给出文件中各个字符的出现频率，求各个字符的哈夫曼编码方案的问题分析和JAVA程序

问题分析：

哈夫曼编码是一种无损数据压缩算法，可以通过对字符出现频率进行编码来减少数据传输的大小。具体实现步骤如下：

1. 统计文件中各个字符出现的频率；
2. 将字符频率构建成一个哈夫曼树；
3. 对哈夫曼树进行遍历，生成哈夫曼编码；
4. 将哈夫曼编码应用到文件中对应的字符，生成压缩文件。

JAVA程序：

下面是一个简单的JAVA程序，实现了对文件中各个字符出现频率的统计和哈夫曼编码的生成。

import java.io.*;
import java.util.*;

public class Huffman {
    // 统计文件中各个字符出现的频率
    public static Map<Character, Integer> getCharFrequency(String filename) throws IOException {
        Map<Character, Integer> frequency = new HashMap<>();
        try (BufferedReader reader = new BufferedReader(new FileReader(filename))) {
            int c;
            while ((c = reader.read()) != -1) {
                char ch = (char) c;
                frequency.put(ch, frequency.getOrDefault(ch, 0) + 1);
            }
        }
        return frequency;
    }

    // 构建哈夫曼树
    public static HuffmanNode buildHuffmanTree(Map<Character, Integer> frequency) {
        PriorityQueue<HuffmanNode> pq = new PriorityQueue<>();
        for (Map.Entry<Character, Integer> entry : frequency.entrySet()) {
            pq.offer(new HuffmanNode(entry.getKey(), entry.getValue()));
        }
        while (pq.size() > 1) {
            HuffmanNode left = pq.poll();
            HuffmanNode right = pq.poll();
            pq.offer(new HuffmanNode('\0', left.count + right.count, left, right));
        }
        return pq.poll();
    }

    // 生成哈夫曼编码
    public static Map<Character, String> getHuffmanCode(HuffmanNode root) {
        Map<Character, String> code = new HashMap<>();
        getHuffmanCodeHelper(root, "", code);
        return code;
    }

    private static void getHuffmanCodeHelper(HuffmanNode node, String path, Map<Character, String> code) {
        if (node.left == null && node.right == null) {
            code.put(node.ch, path);
            return;
        }
        getHuffmanCodeHelper(node.left, path + "0", code);
        getHuffmanCodeHelper(node.right, path + "1", code);
    }

    // 压缩文件
    public static void compress(String inputFilename, String outputFilename, Map<Character, String> code) throws IOException {
        try (BufferedReader reader = new BufferedReader(new FileReader(inputFilename));
             BufferedWriter writer = new BufferedWriter(new FileWriter(outputFilename))) {
            int c;
            StringBuilder sb = new StringBuilder();
            while ((c = reader.read()) != -1) {
                char ch = (char) c;
                sb.append(code.get(ch));
            }
            writer.write(sb.toString());
        }
    }

    public static void main(String[] args) throws IOException {
        String inputFilename = "input.txt";
        String outputFilename = "output.txt";

        // 统计字符出现频率
        Map<Character, Integer> frequency = getCharFrequency(inputFilename);

        // 构建哈夫曼树
        HuffmanNode root = buildHuffmanTree(frequency);

        // 生成哈夫曼编码
        Map<Character, String> code = getHuffmanCode(root);

        // 压缩文件
        compress(inputFilename, outputFilename, code);
    }
}

class HuffmanNode implements Comparable<HuffmanNode> {
    char ch;
    int count;
    HuffmanNode left;
    HuffmanNode right;

    public HuffmanNode(char ch, int count) {
        this.ch = ch;
        this.count = count;
    }

    public HuffmanNode(char ch, int count, HuffmanNode left, HuffmanNode right) {
        this.ch = ch;
        this.count = count;
        this.left = left;
        this.right = right;
    }

    @Override
    public int compareTo(HuffmanNode o) {
        return Integer.compare(count, o.count);
    }
}

注意事项：

1. 该程序仅供参考，具体实现可能需要根据实际情况进行调整；
2. 该程序只处理了ASCII码范围内的字符，如果需要处理其他字符集，可能需要进行相应的修改；
3. 哈夫曼编码的生成过程可以使用递归或非递归的方式实现，上面的程序使用了递归实现。