注意：該項目只展示部分功能

1.開發(fā)環(huán)境

發(fā)語言：python
采用技術(shù)：Spark、Hadoop、Django、Vue、Echarts等技術(shù)框架
數(shù)據(jù)庫：MySQL
開發(fā)環(huán)境：PyCharm

2 系統(tǒng)設(shè)計

隨著深圳市人口老齡化進(jìn)程加速，養(yǎng)老服務(wù)需求急劇增長，傳統(tǒng)的養(yǎng)老機(jī)構(gòu)管理和決策模式已無法滿足現(xiàn)代化、精細(xì)化的服務(wù)要求。當(dāng)前深圳市養(yǎng)老機(jī)構(gòu)數(shù)據(jù)存在分散存儲、信息孤島、缺乏統(tǒng)一分析等問題，政府部門和市民在進(jìn)行養(yǎng)老資源配置決策時往往依賴經(jīng)驗判斷，缺少數(shù)據(jù)支撐。基于此現(xiàn)狀，迫切需要構(gòu)建一個集數(shù)據(jù)采集、存儲、分析和可視化于一體的基于大數(shù)據(jù)技術(shù)的養(yǎng)老機(jī)構(gòu)資源配置可視化分析平臺，運用大數(shù)據(jù)技術(shù)深度挖掘養(yǎng)老機(jī)構(gòu)的空間分布規(guī)律、服務(wù)能力特征和資源配置效率，為深圳市養(yǎng)老事業(yè)的科學(xué)發(fā)展提供數(shù)據(jù)驅(qū)動的決策支持。

本系統(tǒng)基于深圳市養(yǎng)老機(jī)構(gòu)的多維度數(shù)據(jù)，運用Spark+Hadoop大數(shù)據(jù)處理架構(gòu)和Python數(shù)據(jù)分析技術(shù)，構(gòu)建了一套完整的基于大數(shù)據(jù)技術(shù)的養(yǎng)老機(jī)構(gòu)資源配置可視化分析平臺。研究內(nèi)容主要圍繞四個核心維度展開：首先是空間分布維度分析，通過對各行政區(qū)養(yǎng)老機(jī)構(gòu)數(shù)量、床位資源、性質(zhì)構(gòu)成、平均規(guī)模和護(hù)理能力的深入剖析，全面揭示深圳市養(yǎng)老資源的地理配置格局和區(qū)域差異特征；其次是類型性質(zhì)維度分析，重點探究不同性質(zhì)機(jī)構(gòu)在供給貢獻(xiàn)、平均規(guī)模、護(hù)理能力等方面的差異化特征，以及不同規(guī)模等級機(jī)構(gòu)的分布結(jié)構(gòu)和服務(wù)能力關(guān)聯(lián)性；第三是服務(wù)能力標(biāo)桿分析，通過構(gòu)建多重排名體系和區(qū)間分布統(tǒng)計，識別行業(yè)領(lǐng)先機(jī)構(gòu)和整體服務(wù)質(zhì)量水平；最后是基于算法的特征聚類與熱點分析，運用K-Means聚類算法實現(xiàn)機(jī)構(gòu)的智能分類，結(jié)合地址信息的文本挖掘技術(shù)發(fā)現(xiàn)地理聚集熱點，為養(yǎng)老服務(wù)的個性化推薦和空間規(guī)劃提供科學(xué)支撐。整個研究體系通過Vue+Echarts技術(shù)實現(xiàn)可視化呈現(xiàn)，為政府決策者、投資者和市民提供了直觀、準(zhǔn)確、實用的數(shù)據(jù)分析服務(wù)。

基于大數(shù)據(jù)技術(shù)的養(yǎng)老機(jī)構(gòu)資源配置可視化分析平臺具體功能研究內(nèi)容如下所示。
1.空間分布維度分析：各行政區(qū)養(yǎng)老機(jī)構(gòu)數(shù)量分布統(tǒng)計為資源規(guī)劃提供基礎(chǔ)數(shù)據(jù)支撐，總床位數(shù)與護(hù)理型床位數(shù)對比分析衡量各區(qū)實際承載能力。各行政區(qū)性質(zhì)構(gòu)成分析揭示市場結(jié)構(gòu)特征，平均規(guī)模分析反映區(qū)域發(fā)展模式，護(hù)理能力分析評估專業(yè)化服務(wù)水平。
2.類型性質(zhì)維度分析：不同性質(zhì)機(jī)構(gòu)供給貢獻(xiàn)分析明確各類機(jī)構(gòu)功能定位，平均規(guī)模與護(hù)理能力對比探究發(fā)展策略差異。規(guī)模等級劃分統(tǒng)計描繪產(chǎn)業(yè)整體結(jié)構(gòu)，不同規(guī)模護(hù)理能力分析驗證規(guī)模與服務(wù)水平關(guān)聯(lián)性。
3.服務(wù)能力標(biāo)桿分析：總床位數(shù)TOP10排名展示市場重要力量，護(hù)理型床位數(shù)TOP10篩選最強(qiáng)醫(yī)療護(hù)理資源。護(hù)理型床位占比TOP10識別專業(yè)型標(biāo)桿機(jī)構(gòu)，護(hù)理能力區(qū)間分布揭示行業(yè)整體質(zhì)量水平。
4.算法聚類與熱點分析：地址詞云分析發(fā)現(xiàn)地理聚集熱點區(qū)域，K-Means聚類實現(xiàn)數(shù)據(jù)驅(qū)動的科學(xué)分類和個性化推薦。聚類群組分布分析結(jié)合算法與地理空間，雷達(dá)圖分析提供多維度特征對比展示。

3 系統(tǒng)展示

3.1 大屏頁面

大屏上.png

大屏下.png

3.2 分析頁面

服務(wù)能力.png

機(jī)構(gòu)空間.png

機(jī)構(gòu)類型.png

熱點聚類.png

3.3 基礎(chǔ)頁面

登錄.png

數(shù)據(jù)管理.png

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5 部分功能代碼

spark = SparkSession.builder.appName("ElderCareAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/eldercare").option("dbtable", "institutions").option("user", "root").option("password", "password").load()
def kmeans_clustering_analysis():
    feature_cols = ['beds', 'nursing_beds_ratio']
    assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
    feature_df = assembler.transform(df.withColumn("nursing_beds_ratio", col("nursing_beds") / col("beds")))
    scaler = StandardScaler(inputCol="features", outputCol="scaled_features", withStd=True, withMean=False)
    scaler_model = scaler.fit(feature_df)
    scaled_df = scaler_model.transform(feature_df)
    kmeans = KMeans(featuresCol="scaled_features", predictionCol="cluster", k=4, seed=42, maxIter=100)
    kmeans_model = kmeans.fit(scaled_df)
    clustered_df = kmeans_model.transform(scaled_df)
    cluster_centers = kmeans_model.clusterCenters()
    cluster_summary = clustered_df.groupBy("cluster").agg(
        count("*").alias("institution_count"),
        avg("beds").alias("avg_beds"),
        avg("nursing_beds_ratio").alias("avg_nursing_ratio"),
        avg("nursing_beds").alias("avg_nursing_beds")
    ).orderBy("cluster")
    silhouette_evaluator = ClusteringEvaluator(featuresCol="scaled_features", metricName="silhouette")
    silhouette_score = silhouette_evaluator.evaluate(clustered_df)
    cluster_labels = {0: "小規(guī)?；A(chǔ)照料型", 1: "中等規(guī)模均衡型", 2: "大規(guī)模高護(hù)理型", 3: "小規(guī)模專業(yè)護(hù)理型"}
    labeled_df = clustered_df.withColumn("cluster_label", 
        when(col("cluster") == 0, cluster_labels[0])
        .when(col("cluster") == 1, cluster_labels[1])
        .when(col("cluster") == 2, cluster_labels[2])
        .otherwise(cluster_labels[3]))
    regional_distribution = labeled_df.groupBy("district", "cluster_label").count().orderBy("district", "count")
    result = {
        "cluster_summary": cluster_summary.collect(),
        "silhouette_score": silhouette_score,
        "cluster_centers": cluster_centers.tolist(),
        "regional_distribution": regional_distribution.collect(),
        "total_clustered": clustered_df.count()
    }
    return result
def regional_resource_distribution_analysis():
    district_stats = df.groupBy("district").agg(
        count("institution_name").alias("institution_count"),
        sum("beds").alias("total_beds"),
        sum("nursing_beds").alias("total_nursing_beds"),
        avg("beds").alias("avg_institution_size"),
        avg(col("nursing_beds") / col("beds")).alias("avg_nursing_ratio"),
        countDistinct("nature").alias("nature_types")
    ).orderBy(desc("total_beds"))
    nature_composition = df.groupBy("district", "nature").count().orderBy("district", "nature")
    nature_pivot = nature_composition.groupBy("district").pivot("nature").agg(sum("count")).fillna(0)
    bed_capacity_ranking = district_stats.select("district", "total_beds", "institution_count").orderBy(desc("total_beds"))
    nursing_capacity_ranking = district_stats.select("district", "total_nursing_beds", "avg_nursing_ratio").orderBy(desc("total_nursing_beds"))
    resource_efficiency = district_stats.withColumn("beds_per_institution", col("total_beds") / col("institution_count")).withColumn("nursing_beds_per_institution", col("total_nursing_beds") / col("institution_count"))
    district_comparison = resource_efficiency.select("district", "beds_per_institution", "nursing_beds_per_institution", "avg_nursing_ratio").orderBy(desc("beds_per_institution"))
    total_resources = df.agg(sum("beds").alias("city_total_beds"), sum("nursing_beds").alias("city_total_nursing_beds"), count("*").alias("city_total_institutions")).collect()[0]
    resource_share = district_stats.withColumn("beds_share_percent", (col("total_beds") / total_resources["city_total_beds"] * 100)).withColumn("institutions_share_percent", (col("institution_count") / total_resources["city_total_institutions"] * 100))
    top_districts = resource_share.select("district", "beds_share_percent", "institutions_share_percent", "avg_nursing_ratio").orderBy(desc("beds_share_percent")).limit(5)
    regional_gaps = district_stats.select("district", "avg_institution_size", "avg_nursing_ratio").withColumn("size_gap_from_avg", col("avg_institution_size") - district_stats.agg(avg("avg_institution_size")).collect()[0][0]).withColumn("nursing_gap_from_avg", col("avg_nursing_ratio") - district_stats.agg(avg("avg_nursing_ratio")).collect()[0][0])
    result = {
        "district_overview": district_stats.collect(),
        "nature_composition": nature_pivot.collect(),
        "bed_capacity_ranking": bed_capacity_ranking.collect(),
        "nursing_capacity_ranking": nursing_capacity_ranking.collect(),
        "resource_efficiency": district_comparison.collect(),
        "city_totals": total_resources.asDict(),
        "top_performing_districts": top_districts.collect(),
        "regional_gaps_analysis": regional_gaps.collect()
    }
    return result
def service_capacity_benchmark_analysis():
    bed_top10 = df.select("institution_name", "district", "nature", "beds", "nursing_beds").orderBy(desc("beds")).limit(10)
    nursing_bed_top10 = df.select("institution_name", "district", "nature", "beds", "nursing_beds").orderBy(desc("nursing_beds")).limit(10)
    nursing_ratio_df = df.withColumn("nursing_ratio", col("nursing_beds") / col("beds")).filter(col("nursing_ratio").isNotNull() & (col("nursing_ratio") > 0))
    nursing_ratio_top10 = nursing_ratio_df.select("institution_name", "district", "nature", "beds", "nursing_beds", "nursing_ratio").orderBy(desc("nursing_ratio")).limit(10)
    ratio_intervals = nursing_ratio_df.withColumn("ratio_interval", 
        when(col("nursing_ratio") == 0, "0%")
        .when((col("nursing_ratio") > 0) & (col("nursing_ratio") <= 0.2), "1-20%")
        .when((col("nursing_ratio") > 0.2) & (col("nursing_ratio") <= 0.4), "21-40%")
        .when((col("nursing_ratio") > 0.4) & (col("nursing_ratio") <= 0.6), "41-60%")
        .when((col("nursing_ratio") > 0.6) & (col("nursing_ratio") <= 0.8), "61-80%")
        .otherwise("81-100%"))
    interval_distribution = ratio_intervals.groupBy("ratio_interval").agg(count("*").alias("institution_count"), avg("beds").alias("avg_beds_in_interval")).orderBy("ratio_interval")
    excellence_criteria = df.filter((col("beds") >= 200) & ((col("nursing_beds") / col("beds")) >= 0.6))
    excellent_institutions = excellence_criteria.select("institution_name", "district", "nature", "beds", "nursing_beds", (col("nursing_beds") / col("beds")).alias("nursing_ratio")).orderBy(desc("beds"))
    industry_benchmarks = df.agg(
        avg("beds").alias("industry_avg_beds"),
        avg("nursing_beds").alias("industry_avg_nursing_beds"),
        avg(col("nursing_beds") / col("beds")).alias("industry_avg_nursing_ratio"),
        max("beds").alias("max_beds"),
        max("nursing_beds").alias("max_nursing_beds")
    ).collect()[0]
    performance_tiers = df.withColumn("nursing_ratio", col("nursing_beds") / col("beds")).withColumn("performance_tier",
        when((col("beds") >= 300) & (col("nursing_ratio") >= 0.7), "頂級綜合型")
        .when((col("beds") >= 200) & (col("nursing_ratio") >= 0.6), "優(yōu)質(zhì)大型")
        .when((col("beds") >= 100) & (col("nursing_ratio") >= 0.5), "標(biāo)準(zhǔn)中型")
        .when(col("nursing_ratio") >= 0.8, "專業(yè)護(hù)理型")
        .otherwise("基礎(chǔ)服務(wù)型"))
    tier_statistics = performance_tiers.groupBy("performance_tier").agg(count("*").alias("count"), avg("beds").alias("avg_beds"), avg("nursing_ratio").alias("avg_nursing_ratio")).orderBy(desc("count"))
    result = {
        "bed_capacity_top10": bed_top10.collect(),
        "nursing_bed_top10": nursing_bed_top10.collect(),
        "nursing_ratio_top10": nursing_ratio_top10.collect(),
        "nursing_ratio_distribution": interval_distribution.collect(),
        "excellent_institutions": excellent_institutions.collect(),
        "industry_benchmarks": industry_benchmarks.asDict(),
        "performance_tier_stats": tier_statistics.collect(),
        "total_analyzed_institutions": df.count()
    }
    return result

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