Interactive evolution of images

Interactive Evolution of Images

Jeanine Graf and Wolfgang Banzhaf

Abstract

Systems of selection and variation by recombination and/or mutation can be used to evolve images for computer graphics and animation.Interactive evolution can be used to direct the development of favorite designs in various application areas.Examples of the application of evolutionary algorithms to two-dimensional(2-D)bitmap images and the methods for three-dimensional(3-D)voxel images are indicated.We show that arti?cial evolution can serve as a useful tool for achieving?exibility and complexity in image design with only a moderate amount of user-input and detailed knowledge.

1INTRODUCTION

Dawkins(Dawkins1986)demonstrated convincingly the potential of Dar-winian variation and selection in graphics.He evolved biomorphs2-D gra-phic objects,from a collection of genetic parameters with interaction with the user(Dawkins1986;Smith1984).Recently,much research has been directed into the application of genetic algorithms to image and graphics problems,such as the segmentation of range images(Meygret,Levine,and Roth1992)or pattern identi?cation(Hill and Taylor1992).Sims(Sims1991) used genetic algorithms for interactive generation of color art;Todd and Latham(Todd and Latham1991)have considered similar ideas to reprodu-ce computer sculptures through structural geometric techniques.Caldwell and Johnson(Caldwell and Johnston1991)have applied the concept of ge-netic algorithms to search interactively in face space of criminal suspects with the help of witnesses.

The novel idea offered in this paper is to provide a user with new technique to evolve2-D(bitmap)and3-D(voxel)images that can be applied universally in any?eld of interest.We have developed a system which presents progressively evolving solutions for graphical design problems by means of interactive processes.

Interactive evolution is a technique from the class of evolutionary algo-rithms(EAs)which are based upon a simple model of organic evolution. Most of these algorithms operate on a population of individuals which re-present search points in the space of the decision variables(either directly,or by using coding mappings).Evolution proceeds from generation to gene-ration by exchanging genetic material between individuals(recombination), i.e.by trying out new combinations of partial solutions,and by random changes of individuals(mutation).New variations are subjected to selection

based on an evaluation of features of the individuals according to certain (?tness)criteria.

The best-known representatives of this class of algorithms are evolutio-nary programming(EP),developed in the U.S.by L.J.Fogel(Fogel,Owens, and Walsh1966),evolution strategies(ESs),developed in Germany by I. Rechenberg(Rechenberg1973)and H.–P.Schwefel(Schwefel1981),and genetic algorithms(GAs),developed in the U.S.by J.H.Holland(Holland 1975).

Evolutionary programming(EP)tries to apply the variation-selection prin-ciple to ameliorate computer systems.The important and elementary steps of the search process are(i)a de?nition of the task and its?tness criteria, (ii)creation of a?rst representation,(iii)variation of the statement leading to offspring representations,(iv)performance quali?cation of the offspring representations,(v)reproduction of the best performing representations. The process is repeated until the task is accomplished(Fogel1993).

Evolutionary programming uses real-valued object variables and normal-ly distributed random mutation with expectation zero.The variance of the distribution evolves during the optimization process.It is calculated se-parately for each representation as transformation of its own?tness value. Mutation is the primary operator.Recombination is not used in the standard EP algorithm.Objective function values are scaled and possibly randomly altered to obtain the?tness.Selection of new parent representations is done probabilistically using a competition procedure which guarantees that the best representation is always retained and the worst always discarded.

The?rst applications of evolution strategies(ES)came in the?eld of experimental optimization and used discrete mutations.When computers became available,algorithms were devised that operated with continuous-valued variables.

The ES uses,like EP,mutation as its main search operator.In addition re-combination operators(e.g.discrete recombination)are applied.Parameters like the standard deviation of the mutation are added to the representation (individual)as strategy parameters,which are adapted during the simulation via heuristics like Rechenberg’s success rule(Rechenberg1973).Only the best individuals out of the offsprings or out of the offsprings and parents are selected to form the next population(Schwefel1981;B¨a ck1994; Rechenberg1973).

The genetic algorithm(GA)works on a genotypic level of binary encoded individuals,a choice that is founded by the argument of maximizing the number of schemata available with respect to a given code(Goldberg1989; Davis1991).Various selection schemes,such as proportional selection,are applied with respect to the relative?tness of the individuals.The recombi-nation(e.g.,1-point crossover)serves as the main search operator.Mutation (e.g.,bit-mutation)is used at a low rate to maintain diversity.Nearly no knowledge about the properties of the object function is required.In order to use a GA for optimization,a mapping from the genotype(bitstring)to phenotype(realised behavior)has to be de?ned.This could be a very com-plicated task,because the mapping is absolutely crucial for the performance of the GA.

In all of these cases,the selection criteria are traditionally?xed and are held constant from the start of the simulation,therefore these criteria must

be detailed explicitly beforehand.This constitutes a signi?cant problem in many realistic applications(apart from optimization),because an explicit ?tness function may not be available in closed form.Recently,various work-arounds have been tried,one of the most prominent being co-evolution. In this method,rather than using one population to search for the best solution,two or more antagonistic populations are run which compete against each other.The realized?tness in this case is in part determined by the relationship of one population to the other,and does not have to be de?ned explicitly beforehand(Hillis1990).

This paper makes use of an alternative method to generate?tness by involving the user into the selection process of arti?cial evolution.Our work in computer graphics,a natural domain for humans,easily engages the user by relying on human visual capacity.

2INTERACTIVE EVOLUTION

In interactive evolution,the user selects one or more favourite model(s)which survive(s)and reproduce(s)(with variation)to constitute a new generation. These techniques can be applied to the production of computer graphics, animation,creating forms,textures,and motions(Glassner1990;Arvo1991; Foley1992).Potential applications of interactive evolution include product design,e.g.,cars,engineering of components,and architectural design.

Phenotypes and Genotypes

We shall need to discern between genotypes and phenotypes in interac-tive evolution,both terms are also basic concepts for biological evolution. The biological genotype is the information that codes the development of an individual.Genotypes most often consist of DNA sequences.In interactive evolution,genotypes are represented as numerical data and real values,col-lections of procedural parameters,symbolic expressions or compound data structures(e.g.,trees).The phenotype is the realised behavior of the indivi-dual itself,i.e.,the product of an interpretation of the underlying genotypic representation.In our case,the phenotype is the resulting graphical image.

The relation between genotypes and phenotypes in nature is determined by the genotype-phenotype mapping.This transformation is very compli-cated and draws heavily from the individual’s current environment.In principle,a similar mapping can be introduced in arti?cial evolution(Banz-haf1994).In this way,a part of the complexity of the developing solution might be rendered by the environment.

Fitness and Selection

The term?tness in interactive evolution is the capability of an individu-al or model to survive into the next generation,and therefore is tied directly to https://www.wendangku.net/doc/0410473120.html,ually,?tness is not de?ned explicitly but is instead a relative measure of success following from the selection activity of a human user. Here,it is even based on non-quanti?able measures like visual preference or personal taste.

Hybrid systems,however,are reasonable as well.Certain prede?ned

criteria(for example:drag coef?cient()or air resistance of an airpla-ne model)help to sort candidates for survival from the set of all variants, among which a human user?nally selects the next generation.

Variation

In interactive evolution,one of the main bene?ts is the automatic gene-ration of variants.Variation is accomplished by de?ning problem-speci?c mutation and recombination operators that constantly propose new vari-ants to the presently existing population of graphic models on the screen.A certain amount of knowledge has to be invested in order to?nd appropriate operators for an application domain.In the next section we shall provide appropriate operators for manipulation of bitmap images and voxel gra-phics.

Figure1:Generation of variants by recombination and mutation of graphical mo-dels.A*A and B*B are the unchanged parents,B*A and A*B are generated by recombination,and A’and B’are generated by mutation

3EVOLVING2-D IMAGES

In our approach we try to evolve two-dimensional images speci?ed either directly as bitmaps or as parameterized geometric models,such as those provided by vector graphics.Figure1gives a sketch of the variants that may arise from the graphical parent objects.Whereas the latter is more or less straight-forward in EAs,once the parameters have been?xed,the former is a challenging task.To our knowledge no effort has been made to apply evolutionary algorithms to the evolution of bitmap images directly.

Application to2-D Images

Bitmaps and other forms of direct encoding of images have found an excel-lent niche in computer graphics,video composition and image rendering (Foley1992;Kirik1992).Any2-D shape can be represented as a sequence of

points or vertices,with each vertex consisting of an ordered pair of numbers ,its coordinates.The array of pixel values of a2-D image,however, have nothing to do with the structure being represented in the image.This constitutes the challenge to?nding appropriate operators for the genera-tion of new variants of an existing image,because structural or functional conservation of the image content is of utmost importance in application.

We solve the problem for realizing evolutionary operations by using warping and morphing to create variations.

Warping is a method which,by using tiepoints in two images“A”and “B”,allows for the creation of intermediate images(Woldberg1990;Ru-precht1994).Basically,these intermediate images are interpolations along an abstract axis from image“A”and image“B”.The tiepoints are cons-traints of the interpolation because corresponding tiepoints in“A”have to be transformed into those of“B”.

Morphing is an application of digital image warping.It involves dis-tributing tiepoints over two images in such a way as to conserve essential structures in interpolated(intermediate)images.In this way an arbitrary initial image can evolve via intermediate steps of interpolation into a?nal image without leaving visual irritations.

It is interesting to note that even without any concept of structure in image warping and morphing,visually appealing images are generated. Structure has been substituted by tiepoints that extend their in?uence into the surrounding image by the help of interpolation algorithms.

We adopt this novel approach for the arti?cial evolution of images.By specifying tiepoints,suf?cient control can be exerted about structure in2-D images as to provide useful variants to the images being varied.Whereas in conventional computer graphics morphing is used for the purpose of transforming images in a dynamic animation series,we use the generated intermediate images as variants of the original images in the process of evolution.

Let us look more closely at the operations usually implemented in EAs: Mutation is commonly considered to be a local operation that does not radically change the resulting phenotype.In order to provide this feature in bitmap image evolution,we propose to use a very small number of tiepoints.A one-point mutation would select one tiepoint in an image.The corresponding tiepoint in a second image would then be used as a source for novelty,by providing information into which direction to evolve the original image.Structure is conserved because tiepoints in both images correspond to each other.A parameter would then be used to quantify the degree of substitution in the image.

Note that the second image,from which novelty is gained,is not ne-cessarily in the present generation of the evolutionary process.Instead,a generation0of images,equipped with a number of tiepoints is used for mutation.By selecting one tiepoint in an image of generation n that corre-sponds to a tiepoint in an image of generation0and constraining the effect to a local neighborhood,we provide a path for morphing between the two images.The generation0images in a way help to form equivalence classes between structures expressed as tiepoints.Some domain knowledge must be used in the process of tiepoint selection for generation0.

Figure2:This?gure shows an example recombination of two bitmap images through image interpolation.The percentage represents the proportion of inheritance from

the parent images

Recombination is implemented as a more global operation by which two images exchange information.We propose to use as many tiepoints as necessary to conserve the underlying structure in two images“A”and“B”.

A recombination would then be quanti?ed in the image space between“A”and“B”by a certain parameter indicating the degree of“intermediateness”of a variant.Figure2demonstrates the method of recombination.Different variants between the two original cars are shown.Note that recombination always operates within the present generation.

Figure3and4demonstrates the mutation process by using2-D images of cars.A local variation takes place by substituting one sort of wheel by another(Figure3).

Figure3:Local mutation by substituting one wheel by another.

Figure4:Whole mutation of a car by slightly random warping.

An arbitrary warping of an image at different locations,without using the proper equivalence class of image structures is shown in a contrasting image in Figure4.It gives the impression of a damaged car.That is the case because arbitrary operations have been applied without being constrained by an otherwise existing path of variation between structures.

First generation

Third generation

Fifth generation

Figure5:This example shows the evolution of nine models.After?ve generations some models are found which are closer to the users taste and to his target model.

Structurally,the content of an image is usually composed of components. In our example,a car is composed of a body,wheels,seats,chassis,engine, windows and doors.The same method that was applied before to the entire image representing the whole structure can also be applied to its components.By using many tiepoints in a component such as,say a wheel, in?uence can be exerted to any necessary degree about the details of the evolving structure.

Before presenting the entire interactive evolution system,we now turn to other representations of images.

Application to2-D structural descriptions of images

Procedural models of images can be characterized by certain parameters that must be interpreted in the appropriate context.The parameters consti-tute the genotype of an image.Its interpretation is the genotype-phenotype mapping and the resulting image is the phenotype.

Because the number of structural elements usually varies from image to image,it is necessary to allow for variable-length genotypes.

Variation takes place on the level of parameters that are subjected to nor-mally distributed random mutations as well as to intermediate or discrete recombination operations.

The resulting images are subjected to the same selection procedures as are those of the bitmap manipulation procedures discussed before.

4EVOLVING3-D IMAGES

For various applications in computer graphics it is often useful to represent objects in a3-D grid of cubes or voxels(volume elements)according to their position in space(Young and Pew1992).

Figure6:3-D mutation

From objects in3-D space an image can be constructed using establis-hed methods of computer graphics(Watt1993).Alternatively,procedural models of3-D objects can be combined into an image.

The generalisation of the above methods into the realm of3-D graphics is straight-forward.Tiepoints in3-D voxel graphics in?uence3-D areas instead of2-D areas in pixel graphics.Apart from that,any other compo-nent of the mechanism remains in place,especially interpolation and the generation of local and global variation to an original parent generation of images.

The same applies to procedural models that are generated by applying a genotype-phenotype mapping starting from a collection of appropriate parameters.Figure5and6show two examples of3-D mutations that can be used.

Figure7:Mutation through deformation[26].A set of transformations that deform the object.Linear transformation rotate,translate or scale the object.

5JARDIN–A SYSTEM FOR INTERACTIVE EVOLUTION

Jardin is a digital image warping and morphing program,that allows the evolution of images,and runs under the X Window System(Cutler,Gilly, and O‘Reilly1993;Gaskin1992;Young and Pew1992).Jardin loads and saves image populations.It provides facilities to store tiepoints in images, to warp images and to apply the evolutionary process.Tiepoints are inhe-rited from generation to generation,with generation“0”provided by the user.With a very small population,between2and20graphical models per generation and over a short time,a human user can select new generations of images.This process will be repeated until a favourite individual in the population has been generated.

6SUMMARY AND CONCLUSIONS

We demonstrate how interactive evolution can be applied to2-D bitmap images and a generalization to3-D representation is outlined.The main idea is to combine the concepts from interactive evolutionary algorithms with the concepts of warping and morphing from computer graphics.Structure within images is substituted by a collection of tiepoints.By providing a ?rst generation of images,where a structure in the images can manually be de?ned by the user.Evolution then proceeds along the paths constrained by the set of these tiepoints in all of the images.The original generation is kept as a source for mutations,which allows for new models to be created.In contrast recombination always works on images of the present generation.

In our version of interactive evolution,the user selects his favourite indi-vidual which then is reproduced to constitute the next generation.These techniques can be applied to the production of computer graphics and in-clude e.g.,product visualisation of cars,planes,engineering components and construction projects.Our interactive evolution system has the poten-tial for a large number of other application areas like:interactive plotting in business,electronic publishing,computer aided design,drafting and manufacturing,simulation and animation for scienti?c visualisation,enter-tainment,architecture,etc.Our interactive simulations have shown that interesting results can be achieved even with low population sizes and few generations.This makes the system applicable to quick design and proto-typing,in a large variety of application areas.

Acknowledgements

Funding from the German Bundesministerium f¨ur Forschung und Techno-logie(BMFT)under project EVOALG is gratefully acknowledged.Thanks to Detlef Ruprecht for help and morphing graphics software support.Thanks to Thomas B¨a ck for discussions.And special thanks to David Fogel for his helpful comments and suggestions.

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青岛恒源热电

注意：以下内容请进一步总结！ 青岛恒源热电有限公司 目标公司主要从事蒸汽、热水的生产及供应、蒸汽余热发电业务，同时提供供热管道及设施维修、安装业务。据介绍，目标公司开发了循环水供热工程项目，该项目是青岛市获批的第一个清洁发展机制(CDM)项目；前处该项目处于施工建设阶段，预计将于2009年上半年内正式投产。据介绍，目标公司主要负责临港工业区辖区内的蒸汽供应及热网管理，发电业务，对居民的用热服务。 公司成立于2001年，主要从事蒸汽、热水的生产及供应、蒸汽余热发电业务。 青岛恒源热电有限公司位于开发区B区供热范围，拥有12MW的抽凝式汽轮发电机组1台及12MW的背压机组1台，75t/h循环流化床锅炉3台和150t/h锅炉1台，最大供热能力是355t/h，担负着B区的生产、民用供热负荷，主要满足热电厂东部居民小区供热和山东科技大学供热。 青岛恒源热电有限公司位于青岛经济技术开发区临港工业区的中北部，海尔大道与渭河路交界处东北角，渭河路777号。厂区所在地东侧隔宽约100m绿化地为鑫龙物流公司，该公司东侧、距离本项目最近300m处为澳柯玛人才公寓；厂区南侧隔渭河路、绿化带100m处为东小庄村（原村庄平房已搬迁，现建有多座两层复式楼房），该村庄南侧、距离本项目约420m处为山孚日水食品有限公司；项目隔渭河路东南方向约200m处为澳柯玛工业园；西及西南方向隔海尔大道、渭河路均为浦项制铁有限公司；北侧与开发区消防大队以及正友砼业相邻。 企业所在地厂址东南距市中心约8km，东面距前湾港区约4.5km。 现有工程内容：青岛恒源热电有限公司主要服务于黄岛供热分区B 区（齐长城路以北、疏港高速以南、镰湾河以西、柳花泊和珠山以东片区（包括柳花泊），总占地面积约60平方公里）。企业现有锅炉规模为3×75t/h+1×130t/h 循环流化床蒸汽锅炉，总计约355t/h锅炉容量；发电机组规模为1×12MW C12-34.9/0.98（抽凝）+1×12MW B12-4.9/0.98（背压），总计发电装机容量24 MW。 近几年，恒源热电强化能源管理，合理调整运行方式，加强节能技术改造，企业能源管理工作上了一个新台阶，先后通过了“企业能源审计”、“热电联产机组认定”等审核认证工作，被评为“青岛市清洁生产企业”，2007年度“山东省节能先进企业”。 为进一步加强企业能源管理，完善优化企业节能减排工作，公司在本年度开始推行循环经济试点工作。目前，作为试点工作重点项目之一的企业冷渣机改造项目已基本完成，初步具备投运条件，预计本年度六月份正式投入运行。该项目是将循环流化床锅炉的人工排渣（温度一般在900℃），通过加装冷渣机把炉渣余热加热除盐水，将锅炉效率提高1-3%，同时解决人工放渣存在安全隐患、能源浪费以及不环保等问题，项目投资为85万元，年可节标煤700吨。

认识实习报告(青岛东亿热电厂)

热能与动力工程专业制热方向认识 实习报告 学院：机电工程学院 班级：热能一班 姓名：徐国庆 学号：201240502013

一．认识实习的目的和任务 1.认识实习的目的： （1）认识实习是四年制高等学校教学活动的实践环节之一； （2）认识实习是对学生进行火力发电厂主机（锅炉、汽轮机）、辅机（换热器、风机、水泵）及其制造厂的设备系统、生产工艺进行认识性训 练，对发电厂热力系统进行整体初步了解。 2.认识实习的任务： （1）对火力发电厂主机的认识实习 实习对象：锅炉本体、汽轮发电机本体。锅炉形式包括煤粉锅炉、循 环流化床锅炉、链条炉、余热锅炉等。汽轮机形式包括凝气式汽轮机、 背压式汽轮机、调节抽汽式汽轮机。 认识内容：设备外形特点、摆放位置、主要性能参数、安全生产常识。 （2）对火力发电厂辅助机械设备的认识实习 实习对象：制粉系统、除尘除灰系统、烟风系统、回热系统、润滑冷 却系统、水油净化系统等。 认识内容：设备外形特点、摆放位置、主要性能参数、安全生产常识。 （3）对火力发电厂设备系统的认识实习 实习对象：火力发电厂主机和辅机工程的系统。 认识内容：设备之间的空间关系、安全生产常识。 3.认识实习的意义 （1）强化学生对专业基础课程的理解 （2）国内火力发电厂的技术发展出现了新进展 CFB锅炉、燃气轮机、余热锅炉、超临界机组、烟气脱硫、布袋除尘、集中控制运行等新技术。 （3）认识实习有利于培养学生的职业精神 （4）认识实习有利于了解机组 （5）认识实习有利于了解机组建设过程 二．捷能汽轮机厂 （1）简介：汽轮机是火力发电厂三大主要设备之一。它是以蒸汽为工质，将热能转变为机械能的高速旋转式原动机。它为发电机的能量转换提供机 械能。 青岛捷能汽轮机集团股份有限公司始建于1950年，是我国汽轮机行业重 点骨干企业。拥有各种数控、数显等机械加工设备2200余台，以200MW 及以下“捷能牌”汽轮机为主导产品，拥有电站汽轮机和工业拖动汽轮 机两大系列产品，能够满足发电、石化、水泥、冶金、造纸、垃圾处理、燃气-蒸汽联合循环、城市集中供热等领域需求，年产能达500台/600万 千瓦以上。中小型汽轮机市场占有率居国内同行业首位，是目前国内中 小型汽轮机最大最强的设计制造供应商和电站成套工程总包商。 公司积极推进品牌战略，率先在汽轮机行业内取得了美国FMRC公司双重 ISO9001国际质量体系认证和ISO1400环境管理体系认证，率先在汽轮机 行业内第一个获得了“中国名牌产品”称号，先后获得了“全国AAA级 信用企业”、“中国优秀诚信企业”、“全国用户满意产品”、“山东

供热管网检修作业指导手册[青岛热电集团]

供热管网检修作业指导手册[青岛热电集团] 供热管网检修作业指导手册[青岛热电集团] 供热管网检修作业指导手册[青岛热电集团] 作者:佚名更新时间:2008-12-5 15:55:38 字体: 供热管网检修作业指导手册 1 总则 1.1 为使公司供热管网的维护、检修工作更为规范和科学合理，确保安全运行，制定作业指导手册。 1.2 本作业指导手册适用于公司供热管网的维护、检修及事故抢修。 本作业指导手册供热管网的工作压力限定为: 工作压力不大于1.6MPa(表压)，介质温度不大于300?的蒸汽供热管网。 1.3 管网的检修工作应符合原设计要求。 1.4 执行本作业指导手册时，尚应符合国家现行有关标准的规定。 2 术语 2.1 热网维修 热网的维护和检修。本作业指导手册中简称维修。 2.2 热网维护 供热运行期间，在不停热条件下对热网进行的维护工作。本作业指导手册中简称维护。 2.3 热网检修 在停热条件下对热网进行的检修工作。本作业指导手册中简称检修。 2.4 热网抢修

供热管道设备突发故障引起蒸汽大量泄漏，危及管网安全运行或对周边环境、人身安全造成威胁时进行的紧急检修工作。本作业指导手册中简称抢修。 2.5 供热管网 由热源向热用户输送和分配供热介质的管线系统。本作业指导手册中简称热网。 3 维护、检修机构设置、检修人员及设备 3.1 维护、检修机构设置及人员要求 3.1.1客户服务中心是公司高新区内供热管网运行、调度、维护、检修的责任机构，负责高新区内供热管网的维护、检修工作。 3.1.2 供热管冈的维护、检修人员必须经过培训和专业资格考 试合格后，方可独立进行维护、检修工作。供热管网维护、检修人员必须熟悉管辖范围内的管道分布情况、设备及附件位置。维护、检修人员必须掌握管辖范国内供热管线各种附件的作用、性能、构造以及安装操作和维护、检修方法。 3.1.3检修人员出门检修时应穿公司工作服，配戴上岗证，注意礼貌用语，维护公司形象。 3.2 维护、检修用主要设备与器材 3.2.1 供热管网的维护检修部门，应备有维护、检修及故障抢修时常用的设备与器材。 3.2.2检修设备、工具平时摆放在规定位置，检修设备和专用工具要有专人保管，所有设备、工具应保证完好，须保证检修时能够立即投入使用。检修物资也应分门别类码放整齐，方便查找，以保证检修、抢修时不会因为寻找物资配件而耽误时间。每次检修完后都应检查备品备件数量，发现不够时要及时与物质采购部联系进行必要地补充，确保检修时不会因无备品备件而影响检修时间与质量。

青岛西海岸公用事业集团易通热电有限公司新能源分公司_中标190922

招标投标企业报告 青岛西海岸公用事业集团易通热电有限公司新 能源分公司

本报告于 2019年9月22日 生成 您所看到的报告内容为截至该时间点该公司的数据快照 目录 1. 基本信息：工商信息 2. 招投标情况：中标/投标数量、中标/投标情况、中标/投标行业分布、参与投标 的甲方排名、合作甲方排名 3. 股东及出资信息 4. 风险信息：经营异常、股权出资、动产抵押、税务信息、行政处罚 5. 企业信息：工程人员、企业资质 * 敬启者：本报告内容是中国比地招标网接收您的委托，查询公开信息所得结果。中国比地招标网不对该查询结果的全面、准确、真实性负责。本报告应仅为您的决策提供参考。

一、基本信息 1. 工商信息 企业名称：青岛西海岸公用事业集团易通热电有限公司新能 源分公司 统一社会信用代码：91370211334195493K 工商注册号：370211120004502组织机构代码：334195493法定代表人：赵军田成立日期：2015-04-23 企业类型：有限责任公司分公司(非自然人投资或控股的法人 独资) 经营状态：注销 注册资本：/ 注册地址：山东省青岛市黄岛区相公山路723号 营业期限：2015-04-23 至 / 营业范围：为上级公司联系业务。（依法须经批准的项目，经相关部门批准后方可开展经营活动）联系电话：*********** 二、招投标分析 2.1 中标/投标数量 企业中标/投标数： 个 （数据统计时间：2017年至报告生成时间）

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