Method and system for determining optimal proportion of long-afterglow self-luminous road marking paint

Abstract

The invention discloses a method and a system for determining the optimal proportion of a long-afterglow self-luminous road marking coating, and the method comprises the steps: firstly, carrying out the combination under the multi-factor and multi-level conditions based on a uniform or orthogonal rule to obtain a proportion scheme which envelopes the full information of raw materials and has the minimum quantity; and testing the performances of the optical vision system and the road mechanical system of the coating in each scheme. And then two series of performance index weights are respectively calculated based on an entropy weight method, grey correlation analysis is coupled, and performance indexes with relatively large weight values and relatively good independence are preferably selected from each group to form a set of new coating performance evaluation index system. And finally, based on the new index system, analyzing by coupling with a multi-criterion sorting method, and carrying out comprehensive performance objective sorting on each matching scheme, so as to obtain the optimal matching of the long-afterglow self-luminous road marking paint. According to the invention, TOPSIS + GRA + VIKOR is innovatively coupled, the optimal ratio of the long-afterglow self-luminous road marking paint is scientifically obtained by a three-step design method, and a large amount of test and calculation work is saved.

Description

Technical Field

[0001] The invention relates to the field of coating product ratio composition design method and performance evaluation, and in particular to a method and system for determining the optimal ratio of long afterglow self-luminous road marking coating. Background Art

[0002] In recent years, the gradual increase in energy consumption has led to a serious shortage of electricity supply. The demand for road lighting in some economically underdeveloped areas and remote mountainous areas has become an urgent problem to be solved. With the iteration and update of new materials and the emergence of new products, long-afterglow self-luminous materials have gradually entered the public eye. It is compounded with traditional coatings to prepare long-afterglow self-luminous road marking paint products and applied to the surface of the road surface. It fully absorbs and stores solar energy during the day, gradually releases energy in the form of visible light at night, and can further absorb vehicle lights, moonlight and other irradiation energy to continue to emit light, thereby maximizing the provision of light illumination and direction guidance for vehicles and pedestrians on the road at night, and simultaneously improving the road safety driving operation management and control capabilities.

[0003] However, there are currently no design criteria for the product ratio of road marking paints, especially in the field of the best ratio of long afterglow self-luminous road marking paints, and no simple, scientific and universal design method has been proposed. If the ratio of long afterglow self-luminous road marking paints is designed directly based on relevant performance indicators, the required experimental and calculation workload will be too large. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a method and system for determining the optimal ratio of long afterglow self-luminous road marking paint, which can obtain the optimal ratio of long afterglow self-luminous road marking paint and save a lot of experimental and calculation work.

[0005] The technical solution of the present invention is as follows:

[0006] In a first aspect of the present invention, a method for determining an optimal ratio of a long afterglow self-luminous road marking paint is provided, characterized in that it comprises the following steps:

[0007] Step 1: Under various raw material types and various raw material dosages, a variety of ratio schemes are obtained based on the uniformity rule or the orthogonality rule, and the optical visual system performance index and road mechanical system performance index of the coating under each ratio scheme are tested;

[0008] Step 2: Based on the entropy weight method, the weights of the optical vision system performance index and the road mechanics system performance index are calculated respectively, and combined with the results of grey correlation analysis, a new coating performance evaluation index system is determined;

[0009] Step 3: Based on the new coating performance evaluation index system, the multi-criteria ranking method is combined to objectively rank the comprehensive performance of each ratio scheme, and the comprehensive score is used to determine the optimal ratio of long-lasting self-luminous road marking paint.

[0010] In some embodiments of the present invention, the raw material types include film-forming substances, phosphors, pigments, fillers and film-forming aids; the dosage of the film-forming substances is fixed at 100%, and the remaining types of raw materials are weighed and configured based on the film-forming substances; among them, the dosage range of phosphors is 30% to 50%, the dosage range of pigments is 3% to 7%, and the dosage range of fillers is 3% to 7%; the film-forming aids are added based on engineering requirements and on-site environment, and the dosage range is 0% to 1.5%.

[0011] In some embodiments of the present invention, the optical vision system performance indicators include initial brightness, transition brightness, terminal brightness, afterglow time and brightness factor; the road mechanics system performance indicators include anti-skid coefficient, adhesion strength, wear loss, dust pollution resistance, oil pollution resistance, and UV aging resistance.

[0012] In some embodiments of the present invention, the determination of a new coating performance evaluation index system includes:

[0013] Group all performance indicators;

[0014] The entropy weight method is used to calculate the weight value of each performance indicator in each group;

[0015] Grey correlation analysis was used to calculate the correlation coefficient between the two performance indicators in each group;

[0016] Performance indicators with large weight values ​​and good independence are selected from each group to form a new coating performance evaluation index system.

[0017] In some embodiments of the present invention, the multi-criteria ranking method is combined to objectively rank the comprehensive performance of each ratio scheme, and the comprehensive score is used to determine the optimal ratio of the long afterglow self-luminous road marking paint, including:

[0018] The performance indicators are divided into positive performance indicators and negative performance indicators, and the performance indicator data of the test are normalized;

[0019] Sort the normalized data by vector quality;

[0020] Calculate the distance between the performance index test data of each ratio scheme and the optimal vector and the worst vector respectively;

[0021] The scoring values ​​of various mixing schemes are calculated according to the distance, and the optimal mixing ratio of the long afterglow self-luminous road marking paint is selected according to the scoring values.

[0022] In some embodiments of the present invention, the normalization process includes:

[0023] For the positive performance indicator, the optimization and improvement calculation formula for normalizing the test data is:

[0024]

[0025] For negative performance indicators, the optimization and improvement calculation formula for normalizing the test data is:

[0026]

[0027] Where: A i-j is the data before normalization, B i-j is the normalized data; A -j To normalize the test data of a certain performance index under all ratio schemes, B -j It is the test data of a certain performance index under all the proportion schemes after normalization; i represents the proportion scheme number, ranging from 1 to m, and the value of m varies with the selected raw material type, raw material dosage and law; j represents the performance index, and the value of j ranges from 1 to n, n = 17; for example, A 3-2 It represents the test data obtained by testing the performance index 2 of the coating product under the ratio of scheme 3 before normalization.

[0028] In some embodiments of the present invention, the vector ranking method is as follows:

[0029] Optimal vector: (max(B -1 ), max(B -2 ),max(B -3 ), …, max(B -j ), …, max(B -n ));

[0030] Worst vector: (min(B -1 ), min(B -2 ),min(B -3 ), …, min(B -j ), …, min(B -n )).

[0031] In some embodiments of the present invention, respectively calculating the distances between the performance index test data of each ratio scheme and the optimal vector and the worst vector includes:

[0032] The distance between the performance index test data of each ratio scheme and the optimal vector The calculation formula is as follows:

[0033]

[0034] The distance between the performance index test data of each ratio scheme and the worst vector The calculation formula is as follows:

[0035]

[0036] In some embodiments of the present invention, the scoring value D of each ratio scheme is i The calculation formula is as follows:

[0037]

[0038] Among them, D i The larger the value, the better the performance of the i-th matching scheme, and the higher the ranking will be.

[0039] In a second aspect of the present invention, a system for determining an optimal ratio of a long afterglow self-luminous road marking paint is provided, comprising:

[0040] The performance index test module is configured to: obtain multiple ratio schemes based on the uniformity rule or the orthogonal rule under multiple raw material types and multiple raw material dosages, test the optical visual system performance index and road mechanical system performance index of the coating under each ratio scheme, and obtain the test results of each performance index;

[0041] The coating performance evaluation index system determination module is configured to: calculate the weights of the optical vision system performance index and the road mechanics system performance index based on the entropy weight method, and determine a new coating performance evaluation index system in combination with the grey correlation analysis results;

[0042] The module for obtaining the best paint ratio is configured as follows: based on the new paint performance evaluation index system, the module combines the multi-criteria ranking method to objectively rank the comprehensive performance of various ratio schemes, and uses the comprehensive score to determine the best ratio of long-lasting self-luminous road marking paint.

[0043] One or more technical solutions of the present invention have the following beneficial effects:

[0044] (1) The design method proposed in the present invention is simple, convenient, flexible and diverse. It can not only evaluate the schemes and optimize the performance of products formed under the influence of a relatively small number of changes in conditions such as raw material type and raw material dosage, but also when the number of conditions changes more, the technical advantages of the method of the present invention are more obvious, thus filling the unfavorable situation of the lack of the optimal ratio design method for road luminous coatings.

[0045] (2) The present invention obtains a new set of coating performance evaluation index systems that match specific conditions by innovatively coupling TOPSIS weight analysis, GRA grey correlation analysis, and VIKOR multi-criteria ranking analysis. At the same time, the optimal ratio of long-lasting self-luminous road marking coatings can be determined with minimal experimental work, and the optical performance, road mechanical properties, and comprehensive performance of all design schemes can be objectively ranked.

[0046] (3) The design method proposed in the present invention is scientific and reasonable. The final set of a new coating performance evaluation index system not only covers most of the performance indicators in the fields of optics and road mechanics of luminous coatings for road markings, but also takes into account the objective weights of various performance indicators and their degree of interaction or independence. The optimal ratio scheme obtained by this evaluation is very objective and scientific, eliminating the interference of factors such as insufficient consideration of factors in the current design method, subjective weights, and repeated correlation of indicators. At the same time, it also avoids the disadvantages of the current design method, such as huge experimental workload, long result acquisition cycle, and low accuracy of optimization results. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The 9 groups of coating products with the mixing ratio scheme in implementation mode 1;

[0048] Figure 2 An optical performance test is performed using the 9-group ratio scheme in Implementation Mode 1;

[0049] Figure 3 A mechanical property test conducted using the 9 mix ratio schemes in Implementation Method 1;

[0050] Figure 4 The UV aging resistance test is carried out using the 9 groups of ratio schemes in Implementation Method 1;

[0051] Figure 5 The optical performance test results are obtained by using the 9 groups of ratio schemes in Implementation Method 1;

[0052] Figure 6 It is the on-site laying of the coating project carried out according to the optimal ratio scheme in implementation mode 1. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0054] Example 1

[0055] In a typical embodiment of the present invention, a method for determining the optimal ratio of long afterglow self-luminous road marking paint is proposed, comprising the following steps:

[0056] Step 1: Under various raw material types and various raw material dosages, obtain various ratio schemes based on the uniformity rule or the orthogonality rule, and test the optical visual system performance indicators and road mechanical system performance indicators of the coating under each ratio scheme.

[0057] Among them, the types of raw materials include film-forming substances, fluorescent powder, pigments, fillers and film-forming aids; the dosage of the film-forming substance is fixed at 100%, and the remaining types of raw materials are weighed and configured based on the film-forming substance; among them, the dosage range of fluorescent powder is 30% to 50%, the dosage range of pigment is 3% to 7%, and the dosage range of filler is 3% to 7%; the addition of the film-forming aid is determined according to the engineering requirements and the on-site environment, and the dosage range is 0% to 1.5%, and the film-forming aid includes leveling agents, defoaming agents, anti-settling agents, dispersants and pH adjusters.

[0058] Film-forming substances include one or more combinations of silicone-acrylic emulsion, acrylic emulsion, epoxy resin emulsion, alkyd resin emulsion, styrene-butadiene latex, polyurea latex, etc.; phosphors include one or more combinations of aluminates, silicates, stannates, germanates, etc.; pigments include one or more combinations of titanium dioxide, forging white, antimony white, iron white, zinc oxide, lead powder, talc, kaolin, etc.; fillers include one or more combinations of glass powder beads, ceramic microbeads, polytetrafluoroethylene nanospheres, silica nanospheres, light calcium carbonate, light barium sulfate, light muscovite powder, etc.; film-forming aids include one or more combinations of leveling agents, defoamers, anti-settling agents, dispersants, pH adjusters, etc.

[0059] Further, aluminate type phosphors include SrAl2O4:Eu 2+ ,Dy 3+ 、BaAl2O4:Eu 2+ ,Dy 3+ 、CaAl2O4:Eu 2+ ,Dy 3+ 、CaAl2O4:Eu 2+ ,Nd 3+ 、MgAl2O4:Eu 3+ ,Tb 3+ 、MgAl5O 12 :Ce 3+ , BaAl 12 O 19 :Ce 3+ A combination of one or more of the above; Silicate type phosphors include Sr2MgSi2O7:Eu 2+ ,Dy 3+ 、SrAl2Si2O8:Ce 3+ ,Tb 3+ 、SrSi2O2N2:Eu 2+,Mn 2+ 、BaSi2O2N2:Eu 2+ ,Mn 2+ 、CaSi2O2N2:Eu 2+ ,Mn 2+ 、Na2SiF6:Mn 4+ 、K2SiF6:Mn 4+ One or more combinations thereof; stannate type phosphors include Mg2SnO4:Eu 3+ ,Pr 3+ 、Mg2SnO4:Tm 3+ 、Mg2SnO4:Er 3+ 、Zn2SnO4:Cr 3+ One or more combinations thereof; Germanate phosphors include MgGeO3:Eu 3+ ,Pr 3+ 、MgGeO3:Tm 3+ 、MgGeO3:Er 3+ One or more combinations of the above.

[0060] Furthermore, the types of leveling agents include one or more combinations of polyacrylate, polydimethylsiloxane, polymethylphenylsiloxane, polyvinyl alcohol, polypropylene alcohol, etc.; the types of defoaming agents include BYK022, BYK065, BYK141, BYK1730, TEGO Airex900, TEGO Airex916, TEGO Foamex1488, BASF Foam Star ED 2522, BASF Efka 2527, BASF Efka 2560, etc.; types of anti-settling agents include one or more combinations of sodium hydroxymethyl cellulose, sodium hydroxyethyl cellulose, sodium hydroxypropyl methyl cellulose, sodium alginate, fumed silica, silica gel, gelatin, agar, etc.; types of dispersants include one or more combinations of sodium hexametaphosphate, silane coupling agent, sodium dodecylbenzene sulfonate, sodium octanoate, polyacrylate, polyacrylate, stearic acid, etc.; types of pH adjusters include one or more combinations of ammonia water, saturated aqueous solution of sodium silicate, 1% to 5% diluted sulfuric acid, 1% to 5% diluted hydrochloric acid, 1% to 5% diluted nitric acid, etc.

[0061] A variety of raw material types and a variety of raw material dosages form multi-factor and multi-level conditions. The factors correspond to the types of raw materials required to configure the coating products, and the levels correspond to the raw material dosages required to configure the coating products. The uniform rule or the orthogonal rule is used to combine and obtain a ratio scheme that can envelop all the information of the raw material variables and has the least number.

[0062] The number of matching schemes varies according to the selected factors, levels and rules. Taking the three-factor three-level condition as an example, the number of matching schemes for the whole experiment is 27 groups, the number of matching schemes that can be selected based on the uniform rule is 9 groups, 12 groups, 15 groups, 18 groups, 21 groups and 24 groups, and the number of matching schemes that can be selected based on the orthogonal rule is 9 groups, 18 groups and 27 groups. Therefore, under the three-factor three-level condition, the number of matching schemes that can envelop all the information of the raw materials and the least number based on the uniform or orthogonal rule is 9 groups; similarly, taking the five-factor five-level condition as an example, the number of matching schemes for the whole experiment is 3125 groups, the number of matching schemes that can be selected based on the uniform rule is at least 10 groups, and the number of matching schemes that can be selected based on the orthogonal rule is at least 25 groups.

[0063] Furthermore, to facilitate subsequent experimental performance testing and computational analysis, the number of factors and levels in multi-factor and multi-level analysis is usually no less than 2 and no more than 8.

[0064] Step 2: Based on the TOPSIS method, the weights of the optical vision system performance indicators and the road mechanics system performance indicators are calculated respectively, and combined with the grey relational analysis (GRA) results to determine a new coating performance evaluation index system.

[0065] Among them, the performance indicators of the optical vision system include initial brightness, turning brightness, terminal brightness, afterglow time and brightness factor; the performance indicators of the road mechanics system include anti-skid coefficient, adhesion strength, wear loss, dust pollution resistance, oil pollution resistance, and UV aging resistance.

[0066] Furthermore, the dust pollution resistance includes the color difference before and after dust pollution and the brightness decrease rate before and after dust pollution; the oil pollution resistance includes the color difference before and after oil pollution and the brightness decrease rate before and after oil pollution; the UV aging resistance includes the decrease in anti-slip coefficient before and after UV aging, the decrease in adhesion strength before and after UV aging, the increase in abrasion loss before and after UV aging, the color difference before and after UV aging, and the brightness decrease rate before and after UV aging.

[0067] Furthermore, according to the order of listing of the above five optical vision system performance indicators, they are defined as indicators 1 to 5, that is, the initial brightness is indicator 1, the turning brightness is indicator 2, the terminal brightness is indicator 3, the afterglow time is indicator 4, and the brightness factor is indicator 5. Similarly, according to the order of listing of the above twelve road mechanics system performance indicators, they are defined as indicators 6 to 17, that is, the anti-skid coefficient is indicator 6, the adhesion strength is indicator 7, the wear loss is indicator 8, the color difference before and after dust pollution is indicator 9, the brightness reduction rate before and after dust pollution is indicator 10, the color difference before and after oil pollution is indicator 11, the brightness reduction rate before and after oil pollution is indicator 12, the anti-skid coefficient reduction value before and after UV aging is indicator 13, the adhesion strength reduction value before and after UV aging is indicator 14, the wear loss increase value before and after UV aging is indicator 15, the color difference before and after UV aging is indicator 16, and the brightness reduction rate before and after UV aging is indicator 17.

[0068] In this embodiment, the determination of a new coating performance evaluation index system includes:

[0069] (1) Group all performance indicators.

[0070] The performance indicators with the same attributes in the same system are grouped into the same group, and the performance indicators in the same group are judged, selected and optimally combined in combination with the subsequent operation steps. Among them, the attributes of indicators 1 to 5 in the optical vision system, indicators 6 to 8, indicators 9 to 12, indicators 13 to 15, and indicators 16 to 17 in the road mechanics system are basically consistent, and they are grouped into the same performance indicator group.

[0071] (2) The entropy weight method is used to calculate the weight value of each performance indicator in each group.

[0072] The criterion for judging whether the weight of an indicator in the same group is too low is that the weight of the performance indicator with the lowest weight in the group is less than half of the weight of the performance indicator with the highest weight.

[0073] (3) Grey correlation analysis is used to calculate the correlation coefficient between any two performance indicators in each group.

[0074] Whether there is a correlation between several indicators in a group needs to be judged by referring to the correlation coefficient calculation results obtained by grey correlation analysis. When the correlation coefficient calculation result between two indicators is greater than or equal to 0.85, it means that there is a correlation between the two indicators in the group; when the correlation coefficient calculation result between the two indicators is less than 0.85, it means that there is no correlation between the two indicators in the group, and it can be considered that the two indicators are independent of each other, and the independence of a single indicator is good.

[0075] (4) Select performance indicators with large weight values ​​and good independence from each group to form a new coating performance evaluation index system.

[0076] When the group selects and comprehensively forms a new coating performance evaluation index system, the reference standard is: when there is a correlation between several performance indicators in the same group, only the indicator with the highest weight ratio is retained, and the remaining indicators with lower weight ratios are discarded. When only some performance indicators in the same group are correlated, it is only necessary to judge and choose these indicators, take the indicator with the highest weight ratio in this part, and discard the remaining indicators with lower weight ratios. The remaining indicators in the group that are not correlated can be retained. When there is no correlation between several performance indicators in the same group, if the weight distribution of each indicator is basically the same and the difference is not large, then all indicators in the group are retained. When there is no correlation between several performance indicators in the same group, if the weight of a certain indicator is too low, it will be discarded, and the remaining indicators can be retained.

[0077] Furthermore, at least one performance indicator must be selected from the same group, and at most all indicators of the group can be retained.

[0078] Step 3: Based on the new coating performance evaluation index system, the multi-criteria ranking method (VIKOR) is combined to objectively rank the comprehensive performance of each ratio scheme, and the optimal ratio of long-lasting self-luminous road marking paint is determined by the comprehensive score.

[0079] Among them, the method of combining the multi-criteria ranking method to objectively rank the comprehensive performance of each ratio scheme, and to judge the best ratio of the long afterglow self-luminous road marking paint by the comprehensive score, including:

[0080] (1) The performance indicators are divided into positive performance indicators and negative performance indicators, and the test performance indicator data are normalized.

[0081] When objectively ranking the comprehensive performance of each ratio scheme, indicators 1 to 7 are all positive performance indicators, and the larger their values ​​are, the better the performance is; indicators 8 to 17 are all negative performance indicators, and the larger their values ​​are, the worse the performance is.

[0082] For the positive performance indicator, the optimization and improvement calculation formula for normalizing the test data is:

[0083]

[0084] For negative performance indicators, the optimization and improvement calculation formula for normalizing the test data is:

[0085]

[0086] Where: A iij is the data before normalization, B i-j is the normalized data; A -j To normalize the test data of a certain performance index under all ratio schemes, B -j It is the test data of a certain performance index under all the proportion schemes after normalization; i represents the proportion scheme number, ranging from 1 to m, and the value of m varies with the selected raw material type, raw material dosage and law; j represents the performance index, and the value of j ranges from 1 to n, n = 17; for example, A 3-2 It represents the test data obtained by testing the performance index 2 of the coating product under the ratio of scheme 3 before normalization.

[0087] The optimized and improved calculation formula for test data normalization combines one-dimensional and two-dimensional scale performances, and is therefore more reasonable than the original data normalization calculation formula based on single-scale performance.

[0088] (2) Sort the normalized data by vector quality.

[0089] The vector quality sorting method is as follows:

[0090] Optimal vector: (max(B -1 ), max(B -2 ),max(B -3 ), …, max(B -j ), …, max(B -n ));

[0091] Worst vector: (min(B -1 ), min(B -2 ),min(B -3 ), …, min(B -j ), …, min(B -n )).

[0092] (3) Calculate the distance between the performance index test data of each matching scheme and the optimal vector and the worst vector respectively.

[0093] Specifically include:

[0094] The distance between the performance index test data of each ratio scheme and the optimal vector The calculation formula is as follows:

[0095]

[0096] The distance between the performance index test data of each ratio scheme and the worst vector The calculation formula is as follows:

[0097]

[0098] (4) Calculate the score of each mixing ratio according to the distance, and select the best ratio of long afterglow self-luminous road marking paint according to the score.

[0099] The score of each ratio scheme D i The calculation formula is as follows:

[0100]

[0101] Among them, D i The larger the value, the better the performance of the i-th matching scheme, and the higher the ranking will be.

[0102] Furthermore, it also includes the production and preparation of the long afterglow self-luminous road marking paint according to the design method. After obtaining the optimal ratio of the long afterglow self-luminous road marking paint under the corresponding factors and the corresponding level range, the process of product production and preparation is as follows: first, the film-forming substance and half of the mass of the anti-settling agent are mixed evenly as liquid components. At the same time, the fluorescent powder, pigment, filler and other selected types of film-forming aids except the defoamer are mixed evenly as solid components. Subsequently, the solid component is slowly and batchwise added to the liquid component, and a four-blade cross mechanical mixing equipment is used to shear and stir at a rate of 300 to 500 r / min for 20 to 40 minutes. Finally, the remaining half of the mass of the anti-settling agent and the defoamer required by the ratio scheme are added to the obtained liquid-solid mixed product, and the glass rod is continued to be manually stirred for 10 to 20 minutes to obtain the final long afterglow self-luminous road marking paint.

[0103] Furthermore, the paint is applied to the surface of an object by spraying, rotating, scraping, rolling or the like, or poured into a corresponding mold to prepare a long afterglow self-luminous road marking paint product.

[0104] Implementation Method 1

[0105] Step 1: Select the phosphor type SrAl2O4:Eu 2+ ,Dy 3+, the film-forming material type is silicone acrylic emulsion, the pigment type is titanium dioxide, the filler type is glass powder beads, and the film-forming aid type is leveling agent, defoaming agent and anti-settling agent, etc. Among them, the scheme design is carried out based on the mass proportion of different components. The silicone acrylic emulsion is fixed at 100%. Based on this, the phosphor dosage range is 30% to 50%, the pigment dosage range is 3% to 7%, the filler dosage range is 3% to 7%, the leveling agent dosage is fixed at 1%, the defoaming agent dosage is fixed at 1%, and the anti-settling agent dosage is fixed at 0.5%. Therefore, the variables in this embodiment are the three factors of phosphor dosage, pigment dosage and filler dosage. Three levels are selected for each factor. Nine groups of ratio schemes are designed based on orthogonal combination, as shown in Table 1. The coating products are as follows: Figure 1 shown.

[0106] Table 1. Implementation Method 1 Orthogonal Combination Design Experimental Scheme

[0107]

[0108] Then the optical visual system performance and road mechanical system performance of the coating products mixed under each scheme were tested, such as Figure 2-Figure 5 The results are shown in Table 2 and Table 3 respectively.

[0109] Table 2. Test results of optical visual system performance of coating products under different test schemes

[0110]

[0111] Table 3. Test results of road mechanical system performance of coating products under different test schemes

[0112]

[0113] Step 2: The TOPSIS entropy weight method is used to assign objective weights to each performance index in the optical vision system performance and the road mechanics system performance. At the same time, a grey relational analysis (GRA) is performed on the degree of connection between the various performance indicators. The calculation results are listed in Tables 4 to 7.

[0114] Table 4. Calculation results of optical visual performance index weights for implementation mode 1

[0115] Indicator system Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Weight / % 26.37 18.17 18.62 26.60 10.24

[0116] Table 5. Calculation results of correlation coefficients of optical visual performance indicators in implementation mode 1

[0117] Relevance Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Indicator 1 1.00 0.75 0.70 0.57 0.56 Indicator 2 0.75 1.00 0.60 0.88 0.50 Indicator 3 0.70 0.60 1.00 0.74 0.51 Indicator 4 0.57 0.88 0.74 1.000 0.58 Indicator 5 0.56 0.50 0.51 0.58 1.00

[0118] Table 6. Calculation results of road mechanical performance index weights for implementation mode 1

[0119]

[0120] Table 7. Calculation results of correlation between road mechanical performance indicators in implementation mode 1

[0121]

[0122] Then, the above-mentioned indicators are combined optimally, and performance indicators with larger weight values ​​and better independence are selected to form a new coating performance evaluation index system. Analysis of the calculation results in Tables 4 and 5 shows that: First, it can be seen from Table 5 that, except for the high degree of correlation between indicator 2 and indicator 4, the other indicators are independent of each other. Combined with Table 4, it can be seen that the weight of indicator 4 is much greater than that of indicator 2, so indicator 2 can be excluded by combining the two. In addition, it can be seen from Table 4 that the weight of indicator 5 is too low in the optical vision indicator system, so indicator 5 can also be excluded. In summary, by coupling TOPSIS+GRA, three indicators, namely initial brightness, terminal brightness and afterglow time, are optimized and selected in the optical vision performance index system.

[0123] From the analysis of the calculation results in Tables 6 and 7, we can see that: First, it can be seen from Table 7 that there is a high degree of correlation between indicators 6, 7 and 8. Combined with Table 6, it can be seen that the weight of indicator 8 is the largest among the three, so indicators 6 and 7 can be excluded. Similarly, it can be seen from Table 7 that there is a high degree of correlation between indicators 9 and 11, and between indicators 10 and 12 in indicators 9 to 12, and the remaining indicators are independent of each other. Combined with Table 6, it can be seen that the weight of indicator 11 is much greater than that of indicator 9, and the weight of indicator 12 is much greater than that of indicator 10, so indicators 9 and 10 can be excluded. Then, it can be seen from Table 7 that indicators 13, 14 and 15 are independent of each other, and combined with Table 6, it can be seen that the weights of each indicator are basically the same, so they are all retained. Finally, it can be seen from Table 7 that there is a high degree of correlation between indicators 16 and 17. Combined with Table 6, it can be seen that the weight of indicator 16 is much greater than that of indicator 17, so indicator 17 can be excluded. In summary, by coupling TOPSIS+GRA, seven indicators are optimized and selected in the road mechanical performance index system, including wear loss value, color difference before and after oil pollution, brightness decrease rate before and after oil pollution, anti-skid coefficient decrease before and after UV aging, adhesion strength decrease before and after UV aging, wear loss increase before and after UV aging, and color difference before and after UV aging.

[0124] Step 3: Summarize the three selected optical visual performance indicators and seven road mechanical performance indicators to obtain a new set of coating performance evaluation index system suitable for implementation mode 1. Based on this, the VIKOR method is further combined to objectively rank the comprehensive performance of each group of ratio schemes. The results are shown in Table 8.

[0125] Table 8. Objective ranking results of comprehensive performance of implementation mode 1

[0126] plan ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ Sorting 9 8 7 6 1 5 2 3 4

[0127] It can be determined that the best ratio of the long afterglow self-luminous road marking paint of implementation mode 1 is: based on silicone acrylic emulsion, silicone acrylic emulsion is 100%, phosphor content is 40%, pigment content is 5%, filler content is 7%, leveling agent content is 1%, defoaming agent content is 1%, anti-settling agent content is 0.5%, and the best ratio scheme is used for the on-site laying of the coating project. Figure 6 shown.

[0128] Implementation Method 2

[0129] Step 1: Select the phosphor type SrAl2O4:Eu 2+ ,Dy 3+ , the film-forming material type is silicone acrylic emulsion, the pigment type is titanium dioxide, the filler type is glass powder beads, and the film-forming aid type is leveling agent, defoaming agent and anti-settling agent, etc. Among them, the scheme design is based on the mass proportion of different components. The silicone acrylic emulsion is fixed at 100%. Based on this, the phosphor dosage range is 30% to 50%, the pigment dosage range is 3% to 7%, the filler dosage range is 3% to 7%, the leveling agent dosage is fixed at 1%, the defoaming agent dosage is fixed at 1%, and the anti-settling agent dosage is fixed at 0.5%. Therefore, the variables of this embodiment are the three factors of phosphor dosage, pigment dosage and filler dosage. Three levels are selected for each factor. Nine groups of ratio schemes are designed based on uniform combination, as shown in Table 9.

[0130] Table 9. Orthogonal combination design experimental scheme for implementation 2

[0131]

[0132] Subsequently, the optical visual system performance and road mechanical system performance of the coating products mixed under each scheme were tested, and the results are listed in Table 10 and Table 11 respectively.

[0133] Table 10. Test results of optical visual system performance of coating products under different test schemes

[0134]

[0135]

[0136] Table 11. Test results of road mechanical system performance of coating products under different test schemes

[0137]

[0138] Step 2: The TOPSIS entropy weight method is used to assign objective weights to each performance index in the optical vision system performance and the road mechanics system performance. At the same time, a grey correlation analysis (GRA) is performed on the degree of connection between the various performance indicators. The calculation results are listed in Tables 12 to 15.

[0139] Table 12. Calculation results of optical visual performance index weights for implementation 2

[0140] Indicator system Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Weight / % 26.82 17.58 16.90 27.33 11.37

[0141] Table 13. Calculation results of correlation between optical visual performance indicators in implementation mode 2

[0142] Relevance Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Indicator 1 1.00 0.71 0.65 0.70 0.59 Indicator 2 0.71 1.00 0.68 0.85 0.53 Indicator 3 0.65 0.68 1.00 0.77 0.56 Indicator 4 0.70 0.85 0.77 1.00 0.60 Indicator 5 0.59 0.53 0.56 0.60 1.00

[0143] Table 14. Calculation results of road mechanical performance index weights for implementation mode 2

[0144]

[0145] Table 15. Calculation results of correlation between road mechanical performance indicators in implementation mode 2

[0146]

[0147] Then, the above-mentioned indicators are combined optimally, and the performance indicators with larger weight values ​​and better independence are selected to form a new coating performance evaluation index system. Analysis of the calculation results in Tables 12 and 13 shows that: First, it can be seen from Table 13 that, except for the high degree of correlation between indicators 2 and 4, the other indicators are independent of each other. Combined with Table 12, it can be seen that the weight of indicator 4 is much greater than that of indicator 2, so indicator 2 can be excluded by combining the two. In addition, it can be seen from Table 12 that the weight of indicator 5 is too low in the optical vision indicator system, so indicator 5 can also be excluded. In summary, by coupling TOPSIS+GRA, three indicators, namely initial brightness, terminal brightness and afterglow time, are optimized and selected in the optical vision performance index system.

[0148] From the analysis of the calculation results in Table 14 and Table 15, it can be seen that: first, it can be seen from Table 15 that there is a high degree of correlation between indicators 6, 7 and 8. Combined with Table 14, it can be seen that the weight of indicator 8 is the largest among the three, so indicators 6 and 7 can be excluded. Similarly, it can be seen from Table 15 that there is a high degree of correlation between indicators 9 and 11, and between indicators 10 and 12 in indicators 9 to 12, and the remaining indicators are independent of each other. Combined with Table 14, it can be seen that the weight of indicator 11 is much greater than that of indicator 9, and the weight of indicator 12 is much greater than that of indicator 10, so indicators 9 and 10 can be excluded. Then, it can be seen from Table 15 that indicators 13, 14 and 15 are independent of each other, and combined with Table 14, it can be seen that the weights of each indicator are basically the same, so they are all retained. Finally, it can be seen from Table 15 that there is a high degree of correlation between indicators 16 and 17. Combined with Table 14, it can be seen that the weight of indicator 16 is much greater than that of indicator 17, so indicator 17 can be excluded. In summary, by coupling TOPSIS+GRA, seven indicators are optimized and selected in the road mechanical performance index system, including wear loss value, color difference before and after oil pollution, brightness decrease rate before and after oil pollution, anti-skid coefficient decrease before and after UV aging, adhesion strength decrease before and after UV aging, wear loss increase before and after UV aging, and color difference before and after UV aging.

[0149] Step 3: Summarize the three selected optical visual performance indicators and seven road mechanical performance indicators to obtain a new set of coating performance evaluation index system suitable for implementation mode 2. Based on this, the VIKOR method is further combined to objectively rank the comprehensive performance of each group of ratio schemes. The results are shown in Table 16.

[0150] Table 16. Objective ranking results of comprehensive performance of implementation method 2

[0151] plan ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ Sorting 7 9 8 6 1 5 4 3 2

[0152] It can be determined that the optimal ratio of the long afterglow self-luminous road marking paint of embodiment 2 is: based on silicone acrylic emulsion, the silicone acrylic emulsion is 100%, the phosphor content is 40%, the pigment content is 5%, the filler content is 5%, the leveling agent content is 1%, the defoaming agent content is 1%, and the anti-settling agent content is 0.5%.

[0153] By comparing the optimal ratio results of implementation mode 1 and implementation mode 2, it is found that when the number of factors or levels is small, under the same conditions, the new coating performance evaluation index system calculated based on the test scheme based on uniform combination design and the test scheme based on orthogonal combination design are completely consistent, and the difference in the optimal ratio is not large.

[0154] Implementation 3

[0155] Step 1: Select the phosphor type SrAl2O4:Eu 2+ ,Dy 3+ , the film-forming material type is polyurethane emulsion, the pigment type is talcum powder, the filler type is glass powder beads, and the film-forming aid type is leveling agent, defoaming agent and anti-settling agent, etc. Among them, the scheme design is based on the mass proportion of different components, and the polyurethane emulsion is fixed at 100%. Based on this, the phosphor dosage range is 30% to 50%, the pigment dosage range is 3% to 7%, the filler dosage range is 3% to 7%, the leveling agent dosage range is 0.5% to 1.5%, the defoaming agent dosage is fixed at 1%, and the anti-settling agent dosage is fixed at 0.5%. Therefore, the variables of this embodiment are four factors: phosphor dosage, pigment dosage, filler dosage and leveling agent dosage. Three levels are selected for each factor. Nine groups of ratio schemes are designed based on orthogonal combination, as shown in Table 17.

[0156] Table 17. Orthogonal combination design experimental plan for implementation method 3

[0157]

[0158] Subsequently, the optical visual system performance and road mechanical system performance of the coating products mixed under each scheme were tested, and the results are listed in Table 18 and Table 19 respectively.

[0159] Table 18. Test results of optical visual system performance of coating products under different test schemes

[0160]

[0161]

[0162] Table 19. Test results of road mechanical system performance of coating products under different test schemes

[0163]

[0164] Step 2: The TOPSIS entropy weight method is used to assign objective weights to each performance indicator in the optical vision system performance and the road mechanics system performance. At the same time, a grey correlation analysis (GRA) is performed on the degree of connection between the various performance indicators. The calculation results are listed in Tables 20 to 23.

[0165] Table 20. Calculation results of optical visual performance index weights for implementation 3

[0166] Indicator system Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Weight / % 23.73 17.53 16.83 29.86 12.05

[0167] Table 21. Calculation results of correlation between optical visual performance indicators of implementation mode 3

[0168] Relevance Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Indicator 1 1.00 0.76 0.61 0.73 0.77 Indicator 2 0.76 1.00 0.66 0.80 0.55 Indicator 3 0.61 0.66 1.00 0.73 0.56 Indicator 4 0.73 0.80 0.73 1.000 0.55 Indicator 5 0.77 0.55 0.56 0.55 1.00

[0169] Table 22. Calculation results of road mechanical performance index weights for implementation method 3

[0170]

[0171] Table 23. Calculation results of correlation between road mechanical performance indicators in implementation mode 3

[0172]

[0173] Then, the above indicators are combined optimally, and the performance indicators with larger weight values ​​and better independence are selected to form a new coating performance evaluation index system. Analysis of the calculation results of Table 20 and Table 21 shows that: First, it can be seen from Table 21 that the various indicators in the optical visual performance are independent of each other. Combined with Table 20, it can be seen that the weight of indicator 5 is too low in the optical visual index system, so indicator 5 is excluded in this example. In summary, by coupling TOPSIS+GRA, four indicators, namely initial brightness, turning brightness, terminal brightness and afterglow time, are optimized and selected in the optical visual performance index system.

[0174] From the analysis of the calculation results in Table 22 and Table 23, it can be seen that: First, it can be seen from Table 23 that among indicators 6 to 8, only indicator 6 and indicator 7 have a high degree of correlation, and the other indicators are independent of each other. Combined with Table 22, it can be seen that the weight of indicator 6 is the largest among the three, so indicator 7 is excluded. Similarly, it can be seen from Table 23 that among indicators 9 to 12, only indicators 10 and 11, indicators 10 and 12, and indicators 11 and 12 have a high degree of correlation, and the other indicators are independent of each other. Combined with Table 22, it can be seen that the weight of indicator 11 is much greater than that of indicator 10, the weight of indicator 10 is much greater than that of indicator 12, and the weight of indicator 11 is also much greater than that of indicator 12, so indicators 10 and 12 can be excluded. Then, it can be seen from Table 23 that indicators 13, 14 and 15 are independent of each other, and combined with Table 22, it can be seen that the weights of each indicator are basically the same, so they are all retained. Finally, it can be seen from Table 23 that there is a high degree of correlation between indicator 16 and indicator 17. Combined with Table 22, it can be seen that the weight of indicator 17 is much greater than that of indicator 16, so indicator 16 can be excluded. In summary, by coupling TOPSIS+GRA, eight indicators are optimized in the road mechanical performance index system, including anti-skid coefficient value, wear loss value, color difference difference before and after dust pollution, color difference difference before and after oil pollution, anti-skid coefficient decrease value before and after UV aging, adhesion strength decrease value before and after UV aging, wear loss increase value before and after UV aging, and brightness decrease rate before and after UV aging.

[0175] Step 3: Summarize the selected four optical visual performance indicators and eight road mechanical performance indicators to obtain a new set of coating performance evaluation index system suitable for implementation mode 3. Based on this, the VIKOR method is further combined to objectively rank the comprehensive performance of each group of ratio schemes. The results are shown in Table 24.

[0176] Table 24. Objective ranking results of comprehensive performance of implementation method 3

[0177] plan ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ Sorting 9 8 7 6 2 3 4 5 1

[0178] It can be determined that the optimal ratio of the long afterglow self-luminous road marking paint of embodiment 3 is: based on polyurethane emulsion, the polyurethane emulsion is 100%, the fluorescent powder is 50%, the pigment is 7%, the filler is 5%, the leveling agent is 0.5%, the defoaming agent is 1%, and the anti-settling agent is 0.5%.

[0179] Implementation 4

[0180] Step 1: Select the phosphor type SrAl2O4:Eu 2+ ,Dy 3+ , the type of film-forming material is polyurethane emulsion, the type of pigment is talcum powder, the type of filler is glass powder beads, and the type of film-forming aid is leveling agent, defoaming agent and anti-settling agent. Among them, the scheme design is based on the mass proportion of different components, and the polyurethane emulsion is fixed at 100%. Based on this, the phosphor dosage range is 30% to 50%, the pigment dosage range is 3% to 7%, the filler dosage range is 3% to 7%, the leveling agent dosage range is 0.5% to 1.5%, the defoaming agent dosage is fixed at 1%, and the anti-settling agent dosage is fixed at 0.5%. Therefore, the variables of this embodiment are four factors: phosphor dosage, pigment dosage, filler dosage and leveling agent dosage. Three levels are selected for each factor. Nine groups of ratio schemes are designed based on orthogonal combination, as shown in Table 25.

[0181] Table 25. Experimental plan for uniform combination design of implementation mode 4

[0182]

[0183] Subsequently, the optical visual system performance and road mechanical system performance of the coating products mixed under each scheme were tested, and the results are listed in Table 26 and Table 27 respectively.

[0184] Table 26. Test results of optical visual system performance of coating products under different test schemes

[0185]

[0186] Table 27. Test results of road mechanical system performance of coating products under different test schemes

[0187]

[0188]

[0189] Step 2: The TOPSIS entropy weight method is used to assign objective weights to each performance indicator in the optical vision system performance and the road mechanics system performance. At the same time, a grey correlation analysis (GRA) is performed on the degree of connection between the various performance indicators. The calculation results are listed in Tables 28 to 31.

[0190] Table 28. Calculation results of optical visual performance index weights for implementation 4

[0191] Indicator system Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Weight / % 23.02 19.37 11.87 26.72 19.02

[0192] Table 29. Calculation results of correlation of optical visual performance indicators of implementation mode 4

[0193] Relevance Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Indicator 1 1.00 0.727 0.579 0.722 0.713 Indicator 2 0.727 1.00 0.609 0.732 0.485 Indicator 3 0.579 0.609 1.00 0.671 0.523 Indicator 4 0.722 0.732 0.671 1.000 0.541 Indicator 5 0.713 0.485 0.523 0.541 1.00

[0194] Table 30. Calculation results of road mechanical performance index weights for implementation mode 4

[0195]

[0196] Table 31. Calculation results of correlation between road mechanical performance indicators in implementation mode 4

[0197]

[0198]

[0199] Then, the above indicators are combined optimally, and the performance indicators with larger weight values ​​and better independence are selected to form a new coating performance evaluation index system. Analysis of the calculation results of Table 28 and Table 29 shows that: First, it can be seen from Table 29 that the various indicators in the optical visual performance are independent of each other. Combined with Table 28, it can be seen that the weight of indicator 3 is too low in the optical visual index system, so indicator 3 is excluded in this example. In summary, by coupling TOPSIS+GRA, four indicators, including initial brightness, transition brightness, and afterglow time brightness factor, are optimized and selected in the optical visual performance index system.

[0200] From the analysis of the calculation results of Table 30 and Table 31, it can be seen that: First, it can be seen from Table 31 that among indicators 6 to 8, only indicator 6 and indicator 7 have a high degree of correlation, and the other indicators are independent of each other. Combined with Table 30, it can be seen that the weight of indicator 6 is the smallest among the three, so indicator 6 is excluded. Similarly, it can be seen from Table 31 that among indicators 9 to 12, only indicators 10 and 11, indicators 10 and 12, and indicators 11 and 12 have a high degree of correlation, and the other indicators are independent of each other. Combined with Table 30, it can be seen that the weight of indicator 11 is much greater than that of indicator 10, and the weight of indicator 12 is much greater than that of indicator 11, so indicators 10 and 11 can be excluded. Then, it can be seen from Table 31 that among indicators 13 to 15, only indicators 14 and 15 have a high degree of correlation, and the other indicators are independent of each other. Combined with Table 30, it can be seen that the weight of indicator 15 is the smallest among the three, so indicator 15 is excluded. Finally, it can be seen from Table 31 that there is a high degree of correlation between indicator 16 and indicator 17. Combined with Table 30, it can be seen that the weight of indicator 16 is much greater than that of indicator 17, so indicator 17 can be excluded. In summary, by coupling TOPSIS+GRA, seven indicators are optimized in the road mechanical performance index system, including adhesion strength value, wear loss value, color difference value before and after dust pollution, brightness reduction rate before and after oil pollution, anti-skid coefficient reduction value before and after UV aging, adhesion strength reduction value before and after UV aging, and color difference value before and after UV aging.

[0201] Step 3: Summarize the selected four optical visual performance indicators and seven road mechanical performance indicators to obtain a new set of coating performance evaluation index system suitable for implementation mode 4. Based on this, the VIKOR method is further combined to objectively rank the comprehensive performance of each group of ratio schemes. The results are shown in Table 32.

[0202] Table 32. Objective ranking results of comprehensive performance of implementation mode 4

[0203] plan ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ Sorting 7 9 8 4 2 6 5 3 1

[0204] It can be determined that the optimal ratio of the long afterglow self-luminous road marking paint of embodiment 4 is: based on polyurethane emulsion, polyurethane emulsion is 100%, phosphor content is 50%, pigment content is 5%, filler content is 7%, leveling agent content is 0.5%, defoaming agent content is 1%, and anti-settling agent content is 0.5%.

[0205] By comparing the optimal ratio results of implementation mode 3 and implementation mode 4, it is found that when the number of factors or levels is large, under the same conditions, the new coating performance evaluation index system calculated based on the uniform combination design and the experimental scheme based on the orthogonal combination design have a large difference, and the optimal ratio is also large.

[0206] Implementation method 5

[0207] Step 1: Select the phosphor type SrAl2O4:Eu 2+ ,Dy 3+ , acrylic emulsion is selected as the film-forming material type, zinc oxide is selected as the pigment type, kaolin is selected as the filler type, and leveling agent, defoamer and anti-settling agent are selected as the film-forming aid type. Among them, the scheme design is carried out based on the mass proportion of different components, acrylic emulsion is fixed at 100%, and based on this, the phosphor dosage range is 30% to 50%, the pigment dosage is fixed at 5%, the filler dosage range is 3% to 7%, the leveling agent dosage range is 0.5% to 1.5%, the defoamer dosage range is 0.5% to 1.5%, and the anti-settling agent dosage range is 0.3% to 0.7%. Therefore, the variables of this embodiment are five factors: phosphor dosage, filler dosage, leveling agent dosage, defoamer dosage and anti-settling agent dosage. Five levels are selected for each factor, and 25 groups of ratio schemes are selected based on orthogonal combination design, as shown in Table 33.

[0208] Table 33. Orthogonal combination design experimental plan for implementation mode 5

[0209]

[0210]

[0211] Subsequently, the optical visual system performance and road mechanical system performance of the coating products mixed under each scheme were tested, and the results are listed in Table 34 and Table 35 respectively.

[0212] Table 34. Test results of optical visual system performance of coating products under different test schemes

[0213]

[0214]

[0215] Table 35. Test results of road mechanical system performance of coating products under different test schemes

[0216]

[0217]

[0218] Step 2: The TOPSIS entropy weight method is used to assign objective weights to each performance indicator in the optical vision system performance and the road mechanics system performance. At the same time, a grey correlation analysis (GRA) is performed on the degree of connection between the various performance indicators. The calculation results are listed in Tables 36 to 39 respectively.

[0219] Table 36. Calculation results of optical visual performance index weights for implementation mode 5

[0220] Indicator system Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Weight / % 21.43 13.68 19.94 25.36 19.59

[0221] Table 37. Calculation results of correlation of optical visual performance indicators of implementation mode 5

[0222] Relevance Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Indicator 1 1.00 0.77 0.60 0.73 0.83 Indicator 2 0.77 1.00 0.64 0.87 0.64 Indicator 3 0.60 0.64 1.00 0.73 0.58 Indicator 4 0.73 0.87 0.73 1.000 0.59 Indicator 5 0.83 0.64 0.58 0.59 1.00

[0223] Table 38. Calculation results of road mechanical performance index weights for implementation mode 5

[0224]

[0225] Table 39. Calculation results of correlation between road mechanical performance indicators in implementation mode 5

[0226]

[0227] Then, the above-mentioned indicators are combined optimally, and performance indicators with larger weight values ​​and better independence are selected to form a new coating performance evaluation index system. Analysis of the calculation results of Tables 36 and 37 shows that: First, it can be seen from Table 37 that, except for the high correlation between indicators 2 and 4, the other indicators are independent of each other. Combined with Table 36, it can be seen that the weight of indicator 4 is much greater than that of indicator 2, so the combination of the two can exclude indicator 2. In addition, it can be seen from Table 36 that the weights of the remaining indicators are basically the same in the optical vision indicator system, so they are all retained. In summary, it can be seen that by coupling TOPSIS+GRA, four indicators, namely initial brightness, terminal brightness, afterglow time and brightness factor, are optimized and selected in the optical vision performance indicator system. Analysis of the calculation results of Tables 38 and 39 shows that: First, it can be seen from Table 39 that among indicators 6 to 8, only indicator 6 and indicator 7 have a high correlation, and the other indicators are independent of each other. Combined with Table 38, it can be seen that the weight of indicator 7 is the smallest among the three, so indicator 7 is excluded. Similarly, it can be seen from Table 39 that there is a high degree of correlation between the indicators 9 to 12. Combined with Table 38, it can be seen that the weight of indicator 12 is much greater than the other indicators, so indicators 9, 10 and 11 can be excluded. Then, it can be seen from Table 39 that the indicators 13 to 15 are independent of each other. Combined with Table 38, it can be seen that the weight of indicator 15 is too small among the three, so indicator 15 is excluded. Finally, it can be seen from Table 39 that indicators 16 and 17 are independent of each other. Combined with Table 38, it can be seen that the weights of each indicator are basically the same, so they are all retained. In summary, by coupling TOPSIS+GRA, seven indicators are optimized and selected in the road mechanical performance index system, including anti-skid coefficient value, wear loss value, brightness reduction rate before and after oil stain pollution, anti-skid coefficient reduction value before and after ultraviolet aging, adhesion strength reduction value before and after ultraviolet aging, color difference difference before and after ultraviolet aging, and brightness reduction rate before and after ultraviolet aging.

[0228] Step 3: Summarize the selected four optical visual performance indicators and seven road mechanical performance indicators to obtain a new set of coating performance evaluation index system suitable for implementation mode 5. Based on this, the VIKOR method is further combined to objectively rank the comprehensive performance of each group of ratio schemes. The results are shown in Table 40.

[0229] Table 40. Objective ranking results of comprehensive performance of implementation mode 5

[0230]

[0231] Table 40 (continued). Objective ranking results of comprehensive performance of implementation mode 5

[0232]

[0233] It can be determined that the optimal ratio of the long afterglow self-luminous road marking paint of implementation mode 5 is: based on acrylic emulsion, acrylic emulsion is 100%, phosphor content is 45%, pigment content is 5%, filler content is 5%, leveling agent content is 0.5%, defoaming agent content is 1.25%, and anti-settling agent content is 0.4%.

[0234] Implementation 6

[0235] Step 1: Select the phosphor type SrAl2O4:Eu 2+ ,Dy 3+ , acrylic emulsion is selected as the film-forming material type, zinc oxide is selected as the pigment type, kaolin is selected as the filler type, and leveling agent, defoamer and anti-settling agent are selected as the film-forming aid type. Among them, the scheme design is carried out based on the mass proportion of different components, and the epoxy resin emulsion is fixed at 100%. Based on this, the phosphor dosage range is 30% to 50%, the pigment dosage is fixed at 5%, the filler dosage range is 3% to 7%, the leveling agent dosage range is 0.5% to 1.5%, the defoamer dosage range is 0.5% to 1.5%, and the anti-settling agent dosage range is 0.3% to 0.7%. Therefore, the variables of this embodiment are five factors: phosphor dosage, filler dosage, leveling agent dosage, defoamer dosage and anti-settling agent dosage. Five levels are selected for each factor, and 10 groups of ratio schemes are designed based on uniform combination, as shown in Table 41.

[0236] Table 41. Experimental plan for uniform combination design of implementation mode 6

[0237]

[0238]

[0239] Subsequently, the optical visual system performance and road mechanical system performance of the coating products mixed under each scheme were tested, and the results are listed in Table 42 and Table 43 respectively.

[0240] Table 42. Test results of optical visual system performance of coating products under different test schemes

[0241]

[0242] Table 43. Test results of road mechanical system performance of coating products under different test schemes

[0243]

[0244]

[0245] Step 2: The TOPSIS entropy weight method is used to assign objective weights to each performance indicator in the optical vision system performance and the road mechanics system performance. At the same time, a grey correlation analysis (GRA) is performed on the degree of connection between the various performance indicators. The calculation results are listed in Tables 44 to 47 respectively.

[0246] Table 44. Calculation results of optical visual performance index weights for implementation 6

[0247] Indicator system Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Weight / % 19.26 14.80 21.39 25.32 19.23

[0248] Table 45. Calculation results of correlation between optical visual performance indicators of implementation mode 6

[0249] Relevance Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Indicator 1 1.00 0.77 0.54 0.75 0.83 Indicator 2 0.77 1.00 0.55 0.88 0.61 Indicator 3 0.54 0.55 1.00 0.72 0.59 Indicator 4 0.75 0.88 0.72 1.000 0.57 Indicator 5 0.83 0.61 0.59 0.57 1.00

[0250] Table 46. Calculation results of road mechanical performance index weights for implementation mode 6

[0251]

[0252] Table 47. Calculation results of correlation between road mechanical performance indicators in implementation mode 6

[0253]

[0254]

[0255] Then, the above indicators are combined optimally, and the performance indicators with larger weight values ​​and better independence are selected to form a new coating performance evaluation index system. Analysis of the calculation results of Table 44 and Table 45 shows that: First, it can be seen from Table 45 that, except for the high correlation between indicator 2 and indicator 4, the other indicators are independent of each other. Combined with Table 44, it can be seen that the weight of indicator 4 is much greater than that of indicator 2, so the combination of the two can exclude indicator 2. In addition, it can be seen from Table 45 that the weights of the other indicators are basically the same in the optical vision indicator system, so they are all retained. In summary, it can be seen that by coupling TOPSIS+GRA, four indicators, namely initial brightness, terminal brightness, afterglow time and brightness factor, are optimized and selected in the optical vision performance index system. Analysis of the calculation results of Table 46 and Table 47 shows that: First, it can be seen from Table 47 that among indicators 6 to 8, only indicator 6 and indicator 7 have a high correlation, and the other indicators are independent of each other. Combined with Table 46, it can be seen that the weight of indicator 7 is the smallest among the three, so indicator 7 is excluded. Similarly, from Table 47, it can be seen that among indicators 9 to 12, only indicators 9 and 11, and indicators 10 and 12 have a high degree of correlation, and the remaining indicators are independent of each other. Combined with Table 46, it can be seen that the weight of indicator 11 is much greater than that of indicator 9, and the weight of indicator 10 is much greater than that of indicator 12, so indicators 9 and 12 can be excluded. Then, from Table 47, it can be seen that the indicators from indicators 13 to 15 are all independent of each other. Combined with Table 46, it can be seen that the weight of indicator 14 is too small among the three, so indicator 14 is excluded. Finally, from Table 47, it can be seen that indicators 16 and 17 are independent of each other. Combined with Table 46, it can be seen that the weights of each indicator are basically the same, so they are all retained. In summary, by coupling TOPSIS+GRA, eight indicators are optimized and selected in the road mechanical performance index system, including anti-skid coefficient value, wear loss value, brightness decrease rate before and after dust pollution, color difference difference before and after oil pollution, anti-skid coefficient decrease value before and after UV aging, wear loss increase value before and after UV aging, color difference difference before and after UV aging, and brightness decrease rate before and after UV aging.

[0256] Step 3: Summarize the selected four optical visual performance indicators and eight road mechanical performance indicators to obtain a new set of coating performance evaluation index system suitable for implementation mode 6. Based on this, the VIKOR method is further combined to objectively rank the comprehensive performance of each group of ratio schemes. The results are shown in Table 48.

[0257] Table 48. Objective ranking results of comprehensive performance of implementation mode 6

[0258] plan ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ <![CDATA[①0]]> Sorting 10 9 8 7 1 5 3 4 2 6

[0259] It can be determined that the optimal ratio of the long afterglow self-luminous road marking paint of embodiment 6 is: based on acrylic emulsion, acrylic emulsion is 100%, phosphor content is 40%, pigment content is 5%, filler content is 4%, leveling agent content is 1.25%, defoaming agent content is 0.5%, and anti-settling agent content is 0.7%.

[0260] By comparing the optimal ratio results of implementation mode 5 and implementation mode 6, it is found that when the number of factors or levels is large, under the same conditions, the new coating performance evaluation index system calculated based on the uniform combination design and the experimental scheme based on the orthogonal combination design have a large difference, and the optimal ratio is also large.

[0261] Example 2

[0262] In a typical embodiment of the present invention, a system for determining the optimal ratio of long afterglow self-luminous road marking paint is proposed, comprising:

[0263] The performance index test module is configured to: obtain multiple ratio schemes based on the uniformity rule or the orthogonal rule under multiple raw material types and multiple raw material dosages, test the optical visual system performance index and road mechanical system performance index of the coating under each ratio scheme, and obtain the test results of each performance index;

[0264] The coating performance evaluation index system determination module is configured to: calculate the weights of the optical vision system performance index and the road mechanics system performance index based on the entropy weight method, and determine a new coating performance evaluation index system in combination with the grey correlation analysis results;

[0265] The module for obtaining the best paint ratio is configured as follows: based on the new paint performance evaluation index system, the module combines the multi-criteria ranking method to objectively rank the comprehensive performance of various ratio schemes, and uses the comprehensive score to determine the best ratio of long-lasting self-luminous road marking paint.

[0266] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for determining the optimal ratio of long afterglow self-luminous road marking paint, characterized in that: The following steps are involved: Step 1: Under various raw material types and various raw material dosages, a variety of ratio schemes are obtained based on the uniformity rule or the orthogonality rule, and the optical visual system performance index and road mechanical system performance index of the coating under each ratio scheme are tested; Step 2: Based on the entropy weight method, the weights of the optical vision system performance index and the road mechanics system performance index are calculated respectively, and combined with the results of grey correlation analysis, a new coating performance evaluation index system is determined; Step 3: Based on the new coating performance evaluation index system, the multi-criteria ranking method is combined to objectively rank the comprehensive performance of each ratio scheme, and the comprehensive score is used to determine the optimal ratio of long-lasting self-luminous road marking paint.

2. The method for determining the optimal ratio of long afterglow self-luminous road marking paint according to claim 1, characterized in that: The types of raw materials include film-forming substances, fluorescent powder, pigments, fillers and film-forming aids; the dosage of the film-forming substance is fixed at 100%, and the remaining types of raw materials are weighed and configured based on the film-forming substance; among them, the dosage range of fluorescent powder is 30% to 50%, the dosage range of pigment is 3% to 7%, and the dosage range of filler is 3% to 7%; the addition of the film-forming aid is determined according to the engineering requirements and the on-site environment, and the dosage range is 0% to 1.5%.

3. The method for determining the optimal ratio of long afterglow self-luminous road marking paint according to claim 1, characterized in that: The performance indicators of the optical vision system include initial brightness, transition brightness, terminal brightness, afterglow time and brightness factor; the performance indicators of the road mechanics system include anti-skid coefficient, adhesion strength, wear loss, resistance to dust pollution, resistance to oil pollution, and resistance to ultraviolet aging.

4. The method for determining the best ratio of long afterglow self-luminous road marking paint according to claim 3, characterized in that: The new coating performance evaluation index system is determined, including: Group all performance indicators; The entropy weight method is used to calculate the weight value of each performance indicator in each group; Grey correlation analysis was used to calculate the correlation coefficient between the two performance indicators in each group; Performance indicators with large weight values ​​and good independence are selected from each group to form a new coating performance evaluation index system.

5. The method for determining the best ratio of long afterglow self-luminous road marking paint according to claim 1, characterized in that: The above-mentioned multi-criteria ranking method is combined to objectively rank the comprehensive performance of each ratio scheme, and the optimal ratio of the long afterglow self-luminous road marking paint is determined by the comprehensive score, including: The performance indicators are divided into positive performance indicators and negative performance indicators, and the performance indicator data of the test are normalized; Sort the normalized data by vector quality; Calculate the distance between the performance index test data of each ratio scheme and the optimal vector and the worst vector respectively; The scoring values ​​of various mixing schemes are calculated according to the distance, and the optimal mixing ratio of the long afterglow self-luminous road marking paint is selected according to the scoring values.

6. The method for determining the best ratio of long afterglow self-luminous road marking paint according to claim 5, characterized in that: The normalization process includes: For the positive performance indicator, the optimization and improvement calculation formula for normalizing the test data is: For negative performance indicators, the optimization and improvement calculation formula for normalizing the test data is: Where: A i-j is the data before normalization, B i-j is the normalized data; A -j To normalize the test data of a certain performance index under all ratio schemes, B -j It is the test data of a certain performance index under all the proportion schemes after normalization; i represents the proportion scheme number, ranging from 1 to m, and the value of m varies with the selected raw material type, raw material dosage and law; j represents the performance index, and the value of j ranges from 1 to n, n = 17; for example, A 3-2 It represents the test data obtained by testing the performance index 2 of the coating product under the ratio of scheme 3 before normalization.

7. The method for determining the best ratio of long afterglow self-luminous road marking paint according to claim 6, characterized in that: The vector quality sorting method is as follows: Optimal vector: (max(B -1 ), max(B -2 ),max(B -3 ), …, max(B -j ), …, max(B -n )); Worst vector: (min(B -1 ), min(B -2 ),min(B -3 ), …, min(B -j ), …, min(B -n )).

8. The method for determining the best ratio of long afterglow self-luminous road marking paint according to claim 7, characterized in that: The respectively calculating the distances between the performance index test data of each ratio scheme and the optimal vector and the worst vector includes: The distance between the performance index test data of each ratio scheme and the optimal vector The calculation formula is as follows: The distance between the performance index test data of each ratio scheme and the worst vector The calculation formula is as follows:

9. The method for determining the best ratio of long afterglow self-luminous road marking paint according to claim 8, characterized in that: The score of each ratio scheme D i The calculation formula is as follows: Among them, D i The larger the value, the better the performance of the i-th matching scheme, and the higher the ranking will be.

10. A system for determining the optimal ratio of long afterglow self-luminous road marking paint, characterized in that: include: The performance index test module is configured to: obtain multiple ratio schemes based on the uniformity rule or the orthogonal rule under multiple raw material types and multiple raw material dosages, test the optical visual system performance index and road mechanical system performance index of the coating under each ratio scheme, and obtain the test results of each performance index; The coating performance evaluation index system determination module is configured to: calculate the weights of the optical vision system performance index and the road mechanics system performance index based on the entropy weight method, and determine a new coating performance evaluation index system in combination with the grey correlation analysis results; The module for obtaining the best paint ratio is configured as follows: based on the new paint performance evaluation index system, the module combines the multi-criteria ranking method to objectively rank the comprehensive performance of various ratio schemes, and uses the comprehensive score to determine the best ratio of long-lasting self-luminous road marking paint.

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