street lamp S (l,5) lasts less than 10Lux for more than 5 minutes, which is determined as failure, the compensation radius is determined to be 30m based on the compensation radius formula, the participating compensation street lamps are S (l,4) , S (l,6) , S (r,4) , S (r,5) , S (r,6) , the road minimum illumination is calculated to be 15Lux, the brightness gap is 10Lux, the compensation coefficient of the same side street lamp is 0.7, the compensation coefficient of the opposite side street lamp is 0.3, the lighting brightness of the participating compensation street lamp needs to be adjusted is calculated respectively, the Pareto optimal solution set is solved by NSGA-II algorithm by establishing an optimization equation.

[0069] The application discloses a street lamp intelligent control system based on data analysis, which comprises a data acquisition module, a brightness difference analysis module, a brightness adjustment module and an abnormal compensation module. The data acquisition module is used for acquiring data related to street lamps, cleaning and preprocessing the acquired data, and obtaining a standardized street lamp data set. The brightness difference analysis module is used for calculating the brightness difference ratio of adjacent street lamps, comparing the brightness difference ratio with a preset threshold value, and generating a compensation instruction when the difference exceeds the set threshold value. The brightness adjustment module is used for dynamically adjusting the brightness of street lamps based on the brightness and distance of adjacent street lamps. The abnormal compensation module is used for compensating light for the surrounding street lamps on the same side and the opposite side of the street lamp when the street lamp appears abnormal and there is an adjacent street lamp on the same side in the face-ordered street lamp layout, calculating the compensation radius with the faulty street lamp as the center, determining the brightness adjustment range of each participating compensation street lamp according to the rules, and solving the optimal compensation strategy through an intelligent algorithm.

[0070] Further, the data acquisition module comprises a spatial data acquisition unit, an operating state data acquisition unit and a preprocessing unit. The spatial data acquisition unit is used for acquiring data related to street lamps, including the unique identification code of each street lamp and the physical interval distance information of adjacent street lamps in the road layout. The operating state data acquisition unit is used for acquiring the real-time luminous brightness parameters of street lamps. The preprocessing unit is used for cleaning and standardizing the collected raw data, storing the cleaned and standardized data in a unified database structure, forming a standardized street lamp data set, and connecting the output end of the data acquisition module to the input end of the brightness difference analysis module.

[0071] Further, the brightness difference analysis module comprises a brightness gradient calculation unit, a threshold value comparison unit and a compensation instruction generation unit. The brightness gradient calculation unit is used for calculating the brightness gradient of adjacent street lamp nodes. The threshold value comparison unit is used for comparing the calculated brightness gradient with a preset brightness gradient threshold value to determine whether the brightness difference exceeds the set range. The compensation instruction generation unit is used for generating a brightness compensation instruction when the brightness gradient is greater than the preset threshold value, triggering the brightness adjustment of street lamps. The output end of the brightness gradient calculation unit is connected to the input end of the threshold value comparison unit, the output end of the threshold value comparison unit is connected to the input end of the compensation instruction generation unit, and the output end of the compensation instruction generation unit is connected to the output end of the brightness adjustment module.

[0072] Further, the brightness adjustment module comprises a brightness relationship judging unit, an adjustment calculating unit and a brightness control executing unit, the brightness relationship judging unit is used for judging the size relationship between the current street lamp brightness and the adjacent street lamp brightness, and determining the adjustment direction, the adjustment calculating unit calculates the street lamp brightness adjustment value based on the adjacent street lamp brightness and the distance parameter, and the brightness control executing unit is used for executing the brightness adjustment instruction, adjusting the street lamp brightness, and limiting the adjusted brightness in the range stipulated by the national standard, the output end of the brightness relationship judging unit is connected with the input end of the adjustment calculating unit, the output end of the adjustment calculating unit is connected with the input end of the brightness control executing unit, and the output end of the brightness control executing unit is connected with the input end of the abnormality compensation module.

[0073] Further, the abnormality compensation module comprises an abnormality detecting unit, a compensation range determining unit, a compensation calculating unit and an optimal strategy solving unit, the abnormality detecting unit is used for judging whether the current street lamp has abnormality and whether there is an adjacent street lamp in a detection period, the compensation range determining unit is used for calculating the compensation radius with the fault street lamp as the center, and determining the range of the surrounding street lamps participating in the compensation, the compensation calculating unit is used for calculating the brightness adjustment amplitude of each participating compensation street lamp according to the distance between the participating compensation street lamp and the fault street lamp and the compensation coefficient parameter, and the optimal strategy solving unit is used for solving the Pareto optimal solution set by establishing an optimization equation and using the NSGA-II algorithm, the output end of the abnormality detecting unit is connected with the input end of the compensation range determining unit, the output end of the compensation range determining unit is connected with the input end of the compensation calculating unit, and the output end of the compensation calculating unit is connected with the input end of the optimal strategy solving unit.

[0074] It should be noted that, in this text, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between the entities or operations. Moreover, the term "comprises", "includes" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0075] Finally, it should be noted that: the above only describes the preferred embodiments of the present application, and does not limit the present application, although the present application has been described in detail with reference to the foregoing embodiments, and those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A data analysis-based intelligent control method for street lamps, characterized in that: Obtaining data related to street lamps, cleaning and preprocessing the obtained data, and obtaining a standardized street lamp data set Calculate the brightness difference ratio of adjacent street lamps, compare with the preset threshold, and generate a compensation instruction when the difference exceeds the set threshold Based on the brightness and distance of adjacent street lamps, dynamically adjust the brightness of the street lamp In the opposite ordering of the street lamp layout, when the street lamp appears abnormal and there is a same side adjacent street lamp, link the same side and opposite side surrounding street lamps for light compensation, calculate the compensation radius with the fault street lamp as the center, determine the brightness adjustment amplitude of each participating compensation street lamp according to the rules, and solve the optimal compensation strategy through intelligent algorithm.

2. The intelligent control method of street lamps based on data analysis according to claim 1, characterized in that: Obtaining data related to street lamps, cleaning and preprocessing the obtained data, and obtaining a standardized street lamp data set, the specific steps comprising: Obtaining data related to street lamps, including the unique identification code of each street lamp, the real-time luminous brightness parameter, and the physical interval distance information of adjacent street lamps in the road layout; Cleaning and preprocessing the obtained data related to street lamps, and standardizing the data format, storing all data according to a unified database structure; Using Min-Max normalization algorithm to process the obtained data related to street lamps, and mapping the data to the [0,1] interval; The cleaned data is composed into a standardized street lamp data set, denoted as DS = {d s1 ,d s2 ,....,d sn}; wherein d s1 ,d s2 ,....,d sn represent the relevant data of the 1st, 2nd,..., nth street lamp respectively.

3. The method of claim 2, wherein: Calculate the brightness difference ratio of adjacent street lamps, compare with the preset threshold, and generate a compensation instruction when the difference exceeds the set threshold, the specific steps comprising: The luminance gradient of the adjacent street light nodes is calculated, and the calculation formula is as follows: ΔL i,i+1 = |L i -L i+1 | / max(L i ,L i+1 ); wherein, L i and L i+1 respectively represent the luminance of the i th and i+1 th street light, and ΔL i,i+1 represents the luminance gradient of the i th and i+1 th street light; The relationship between the luminance gradient of the adjacent street light nodes and the preset luminance gradient threshold is compared. For the luminance gradient of the adjacent street light nodes being greater than the preset luminance gradient threshold, a luminance compensation instruction is generated, and the luminance of the i-th street light is adjusted according to a formula f(L i , L i+1 , d i,i+1 ) where d i,i+1 represents the distance between the i-th street light and the i+1-th street light.

4. The intelligent control method of street lamps based on data analysis according to claim 3, characterized in that: Based on the brightness and distance of adjacent street lamps, dynamically adjust the brightness of the street lamp, the specific steps comprising: Adjust the brightness of the i-th street lamp, the calculation formula is as follows: When L i Less than L i+1 At that time, according to L i ’ =f(L i ,L i+1 ,d i,i+1 ) = L i +L i+1 / (1+e^(-d i,i+1 / d0))*ΔL i,i+1 *(L i+1 -L i The brightness of the i-th street light is adjusted, where L i ’ d represents the adjusted brightness of the i-th street light. i,i+1 Let di represent the distance between streetlight i and streetlight i+1, d0 represent the standard distance, and Lmin ≤ Li+1. i ’ ≤Lmax, Lmin, and Lmax represent the brightness range specified by the national standard, respectively; When L i ≥ L i+1 , the brightness of the i-th street lamp is adjusted according to f(L i ,L i+1 ,d i,i+1 ) = L i -L i+1 / (1+e^(-d i,i+1 / d0))*ΔL i,i+1 *(L i+1 -L i ).

5. The method of claim 4, wherein: In the opposite ordering of the street lamp layout, when the street lamp appears abnormal and there is a same side adjacent street lamp, link the same side and opposite side surrounding street lamps for light compensation, calculate the compensation radius with the fault street lamp as the center, determine the brightness adjustment amplitude of each participating compensation street lamp according to the rules, and solve the optimal compensation strategy through intelligent algorithm, the specific steps comprising: In one detection cycle, the current street lamp S (l,i) is judged whether there is a neighboring street lamp, for the street lamp S (l,i) , if there is a neighboring street lamp S (l,i-1) , when the street lamp S (l,i) has abnormal situation, the illumination intensity of the surrounding street lamps is adjusted to meet the lighting demand in the lighting range, wherein the surrounding street lamps include the street lamp S (l,i) , S (l,i) , the street lamp S (l,i-1) , the street lamp S (l,i+1) , the street lamp S (r,i-1) , the street lamp S (r,i) , the street lamp S (r,i+1) , S (l,i-1) , S (l,i+1) respectively represent the i-1 and i+1 street lamps on the same side with the street lamp S (l,i) , S (r,i-1) , S (r,i) , S (r,i+1) respectively represent the i-1, i and i+1 street lamps on the opposite side with the street lamp S (l,i) . When the street lamp S (l,i) fails, a compensation radius R is calculated with the street lamp S (l,i) as the center, and the compensation radius is defined as shown below: R = max(d l,i-1 , d l,i+1 ); wherein d l,i-1 and d l,i+1 respectively represent the distances of the i-1th street lamp and the i+1th street lamp on the same side as the street lamp S (l,i) from the street lamp S (l,i) . For the participating compensation street lamp k∈{(l,i+1),(l,i+1),(r,i),(r,i+1)}, the linkage compensation is calculated, defined as shown below: L k ’ = L k + γ k *(R-d i,k ) / R*(L req -L i ); wherein, L req represents the lowest illumination standard of the road, γ k represents the compensation coefficient of the participating compensation street lamp pair to the failed street lamp, d i,k represents the distance between the ith street lamp and the participating compensation street lamp. By establishing an optimization equation, the NSGA-II algorithm is used to solve the Pareto optimal solution set.

6. A data analysis based intelligent control system for street lights, applied to the data analysis based intelligent control method for street lights according to any one of claims 1-5, characterized in that: The system comprises a data acquisition module, a brightness difference analysis module, a brightness adjustment module and an abnormal compensation module, the data acquisition module is used for obtaining data related to street lamps, cleaning and preprocessing the obtained data, and obtaining a standardized street lamp data set; the brightness difference analysis module is used for calculating the brightness difference ratio of adjacent street lamps, comparing with the preset threshold, and generating a compensation instruction when the difference exceeds the set threshold; the brightness adjustment module is used for dynamically adjusting the brightness of the street lamp based on the brightness and distance of adjacent street lamps; the abnormal compensation module is used for linking the same side and opposite side surrounding street lamps for light compensation in the opposite ordering of the street lamp layout when the street lamp appears abnormal and there is a same side adjacent street lamp, calculating the compensation radius with the fault street lamp as the center, determining the brightness adjustment amplitude of each participating compensation street lamp according to the rules, and solving the optimal compensation strategy through intelligent algorithm.

7. The data analytics based intelligent control system for street light as claimed in claim 6 wherein: The data acquisition module comprises a spatial data acquisition unit, an operating state data acquisition unit and a preprocessing unit, the spatial data acquisition unit is used for acquiring data related to street lamps, including the unique identification code of each street lamp and the physical interval distance information of adjacent street lamps in road layout; the operating state data acquisition unit is used for acquiring the real-time luminous brightness parameter of the street lamp; the preprocessing unit is used for cleaning and standardizing the collected original data, storing the cleaned and standardized data according to a unified database structure, forming a standardized street lamp data set, and the output end of the data acquisition module is connected to the input end of the luminance difference analysis module.

8. The data analysis based intelligent control system for street light as claimed in claim 7 wherein: The luminance difference analysis module comprises a luminance gradient calculation unit, a threshold comparison unit and a compensation instruction generation unit, the luminance gradient calculation unit is used for calculating the luminance gradient of adjacent street lamp nodes; the threshold comparison unit is used for comparing the calculated luminance gradient with a preset luminance gradient threshold to determine whether the luminance difference exceeds the set range; The compensation instruction generation unit is used for generating a luminance compensation instruction when the luminance gradient is greater than the preset threshold, triggering street lamp luminance adjustment, the output end of the luminance gradient calculation unit is connected to the input end of the threshold comparison unit, the output end of the threshold comparison unit is connected to the input end of the compensation instruction generation unit, and the output end of the compensation instruction generation unit is connected to the output end of the luminance adjustment module.

9. The data analysis based intelligent control system for street light as claimed in claim 8 wherein: The luminance adjustment module comprises a luminance relationship judgment unit, an adjustment calculation unit and a luminance control execution unit, the luminance relationship judgment unit is used for judging the size relationship between the current street lamp luminance and the adjacent street lamp luminance to determine the adjustment direction; the adjustment calculation unit calculates the street lamp luminance adjustment value based on the adjacent street lamp luminance and the interval distance parameter; The luminance control execution unit is used for executing the luminance adjustment instruction to adjust the street lamp luminance, and limiting the adjusted luminance within the range specified in the national standard, the output end of the luminance relationship judgment unit is connected to the input end of the adjustment calculation unit, the output end of the adjustment calculation unit is connected to the input end of the luminance control execution unit, and the output end of the luminance control execution unit is connected to the input end of the abnormality compensation module.

10. The data analysis based intelligent control system for street light as claimed in claim 9 wherein: The abnormality compensation module comprises an abnormality detection unit, a compensation range determination unit, a compensation calculation unit and an optimal strategy solving unit, the abnormality detection unit is configured to determine whether the current street lamp has an abnormality and whether there is a neighboring street lamp in a detection period; the compensation range determination unit is configured to calculate a compensation radius with the fault street lamp as the center and determine the range of surrounding street lamps participating in compensation; the compensation calculation unit is configured to calculate the brightness adjustment amplitude of each participating compensation street lamp according to the distance between the participating compensation street lamp and the fault street lamp and a compensation coefficient parameter; the optimal strategy solving unit is configured to solve a Pareto optimal solution set by establishing an optimization equation and using an NSGA-II algorithm, and an output end of the abnormality detection unit is connected to an input end of the compensation range determination unit, an output end of the compensation range determination unit is connected to an input end of the compensation calculation unit, and an output end of the compensation calculation unit is connected to an input end of the optimal strategy solving unit.

Images