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HL7 \u6216\u4fdd\u9669\u9886\u57df\u7684 ACORD\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5730\u7406\u7a7a\u95f4\u6807\u51c6\u5316<\/strong>\uff1a\u5730\u7406\u7a7a\u95f4\u6570\u636e\u7ecf\u8fc7\u6807\u51c6\u5316\uff0c\u4ee5\u4fc3\u8fdb\u57fa\u4e8e\u4f4d\u7f6e\u7684\u5206\u6790\u548c\u5e94\u7528\uff0c\u4f8b\u5982\u5730\u7406\u4fe1\u606f\u7cfb\u7edf (GIS)\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u4ea4\u6362\u6807\u51c6\u5316<\/strong>\uff1a\u65e8\u5728\u786e\u4fdd\u4e0d\u540c\u7cfb\u7edf\u548c\u5e73\u53f0\u4e4b\u95f4\u7684\u65e0\u7f1d\u6570\u636e\u4ea4\u6362\u3002\u793a\u4f8b\u5305\u62ec XML\u3001JSON \u548c EDI\uff08\u7535\u5b50\u6570\u636e\u4ea4\u6362\uff09\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u4e0b\u8868\u603b\u7ed3\u4e86\u4e00\u4e9b\u5e38\u89c1\u7684\u6570\u636e\u6807\u51c6\u5316\u7c7b\u578b\u53ca\u5176\u5e94\u7528\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>\u6570\u636e\u6807\u51c6\u5316\u7c7b\u578b<\/th>\n<th>\u5e94\u7528<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u7ed3\u6784\u6807\u51c6\u5316<\/td>\n<td>\u6570\u636e\u5e93\u8bbe\u8ba1\u548c\u6587\u4ef6\u683c\u5f0f<\/td>\n<\/tr>\n<tr>\n<td>\u672f\u8bed\u6807\u51c6\u5316<\/td>\n<td>\u4e00\u81f4\u7684\u8bcd\u6c47\u548c\u672c\u4f53<\/td>\n<\/tr>\n<tr>\n<td>\u4ee3\u7801\u6807\u51c6\u5316<\/td>\n<td>\u53ef\u8bfb\u4e14\u53ef\u7ef4\u62a4\u7684\u4ee3\u7801<\/td>\n<\/tr>\n<tr>\n<td>\u884c\u4e1a\u7279\u5b9a\u6807\u51c6\u5316<\/td>\n<td>\u533b\u7597\u4fdd\u5065 (HL7)\u3001\u4fdd\u9669 (ACORD)<\/td>\n<\/tr>\n<tr>\n<td>\u5730\u7406\u7a7a\u95f4\u6807\u51c6\u5316<\/td>\n<td>\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\uff08GIS\uff09<\/td>\n<\/tr>\n<tr>\n<td>\u6570\u636e\u4ea4\u6362\u6807\u51c6\u5316<\/td>\n<td>\u53ef\u4e92\u64cd\u4f5c\u7684\u6570\u636e\u4ea4\u6362\u683c\u5f0f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u4f7f\u7528\u65b9\u6cd5\u6570\u636e\u6807\u51c6\u5316\u3001\u4f7f\u7528\u76f8\u5173\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n<p>\u6570\u636e\u6807\u51c6\u5316\u7684\u5e94\u7528\u573a\u666f\u975e\u5e38\u5e7f\u6cdb\uff0c\u5176\u5e94\u7528\u5ef6\u4f38\u5230\u5404\u4e2a\u884c\u4e1a\u548c\u9886\u57df\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u5546\u4e1a\u667a\u80fd\u548c\u5206\u6790<\/strong>\uff1a\u6807\u51c6\u5316\u6570\u636e\u53ef\u786e\u4fdd\u62a5\u544a\u51c6\u786e\u4e00\u81f4\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u597d\u7684\u4e1a\u52a1\u6d1e\u5bdf\u548c\u6570\u636e\u9a71\u52a8\u7684\u51b3\u7b56\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u6574\u5408<\/strong>\uff1a\u6574\u5408\u591a\u4e2a\u6765\u6e90\u7684\u6570\u636e\u65f6\uff0c\u6807\u51c6\u5316\u53ef\u786e\u4fdd\u65e0\u7f1d\u96c6\u6210\u548c\u517c\u5bb9\u6027\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u8fc1\u79fb<\/strong>\uff1a\u5728\u7cfb\u7edf\u5347\u7ea7\u6216\u6570\u636e\u4f20\u8f93\u671f\u95f4\uff0c\u6807\u51c6\u5316\u7b80\u5316\u4e86\u8fc1\u79fb\u8fc7\u7a0b\uff0c\u964d\u4f4e\u4e86\u6570\u636e\u4e22\u5931\u6216\u635f\u574f\u7684\u98ce\u9669\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u76d1\u7ba1\u5408\u89c4\u6027<\/strong>\uff1a\u6807\u51c6\u5316\u6570\u636e\u6709\u52a9\u4e8e\u9075\u5b88\u884c\u4e1a\u6cd5\u89c4\u548c\u6570\u636e\u9690\u79c1\u6cd5\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u5171\u4eab<\/strong>\uff1a\u6807\u51c6\u5316\u4f7f\u5408\u4f5c\u4f19\u4f34\u548c\u5229\u76ca\u76f8\u5173\u8005\u4e4b\u95f4\u80fd\u591f\u987a\u5229\u3001\u5b89\u5168\u5730\u5171\u4eab\u6570\u636e\u3002<\/p>\n<\/li>\n<\/ol>\n<h3>\u95ee\u9898\u53ca\u89e3\u51b3\u65b9\u6848\uff1a<\/h3>\n<p>\u867d\u7136\u6570\u636e\u6807\u51c6\u5316\u63d0\u4f9b\u4e86\u8bb8\u591a\u4f18\u52bf\uff0c\u4f46\u5b83\u4e5f\u5e76\u975e\u6ca1\u6709\u6311\u6218\u3002\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u6570\u636e\u4e0d\u4e00\u81f4<\/strong>\uff1a\u4e0d\u540c\u6765\u6e90\u7684\u6570\u636e\u53ef\u80fd\u4f1a\u6709\u6240\u4e0d\u540c\uff0c\u4ece\u800c\u5bfc\u81f4\u4e0d\u4e00\u81f4\u3002\u81ea\u52a8\u5316\u6570\u636e\u6e05\u7406\u548c\u9a8c\u8bc1\u8fc7\u7a0b\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u4e22\u5931<\/strong>\uff1a\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6807\u51c6\u5316\u53ef\u80fd\u4f1a\u5bfc\u81f4\u67d0\u4e9b\u7ec6\u8282\u6216\u7ec6\u5fae\u5dee\u522b\u7684\u4e22\u5931\u3002\u4e3a\u4e86\u7f13\u89e3\u8fd9\u79cd\u60c5\u51b5\uff0c\u5fc5\u987b\u5728\u6807\u51c6\u5316\u548c\u4fdd\u7559\u6709\u4ef7\u503c\u7684\u4fe1\u606f\u4e4b\u95f4\u53d6\u5f97\u5e73\u8861\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6539\u53d8\u6807\u51c6<\/strong>\uff1a\u968f\u7740\u6280\u672f\u548c\u884c\u4e1a\u7684\u53d1\u5c55\uff0c\u6570\u636e\u6807\u51c6\u5316\u6307\u5357\u53ef\u80fd\u4f1a\u53d1\u751f\u53d8\u5316\u3002\u5b9a\u671f\u66f4\u65b0\u5e76\u968f\u65f6\u4e86\u89e3\u65b0\u5174\u6807\u51c6\u6709\u52a9\u4e8e\u514b\u670d\u8fd9\u4e00\u6311\u6218\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6210\u672c\u548c\u8d44\u6e90<\/strong>\uff1a\u5b9e\u65bd\u6570\u636e\u6807\u51c6\u5316\u9700\u8981\u5728\u5de5\u5177\u3001\u4e13\u4e1a\u77e5\u8bc6\u548c\u7ef4\u62a4\u65b9\u9762\u8fdb\u884c\u6295\u8d44\u3002\u7136\u800c\uff0c\u957f\u671f\u6536\u76ca\u5f80\u5f80\u8d85\u8fc7\u521d\u59cb\u6210\u672c\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u62b5\u5236\u53d8\u9769<\/strong>\uff1a\u5458\u5de5\u53ef\u80fd\u4f1a\u62b5\u5236\u9002\u5e94\u65b0\u7684\u6807\u51c6\u5316\u5b9e\u8df5\u3002\u9002\u5f53\u7684\u57f9\u8bad\u548c\u6c9f\u901a\u53ef\u4ee5\u5e2e\u52a9\u5e94\u5bf9\u8fd9\u4e00\u6311\u6218\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u4ee5\u8868\u683c\u548c\u5217\u8868\u7684\u5f62\u5f0f\u5217\u51fa\u4e3b\u8981\u7279\u5f81\u4ee5\u53ca\u4e0e\u7c7b\u4f3c\u672f\u8bed\u7684\u5176\u4ed6\u6bd4\u8f83\u3002<\/p>\n<h3>\u6570\u636e\u6807\u51c6\u5316\u7684\u7279\u70b9\uff1a<\/h3>\n<ol>\n<li>\n<p><strong>\u5747\u5300\u5ea6<\/strong>\uff1a\u6570\u636e\u6807\u51c6\u5316\u786e\u4fdd\u6240\u6709\u6570\u636e\u5b9e\u4f8b\u7684\u683c\u5f0f\u4e00\u81f4\uff0c\u4fc3\u8fdb\u65e0\u7f1d\u96c6\u6210\u548c\u6bd4\u8f83\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u51c6\u786e\u6027<\/strong>\uff1a\u901a\u8fc7\u6e05\u7406\u548c\u9a8c\u8bc1\u6570\u636e\uff0c\u6807\u51c6\u5316\u63d0\u9ad8\u4e86\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u4e92\u64cd\u4f5c\u6027<\/strong>\uff1a\u6807\u51c6\u5316\u6570\u636e\u53ef\u4ee5\u5728\u4e0d\u540c\u7cfb\u7edf\u548c\u5e73\u53f0\u4e4b\u95f4\u8f7b\u677e\u4ea4\u6362\u548c\u5171\u4eab\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6548\u7387<\/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href=\"https:\/\/www.ibm.com\/cloud\/learn\/data-standardization\" target=\"_new\" rel=\"noopener nofollow\">\u6570\u636e\u6807\u51c6\u5316\uff1a\u5b83\u662f\u4ec0\u4e48\u4ee5\u53ca\u4e3a\u4ec0\u4e48\u5b83\u5f88\u91cd\u8981<\/a><\/li>\n<li><a href=\"https:\/\/datascience.nih.gov\/data-standards\" target=\"_new\" rel=\"noopener nofollow\">\u6570\u636e\u6807\u51c6\u5316 - \u7f8e\u56fd\u56fd\u7acb\u536b\u751f\u7814\u7a76\u9662<\/a><\/li>\n<li><a href=\"https:\/\/www.w3.org\/standards\/semanticweb\/\" target=\"_new\" rel=\"noopener nofollow\">\u8bed\u4e49\u7f51\u6280\u672f<\/a><\/li>\n<\/ol>\n<p>\u901a\u8fc7\u6df1\u5165\u7814\u7a76\u8fd9\u4e9b\u8d44\u6e90\uff0c\u60a8\u53ef\u4ee5\u66f4\u6df1\u5165\u5730\u4e86\u89e3\u6570\u636e\u6807\u51c6\u5316\u5728\u5f53\u4eca\u4ee5\u6570\u636e\u4e3a\u4e2d\u5fc3\u7684\u4e16\u754c\u4e2d\u7684\u91cd\u8981\u6027\u3002<\/p>","protected":false},"featured_media":468148,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476708","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Data Standardization: Streamlining Information for a Connected World<\/mark>","faq_items":[{"question":"What is data standardization?","answer":"<p>Data standardization is a crucial process in information management that involves structuring and formatting data in a consistent and uniform manner. It ensures that data can be seamlessly shared, exchanged, and analyzed across various platforms and systems.<\/p>"},{"question":"How did data standardization originate?","answer":"<p>The concept of data standardization emerged as computing evolved, and the need for interoperability between systems and organizations became evident. One of the earliest mentions of data standardization can be traced back to the development of ASCII in the early 1960s.<\/p>"},{"question":"What does data standardization entail?","answer":"<p>Data standardization involves data cleaning, normalization, formatting, categorization, integration, and validation. These steps transform data into a standardized structure, improving data quality and facilitating analysis.<\/p>"},{"question":"How does data standardization work internally?","answer":"<p>Data standardization involves data profiling, rule definition, and execution. Data analysts examine the dataset, define rules, and use automated tools to apply transformations for standardized results.<\/p>"},{"question":"What are the key features of data standardization?","answer":"<p>Data standardization offers uniformity, accuracy, interoperability, and efficiency. It enhances data quality, supports data governance, and enables data-driven decision-making.<\/p>"},{"question":"What are the types of data standardization?","answer":"<p>Various types of data standardization include structural, terminological, code standardization, industry-specific standardization, geospatial standardization, and data exchange standardization.<\/p>"},{"question":"How can data standardization be used?","answer":"<p>Data standardization finds application in business intelligence, data integration, migration, compliance, and data sharing, among other areas.<\/p>"},{"question":"What are the challenges and solutions related to data standardization?","answer":"<p>Challenges include data inconsistencies, data loss, changing standards, costs, and resistance to change. Solutions involve automated data cleaning, careful balancing of standardization, and regular updates.<\/p>"},{"question":"How does data standardization compare to similar terms?","answer":"<p>Data standardization differs from data normalization and data cleansing, as it encompasses a broader range of processes for consistent data formatting.<\/p>"},{"question":"How will data standardization evolve in the future?","answer":"<p>The future of data standardization will see advancements in semantic web technologies, AI-driven tools, blockchain integration, and industry-specific standards.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476708","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":1,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476708\/revisions"}],"predecessor-version":[{"id":505252,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476708\/revisions\/505252"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/468148"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=476708"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}