cardSrv/src/performance_monitor.erl

-module(performance_monitor).
-behaviour(gen_server).

-export([start_link/0, init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).
-export([start_monitoring/1, stop_monitoring/1, get_metrics/1, generate_report/1]).

-record(state, {
    monitors = #{},       % 监控对象集合
    metrics = #{},        % 性能指标数据
    alerts = [],          % 告警信息
    start_time = undefined
}).

-record(monitor_data, {
    type,                 % 监控类型
    metrics = [],         % 指标列表
    threshold = #{},      % 阈值设置
    callback              % 回调函数
}).

%% API
start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

start_monitoring(Target) ->
    gen_server:call(?MODULE, {start_monitoring, Target}).

stop_monitoring(Target) ->
    gen_server:call(?MODULE, {stop_monitoring, Target}).

get_metrics(Target) ->
    gen_server:call(?MODULE, {get_metrics, Target}).

generate_report(Target) ->
    gen_server:call(?MODULE, {generate_report, Target}).

%% 内部函数

collect_metrics(Target) ->
    % 收集各种性能指标
    #{
        cpu_usage => get_cpu_usage(Target),
        memory_usage => get_memory_usage(Target),
        response_time => get_response_time(Target),
        throughput => get_throughput(Target)
    }.

analyze_performance(Metrics) ->
    % 分析性能数据
    #{
        avg_response_time => calculate_average(maps:get(response_time, Metrics)),
        peak_memory => get_peak_value(maps:get(memory_usage, Metrics)),
        bottlenecks => identify_bottlenecks(Metrics)
    }.

generate_alerts(Metrics, Thresholds) ->
    % 生成性能告警
    lists:filtermap(
        fun({Metric, Value}) ->
            case check_threshold(Metric, Value, Thresholds) of
                {true, Alert} -> {true, Alert};
                false -> false
            end
        end,
        maps:to_list(Metrics)
    ).

create_report(Target, Metrics) ->
    % 生成性能报告
    #{
        target => Target,
        timestamp => os:timestamp(),
        metrics => Metrics,
        analysis => analyze_performance(Metrics),
        recommendations => generate_recommendations(Metrics)
    }.