CN118052593A - Campus abnormal energy user identification method and system based on big data - Google Patents
Campus abnormal energy user identification method and system based on big data Download PDFInfo
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Abstract
The invention discloses a method and a system for identifying abnormal energy users in a park based on big data, wherein the method comprises the following steps: acquiring target electricity load information of each electricity user in a unit monitoring period and real-time running state of production equipment at each moment; determining standard electricity load information corresponding to the real-time running state of the production equipment based on the association relation between the standard running state of each electricity user and the electricity load of the production equipment; and judging whether each electricity user has abnormal energy caused by equipment failure according to the target electricity load information and the standard electricity load information. According to the method and the system, the electricity consumption requirements and the electricity consumption habits of each electricity consumption user are considered, the electricity consumption load information is compared with the standard electricity consumption load information, whether the electricity consumption user has abnormal energy consumption caused by equipment faults or not is judged, abnormal energy consumption users with different electricity consumption conditions in a park can be rapidly and accurately identified, the equipment faults are timely found, and the electricity consumption safety and the production efficiency of park production are improved.
Description
Technical Field
The invention relates to the technical field of abnormal electricity consumption identification, in particular to a method and a system for identifying park abnormal energy consumption users based on big data.
Background
The campus refers to a unified planning and designating area by government, and enterprises, companies and the like which are specially provided with a certain specific industry and form in the area are unified management, such as an industrial park.
In the field of industrial production, an unusual increase in electrical load is often caused by equipment failure, for example, the aging of refrigeration equipment requires a higher cooling drive power to complete the refrigeration task, and for example, the transmission system requires a greater transmission drive force to complete the transmission task when the transmission structure is damaged. On one hand, equipment faults can influence the production progress of electricity users, and certain potential safety hazards can be brought due to overload operation of equipment; on the other hand, the fluctuation of the frequency and the voltage of the power grid can be caused by the increase of the power load of the user, so that the normal operation of other user equipment in a park is influenced; at present, for equipment fault monitoring of different electricity users in a park, manual inspection (whether equipment is faulty or not is checked periodically or regularly by manpower, and the efficiency and accuracy are not high) and sensor monitoring (the sensor is used for detecting the change of related parameters of potential safety hazards caused by equipment faults, and the timeliness is not ideal) are generally adopted; in addition, the power load requirements and the actual power consumption conditions of different power consumption users in the park are different, and the same standard cannot be adopted for detection.
Therefore, how to quickly and accurately identify abnormal energy users in a park, discover equipment faults in time, and improve the electricity safety and the production efficiency of park production is a technical problem which needs to be solved.
Disclosure of Invention
The invention mainly aims to provide a method and a system for identifying abnormal energy users in a park based on big data, and aims to solve the technical problems.
In order to achieve the above purpose, the present invention provides a method for identifying abnormal energy users in a campus based on big data, the method comprising the following steps:
Acquiring target electricity load information of each electricity user in a unit monitoring period according to the identification information of each electricity user in the park;
calling an access interface of a production equipment state database provided by each electricity utilization user, and acquiring the real-time running state of the production equipment of each electricity utilization user at each moment recorded in the production equipment state database;
Determining standard electricity load information corresponding to the real-time running state of the production equipment based on the association relation between the standard running state of each electricity user and the electricity load of the production equipment; wherein the association relation between the standard operation state of the production equipment and the electricity load is configured into a relation comparison table generated according to the historical electricity load data of each electricity user and the historical operation state of the production equipment;
And judging whether each electricity utilization user has abnormal energy caused by equipment failure according to the target electricity utilization load information and the standard electricity utilization load information.
Optionally, the step of obtaining the target electricity load information of each electricity user in the unit monitoring period according to the identification information of each user in the campus specifically includes:
According to the identification information of each electricity utilization user in the park, matching a preset electricity utilization monitoring scheme corresponding to each electricity utilization user in an electricity utilization user monitoring type list; the preset electricity consumption monitoring scheme is configured to record electricity consumption monitoring frequency of each electricity consumption user in the current unit monitoring period;
And in each unit monitoring period, sending a meter reading instruction to the intelligent electric meter of each electricity user in the park according to the preset electricity monitoring scheme so that the intelligent electric meter executes meter reading action and obtains target electricity load information fed back by the intelligent electric meter and applied to the electricity user.
Optionally, before the step of matching the preset electricity consumption monitoring scheme corresponding to each electricity consumption user in the electricity consumption user monitoring type list according to the identification information of each electricity consumption user in the campus, the method further includes:
acquiring historical electricity load data of each unit monitoring period which changes with time in the last monitoring period of each electricity user, and generating an electricity load estimated value of each unit monitoring period of each electricity user in the current monitoring period based on an electricity load estimated adjustment value input by each electricity user;
Generating electricity consumption monitoring frequency of each electricity consumption user in each unit monitoring period according to the proportion relation of the electricity consumption load estimated value of each electricity consumption user in each unit monitoring period in the current monitoring period;
Dividing the electricity consumption user monitoring types of each electricity consumption user in each unit monitoring period based on the interval where the electricity consumption monitoring frequency is located, and generating a preset electricity consumption monitoring scheme of each electricity consumption user in the current monitoring period according to the electricity consumption user monitoring types of each electricity consumption user in each unit monitoring period and the corresponding electricity consumption monitoring frequency.
Optionally, each intelligent ammeter of the electricity user is connected with a park power grid house lead-in wire and a plurality of production devices of the electricity user, and each intelligent ammeter is provided with an electricity load detection module and a communication module;
The power utilization load detection module is configured to detect real-time power utilization load information of a plurality of production devices accessed to a park power grid by the power utilization user in real time;
The communication module is configured to execute meter reading action when receiving meter reading instructions, sample the detected real-time electricity load information once, obtain target electricity load information of a plurality of production devices of an electricity user, and feed back the target electricity load information.
Optionally, in each unit monitoring period, sending a meter reading instruction to the smart meter of each electricity user in the campus according to the preset electricity monitoring scheme, so that the smart meter performs a meter reading action and obtains target electricity load information fed back by the smart meter, where the target electricity load information is applied to the electricity user, specifically including:
Generating a monitoring time stamp of each unit monitoring period according to the power utilization monitoring frequency corresponding to the preset power utilization monitoring scheme of each power utilization user;
transmitting a meter reading instruction to an intelligent ammeter of each electricity user in a park at each monitoring time stamp of each unit monitoring time period to obtain electricity load data corresponding to each monitoring time stamp fed back by the intelligent ammeter;
And taking each monitoring time stamp and the corresponding electricity load data as an electricity load data set, and generating target electricity load information of each electricity user in each unit monitoring period based on a plurality of electricity load data sets in each unit monitoring period.
Optionally, the production equipment state database is configured as a database formed according to the real-time operation state of the production equipment collected by the production equipment operation monitoring component of each electricity utilization user;
The production equipment operation monitoring assembly comprises operation monitoring sensors arranged on each production equipment, and the operation monitoring sensors are configured to sense whether the production equipment operates at the current moment;
Wherein the operation monitoring sensor comprises one or more of a photoelectric sensor, a proximity sensor, a vibration sensor or a volume sensor.
Optionally, before the step of determining the standard power consumption load information corresponding to the real-time running state of the production equipment based on the association relation between the standard running state of each power consumption user and the power consumption load of the production equipment, the method further includes:
Acquiring the real-time running state of production equipment and corresponding historical electricity load data of each time in the past several unit monitoring periods of each electricity user;
The method comprises the steps of grouping historical electricity load data with the same real-time running state of production equipment in the real-time running state of the production equipment at each moment and corresponding historical electricity load data, and selecting the historical electricity load data with the largest occurrence number from each group of real-time running states of the production equipment;
And taking the historical electricity load data with the largest occurrence number as standard electricity load information of the real-time running state of the group of production equipment, and generating the association relation of each electricity user about the standard running state of the production equipment and the electricity load according to the standard electricity load information of the real-time running state of each group of production equipment.
Optionally, the step of determining standard electricity load information corresponding to the real-time running state of the production equipment based on the association relation between the standard running state of each electricity user and the electricity load of the production equipment specifically includes:
Invoking an association relation between a standard running state of each electricity utilization user and an electricity utilization load of production equipment; the association relation comprises a relation comparison table of the standard running state of the production equipment and the electric load;
And inputting the real-time running state of the production equipment into the relation comparison table to obtain standard electricity load information corresponding to the real-time running state of the production equipment.
Optionally, according to the target electricity load information and the standard electricity load information, judging whether each electricity user has abnormal energy consumption caused by equipment failure, including:
Judging whether each user meets the judging condition of abnormal energy consumption caused by equipment failure according to the target electricity consumption load information and the standard electricity consumption load information at each moment in the unit monitoring period;
Wherein the determination condition includes:
the difference value between the target electricity load information and the standard electricity load at the same moment exceeds a preset threshold value and the duration exceeds a preset duration; or (b)
The number of times that the difference value between the target electricity load information and the standard electricity load at the same time in the unit monitoring period exceeds a preset threshold value is not less than the preset number of times.
In addition, in order to achieve the above object, the present invention also provides a system for identifying abnormal energy users in a campus based on big data, comprising:
The acquisition module is used for acquiring target electricity load information of each electricity user in a unit monitoring period according to the identification information of each electricity user in the park;
The calling module is used for calling an access interface of the production equipment state database provided by each electricity utilization user and acquiring the real-time running state of the production equipment of each electricity utilization user at each moment, which is recorded in the production equipment state database;
The determining module is used for determining standard electricity load information corresponding to the real-time running state of the production equipment based on the association relation between the standard running state of each electricity user and the electricity load of the production equipment; wherein the association relation between the standard operation state of the production equipment and the electricity load is configured into a relation comparison table generated according to the historical electricity load data of each electricity user and the historical operation state of the production equipment;
And the judging module is used for judging whether each electricity utilization user has abnormal energy caused by equipment faults according to the target electricity utilization load information and the standard electricity utilization load information.
The invention has the beneficial effects that: according to the method and the system for identifying the abnormal energy users in the park based on big data, the real-time target electricity load information of each electricity user and the real-time operation state of production equipment are obtained, the standard electricity load information corresponding to the real-time operation state of the production equipment is determined by utilizing historical electricity load data according to the real-time operation state of the production equipment, so that whether the abnormal energy users caused by equipment faults exist in each electricity user or not is judged by comparing the electricity load information with the standard electricity load information, the abnormal energy users with different electricity demands and electricity habits in the park can be identified rapidly and accurately, the equipment faults are found timely, and the production electricity safety and the production efficiency of the park are improved.
Drawings
Fig. 1 is a flow chart of a method for identifying a campus abnormal energy user based on big data according to an embodiment of the present invention;
Fig. 2 is a schematic structural diagram of a system for identifying abnormal energy users in a campus based on big data according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a method for identifying a park abnormal energy user based on big data, and referring to fig. 1, fig. 1 is a flow diagram of an embodiment of the method for identifying a park abnormal energy user based on big data.
In this embodiment, the method for identifying a campus abnormal energy user based on big data includes the following steps:
S100: acquiring target electricity load information of each electricity user in a unit monitoring period according to the identification information of each electricity user in the park;
S200: calling an access interface of a production equipment state database provided by each electricity utilization user, and acquiring the real-time running state of the production equipment of each electricity utilization user at each moment recorded in the production equipment state database;
S300: determining standard electricity load information corresponding to the real-time running state of the production equipment based on the association relation between the standard running state of each electricity user and the electricity load of the production equipment; wherein the association relation between the standard operation state of the production equipment and the electricity load is configured into a relation comparison table generated according to the historical electricity load data of each electricity user and the historical operation state of the production equipment;
S400: and judging whether each electricity utilization user has abnormal energy caused by equipment failure according to the target electricity utilization load information and the standard electricity utilization load information.
It should be noted that in the field of industrial production, an irregular increase in electrical load is usually caused by equipment failure, for example, when the refrigeration equipment ages, a higher cooling driving power needs to be provided to complete the refrigeration task, and for example, when the transmission system is damaged, a larger transmission driving force needs to be provided to complete the transmission task. On one hand, equipment faults can influence the production progress of electricity users, and certain potential safety hazards can be brought due to overload operation of equipment; on the other hand, the fluctuation of the frequency and the voltage of the power grid can be caused by the increase of the power load of the user, so that the normal operation of other user equipment in a park is influenced; at present, for equipment fault monitoring of different electricity users in a park, manual inspection (whether equipment is faulty or not is checked periodically or regularly by manpower, and the efficiency and accuracy are not high) and sensor monitoring (the sensor is used for detecting the change of related parameters of potential safety hazards caused by equipment faults, and the timeliness is not ideal) are generally adopted; in addition, the power load requirements and the actual power consumption conditions of different power consumption users in the park are different, and the same standard cannot be adopted for detection. In order to solve the problems, the embodiment determines the standard electricity load information corresponding to the real-time running state of the production equipment according to the real-time running state of the production equipment by acquiring the real-time target electricity load information of each electricity user and the real-time running state of the production equipment, and compares the historical electricity load information with the standard electricity load information to judge whether the electricity user has abnormal energy consumption caused by equipment faults or not, so that abnormal energy consumption users with different electricity demands and electricity habits in a park can be rapidly and accurately identified, the equipment faults can be timely found, and the production electricity safety and the production efficiency of the park can be improved.
In a preferred embodiment, the step of obtaining the target electricity load information of each electricity user in the unit monitoring period according to the identification information of each user in the campus specifically includes: according to the identification information of each electricity utilization user in the park, matching a preset electricity utilization monitoring scheme corresponding to each electricity utilization user in an electricity utilization user monitoring type list; the preset electricity consumption monitoring scheme is configured to record electricity consumption monitoring frequency of each electricity consumption user in the current unit monitoring period; and in each unit monitoring period, sending a meter reading instruction to the intelligent electric meter of each electricity user in the park according to the preset electricity monitoring scheme so that the intelligent electric meter executes meter reading action and obtains target electricity load information fed back by the intelligent electric meter and applied to the electricity user.
Further, according to the identification information of each electricity consumption user in the campus, before the step of matching the preset electricity consumption monitoring scheme corresponding to each electricity consumption user in the electricity consumption user monitoring type list, the method further includes: acquiring historical electricity load data of each unit monitoring period which changes with time in the last monitoring period of each electricity user, and generating an electricity load estimated value of each unit monitoring period of each electricity user in the current monitoring period based on an electricity load estimated adjustment value input by each electricity user; generating electricity consumption monitoring frequency of each electricity consumption user in each unit monitoring period according to the proportion relation of the electricity consumption load estimated value of each electricity consumption user in each unit monitoring period in the current monitoring period; dividing the electricity consumption user monitoring types of each electricity consumption user in each unit monitoring period based on the interval where the electricity consumption monitoring frequency is located, and generating a preset electricity consumption monitoring scheme of each electricity consumption user in the current monitoring period according to the electricity consumption user monitoring types of each electricity consumption user in each unit monitoring period and the corresponding electricity consumption monitoring frequency.
In practical application, each intelligent ammeter of the electricity user is connected with a park power grid house lead-in wire and a plurality of production devices of the electricity user, and each intelligent ammeter is provided with an electricity load detection module and a communication module; the power utilization load detection module is configured to detect real-time power utilization load information of a plurality of production devices accessed to a park power grid by the power utilization user in real time; the communication module is configured to execute meter reading action when receiving meter reading instructions, sample the detected real-time electricity load information once, obtain target electricity load information of a plurality of production devices of an electricity user, and feed back the target electricity load information.
Further, in each unit monitoring period, sending a meter reading instruction to the intelligent electric meter of each electricity user in the park according to the preset electricity monitoring scheme, so that the intelligent electric meter executes meter reading action and obtains target electricity load information of the corresponding electricity user fed back by the intelligent electric meter, and the method specifically comprises the following steps: generating a monitoring time stamp of each unit monitoring period according to the power utilization monitoring frequency corresponding to the preset power utilization monitoring scheme of each power utilization user; transmitting a meter reading instruction to an intelligent ammeter of each electricity user in a park at each monitoring time stamp of each unit monitoring time period to obtain electricity load data corresponding to each monitoring time stamp fed back by the intelligent ammeter; and taking each monitoring time stamp and the corresponding electricity load data as an electricity load data set, and generating target electricity load information of each electricity user in each unit monitoring period based on a plurality of electricity load data sets in each unit monitoring period.
In this embodiment, considering the different electricity demands and electricity habits that different electricity users in a campus may have during different periods of a monitoring period (e.g., one year), food processing, such as seasonal or special periods, will typically have a greater electricity load during only one period of the year, and a lesser electricity load during the remaining periods. Under the condition, because the probability of equipment faults of the electricity users with larger electricity loads is larger and the influence on the park power grid is larger, under the premise that the system data acquisition and processing capacity is limited, corresponding electricity monitoring schemes are needed to be provided for different electricity users in different unit monitoring periods, namely, the electricity users with larger electricity loads in the current unit monitoring period are more likely to have higher electricity loads, the higher meter reading frequency is adopted, the timeliness of data acquisition is improved, the electricity users with abnormal equipment are identified earlier, and the long-term duration of the park electricity anomalies is avoided.
In practical application, the production equipment state database is configured as a database formed according to the real-time operation state of the production equipment collected by the production equipment operation monitoring component of each electricity utilization user; the production equipment operation monitoring assembly comprises operation monitoring sensors arranged on each production equipment, and the operation monitoring sensors are configured to sense whether the production equipment operates at the current moment; wherein the operation monitoring sensor comprises one or more of a photoelectric sensor, a proximity sensor, a vibration sensor or a volume sensor.
It should be noted that, in this embodiment, each electricity consumer further adopts a production equipment operation monitoring component formed by operation monitoring sensors disposed on each production equipment, so as to determine in real time whether each production equipment is in an operation processing state. Therefore, the system can know whether each production equipment is currently operated or not by calling the production equipment state database of each electricity utilization user, and data support is provided for the subsequent standard electricity utilization load according to the operation state.
In a preferred embodiment, before the step of determining the standard electricity load information corresponding to the real-time running state of the production equipment based on the association relation between the standard running state of each electricity consumer and the electricity load of the production equipment, the method further includes: acquiring the real-time running state of production equipment and corresponding historical electricity load data of each time in the past several unit monitoring periods of each electricity user; the method comprises the steps of grouping historical electricity load data with the same real-time running state of production equipment in the real-time running state of the production equipment at each moment and corresponding historical electricity load data, and selecting the historical electricity load data with the largest occurrence number from each group of real-time running states of the production equipment; and taking the historical electricity load data with the largest occurrence number as standard electricity load information of the real-time running state of the group of production equipment, and generating the association relation of each electricity user about the standard running state of the production equipment and the electricity load according to the standard electricity load information of the real-time running state of each group of production equipment.
On the basis, based on the association relation between the standard running state of each electricity utilization user and the electricity utilization load of the production equipment, determining standard electricity utilization load information corresponding to the real-time running state of the production equipment specifically comprises the following steps: invoking an association relation between a standard running state of each electricity utilization user and an electricity utilization load of production equipment; the association relation comprises a relation comparison table of the standard running state of the production equipment and the electric load; and inputting the real-time running state of the production equipment into the relation comparison table to obtain standard electricity load information corresponding to the real-time running state of the production equipment.
In a preferred embodiment, according to the target electricity load information and the standard electricity load information, judging whether each electricity user has abnormal energy consumption caused by equipment failure, specifically including: judging whether each user meets the judging condition of abnormal energy consumption caused by equipment failure according to the target electricity consumption load information and the standard electricity consumption load information at each moment in the unit monitoring period; wherein the determination condition includes: the difference value between the target electricity load information and the standard electricity load at the same moment exceeds a preset threshold value and the duration exceeds a preset duration; or the frequency that the difference value between the target electricity load information and the standard electricity load at the same time in the unit monitoring period exceeds a preset threshold value is not less than the preset frequency.
In this embodiment, after obtaining the real-time operation state of each electric user at each moment, by querying the relationship between the real-time operation state of the production device and the historical electric load data in the historical operation process of the device, statistical analysis is performed on the historical electric load data corresponding to each group of real-time operation states of the production device, and the historical electric load data (or a plurality of groups of historical electric load data in a certain range) with the largest occurrence number is used as the standard electric load information (or standard electric load range information) of the real-time operation state of the production device, so as to obtain the relationship comparison table between the standard operation state and the electric load of each user production device, and the standard electric load information of the real-time operation state of the current device is matched by using the relationship comparison table. And finally, judging whether the current power utilization users have abnormal power utilization caused by equipment faults or not by utilizing the target power utilization load information and the standard power utilization load information. Therefore, by considering the electricity demand and the electricity habit of each electricity user and comparing the electricity load information with the standard electricity load information, whether each electricity user has abnormal energy consumption caused by equipment faults or not is judged, abnormal energy consumption users with different electricity consumption conditions in a park can be rapidly and accurately identified, equipment faults can be timely found, and the electricity safety and the production efficiency of park production are improved.
Referring to fig. 2, fig. 2 is a block diagram illustrating an embodiment of a system for identifying a campus abnormal energy user based on big data according to the present invention.
As shown in fig. 2, the system for identifying a campus abnormal energy user based on big data according to the embodiment of the present invention includes:
the acquisition module 10 is used for acquiring target electricity load information of each electricity user in a unit monitoring period according to the identification information of each electricity user in a park;
The calling module 20 is configured to call an access interface of a production equipment status database provided by each electricity user, and obtain a real-time running status of the production equipment of each electricity user at each moment recorded in the production equipment status database;
A determining module 30, configured to determine standard power consumption load information corresponding to a real-time running state of a production device based on an association relationship between a standard running state and a power consumption load of each power consumption user with respect to the production device; wherein the association relation between the standard operation state of the production equipment and the electricity load is configured into a relation comparison table generated according to the historical electricity load data of each electricity user and the historical operation state of the production equipment;
And a judging module 40, configured to judge whether each electricity consumer has abnormal energy due to equipment failure according to the target electricity load information and the standard electricity load information.
In the embodiment, the real-time target electricity load information of each electricity user and the real-time running state of the production equipment are obtained, the standard electricity load information corresponding to the real-time running state of the production equipment is determined by utilizing the historical electricity load data according to the real-time running state of the production equipment, so that whether the electricity users have abnormal energy consumption caused by equipment faults or not is judged by comparing the electricity load information with the standard electricity load information, abnormal energy consumption users with different electricity demands and electricity habits in a park can be rapidly and accurately identified, the equipment faults are timely found, and the production electricity safety and the production efficiency of the park are improved.
Other embodiments or specific implementation manners of the system for identifying abnormal energy users in a campus based on big data may refer to the above method embodiments, and will not be described herein.
It is appreciated that in the description herein, reference to the terms "one embodiment," "another embodiment," "other embodiments," or "first through nth embodiments," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The method for identifying the abnormal energy utilization users of the park based on the big data is characterized by comprising the following steps:
Acquiring target electricity load information of each electricity user in a unit monitoring period according to the identification information of each electricity user in the park;
calling an access interface of a production equipment state database provided by each electricity utilization user, and acquiring the real-time running state of the production equipment of each electricity utilization user at each moment recorded in the production equipment state database;
Determining standard electricity load information corresponding to the real-time running state of the production equipment based on the association relation between the standard running state of each electricity user and the electricity load of the production equipment; wherein the association relation between the standard operation state of the production equipment and the electricity load is configured into a relation comparison table generated according to the historical electricity load data of each electricity user and the historical operation state of the production equipment;
And judging whether each electricity utilization user has abnormal energy caused by equipment failure according to the target electricity utilization load information and the standard electricity utilization load information.
2. The method for identifying abnormal energy users in a campus based on big data according to claim 1, wherein the step of obtaining the target electricity load information of each electricity user in the unit monitoring period according to the identification information of each user in the campus specifically comprises the following steps:
According to the identification information of each electricity utilization user in the park, matching a preset electricity utilization monitoring scheme corresponding to each electricity utilization user in an electricity utilization user monitoring type list; the preset electricity consumption monitoring scheme is configured to record electricity consumption monitoring frequency of each electricity consumption user in the current unit monitoring period;
And in each unit monitoring period, sending a meter reading instruction to the intelligent electric meter of each electricity user in the park according to the preset electricity monitoring scheme so that the intelligent electric meter executes meter reading action and obtains target electricity load information fed back by the intelligent electric meter and applied to the electricity user.
3. The big data based method for identifying abnormal energy users in a campus as claimed in claim 2, wherein the method further comprises, before the step of matching the preset electricity monitoring scheme corresponding to each electricity user in the list of electricity user monitoring types, according to the identification information of each electricity user in the campus:
acquiring historical electricity load data of each unit monitoring period which changes with time in the last monitoring period of each electricity user, and generating an electricity load estimated value of each unit monitoring period of each electricity user in the current monitoring period based on an electricity load estimated adjustment value input by each electricity user;
Generating electricity consumption monitoring frequency of each electricity consumption user in each unit monitoring period according to the proportion relation of the electricity consumption load estimated value of each electricity consumption user in each unit monitoring period in the current monitoring period;
Dividing the electricity consumption user monitoring types of each electricity consumption user in each unit monitoring period based on the interval where the electricity consumption monitoring frequency is located, and generating a preset electricity consumption monitoring scheme of each electricity consumption user in the current monitoring period according to the electricity consumption user monitoring types of each electricity consumption user in each unit monitoring period and the corresponding electricity consumption monitoring frequency.
4. The method for identifying the abnormal energy users in the park based on big data according to claim 3, wherein the intelligent electric meter of each electricity user is connected with the house lead of the power grid in the park and a plurality of production devices of the applied electricity user, and each intelligent electric meter is provided with an electricity load detection module and a communication module;
The power utilization load detection module is configured to detect real-time power utilization load information of a plurality of production devices accessed to a park power grid by the power utilization user in real time;
The communication module is configured to execute meter reading action when receiving meter reading instructions, sample the detected real-time electricity load information once, obtain target electricity load information of a plurality of production devices of an electricity user, and feed back the target electricity load information.
5. The method for identifying abnormal energy users in a campus based on big data according to claim 4, wherein in each unit monitoring period, a meter reading instruction is sent to a smart meter of each electricity user in the campus according to the preset electricity monitoring scheme, so that the smart meter performs a meter reading action and obtains target electricity load information of the electricity user fed back by the smart meter, and the method specifically comprises the steps of:
Generating a monitoring time stamp of each unit monitoring period according to the power utilization monitoring frequency corresponding to the preset power utilization monitoring scheme of each power utilization user;
transmitting a meter reading instruction to an intelligent ammeter of each electricity user in a park at each monitoring time stamp of each unit monitoring time period to obtain electricity load data corresponding to each monitoring time stamp fed back by the intelligent ammeter;
And taking each monitoring time stamp and the corresponding electricity load data as an electricity load data set, and generating target electricity load information of each electricity user in each unit monitoring period based on a plurality of electricity load data sets in each unit monitoring period.
6. The big data based campus abnormal energy user identification method of claim 1, wherein the production equipment status database is configured as a database of production equipment real-time operational status collected by the production equipment operational monitoring component of each electricity consumer;
The production equipment operation monitoring assembly comprises operation monitoring sensors arranged on each production equipment, and the operation monitoring sensors are configured to sense whether the production equipment operates at the current moment;
Wherein the operation monitoring sensor comprises one or more of a photoelectric sensor, a proximity sensor, a vibration sensor or a volume sensor.
7. The method for identifying abnormal energy users in a campus based on big data according to claim 1, wherein before the step of determining standard electricity load information corresponding to the real-time running state of the production equipment based on the association relation between the standard running state of each electricity user and the electricity load of the production equipment, the method further comprises:
Acquiring the real-time running state of production equipment and corresponding historical electricity load data of each time in the past several unit monitoring periods of each electricity user;
The method comprises the steps of grouping historical electricity load data with the same real-time running state of production equipment in the real-time running state of the production equipment at each moment and corresponding historical electricity load data, and selecting the historical electricity load data with the largest occurrence number from each group of real-time running states of the production equipment;
And taking the historical electricity load data with the largest occurrence number as standard electricity load information of the real-time running state of the group of production equipment, and generating the association relation of each electricity user about the standard running state of the production equipment and the electricity load according to the standard electricity load information of the real-time running state of each group of production equipment.
8. The method for identifying abnormal energy users in a campus based on big data according to claim 7, wherein the step of determining standard electricity load information corresponding to the real-time operation state of the production equipment based on the association relation between the standard operation state of each electricity user and the electricity load of the production equipment specifically comprises the following steps:
Invoking an association relation between a standard running state of each electricity utilization user and an electricity utilization load of production equipment; the association relation comprises a relation comparison table of the standard running state of the production equipment and the electric load;
And inputting the real-time running state of the production equipment into the relation comparison table to obtain standard electricity load information corresponding to the real-time running state of the production equipment.
9. The method for identifying abnormal energy consumption users in a campus based on big data according to claim 8, wherein the step of judging whether each of the power consumption users has abnormal energy consumption caused by equipment failure according to the target power consumption load information and the standard power consumption load information comprises the following steps:
Judging whether each user meets the judging condition of abnormal energy consumption caused by equipment failure according to the target electricity consumption load information and the standard electricity consumption load information at each moment in the unit monitoring period;
Wherein the determination condition includes:
the difference value between the target electricity load information and the standard electricity load at the same moment exceeds a preset threshold value and the duration exceeds a preset duration;
or the number of times that the difference value between the target electricity load information and the standard electricity load at the same time in the unit monitoring period exceeds a preset threshold value is not less than the preset number of times.
10. A big data based campus anomaly energy user identification system, comprising:
The acquisition module is used for acquiring target electricity load information of each electricity user in a unit monitoring period according to the identification information of each electricity user in the park;
The calling module is used for calling an access interface of the production equipment state database provided by each electricity utilization user and acquiring the real-time running state of the production equipment of each electricity utilization user at each moment, which is recorded in the production equipment state database;
The determining module is used for determining standard electricity load information corresponding to the real-time running state of the production equipment based on the association relation between the standard running state of each electricity user and the electricity load of the production equipment; wherein the association relation between the standard operation state of the production equipment and the electricity load is configured into a relation comparison table generated according to the historical electricity load data of each electricity user and the historical operation state of the production equipment;
And the judging module is used for judging whether each electricity utilization user has abnormal energy caused by equipment faults according to the target electricity utilization load information and the standard electricity utilization load information.
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