CN114386822A - 基于加权余弦相似度的重点人员用电行为异常分析方法 - Google Patents
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Abstract
本发明涉及一种基于加权余弦相似度的重点人员用电行为异常分析方法,通过需求部门对重点人员进行信息标注,选取用电行为分析特征,采集其当前时刻的用电负荷辨识数据以及当前时刻的环境变量特征信息,依据当前时刻的环境变量特征信息获取对应的所述的历史时刻的历史用电行为特征,将所述的当前用电行为特征与历史时刻的用电行为特征进行加权余弦相似度计算,获取历史日与待测日的相似程度,并设定相似度阈值,当历史数据量越大时,特征选取越丰富,所得到的相似度越贴近真实的用电行为。本发明能有效提高异常行为研判的精准度,提高效率。更准确反映样本相似性,使计算结果更贴近于实际。减少以人为中心调动监管力量的监督模式。
Description
技术领域
本发明涉及一种基于加权余弦相似度的重点人员用电行为异常分析方法,属于智能电网技术领域。
背景技术
随着全球进入信息化、数字化时代,中国的城镇化和城市化发展日趋成熟,大数据挖掘、云计算、人工智能、高级量测等技术应用日趋广泛,对我国社会治理的思想观念、体制建设、技术手段等方面提出了新的要求。电力数据具有巨大的潜在价值,但电力数据对于服务市域社会治理的多领域主体的支撑相对较弱。与此同时,政府部门也在积极的推进数字化转型发展,考虑社会治理对于城市的管理的精细化、人员配置的精准化、数据采集感知的实时化提出了更高的要求,智能电网应从服务电力系统内部向辅助服务全社会治理发展转型,发挥电力数据强实时性、细粒度等特点结合公众标签信息、天气气象信息、地理位置信息等多维数据融合分析,辅助支撑公安、应急、安监、民政等多领域建设主体数字化建设。
经济社会的不断发展,重点人员漏管、脱管、失控等现象频发,重点人员管控难度增大。针对重点人员的管控目前仅能通过活动路线定位、定期到场汇报、北斗定位、酒店入住登记信息等方式获取重点人员的异常信息,但这些手段多数要耗费大量的人力、物力,且监管的时效性较低。因为,电力是生活中必不可少的能源,通过对重点人员家中的电力负荷的实时监控,可以较好的辅助政府各部门对重点人员的管控,同时通过多元数据融合分析,可以较为精准的对重点人员异常行为进行分析。
可以看出,电力数据与多元数据融合分析在重点人员行为分析中具备得天独厚的天然优势,但是之前受限于居民电力负荷感知的颗粒度不够,而如今非介入式负荷辨识技术为此提供了条件。其可以在重点人员不知情的情况下,不入户安装任何设备,仅通过入户的电流、电压检测,实现空调、冰箱、热水器等电器的使用情况的监测。但是现有技术缺乏通过应用这种技术,针对重点人员电力数据及多元数据挖掘进行行为异常分析,辅助需求部门进行重点人员管理的数据应用方法。
发明内容
发明目的:针对上述现有存在的问题和不足,本发明的目的是提供一种基于加权余弦相似度的重点人员用电行为异常分析方法,有效提高用电异常行为研判的精准度,提高核查的效率。更准确地反映样本之间的相似性,使计算结果更贴近于实际。提升监管工作精准化、精细化、实时化,减少以人为中心调动监管力量的监督模式。
技术方案:为实现上述发明目的,本发明采用以下技术方案:
一种基于加权余弦相似度的重点人员用电行为异常分析方法,包括如下步骤:
步骤1:需求部门输入重点人员的管控标签,根据所述标签确定所述重点人员的小区名称和用户编号,采集待测日该用户全天的电力负荷辨识数据及待测日环境特征数据;
步骤2:读取历史用电数据库中该用户编号,以及和待测日环境特征数据相似的历史电力负荷辨识数据,采用密度聚类算法构建历史日特征向量以及获取的待测日特征向量;
步骤3:通过熵权法对历史日特征数据进行权重计算,得到各个特征在历史日的权重分配;
步骤4:根据步骤3得到的各个特征在历史日的权重分配赋予到余弦相似度中,构建加权余弦相似计算公式,设置相似度阈值;
步骤5:将历史日均值特征向量和待测日特征向量代入加权余弦相似度模型,求解相似度,若相似度超过阈值,则为用电行为正常,若低于阈值,则为用电行为异常,将把用电异常的重点人员以告警形式推送给需求部门。
进一步的,所述步骤1中具体步骤为:
步骤1.1:需求部门输入重点人员的管控标签,根据所述标签对对应的所述重点人员的房屋信息和居住人数统计为居住信息;
步骤1.2:根据步骤1.1所得的所述居住信息读取用电数据库中的重点人员的用户编号,根据所述用户编号,读取重点人员待测日的电力负荷辨识数据;
步骤1.3:获取气象局的气象数据,读取重点人员待测日的环境特征数据;
步骤1.4:将步骤1.1中的管控标签和步骤1.2中的电力负荷辨识数据与步骤1.3中实时的环境特征数据进行关联。
进一步的,所述步骤2中具体步骤为:
步骤2.1:通过待测日的电力负荷辨识数据,得到若干个用电行为特征;
步骤2.3:对步骤2.2选取的个相似历史日的若干个用电行为特征标记为负荷特征,采用密度聚类算法,对个负荷特征直接聚类获取,自动过滤掉异常噪声点,以密度中心线作为该用户当前环境特征数据下的典型负荷特征曲线;
进一步的,所述步骤2.2中的环境特征数据包括季节、天气、最高温、最低温和是否是工作日,当一历史日和待测日的所述季节相同、天气相同、最高温±(0-2)、最低温±(0-2)和是否是工作日相同五个条件同时符合,则认为对应历史日为相似历史日。
进一步的,所述步骤3具体步骤为:
步骤3.3:根据信息熵的计算公式,计算出各个特征参数指标的信息熵为:
进一步的,所述步骤4的具体步骤为:
步骤4.2:通过各个特征参数指标的权重,定义加权余弦相似度计算公式为:
有益效果:与现有技术相比,本发明具有以下优点:
(1)以被标记的需求部门关注的重点人员为分析样本,通过在相似的环境特征条件下,通过加权余弦相似度求解算法,计算长时间粒度的历史的细粒度用电特征数据与实时的细粒度用电特征数据,能够有效提高用电异常行为研判的精准度,提高核查的效率。
(2)特征权重机制,对细粒度用电特征赋予不同的特征权值,并可以根据不同的环境特征信息动态调整用电特征的权重,能够更准确地反映样本之间的相似性,使计算结果更贴近于实际。
(3)数据驱动的监管模式具有针对性的对被监管对象进行预警预判,提升监管工作精准化、精细化、实时化,减少以人为中心调动监管力量的监督模式。
附图说明
图1是本发明的方法流程图;
图2是本发明实施例的各特征标签权重指标示意图。
具体实施方式
下面结合附图和具体实施例,进一步阐明本发明,应理解这些实施例仅用于说明本发明而不用于限制本发明的范围,在阅读了本发明之后,本领域技术人员对本发明的各种等价形式的修改均落于本申请所附权利要求所限定的范围。
如图1的流程所示,本发明提出了一种基于加权余弦相似度的重点人员用电行为异常分析方法,所述需求部门为公安部门、政府职能部门或者电力工等企事业单位,需要通关电力数据融合分析的用户。所述方法包括以下五个步骤:
步骤1:需求部门进行重点人员管控标签输入,根据标签确定重点人员的小区名称、用户编号等信息,采集当前时刻的该用户的电力负荷辨识数据及环境特征数据,
其中步骤1中通过以下步骤对重点人员标签、负荷辨识数据以及环境特征数据进行关联:
步骤1.1根据需求部门提供的重点人员标签信息,根据需求部门提供的重点人员标签为某小区某用户,对所标记的重点人员房屋信息、居住人数等进行数据统计;
步骤1.2根据重点人员居住信息,读取用电数据库中重点人员的用户编号,根据用户编号,读取重点人员的历史及实时的电力负荷辨识数据;
步骤1.3通过获取气象局的气象数据,读取重点人员居住地的某日实时的环境特征数据如表1所示:
表1 待测某日的环境特征数据
户号 | 季节 | 最高温度 | 最低温度 | 天气 | 风向 | 平均空气指数 |
**** | 夏 | 33℃ | 25℃ | 多云 | 东风2级 | 42 |
并根据待测某日的环境特征数据选取与之相似的10日作为历史日。
步骤1.4:将需求部门标签数据、电力负荷辨识数据以及实时的环境特征数据进行关联。
上述的基于加权余弦相似度的重点人员用电行为异常分析方法,其中,步骤2通过以下步骤构建历史日特征向量及待测日特征向量。
步骤2:选取用电行为特征,读取用电数据库中该用户编号的与待测日环境特征数据相似的历史电力负荷辨识数据,并采用密度聚类算法构建历史日特征向量以及获取的待测日特征向量。
步骤2.1:所述的行为特征,通过电力负荷辨识数据所得到包括但不限于的日总用电量、日电器使用种类数量、日空调总电量、日电热总电量、日厨房电器总用电量以及日空调用电时长、日电热总用电时长、厨房电器总用电时长等。
步骤2.2:通过电力负荷辨识数据,通过计算得到:
根据电力负荷辨识数据以及计算得到的上述特征选取10个作为用户的特征标签。
步骤2.3:基于历史负荷辨识数据及环境特征数据,基于重点人员典型日负荷曲线选择个历史日的包括但不限于日总用电量、日电器使用种类数量、日空调总电量、日电热总电量、日厨房电器总用电量以及日空调用电时长、日电热总用电时长以及日空调用电量占比,日电热总用电量占比,日厨房电器总用电量占比等行为特征作为典型标签,采用密度聚类算法,对个历史日负荷特征直接聚类获取,自动过滤掉异常噪声点,以密度中心线作为该用户当前环境特征数据下的典型负荷特征向量如下所示:
步骤3:根据熵权法对历史日特征数据进行权重计算,得到各个特征相对于历史日的权重分配。
步骤4:根据各个特征参数权重,将权重赋予到余弦相似度中,构建加权余弦相似计算公式,设置相似度阈值。
步骤5:将历史日均值特征向量与待测日特征向量带入加权余弦相似度模型,求解相似度。根据相似度求解结果,若相似度超过阈值,则判断为用电行为正常;若相似度低于阈值,则判断用电行为异常,从而将用电异常重点人员以告警的形式推送给需求部门。
步骤3.1:首先需对i个历史日所选取的特征参数数据进行预处理来保证评估结果的客观、合理。
步骤3.4:根据信息熵的计算公式,计算出各个特征参数指标的信息熵为:
根据上述步骤,得到各个特征参数指标的权重为图2所示。
上述的基于加权余弦相似度的重点人员用电行为异常分析方法,其中,步骤4中构建加权余弦相似度计算步骤如下:
步骤4.2:综合各个特征参数指标的权重,定义的加权余弦相似度计算公式为:
根据各个参数的权重指标得到加权后的特征向量:
加权余弦相似度通过测量特征向量为与待测日特征向量两个向量的夹角余弦值来度量它们之间的相似程度,计算结果为,其度量的取值范围为,其取值越大,向量的形态越相似,其用电行为约正常,根据该原则设定相似度的预警阈值为0.7。
上述的基于加权余弦相似度的重点人员用电行为异常分析方法,其中,所述步骤5中,根据加权余弦相似度计算结果,若结算结果,则判断重点人员用电正常,若结算结果,则判断重点人员存在高可能性的用电异常,最终生成重点人员用电异常告警及核查表。若历史数据样本足够大、用电分析特征选取足够丰富,历史日的特征权重更贴近于真实数值,则历史日与待测日的用电相似度分析则更贴近于实际。
Claims (10)
1.一种基于加权余弦相似度的重点人员用电行为异常分析方法,其特征在于:包括如下步骤:
步骤1:需求部门输入重点人员的管控标签,根据所述标签确定所述重点人员的小区名称和用户编号,采集待测日该用户全天的电力负荷辨识数据及待测日环境特征数据;
步骤2:读取历史用电数据库中该用户编号,以及和待测日环境特征数据相似的历史电力负荷辨识数据,采用密度聚类算法构建历史日特征向量以及获取的待测日特征向量;
步骤3:通过熵权法对历史日特征数据进行权重计算,得到各个特征在历史日的权重分配;
步骤4:根据步骤3得到的各个特征在历史日的权重分配赋予到余弦相似度中,构建加权余弦相似计算公式,设置相似度阈值;
步骤5:将历史日均值特征向量和待测日特征向量代入加权余弦相似度模型,求解相似度,若相似度超过阈值,则为用电行为正常,若低于阈值,则为用电行为异常,将把用电异常的重点人员以告警形式推送给需求部门。
2.根据权利要求1所述的基于加权余弦相似度的重点人员用电行为异常分析方法,其特征在于:所述步骤1中具体步骤为:
步骤1.1:需求部门输入重点人员的管控标签,根据所述标签对对应的所述重点人员的房屋信息和居住人数统计为居住信息;
步骤1.2:根据步骤1.1所得的所述居住信息读取用电数据库中的重点人员的用户编号,根据所述用户编号,读取重点人员待测日的电力负荷辨识数据;
步骤1.3:获取气象局的气象数据,读取重点人员待测日的环境特征数据;
步骤1.4:将步骤1.1中的管控标签和步骤1.2中的电力负荷辨识数据与步骤1.3中实时的环境特征数据进行关联。
7.根据权利要求3-5任一项所述的基于加权余弦相似度的重点人员用电行为异常分析方法,其特征在于:所述步骤2.2中的环境特征数据包括季节、天气、最高温、最低温和是否是工作日,当一历史日和待测日的所述季节相同、天气相同、最高温±(0-2)、最低温±(0-2)和是否是工作日相同五个条件同时符合,则认为对应历史日为相似历史日。
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CN116383753B (zh) * | 2023-05-26 | 2023-08-18 | 深圳市博昌智控科技有限公司 | 基于物联网的异常行为提示方法、装置、设备及介质 |
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