CN115358336A - Power utilization abnormity detection method and device and electronic equipment - Google Patents
Power utilization abnormity detection method and device and electronic equipment Download PDFInfo
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- CN115358336A CN115358336A CN202211035293.8A CN202211035293A CN115358336A CN 115358336 A CN115358336 A CN 115358336A CN 202211035293 A CN202211035293 A CN 202211035293A CN 115358336 A CN115358336 A CN 115358336A
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Abstract
The invention discloses a power consumption abnormity detection method and device and electronic equipment. Wherein, the method comprises the following steps: acquiring target attribute data, daily electricity consumption data and hour electricity consumption data of a target enterprise, wherein the target attribute data comprises the target enterprise scale, the target industry type and the target business time type of the target enterprise; determining a first abnormal result of the target enterprise under the target industry type and a second abnormal result of the target enterprise under the target business hour type according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise; and determining whether the target enterprise is the target abnormal result of the abnormal power utilization enterprise or not according to the first abnormal result and the second abnormal result. The method solves the technical problem that abnormal power utilization enterprises are difficult to accurately determine in the related technology.
Description
Technical Field
The invention relates to the field of computers, in particular to a method and a device for detecting electricity utilization abnormity and electronic equipment.
Background
The safe production is a big matter of life and property safety of people and is a mark for the coordinated healthy development of the economy and the society. In order to implement the construction of a safety production supervision system and prevent and solve major safety risks, an enterprise with abnormal electricity utilization needs to be accurately identified, but the abnormal electricity utilization phenomenon in the enterprise is difficult to accurately identify in the related technology.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a power utilization abnormality detection method, a power utilization abnormality detection device and electronic equipment, and aims to at least solve the technical problem that abnormal power utilization enterprises are difficult to accurately determine in the related art.
According to an aspect of an embodiment of the present invention, there is provided a power consumption abnormality detection method including: acquiring target attribute data, daily electricity consumption data and hour electricity consumption data of a target enterprise, wherein the target attribute data comprises the target enterprise scale, the target industry type and the target business time type of the target enterprise; determining a first abnormal result of the target enterprise under the target industry type and a second abnormal result of the target enterprise under the target business time type according to target attribute data, daily electricity consumption data and hour electricity consumption data of the target enterprise; and determining whether the target enterprise is a target abnormal result of the abnormal power utilization enterprise or not according to the first abnormal result and the second abnormal result.
Optionally, after determining whether the target enterprise is the target abnormal result of the abnormal power utilization enterprise according to the first abnormal result and the second abnormal result, the method further includes: and when the target abnormal result is that the target enterprise is an abnormal electricity utilization enterprise, determining the abnormal electricity utilization type of the target enterprise according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise.
Optionally, the determining, according to the first abnormal result and the second abnormal result, whether the target enterprise is a target abnormal result of an abnormal power utilization enterprise includes: inputting the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise into an electricity consumption abnormity identification model to obtain a third abnormity result; and determining whether the target enterprise is a target abnormal result of the abnormal power utilization enterprise or not according to the first abnormal result, the second abnormal result and the third abnormal result.
Optionally, the determining, according to the first abnormal result and the second abnormal result, whether the target enterprise is a target abnormal result of an abnormal power utilization enterprise includes: acquiring historical daily electricity consumption data and historical hourly electricity consumption data of the target enterprise; determining a same ratio and a ring ratio between the daily electricity consumption data of the target enterprise and the historical daily electricity consumption data, and a same ratio and a ring ratio between the hour electricity consumption data of the target enterprise and the historical hour electricity consumption data to obtain a fourth abnormal result; and determining whether the target enterprise is a target abnormal result of the abnormal power utilization enterprise according to the first abnormal result, the second abnormal result and the fourth abnormal result.
Optionally, the determining, according to the target attribute data, the daily electricity consumption data, and the hour electricity consumption data of the target enterprise, a first abnormal result of the target enterprise in the target industry type includes: determining a first upper limit and a first lower limit of the first electric quantity of the target enterprise scale within a preset time period under the target industry type; determining a first target electricity consumption of the target enterprise in the preset time period according to the daily electricity consumption data and the hour electricity consumption data of the target enterprise; and comparing the first target power consumption with the first power consumption upper limit and the first power consumption lower limit, and determining a first abnormal result of the target enterprise under the target industry type.
Optionally, the determining, according to the target attribute data, the daily electricity consumption data, and the hour electricity consumption data of the target enterprise, a second abnormal result of the target enterprise in the target business hour type includes: determining a second upper limit and a second lower limit of the second electric quantity of the target enterprise scale within a preset time period under the target business time type; determining a second target electricity consumption of the target enterprise in the preset time period according to the daily electricity consumption data and the hour electricity consumption data of the target enterprise; and comparing the second target electricity consumption with the second electricity upper limit and the second electricity lower limit, and determining a second abnormal result of the target enterprise in the target business hours.
Optionally, the acquiring target attribute data, daily electricity consumption data, and hour electricity consumption data of the target enterprise includes: acquiring initial attribute data, initial daily electricity consumption data and initial hour electricity consumption data of the target enterprise; and carrying out data standardization on the initial attribute data, the initial daily electricity consumption data and the initial hour electricity consumption data to obtain the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise.
According to an aspect of an embodiment of the present invention, there is provided an electricity abnormality detection apparatus including: the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring target attribute data, daily electricity consumption data and hour electricity consumption data of a target enterprise, and the target attribute data comprises the target enterprise scale, the target industry type and the target business time type of the target enterprise; the first determining module is used for determining a first abnormal result of the target enterprise under the target industry type and a second abnormal result of the target enterprise under the target business time type according to target attribute data, daily electricity consumption data and hour electricity consumption data of the target enterprise; and the second determining module is used for determining whether the target enterprise is a target abnormal result of the abnormal power utilization enterprise according to the first abnormal result and the second abnormal result.
According to an aspect of an embodiment of the present invention, there is provided an electronic apparatus including: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement any of the above-described electricity usage anomaly detection methods.
According to an aspect of the embodiments of the present invention, there is provided a computer-readable storage medium, wherein instructions of the computer-readable storage medium, when executed by a processor of an electronic device, enable the electronic device to perform any one of the above-mentioned power abnormality detection methods.
In the embodiment of the invention, the target attribute data including the target enterprise scale, the target industry type and the target business time type, the daily electricity consumption data and the hour electricity consumption data of the target enterprise are obtained, and further, according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise, a first abnormal result of the target enterprise in the target industry type and a second abnormal result of the target enterprise in the target business time type are determined, and according to the first abnormal result and the second abnormal result, whether the target enterprise is the target abnormal result of the abnormal electricity consumption enterprise is determined together. Since the first abnormal result is an abnormal result in the same industry type obtained from the daily electricity consumption data and the hourly electricity consumption data, the second abnormal result is an abnormal result in an enterprise of the same business hours type obtained from the daily electricity consumption data and the hourly electricity consumption data. Whether the target enterprise is an abnormal power utilization enterprise or not is comprehensively judged through two abnormal results, the abnormal power utilization enterprise can be more accurately determined, and the technical problem that the abnormal power utilization enterprise is difficult to accurately determine in the related technology is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a power consumption abnormality detection method according to an embodiment of the present invention;
FIG. 2 is a schematic flow diagram of a method provided by an alternative embodiment of the present invention;
fig. 3 is a block diagram of the power consumption abnormality detection apparatus according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided an embodiment of a power usage anomaly detection method, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
Fig. 1 is a flowchart of a power consumption abnormality detection method according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S102, acquiring target attribute data, daily electricity consumption data and hour electricity consumption data of a target enterprise, wherein the target attribute data comprises the target enterprise scale, the target industry type and the target business time type of the target enterprise;
step S104, determining a first abnormal result of the target enterprise under the target industry type and a second abnormal result of the target enterprise under the target business hours type according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise;
and step S106, determining whether the target enterprise is the target abnormal result of the abnormal power utilization enterprise or not according to the first abnormal result and the second abnormal result.
Through the steps, the target attribute data including the target enterprise scale, the target industry type and the target business time type, the daily electricity consumption data and the hour electricity consumption data of the target enterprise are obtained, and then a first abnormal result of the target enterprise in the target industry type and a second abnormal result of the target enterprise in the target business time type are determined according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise, and whether the target enterprise is the target abnormal result of the abnormal electricity consumption enterprise or not is determined jointly according to the first abnormal result and the second abnormal result. Since the first abnormal result is an abnormal result in the same industry type obtained from the daily electricity amount data and the hour electricity consumption data, and the second abnormal result is an abnormal result in an enterprise of the same business hour type obtained from the daily electricity amount data and the hour electricity consumption data. Whether the target enterprise is an abnormal power utilization enterprise or not is comprehensively judged through two abnormal results, the abnormal power utilization enterprise can be more accurately determined, and the technical problem that the abnormal power utilization enterprise is difficult to accurately determine in the related technology is solved.
It should be noted that the daily electricity consumption data and the hourly electricity consumption data do not only refer to electricity consumption data of one day or one hour, but can be set by user-defining according to actual applications and scenes, for example, daily electricity consumption data of one week continuously or hourly electricity consumption data within 24 hours continuously.
As an optional embodiment, when the target attribute data, the daily electricity consumption data, and the hour electricity consumption data of the target enterprise are obtained, the initial attribute data, the initial daily electricity consumption data, and the initial hour electricity consumption data of the target enterprise may be obtained first, and data standardization may be performed on the initial attribute data, the initial daily electricity consumption data, and the initial hour electricity consumption data to obtain the target attribute data, the daily electricity consumption data, and the hour electricity consumption data of the target enterprise. Through data standardization processing, the target attribute data, the daily electricity consumption data and the hour electricity consumption data can be standardized as much as possible, the use is easy, the calculated amount in the detection process is reduced, and the detection process is accelerated.
As an alternative embodiment, when determining whether the target enterprise is the target abnormal result of the abnormal power consumption enterprise according to the first abnormal result and the second abnormal result, the power consumption abnormal recognition model may be further added to perform auxiliary judgment, that is, the target attribute data, the daily power consumption data and the hour power consumption data of the target enterprise are input into the power consumption abnormal recognition model to obtain a third abnormal result, and whether the target enterprise is the target abnormal result of the abnormal power consumption enterprise is determined according to the first abnormal result, the second abnormal result and the third abnormal result. The electrical anomaly recognition model may be a model formed by combining multiple algorithms, for example, an anomaly recognition model obtained by combining four algorithms, namely a Mann-Kendall (man-Kendall method) mutation point, a Pettitt algorithm, a Buishand U Test algorithm and a Standard Normal Homogenetic Test (SNHT). Through the power utilization abnormity identification model, the obtained target abnormity result can be more accurate.
As an alternative embodiment, when determining whether the target enterprise is the target abnormal result of the abnormal power utilization enterprise according to the first abnormal result and the second abnormal result, the auxiliary judgment may be performed by using historical data of the enterprise itself: the method comprises the steps of obtaining historical daily electricity consumption data and historical hour electricity consumption data of a target enterprise, determining a same-ratio and ring-ratio between the daily electricity consumption data and the historical daily electricity consumption data of the target enterprise, and a same-ratio and ring-ratio between the hour electricity consumption data and the historical hour electricity consumption data of the target enterprise to obtain a fourth abnormal result, and determining whether the target enterprise is the target abnormal result of the abnormal electricity consumption enterprise according to the first abnormal result, the second abnormal result and the fourth abnormal result. And comparing whether the target enterprise has behavior which is greatly different from the historical activities or not through the fourth abnormal result, and further analyzing whether the target enterprise has abnormal activities or not according to the data. The optional embodiment can catch sudden behavior abnormality of the target enterprise in time.
As an alternative embodiment, when determining the first abnormal result of the target enterprise under the target industry type according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise, the following method may be adopted: determining a first electric quantity upper limit and a first electric quantity lower limit of a target enterprise scale in a preset time period under a target industry type; determining a first target electricity consumption of the target enterprise in a preset time period according to the daily electricity consumption data and the hour electricity consumption data of the target enterprise; and comparing the first target power consumption with the first power consumption upper limit and the first power consumption lower limit, and determining a first abnormal result of the target enterprise under the target industry type. Since the sizes of the enterprises in the same industry type may be different, and therefore the power consumption should not be the same, the power consumption of the enterprise with the same size as the target enterprise is obtained, so as to analyze the upper limit and the lower limit of the power consumption. The enterprise having the same size as the target enterprise may be an enterprise having the same size as the target enterprise, or may be inferred by the same scale. And determining a first abnormal result of the target enterprise under the target industry type by comparing the first target power consumption with the first power consumption upper limit and the first power consumption lower limit. If the first target power consumption exceeds the first power consumption upper limit or is lower than the first power consumption lower limit, it can be known that the first target power consumption is related to the power consumption of the target industry type of the target enterprise, and therefore the first abnormal result can be preliminarily determined to be abnormal, so that the next analysis is carried out, and the abnormal power consumption in the industry is considered.
As an alternative embodiment, when determining the second abnormal result of the target enterprise in the target business hours type according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise, the following method may be adopted: determining a second upper limit and a second lower limit of the second electric quantity of the target enterprise scale in a preset time period under the target business time type; determining a second target electricity consumption of the target enterprise in a preset time period according to the daily electricity consumption data and the hour electricity consumption data of the target enterprise; and comparing the second target electricity consumption with the second upper limit of the electricity consumption and the second lower limit of the electricity consumption, and determining a second abnormal result of the target enterprise in the target business hour type. Since the enterprise scales may be different among a plurality of enterprises of the same business hours type, and therefore the electricity consumption should not be the same, the electricity consumption of an enterprise of the same enterprise scale as the target enterprise scale is obtained, and the upper limit and the lower limit of the electricity consumption are analyzed. The enterprise having the same size as the target enterprise may be an enterprise having the same size as the target enterprise, or may be inferred by the same scale. And determining a second abnormal result of the target enterprise under the target business hour type by comparing the second target electricity consumption with the second upper limit of the electricity consumption and the second lower limit of the electricity consumption. If the second target electricity consumption exceeds the second electricity consumption upper limit or is lower than the second electricity consumption lower limit, it can be known that the second target electricity consumption is related to the electricity consumption of the target business hours type of the target enterprise, therefore, the second abnormal result can be preliminarily determined as abnormal, and further analysis is carried out. The amount of electricity used in business hours is considered to be abnormal.
As an alternative embodiment, after determining whether the target enterprise is the target abnormal result of the abnormal electricity consumption enterprise according to the first abnormal result and the second abnormal result, the following operation may be further performed, and when the target abnormal result is the target enterprise which is the abnormal electricity consumption enterprise, the abnormal electricity consumption type of the target enterprise may be determined according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise. The following common abnormal power utilization types of the enterprise can be obtained: bright stop and dark start: the power utilization is characterized in that the daily power consumption of an enterprise stopping production in a limited period/responsible manner is obviously higher than the daily non-production power consumption of the enterprise; emergency production and shutdown: the power utilization of the system is characterized in that the daily power consumption of an enterprise which normally operates is reduced by more than 70 percent compared with the previous day; overload production: the power consumption is characterized in that the daily power consumption of an enterprise which normally operates is obviously higher than the daily production power consumption of the enterprise; automatic production stop: the power consumption is characterized in that the daily power consumption of an enterprise in normal operation is continuously lower than the daily non-production power consumption of the enterprise for multiple days; stopping production for a long time: the power consumption is characterized in that the daily electricity consumption of an enterprise with automatic production stop is lower than the daily electricity consumption of the enterprise for production for a long time; stopping production and reworking: the power consumption is characterized in that the daily power consumption of an enterprise with long-term production stop/automatic production stop is continuously higher than the non-production power consumption of the enterprise for many days; day stop and night start: the power utilization characteristic is that the night power consumption of the enterprise stopping production in limited period/responsible order is obviously higher than the daytime power consumption. By determining the specific abnormal electricity utilization type, the target enterprise can be better supervised and checked according to the result.
It should be noted that, whether the target enterprise is the target abnormal result of the abnormal electricity consuming enterprise is determined according to the first abnormal result and the second abnormal result. And determining whether the target enterprise is the target abnormal result of the abnormal power utilization enterprise or not according to the first abnormal result, the second abnormal result and the third abnormal result. And when determining whether the target enterprise is the target abnormal result of the abnormal power utilization enterprise according to the first abnormal result, the second abnormal result and the fourth abnormal result, determining a rule of the target abnormal result, and performing custom setting according to actual application and scenes. For example, different weight values can be taken for different abnormal results according to business hours and industry types, and when the integrated abnormal result is greater than a predetermined threshold value, the target enterprise is determined to be an abnormal power utilization enterprise. The rules for determining the target exception result are not limited herein. The optional embodiment can be more flexible and more applicable.
Based on the above embodiments and alternative embodiments, an alternative implementation is provided, which is described in detail below.
The invention provides an enterprise safety production state monitoring method based on electricity consumption data, which gives full play to the advantages of power data resources, data sharing and innovation application, actively promotes the innovation application of the power data in the field of safety production supervision around the fields of intelligent supervision, big data enabling safety supervision and the like, and constructs an enterprise safety state monitoring and analyzing model by combining an enterprise safety production electricity consumption monitoring and analyzing system comprehensively pushed by the state network to identify enterprises with abnormal electricity consumption. The change condition of the power consumption and the electric quantity is combined, the enterprise with abnormal safety production in the enterprise is analyzed and identified, and a new intelligent supervision mode of 'safety supervision + electric power big data' is explored. Fig. 2 is a schematic flow chart of a method provided by an alternative embodiment of the present invention, and as shown in fig. 2, the following method provided by the alternative embodiment of the present invention is described in detail:
s1, acquiring initial attribute data, initial daily electricity consumption data and initial hour electricity consumption data of a target enterprise;
the acquired data sources are power consumption behavior information in the power consumption acquisition system and data in the marketing business application system, such as enterprise information, enterprise file data, enterprise daily electricity consumption data, enterprise hourly power consumption load data measurement data, enterprise early warning result data and the like.
S2, carrying out data standardization on the initial attribute data, the initial daily electricity consumption data and the initial hour electricity consumption data to obtain target attribute data, daily electricity consumption data and hour electricity consumption data of a target enterprise;
wherein, data standardization can be for data cleaning, need carry out data cleaning to the initial data who obtains for improving the index degree of accuracy, data cleaning rule as follows:
(1) Any data loss of each field is defined as data loss, such as null of area name, area number, power failure time, enterprise name, enterprise number and the like.
(2) Repeated occurrence of the detail entry is defined as data redundancy, such as data repetition and data collision of area name, area number, power failure time and the like.
(3) The service data has obvious common sense errors, namely the data is defined as inaccurate, and for example, the power supply unit, the number of the public distribution transformer, the power failure starting time and the like do not accord with common sense.
Common treatment methods include: filling the missing values into a uniform default value or filling the missing values into a specific numerical value (such as a mean value, a minimum value, a median and the like); records containing outliers are deleted.
S3, determining a first abnormal result of the target enterprise under the target industry type according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise;
the method comprises the steps of exploring commonalities among enterprises in the same industry, respectively calculating power consumption ring ratios and the same ratios of the enterprises in the same industry according to daily power consumption data and hour power consumption data of a target enterprise, carrying out correlation analysis on the change conditions of the power consumption ring ratios of the enterprises according to industry classification, drawing a box diagram on the data of the power consumption ring ratios of the enterprises in the industry, calculating an upper edge and a lower edge, respectively taking an upper edge average value and a lower edge average value (the same as the first power upper limit and the first power lower limit) as an abnormal enterprise identification threshold value of the industry, and comparing the abnormal enterprise identification threshold value with the upper edge average value and the lower edge average value to determine a first abnormal result of the target enterprise under the target industry type.
S4, according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise, and a second abnormal result of the target enterprise in the target business hours;
the method comprises the steps of searching commonalities among enterprises in the same time, respectively calculating the electricity consumption ring ratio and the same ratio of each enterprise in the same business time according to daily electricity consumption data and hour electricity consumption data of a target enterprise, carrying out correlation analysis on the variation situation of the electricity consumption ring ratio of the enterprises according to time, drawing a box diagram on the electricity consumption ring ratio data of the enterprises, calculating an upper edge and a lower edge, respectively taking the average value of the upper edge and the average value of the lower edge (the same as the upper limit and the lower limit of the second electricity consumption) as an enterprise identification threshold value of abnormal electricity consumption of the enterprises in the period of time, and comparing the average value of the upper edge and the average value of the lower edge to determine a second abnormal result of the target enterprise in the type of the target business time.
S5, inputting target attribute data, daily electricity quantity data and hour electricity consumption data of a target enterprise into an electricity consumption abnormity identification model to obtain a third abnormal result;
the power utilization abnormity identification model comprises a plurality of algorithms, and because errors may exist in the result of one method for detecting time sequence catastrophe points, the intersection result is finally obtained by combining the plurality of algorithms. And respectively passing the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise through four algorithms of a Mann-Kendall mutation point, a Pettitt algorithm, a Buishand U Test algorithm and a Standard Normal Homogeneeity Test (SNHT). And combining the four algorithm abnormal recognition results, taking the intersection of the algorithm results, extracting the ascending trend result and the descending trend result, and outputting to obtain a third abnormal result.
And S6, determining whether the target enterprise is the target abnormal result of the abnormal power utilization enterprise or not according to the first abnormal result, the second abnormal result and the third abnormal result.
When the third abnormal result output by the power utilization abnormality recognition model is a non-abnormal power utilization enterprise but is actually an abnormal power utilization enterprise, the cause of the error of the model result can be analyzed, and special conditions can be formulated according to the actual data condition to optimize the model.
And S7, determining the abnormal electricity utilization type of the target enterprise according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise under the condition that the target enterprise is the abnormal electricity utilization enterprise as the target abnormal result.
According to the analysis result, the following types of common enterprise electricity utilization abnormity are provided:
(1) the power utilization characteristic is that the daily power consumption of the enterprise stopping production/responsible stopping production is obviously higher than the daily non-production power consumption of the enterprise.
(2) The emergency production stop is characterized in that the electricity consumption of an enterprise which normally operates is reduced by more than 70% in the current day compared with the electricity consumption of the enterprise which normally operates in the previous day.
(3) And (4) overload production, wherein the electricity utilization characteristic is that the daily electricity consumption of an enterprise in normal operation is obviously higher than the daily production electricity consumption of the enterprise.
(4) The power utilization characteristic is that the daily power consumption of an enterprise which is normally in operation is continuously lower than the daily non-production power consumption of the enterprise for multiple days.
(5) The power utilization characteristic is that the daily power consumption of the enterprise which is automatically stopped is lower than the daily power consumption of the enterprise for a long time.
(6) The power consumption characteristic is that the daily power consumption of the enterprise with long-term production halt/automatic production halt is continuously higher than the non-production power consumption of the enterprise for multiple days.
(7) The power utilization is characterized in that the night power consumption of enterprises stopping production in limited periods/responsible for stopping production is obviously higher than the daytime power consumption.
Through the above alternative embodiment, at least the following advantages can be achieved:
(1) Correlation analysis is carried out on the power consumption of the enterprise from multiple dimensions such as industry, time and the like, the power consumption characteristics of the enterprise are accurately described, and whether abnormal power consumption behaviors exist in the enterprise is judged in multiple aspects;
(2) The abnormal recognition is realized through various algorithms, the problem that errors possibly exist in the result of one method for detecting time sequence mutation points is solved, the intersection result is finally obtained and the ascending and descending trend results are extracted by combining the various algorithms, and the enterprises with abnormal electricity utilization can be more accurately and efficiently found out.
It should be noted that for simplicity of description, the above-mentioned method embodiments are shown as a series of combinations of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art will appreciate that the embodiments described in this specification are presently preferred and that no acts or modules are required by the invention.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the method of the embodiments of the present invention.
Example 2
According to an embodiment of the present invention, there is also provided an apparatus for implementing the above power consumption abnormality detection method, and fig. 3 is a block diagram of a power consumption abnormality detection apparatus according to an embodiment of the present invention, and as shown in fig. 3, the apparatus includes: the obtaining module 302, the first determining module 304, and the second determining module 306, which will be described in detail below.
An obtaining module 302, configured to obtain target attribute data of a target enterprise, daily electricity consumption data, and hour electricity consumption data, where the target attribute data includes a target enterprise scale of the target enterprise, a target industry type, and a target business hours type; a first determining module 304, connected to the obtaining module 302, configured to determine a first abnormal result of the target enterprise in the target industry type and a second abnormal result of the target enterprise in the target business hours type according to the target attribute data, the daily electricity consumption data, and the hour electricity consumption data of the target enterprise; the second determining module 306 is connected to the first determining module 304, and configured to determine whether the target enterprise is a target abnormal result of the abnormal power consuming enterprise according to the first abnormal result and the second abnormal result.
It should be noted here that the obtaining module 302, the first determining module 304 and the second determining module 306 correspond to steps S102 to S106 in implementing the electricity consumption abnormality detection method, and a plurality of modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in the above embodiment 1.
Example 3
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement any of the above-described power anomaly detection methods.
Example 4
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, wherein when executed by a processor of an electronic device, instructions of the computer-readable storage medium enable the electronic device to perform any one of the above power abnormality detection methods.
Example 5
According to another aspect of the embodiments of the present invention, there is also provided a computer program product including a computer program, wherein the computer program realizes any one of the above-mentioned power consumption abnormality detection methods when executed by a processor.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A power consumption abnormality detection method characterized by comprising:
acquiring target attribute data, daily electricity consumption data and hour electricity consumption data of the target enterprise, wherein the target attribute data comprises the target enterprise scale, the target industry type and the target business time type of the target enterprise;
determining a first abnormal result of the target enterprise under the target industry type and a second abnormal result of the target enterprise under the target business time type according to the target attribute data, the daily electricity consumption data and the hourly electricity consumption data of the target enterprise;
and determining whether the target enterprise is a target abnormal result of the abnormal power utilization enterprise or not according to the first abnormal result and the second abnormal result.
2. The method of claim 1, wherein after determining whether the target enterprise is a target abnormal result of an abnormal electricity consuming enterprise according to the first abnormal result and the second abnormal result, further comprising:
and when the target abnormal result is that the target enterprise is an abnormal electricity utilization enterprise, determining the abnormal electricity utilization type of the target enterprise according to the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise.
3. The method of claim 1, wherein determining whether the target enterprise is a target abnormal result of an abnormal electricity consuming enterprise according to the first abnormal result and the second abnormal result comprises:
inputting the target attribute data, daily electricity consumption data and hour electricity consumption data of the target enterprise into an electricity consumption abnormity identification model to obtain a third abnormity result;
and determining whether the target enterprise is a target abnormal result of the abnormal power utilization enterprise or not according to the first abnormal result, the second abnormal result and the third abnormal result.
4. The method of claim 1, wherein determining whether the target enterprise is a target abnormal result of an abnormal electricity consuming enterprise according to the first abnormal result and the second abnormal result comprises:
acquiring historical daily electricity consumption data and historical hour electricity consumption data of the target enterprise;
determining a same ratio and a ring ratio between the daily electricity consumption data of the target enterprise and the historical daily electricity consumption data, and a same ratio and a ring ratio between the hour electricity consumption data of the target enterprise and the historical hour electricity consumption data to obtain a fourth abnormal result;
and determining whether the target enterprise is a target abnormal result of the abnormal power utilization enterprise according to the first abnormal result, the second abnormal result and the fourth abnormal result.
5. The method of claim 1, wherein the determining a first abnormal result of the target enterprise under the target industry type according to the target attribute data, the daily electricity consumption data and the hourly electricity consumption data of the target enterprise comprises:
determining a first electric quantity upper limit and a first electric quantity lower limit of the target enterprise scale in a preset time period under the target industry type;
determining a first target electricity consumption of the target enterprise in the preset time period according to the daily electricity consumption data and the hour electricity consumption data of the target enterprise;
and comparing the first target power consumption with the first power consumption upper limit and the first power consumption lower limit, and determining a first abnormal result of the target enterprise under the target industry type.
6. The method of claim 1, wherein the determining a second abnormal result of the target enterprise in the target business hours type according to the target attribute data, the daily electricity consumption data and the hourly electricity consumption data of the target enterprise comprises:
determining a second upper limit and a second lower limit of the second electric quantity of the target enterprise scale within a preset time period under the target business time type;
determining a second target electricity consumption of the target enterprise in the preset time period according to the daily electricity consumption data and the hour electricity consumption data of the target enterprise;
and comparing the second target electricity consumption with the second electricity upper limit and the second electricity lower limit, and determining a second abnormal result of the target enterprise in the target business hours.
7. The method according to any one of claims 1 to 6, wherein the acquiring target attribute data, daily electricity consumption data and hour electricity consumption data of a target enterprise comprises:
acquiring initial attribute data, initial daily electricity consumption data and initial hour electricity consumption data of the target enterprise;
and carrying out data standardization on the initial attribute data, the initial daily electricity consumption data and the initial hour electricity consumption data to obtain the target attribute data, the daily electricity consumption data and the hour electricity consumption data of the target enterprise.
8. An electricity abnormality detection device characterized by comprising:
the system comprises an acquisition module, a management module and a management module, wherein the acquisition module is used for acquiring target attribute data, daily electricity consumption data and hourly electricity consumption data of a target enterprise, and the target attribute data comprises the target enterprise scale, the target industry type and the target business time type of the target enterprise;
the first determining module is used for determining a first abnormal result of the target enterprise under the target industry type and a second abnormal result of the target enterprise under the target business time type according to target attribute data, daily electricity consumption data and hour electricity consumption data of the target enterprise;
and the second determining module is used for determining whether the target enterprise is the target abnormal result of the abnormal power utilization enterprise according to the first abnormal result and the second abnormal result.
9. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the power usage anomaly detection method of any one of claims 1-7.
10. A computer-readable storage medium, wherein instructions in the computer-readable storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the power usage anomaly detection method of any one of claims 1-7.
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| CN202211035293.8A CN115358336A (en) | 2022-08-26 | 2022-08-26 | Power utilization abnormity detection method and device and electronic equipment |
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| CN202211035293.8A CN115358336A (en) | 2022-08-26 | 2022-08-26 | Power utilization abnormity detection method and device and electronic equipment |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117076991A (en) * | 2023-10-16 | 2023-11-17 | 云境商务智能研究院南京有限公司 | Power consumption abnormality monitoring method and device for pollution control equipment and computer equipment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117076991A (en) * | 2023-10-16 | 2023-11-17 | 云境商务智能研究院南京有限公司 | Power consumption abnormality monitoring method and device for pollution control equipment and computer equipment |
| CN117076991B (en) * | 2023-10-16 | 2024-01-02 | 云境商务智能研究院南京有限公司 | Power consumption abnormality monitoring method and device for pollution control equipment and computer equipment |
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