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    专

    Current sampling device for use in battery management system of vehicle, has signal measuring component provided with anode sampling point, where anode and cathode sampling points are arranged on side surface of conductive connecting piece.

    KR200945049E
    发明人
    WANG X, WU Z
    受让人
    NINGDE SHIDAI RUNZHI SOFTWARE TECHNOLOGY (NING-Non-standard), CONTEMPORARY AMPEREX TECHNOLOGY LTD (NDCT-C)
    申请人
    AIRBUS HELICOPTERS
    申请号
    3650776
    申请日
    2022-09-27
    公开(公告)号
    KR200945049E
    公开(公告)日
    2022-07-06
    IPC分类号
    C12P007/44C12F003/10C12P007/06C12P007/20A23K000/00C12P007/46C12F005/00
    CPC分类号
    -
    优先权号
    1020180128481
    优先权日
    2018-10-24
    摘要

    NOVELTY - Physiological characterization in a subject comprises (a) obtaining a physiological sample of the subject, (b) determining biomarker measures of biomarkers in the sample, and (c) classifying the sample based on the biomarker measures using a classification system, where the classification of the sample correlates to a physiologic state or condition, or changes in a disease state in the subject.

    USE - The method is useful for: physiological characterization in a subject; diagnosing reactive airway disease; and diagnosing non-small cell lung cancer in a subject, where the disease state is lung disease including non-small cell cancer and reactive airway disease including asthma.

    DETAILED DESCRIPTION - INDEPENDENT CLAIMS are included for:

    (1) a method-I of diagnosing a lung disease in a subject comprising, step (a) as above per se (b1) determining biomarker measures of the biomarkers that assist in discriminating between the indication of reactive airway disease and non-small cell lung cancer, the biomarkers indicative of reactive airway disease, and the biomarkers indicative of non-small cell lung cancer, in the sample, where the biomarkers are not identical, step (c) as above per se, where the classification of the sample assists in discriminating between the indication of (i) reactive airway disease and non-small cell lung cancer, (ii) presence or absence of reactive airway disease, and (iii) presence or absence of non-small cell lung cancer, and (d) determining the subject to have (1) reactive airway disease, (2) non-small cell lung cancer, or (3) absence of disease, depending on which condition is found in two of the three classifications;

    (2) a method-II of classifying test data, comprising (Aa) receiving test data comprising a biomarker measure for each biomarker of the set of biomarkers in a human test subject, (Ba) evaluating the test data using an electronic representation of a support vector machine or AdaBoost classifier trained using an electronically stored set of training data vectors, (Ca) each training data vector representing an individual human and comprising a biomarker measure of each biomarker of the set of biomarkers for the respective human, (Da) each training data vector further comprising a classification with respect to a disease state of the respective human, and (Ea) outputting a classification of the human test subject based on the evaluating step, where the test data comprises the biomarker measures of each of a set of biomarkers, or (Ea) accessing an electronically stored set of training data vectors, step (Ca)-(Da) as above per se, training an electronic representation of a support vector machine using the electronically stored set of training data vectors, receiving test data comprising a plurality of biomarker measures for the set of biomarkers in a human test subject, evaluating the test data using the electronic representation of the support vector machine, and outputting a classification of the human test subject based on the evaluating step, or (Ea) as above per se, each training data vector representing an individual human and comprising a biomarker measure of each biomarker of the set of biomarkers for the respective human, step (Da) as above per se, selecting a subset of biomarkers from the set of biomarkers, training an electronic representation of a support vector machine using the data from the subset of biomarkers of the electronically stored set of training data vectors; and

    (3) a system for classifying test data.

    权利要求
    1. A computer system comprising: one or more hardware processors in communication with a computer readable medium storing software instructions, the one or more hardware processors configured to execute the software instructions to cause the computer system to: cause display of an interactive map in a graphical user interface, wherein the interactive map comprises a plurality of map layers; determine a list of available map layers; organize the list of available map layers according to a hierarchical layer ontology, wherein like map layers are grouped together; cause display, on the interactive map in the graphical user interface, of the hierarchical layer ontology including indications of the available map layers, wherein each of the available map layers is user selectable, wherein each of the available map layers is associated with one or more feature types, and wherein the available map layers comprise base layers, vector layers, and user-defined layers; receive a first user input selecting or deselecting one or more map layers of the available map layers; and responsive to the first user input, determine whether map layers needed to compose map tiles based on the selecting or deselecting are cached.
    2. The computer system of claim 1 , wherein the hierarchical layer ontology includes at least two hierarchical levels of layers.
    3. The computer system of claim 2 , wherein selection or de-selection of an available map layer in a first hierarchical level causes a corresponding selection or de-selection of all available map layers in any hierarchical levels below the first hierarchical level.
    4. The computer system of claim 1 , wherein fewer than all the map layers are displayed in the hierarchical layer ontology.
    5. The computer system of claim 1 , wherein the base layer comprises at least one of an overhead imagery layer, a topographic layer, a subtle base layer, an aviation layer, a blank Mercator layer, or a blank unprojected layer.
    6. The computer system of claim 1 , wherein the one or more vector layers comprise at least one of a regions layer, a buildings/structures layer, a terrain layer, a transportation layer, or a utilities/infrastructure layer.
    7. The computer system of claim 1 , wherein each of the one or more vector layers is comprised of one or more sub-vector layers.
    8. The computer system of claim 7 , wherein each of the one or more vector layers includes vector data associated with features of respective feature types.
    9. The computer system of claim 8 , wherein the one or more feature types include at least one of regions, buildings/structures, terrain, transportation, or utilities/infrastructure.
    10. The computer system of claim 8 , wherein each of the features represents at least one of a road, a terrain, a lake, a river, a vegetation, a utility, a street light, a sign, a railroad, a hotel, a motel, a school, a hospital, a building or other structure, a region, a transportation object, an entity, an event, or a document.
    11. The computer system of claim 8 , wherein metadata associated with the features includes at least one of a location, a city, a county, a state, a country, an address, a district, a grade level, a phone number, a speed, a width, or other related attributes.
    12. The computer system of claim 11 , wherein the one or more hardware processors are further configured to execute the software instructions to cause the computer system to: receive a second user input from the user selecting one or more of the features; and in response to the second user input, access and cause display of metadata associated with at least some of the selected features.
    13. The computer system of claim 12 , wherein the one or more hardware processors are further configured to execute the software instructions to cause the computer system to: further in response to the second user input, cause display of one or more histograms based on the accessed metadata.
    14. The computer system of claim 1 , wherein the one or more hardware processors are further configured to execute the software instructions to cause the computer system to: further responsive to the first user input: cause the interactive map to be updated with the one or more map tiles including the map layers needed to compose the map tiles based on the selecting or deselecting.
    15. The computer system of claim 14 , wherein causing the interactive map to be updated comprises: composing data associated with each of the map layers needed to compose the map tiles into one or more map tiles; and arranging the one or more map tiles into the interactive map in the graphical user interface.
    16. The computer system of claim 1 , wherein the one or more hardware processors are further configured to execute the software instructions to cause the computer system to: further responsive to the first user input: determine, based on the selecting or deselecting, a map tile composition and the map layers needed to compose the map tiles; compose the map layers needed to compose the map tiles into one or more map tiles; and cause the interactive map to be updated with the one or more map tiles.
    17. The computer system of claim 16 , wherein the one or more hardware processors are further configured to execute the software instructions to cause the computer system to: further responsive to the first user input: calculate any map layers needed in the map tile composition that are not cached; and cache the calculated map layers.
    18. A computer-implemented method comprising: by one or more processors executing program instructions: causing display of an interactive map in a graphical user interface, wherein the interactive map comprises a plurality of map layers; determining a list of available map layers; organizing the list of available map layers according to a hierarchical layer ontology, wherein like map layers are grouped together; causing display, on the interactive map in the graphical user interface, of the hierarchical layer ontology including indications of the available map layers, wherein each of the available map layers is user selectable, wherein each of the available map layers is associated with one or more feature types, and wherein the available map layers comprise base layers, vector layers, and user-defined layers; receiving a first user input selecting or deselecting one or more map layers of the available map layers; and responsive to the first user input, determining whether map layers needed to compose map tiles based on the selecting or deselecting are cached.
    19. The computer-implemented method of claim 18 further comprising: by the one or more processors executing program instructions: further responsive to the first user input: determining, based on the selecting or deselecting, a map tile composition and the map layers needed to compose the map tiles; composing the map layers needed to compose the map tiles into one or more map tiles; and causing the interactive map to be updated with the one or more map tiles.
    20. The computer-implemented method of claim 19 further comprising: by the one or more processors executing program instructions: further responsive to the first user input: calculating any map layers needed in the map tile composition that are not cached; and caching the calculated map layers.
    说明书
    [0001]技术领域
    [0002]本发明涉及一种地下厂房洞室预加锚固力分析计算方法，属于地下工程建设技术领域。
    [0003]背景技术
    [0004]地下厂房洞室的支护设计方法以工程类比为主、数值分析和模型试验为辅。近二十几年来以来我国地下厂房洞室支护采用柔性支护为主，其技术手段主要包括挂网钢筋+喷混凝土、锚杆或钢拱架、预应力锚索等一种或多种组合。而我国地下厂房规模大，普遍存在高边墙问题，所以在系统锚杆以外，大量的工程都会使用预应力锚索。预应力锚索对于松弛范围大，深度深，岩体较差且高边墙的地下厂房，有十分显著的支护作用。地质条件良好的地下厂房不需要施加预应力锚固措施，地质条件差的地下厂房则需要施加大量的预应力锚索。
    [0005]目前，业界并没有一个衡量地下厂房洞室系统锚固支护强度的统一指标，仅对预应力锚索的使用达成了共识，但使用数量的合理范围却缺乏一个相关指标。国内大型水电站地下厂房基本都布置了大量的预应力锚索，特别是地质条件差的地下厂房需施加大规模的预应力锚固措施，但仍有部分地下工程出现围岩大幅变形、开裂等问题。由于没有确切的经验，而且有限元分析因受地质条件模拟、参数取值、已有工程经验验证等的限制，往往也不能准确预测围岩稳定状况。
    [0006]国内仍有大量在建或规划建设的地下厂房，合理地确定地下厂房洞室系统锚固支护强度，准确预测围岩稳定状况，对确保大型地下厂房开挖支护安全有效、降低建设成本等具有极为重要的意义。
    [0007]发明内容
    [0008]为解决上述技术问题，本发明提供了一种地下厂房洞室预加锚固力分析计算方法。
    [0009]本发明通过以下技术方案得以实现：
    [0010]一种地下厂房洞室预加锚固力分析计算方法，包括以下步骤：
    [0011]步骤一、计算地质条件评分S c ；
    [0012]步骤二、计算地下厂房主要洞室的总毛空间体积V；
    [0013]步骤三、根据地质条件评分S c 和地下厂房主要洞室的总毛空间体积V，选取单位空间锚固力F；
    [0014]步骤四、计算地下厂房洞室预加锚固力F 锚 ，F 锚 ＝V·F。
    [0015]所述步骤一中地质条件评分S c 的计算方法如下：
    [0016]S＝R b /σ max (2)，
    [0017]式中，D为地下厂房主要洞室埋深范围的加权平均值，单位为m，G为工程围岩等级评分，S为围岩强度应力比，R b 为岩石单轴饱和抗压强度，σ max 为围岩最大主应力。
    [0018]所述地下厂房主要洞室包括主厂房洞、主变洞和尾水调压室洞。
    [0019]所述D为地下厂房主要洞室水平埋深范围和垂直埋深范围的加权平均值。
    [0020]所述工程围岩等级评分G的取值根据围岩类别、围岩总评分T和围岩强度应力比S进行选取。
    [0021]所述步骤二中地下厂房主要洞室的总毛空间体积V的计算公式如下：
    [0022]V＝ΣL·B·H (3)，
    [0023]式中，L为洞室最大开挖长度，B为洞室最大开挖跨度，H为洞室最大开挖高度。
    [0024]根据所述步骤四中的地下厂房洞室预加锚固力F 锚 ，计算预应力锚索数量N，
    [0025]式中，f为单根预应力锚索的平均锚固力。
    [0026]本发明的有益效果在于：
    [0027]1、定义了地质条件评分S c 、地下厂房主要洞室的总毛空间体积V、单位空间锚固力F和地下厂房洞室预加锚固力F 锚 四个参数，并给出了这四个参数的计算方法或者取值方式，便于合理地确定地下厂房洞室系统锚固支护强度，准确预测围岩稳定状况，对确保大型地下厂房开挖支护安全有效、降低建设成本等具有极为重要的意义。
    [0028]2、同时，给出了预应力锚索数量N的计算方法，能够有效缩小预应力锚索的数量选择范围，科学、合理地确定与地质条件、规模相适应的地下厂房锚固力水平，对于地下厂房前期快速设计，及后期成果验证都有重要作用。
    [0029]附图说明
    [0030]图1为地下厂房主要洞室单位空间锚固力统计图。
    [0031]具体实施方式
    [0032]下面进一步描述本发明的技术方案，但要求保护的范围并不局限于所述。
    [0033]实施例一：
    [0034]本发明所述的一种地下厂房洞室预加锚固力分析计算方法，包括以下步骤：
    [0035]步骤一、计算地质条件评分S c ；
    [0036]步骤二、计算地下厂房主要洞室的总毛空间体积V；
    [0037]步骤三、根据地质条件评分S c 和地下厂房主要洞室的总毛空间体积V，选取单位空间锚固力F；
    [0038]步骤四、计算地下厂房洞室预加锚固力F 锚 ，F 锚 ＝V·F。
    [0039]提供了一种地下厂房洞室预加锚固力F 锚 的计算方法，便于合理地确定地下厂房洞室系统锚固支护强度，准确预测围岩稳定状况，对确保大型地下厂房开挖支护安全有效、降低建设成本等具有极为重要的意义。
    [0040]所述步骤一中地质条件评分S c 的计算方法如下：
    [0041]S＝R b /σ max (2)，
    [0042]式中，D为地下厂房主要洞室埋深范围的加权平均值，单位为m，G为工程围岩等级评分，S为围岩强度应力比，R b 为岩石单轴饱和抗压强度，σ max 为围岩最大主应力。在使用时，收集地下厂房主要洞室的主要围岩类别、水平埋深、垂直埋深以及围岩强度应力等地质资料，便于计算地质条件评分S c 。围岩强度应力比S表示地下厂房所在地的平均地应力水平，按范围取加权平均值。当含有不同等级的围岩时，工程围岩等级评分G取加权平均值，单一等级围岩的工程围岩等级评分G按表1进行选取。
    [0043]表1工程围岩等级评分G取值表
    [0044]所述地下厂房主要洞室包括主厂房洞、主变洞和尾水调压室洞。
    [0045]所述D为地下厂房主要洞室水平埋深范围和垂直埋深范围的加权平均值。
    [0046]所述工程围岩等级评分G的取值根据围岩类别、围岩总评分T和围岩强度应力比S进行选取。
    [0047]所述步骤二中地下厂房主要洞室的总毛空间体积V的计算公式如下：
    [0048]V＝∑L·B·H (3)，
    [0049]式中，L为洞室最大开挖长度，B为洞室最大开挖跨度，H为洞室最大开挖高度。
    [0050]步骤三中根据地质条件评分S c 和地下厂房主要洞室的总毛空间体积V，按照表2选取单位空间锚固力F。
    [0051]表2单位空间锚固力F取值范围表(kN/m 3 )
    [0052]根据所述步骤四中的地下厂房洞室预加锚固力F 锚 ，计算预应力锚索数量N，
    [0053]式中，f为单根预应力锚索的平均锚固力。
    [0054]如图1所示，根据本发明提供的分析计算方法，对国内外大量水电站地下厂房的单位空间锚固力F与主要洞室总毛空间体积V进行了统计分析。从图中可以看出：对于地质条件很好(埋深较浅、地质条件较好、地应力不高)、规模不是很大(中小型地下洞室群)的地下厂房单位空间锚固力F小于2kN/m 3 ；对于地质条件较好的地下厂房，单位空间锚固力F一般在2kN/m 3 ～4kN/m 3 之间；对于地质条件较差(埋深很大、围岩破碎、地应力高)、规模较大(大型、巨型地下洞室群)单位空间锚固力F大于4kN/m 3 ，最大的甚至超过10kN/m 3 。可见，单位空间锚固力F与地下洞室群的地质条件和规模有明显相关关系。
    [0055]实施例二：
    [0056]以大渡河流域某大型水电站地下洞室基础资料为例，利用本发明提供的地下厂房洞室预加锚固力分析计算方法计算其支护方案，结合工程实际资料与本发明提供的方案相互印证。
    [0057]已知该地下厂房主要洞室的主要围岩类别为Ⅲ类，水平埋深为280m～510m，垂直埋深为400m～660m，围岩强度应力比S为3.59。地下洞室群三大洞室平行布置，其主厂房洞尺寸为224.4m×29.7m×74.43m(长×宽×高)，主变洞尺寸为141.1m×18.8m×25.2m(长×宽×高)，尾水调压室洞为135m×23.5m×73.98m(长×宽×高)。
    [0058]根据地下洞室的基本资料，计算其地质条件评分S c 、地下厂房主要洞室的总毛空间体积V、单位空间锚固力F和预应力锚索数量N四个参数。
    [0059]步骤一、计算地质条件评分S c
    [0060]已知该水电站地下厂房主要洞室的水平埋深为280m～510m，垂直埋深为400m～660m，计算地下厂房主要洞室埋深范围的加权平均值D＝0.25×(280+510+400+660)＝462.5m。
    [0061]已知该水电站地下洞室的主要围岩类别为Ⅲ类，其65≥围岩总评分T＞55，所以查表1选取工程围岩等级评分G的值为6。
    [0062]已知该围岩强度应力比S为3.59，计算地质条件评分S c 如下：
    [0063]步骤二、计算地下厂房主要洞室的总毛空间体积V
    [0064]该水电站地下厂房主要洞室的主厂房洞尺寸为224.4m×29.7m×74.43m(长×宽×高)，主变洞尺寸为141.1m×18.8m×25.2m(长×宽×高)，尾水调压室洞为135m×23.5m×73.98m(长×宽×高)。
    [0065]计算地下厂房主要洞室的总毛空间体积V如下：
    [0066]V＝∑L·B·H＝224.4×29.7×74.43+141.1×18.8×25.2+135×23.5×73.98＝79.8万m 3 ＜100万m 3 。
    [0067]步骤三、单位空间锚固力F
    [0068]根据地质条件评分S c 和地下厂房主要洞室的总毛空间体积V，查表2得单位空间锚固力F为4kN/m 3 ～7kN/m 3 。
    [0069]根据现场实际开挖揭露的地质面貌以及地应力测试情况分析，其单位空间锚固力F应在范围内取小值，即F取值范围为4kN/m 3 ～5kN/m 3 。
    [0070]步骤四、计算地下厂房洞室预加锚固力F 锚
    [0071]F 锚 ＝V·F＝79.8×10 4 ×(4～5)＝3192000kN～3990000kN。
    [0072]步骤五、计算预应力锚索数量N
    [0073]工程中常用预应力锚索的平均锚固力f有3000kN、2000kN、1000kN，本实施例中取f＝2000kN进行计算。则预应力锚索数量N计算为：
    [0074]步骤六、分析计算方法验证
    [0075]该水电站实际支护设计方案为采用3000kN预应力锚索76根，采用2500kN预应力锚索875根，采用2000kN预应力锚索596根，采用600kN预应力锚索50根。计算其地下厂房洞室预加锚固力F 锚 ，
    [0076]F 锚 ＝3000kN×76+2500kN×875+2000kN×596+600kN×50＝3637500kN，3192000kN＜3637500kN＜3990000kN，
    [0077]由此可见，该水电站的实际地下厂房洞室预加锚固力3637500kN，位于本发明提供方法所计算的地下厂房洞室预加锚固力F 锚 的值3192000kN与3990000kN之间，说明本发明提供的地下厂房洞室预加锚固力分析计算方法合理。
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