CN112256735A - Power utilization monitoring method and device, computer equipment and storage medium - Google Patents
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Abstract
The invention relates to the technical field of power grids, in particular to a power utilization monitoring method, a power utilization monitoring device, computer equipment and a storage medium, wherein the power utilization monitoring data are obtained and comprise service data and operation data; screening the service data according to a preset rule, and determining a first probability according to the screened service data; preprocessing the operation data, processing the preprocessed operation data by using a preset recognition model and outputting a second probability; determining a probability for irregular power usage based on the first probability and the second probability. According to the power consumption monitoring method provided by the embodiment of the invention, the power consumption monitoring data are divided, different processing modes are adopted for different data to determine the first probability and the second probability, and the first probability and the second probability finally determine the non-standard power consumption probability, so that whether a user does not use power in a standard manner can be more accurately judged, and the degree of automation is high.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to a power utilization monitoring method, a power utilization monitoring device, computer equipment and a storage medium.
Background
The electric energy is an essential energy source in the production and the life of people, and along with the continuous development of the social economy in China, the electric quantity required by the production and the life of people is increased day by day.
In this case, the irregular use of electricity, whether by heart or by accident, is frequent. The irregular power utilization generally comprises power utilization under voltage, power utilization under current, power utilization in phase shift, power utilization without meter and the like. In the past, the actual electricity consumption is not matched with the electricity meter reading, so that the normal charging cannot be realized, and the normal order of the electricity utilization market is damaged.
In the prior art, nonstandard power utilization can be realized only by field inspection, improvement of monitoring capability of the intelligent electric meter, wireless communication monitoring and other modes, however, misjudgment and misjudgment are easily caused by the modes, workload is large, monitoring cost is high, and improvement is needed.
Disclosure of Invention
In view of the above, it is necessary to provide a power consumption monitoring method, apparatus, computer device and storage medium.
The embodiment of the invention is realized in such a way that the electricity utilization monitoring method comprises the following steps:
acquiring power consumption monitoring data, wherein the power consumption monitoring data comprises service data and operation data;
screening the service data according to a preset rule, and determining a first probability according to the screened service data;
preprocessing the operation data, processing the preprocessed operation data by using a preset recognition model and outputting a second probability;
determining a probability for irregular power usage based on the first probability and the second probability.
In one embodiment, an embodiment of the present invention further provides a power consumption monitoring device, where the power consumption monitoring device includes:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring power consumption monitoring data which comprises service data and operation data;
the first probability determination module is used for screening the service data according to a preset rule and determining a first probability according to the screened service data;
the second probability determination module is used for preprocessing the operation data, processing the preprocessed operation data by using a preset recognition model and outputting a second probability;
an irregular power utilization probability determination module for determining a probability for irregular power utilization based on the first probability and the second probability.
In one embodiment, the present invention further provides a computer device, including a memory and a processor, where the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to execute the steps of the power consumption monitoring method.
In one embodiment, the present invention further provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program causes the processor to execute the steps of the electricity usage monitoring method.
According to the power consumption monitoring method provided by the embodiment of the invention, the power consumption monitoring data are divided, different processing modes are adopted for different data to determine the first probability and the second probability, and the first probability and the second probability finally determine the non-standard power consumption probability, so that whether a user does not use power in a standard manner can be more accurately judged, and the degree of automation is high. The method can be adapted to the existing power utilization system, and analysis processing is carried out on the basis of the existing power utilization monitoring data so as to determine whether the user is not in normal power utilization.
Drawings
FIG. 1 is a diagram of an exemplary application environment for a power usage monitoring method;
FIG. 2 is a flow diagram of a method of power usage monitoring in one embodiment;
fig. 3 is a detailed flowchart of the step of determining the first probability according to the screened service data in fig. 2;
FIG. 4 is a detailed flow chart of the steps of FIG. 2 for preprocessing the operational data;
FIG. 5 is a flowchart illustrating steps of processing the preprocessed operating data and outputting a second probability using a predetermined recognition model in FIG. 2;
FIG. 6 is a detailed flowchart of the step of determining the probability for irregular power usage based on the first probability and the second probability of FIG. 2;
FIG. 7 is a block diagram of an electrical monitoring device in one embodiment;
FIG. 8 is a block diagram showing an internal configuration of a computer device according to one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
Fig. 1 is a diagram of an application environment of a power consumption monitoring method provided in an embodiment, as shown in fig. 1, in the application environment, including a power supply grid 110 and a power supply monitoring system 120.
In the embodiment of the present invention, the power supply grid 110 may be a small-sized grid covering a certain city and area, or may be a large-sized and medium-sized grid covering multiple cities and areas, and the embodiment of the present invention is not particularly limited to the scale of the power supply grid and whether the power supply grid operates independently or in a grid-connected manner. In the embodiment of the present invention, it should be understood that the power supply grid 110 needs to be capable of providing the power consumption monitoring data required by the power consumption monitoring method provided by the present invention, as for the specific manner of providing the data, the data may be collected by the power supply monitoring system 120 on line, recorded by using intelligence, and collected by a manual collection manner or a wireless uploading manner, or a combination of the two manners, which is not specifically limited in the embodiment of the present invention.
In the embodiment of the present invention, the power supply monitoring system 120 is mainly responsible for operation control and online monitoring of a power supply grid, and the power consumption monitoring method provided in the embodiment of the present invention operates in the system, may operate as a module in the system to perform online monitoring on the power consumption behavior of a user, and certainly may also operate in an independent system to perform offline analysis and processing on the provided data, which is a specific implementation manner that may be selected, and the embodiment of the present invention is not specifically limited to this.
The power utilization monitoring method provided by the embodiment of the invention can be adapted to the existing power utilization system, and analysis processing is carried out on the basis of the existing power utilization monitoring data so as to determine whether the user does not standardize power utilization.
As shown in fig. 2, in an embodiment, an electricity consumption monitoring method is provided, and this embodiment is mainly exemplified by applying the method to the power supply monitoring system 120 in fig. 1. Specifically, the method may include steps S202 to S208:
step S202, power utilization monitoring data are obtained, and the power utilization monitoring data comprise service data and operation data.
In embodiments of the present invention, the electrical monitoring data includes, but is not limited to, index data, meter data, event data, profile data, and historical electricity usage data in an electrical collection system, a marketing business system. For the data acquired by the meter, in order to ensure the integrity of the data, all data recorded by the electric energy meter during installation and operation can be extracted, and the data specifically comprises a daily frozen electric energy indicating value of a meter reading date of a measuring point, daily measuring point power factor, daily measuring point voltage, daily measuring point current and the like; events in the event data can comprise voltage loss events of the electric energy meter, current loss events of the electric energy meter, meter cover opening events of the electric energy meter, overvoltage events of the electric energy meter, overcurrent events of the electric energy meter and the like; the archive data mainly comprises electric energy meter archives, electric energy meter user archives, metering point archives and the like. In addition, a plurality of non-standard electric case data may be included, which is an optional specific implementation manner, and this is not specifically limited by the embodiment of the present invention.
Step S204, screening the service data according to a preset rule, and determining a first probability according to the screened service data.
In the embodiment of the present invention, the screening is mainly performed through two dimensions of a platform area and a user, and the preset rule may specifically be any one or more of the following forms:
(1) the line loss rate of the line where the user is located is more than 10%, or the line loss rate is close to a cycle fluctuation coefficient is more than 3, or the three-phase unbalance degree daily average value is more than 15%;
(2) the user change relationship of the line where the user is located is correct;
(3) the acquisition coverage rate of the line where the user is located is 100 percent;
(4) the acquisition success rate of the line where the user is located is 100%.
The electricity usage monitoring data of the users satisfying one or more of the above-indicated conditions may be used as the traffic data for determining the first probability. The screening is mainly to eliminate users with low irregular electricity utilization possibility so as to save computing resources.
Step S206, preprocessing the operation data, processing the preprocessed operation data by using a preset recognition model and outputting a second probability.
In the embodiment of the invention, the data processed by the identification module needs to be preprocessed, the preprocessed data can be adapted to the requirements of the model, and part of users with low possibility of irregular power utilization can be removed. It is to be understood that in the embodiment of the present invention, there may be some duplication of the data used for model identification with the traffic data described in the previous step, i.e., for some electricity usage monitoring data, it is used as both traffic data and processing data for identifying the model, but preferably, the non-duplication of the traffic data with the data used for model identification may reduce the duplicate processing of the data to provide operation speed and efficiency.
And step S208, determining the probability of the power utilization for the non-specification according to the first probability and the second probability.
In the embodiment of the invention, the power utilization monitoring data are divided and processed by different methods to obtain the first probability and the second probability, and the probability of unnormalized power utilization is finally determined according to the first probability and the second probability, so that the power utilization monitoring data of a user are analyzed and processed by a multi-dimensional multi-rule, the judgment accuracy can be effectively improved, and the occurrence of misjudgment and misjudgment can be reduced.
According to the power consumption monitoring method provided by the embodiment of the invention, the power consumption monitoring data are divided, different processing modes are adopted for different data to determine the first probability and the second probability, and the first probability and the second probability finally determine the non-standard power consumption probability, so that whether a user does not use power in a standard manner can be more accurately judged, and the degree of automation is high. The method can be adapted to the existing power utilization system, and analysis processing is carried out on the basis of the existing power utilization monitoring data so as to determine whether the user is not in normal power utilization.
In an embodiment, as shown in fig. 3, the step of determining the first probability according to the filtered service data in step S204 may specifically include steps S302 to S306:
in step S302, an evaluation index is determined.
In the embodiment of the invention, the evaluation indexes are preferably historical electric quantity mutation, electric quantity and line loss rate correlation, uncapping and electric quantity mutation, undervoltage and electric quantity mutation, current loss and electric quantity mutation, electric quantity and line loss rate fluctuation, same-industry same-capacity same-season deviation and surface sampling power and calculation power deviation.
And step S304, determining the weight of each evaluation index by using an analytic hierarchy process.
In the embodiment of the invention, the analytic hierarchy process refers to a systematic method which takes a complex multi-objective decision problem as a system, decomposes a target into a plurality of targets or criteria, further decomposes the targets into a plurality of layers of multi-index (or criteria, constraint), calculates the single-layer ordering (weight) and the total ordering by a qualitative index fuzzy quantization method, and takes the single-layer ordering and the total ordering as the target (multi-index) and multi-scheme optimization decision, and is called as an analytic hierarchy process. The analytic hierarchy process includes decomposing the decision problem into different hierarchical structures according to the sequence of the total target, sub targets of each layer, evaluation criteria and specific spare power switching scheme, solving and judging matrix characteristic vector to obtain the priority weight of each element of each layer to one element of the previous layer, and finally conducting hierarchical weighted sum to merge the final weight of each spare power switching scheme to the total target, wherein the maximum weight is the optimal scheme. The term "priority weight" as used herein is a relative measure indicating the relative measure of the superiority of each alternative under the evaluation criteria or sub-objectives of a particular feature, and the relative measure of the importance of each sub-objective to the target of the previous layer. The analytic hierarchy process is suitable for the target system with hierarchical and staggered evaluation indexes, and the target value is difficult to describe quantitatively. The usage is to construct a judgment matrix, find the maximum eigenvalue and the corresponding eigenvector W, and after normalization, obtain the relative importance weight of a certain level index to a certain related index of the previous level. The analytic hierarchy process is characterized in that on the basis of deep analysis of the essence, influence factors, internal relations and the like of a complex decision problem, the thinking process of decision is made to be mathematical by using less quantitative information, so that a simple decision method is provided for the complex decision problem with multiple targets, multiple criteria or no structural characteristics, and the analytic hierarchy process is particularly suitable for occasions where the decision result is difficult to directly and accurately measure.
In the embodiment of the invention, the construction steps of the analytic hierarchy process mainly comprise:
(1) establishing a hierarchical model;
(2) constructing a pair comparison matrix;
(3) calculating the weight vector of the single rank ordering and the maximum eigenvector of the pair-wise comparison matrix A for consistency check;
(4) and calculating the total ranking weight of the layers and checking the consistency.
The problems of the analytic hierarchy process include:
(1) the establishment of the comparison matrix depends on the numerical ratio of pairwise comparison of each index, and is embodied by 1-9 levels of scales, and the establishment of the judgment matrix is reasonable as follows:
B1:B1＝1:1；B1:B2＝1:5；B1:B3＝1:3
B2:B1＝5:1；B2:B2＝1:1；B2:B3＝3:1
B3:B1＝3:1；B3:B2＝1:3；B3:B3＝1:1
to facilitate the mathematical process, we usually write the results in a matrix form, called a pairwise comparison matrix.
(2) And (4) checking consistency, wherein whether the weight is reasonable or not is checked according to the consistency of each layer, and generally, CR is considered to be less than 0.1.
In the embodiment of the present invention, an algorithm specifically adopted for each index is shown in table 1:
table 1: each indication corresponds to a specific algorithm and a weight thereof
Step S306, determining the first probability according to the screened business data, the evaluation index and the corresponding weight.
In an embodiment of the present invention, the first probability is equal to a sum of products of the above indexes and their corresponding weights, that is: the first probability is compared with historical electric quantity, corresponding weight + electric quantity and line loss rate correlation, uncapping and electric quantity mutation, corresponding weight + undervoltage and electric quantity mutation, corresponding weight + current loss and electric quantity mutation, corresponding weight + electric quantity and line loss rate fluctuation, same-industry same-capacity same-season deviation, corresponding weight + table collected power and calculated power deviation.
In an embodiment, as shown in fig. 4, the step of preprocessing the operation data in step S206 may specifically include steps S402 to S406:
step S402, cleaning the operation data to remove abnormal values.
In the embodiment of the invention, abnormal values in data are removed, such as data of electric meter reading flying away and backward away in electric quantity data, abnormal data of files in archive data, abnormal data marked in event data and the like; deleting repeated data in the data, such as data reported repeatedly by the same event in the event data, user data which is not collected in more than 7 days in the near future, and the like; and (4) completing missing data, such as electric energy indicating value data which is not collected.
And S404, standardizing the cleaned operation data.
In the embodiment of the invention, the numerical data in the data is normalized to carry out data standardization processing, such as power consumption data, user voltage and current data.
Step S406, converting the normalized operation data.
In the embodiment of the invention, the numerical data in the data is normalized to carry out data standardization processing, such as power consumption data, user voltage and current data.
In the embodiment of the invention, in addition, the data can be subjected to characteristic processing by combining data distribution and business process. If the standard deviation of the user electric quantity data is calculated, the user electric volatility is measured; the power consumption abnormal coefficient is used for measuring the fluctuation degree of the power consumption of the user near the average value; the number of times of abnormal events of the user, and the like. This is an optional specific implementation, and the embodiment of the present invention is not limited to this specifically.
In an embodiment, as shown in fig. 5, the step of processing the preprocessed operation data by using the preset recognition model and outputting the second probability in step S206 may specifically include steps S502 to S508:
step S502, processing the operation data by using a random forest algorithm model to output a first result.
In the embodiment of the invention, the random forest uses a CART (Classification and regression trees) decision tree as a weak learner, and the establishment of the decision tree is improved. For a common decision tree, an algorithm selects an optimal feature from all sample features on nodes to be used as a basis for dividing sub-trees of the decision tree, but a random forest randomly selects a part of sample features on the nodes and then selects an optimal feature from the randomly selected part of sample features to be used for dividing left and right sub-trees of the decision tree. Due to the uncertainty of feature selection, the generalization capability of the model is further improved.
In the embodiment of the invention, the algorithm calculation flow is as follows:
the model input is sample set D { (x)1，y1)，(x2，y2)，...(xm，ym) And f, iteration times T of the weak classifier. The model output is the final strong classifier f (x).
(1) Weak classifier iteration (T ═ 1,2.., T):
1) random sampling is carried out on the training set for the t time, and m times are collected in total to obtain a sampling set D containing m samplest；
2) Using a sample set DtTraining the t-th decision tree model G (x), when training the nodes of the decision tree model, firstly, selecting one part from all sample characteristics on the nodesDividing sample characteristics, and then selecting an optimal characteristic from the randomly selected partial sample characteristics to divide left and right subtrees of the decision tree;
(2) for classification algorithm prediction, the category with the largest number of votes cast by the T weak learners is taken as the final category. For the regression algorithm, the value obtained by performing arithmetic mean on the regression results obtained by the T weak learners is used as the final model output.
Step S504, the XGboost algorithm model is utilized to process the operation data so as to output a second result.
In the embodiment of the invention, the XGboost algorithm is an algorithm with high computational efficiency and robustness. XGboost supports many other weak learners in addition to decision trees in the selection of weak learner models for the algorithm. In addition to the loss itself, a regularization component is added to the loss function of the algorithm. In the optimization mode of the algorithm, the XGboost loss function performs second-order Taylor expansion on the error part, and the method is more accurate. And (4) performing parallel selection on the process established by each weak learner, and sequencing and grouping the values of all the features before parallel selection. And selecting an appropriate packet size for the characteristics of the packets, and using a CPU (central processing unit) cache for reading acceleration. Individual packets are saved to multiple hard disks to increase IO speed. For the missing value feature, the processing mode of the missing value is determined by enumerating whether all the missing values enter the left sub-tree or the right sub-tree at the current node. The algorithm adds the regularization terms of L1 and L2, so that overfitting can be prevented, and the generalization capability is stronger.
Step S506, the LightGBM algorithm model is used to process the operating data to output a third result.
In an embodiment of the invention the LightGBM algorithm is the classical Boosting series algorithm. Compared with the XGboost algorithm, the computing logic of the XGboost algorithm and the XGboost algorithm is similar, and the main difference is that the LightGBM is better in optimizing communication processing. The LightGBM directly supports the class characteristics, and the class characteristics do not need to be subjected to one-hot coding processing, so that the algorithm efficiency is improved. LightGBM supports data parallelism and feature parallelism simultaneously. When multiple evaluation indexes are used for simultaneous evaluation, the two strategies are different in early stopping, XGboost is used as a stopping standard according to the last evaluation index in the evaluation index list, and LightGBM is influenced by all the evaluation indexes.
Step S508, determining the second probability according to the first result, the second result, the third result, and the respective corresponding preset weights.
In the embodiment of the invention, the constructed identification model selects the historical data of the power utilization sample in the station area of the whole year as data input, wherein the total number of suspected samples is 2994, and the sample data is obtained according to the following steps of 7: and 3, dividing a data training set and a test set. Considering the problem of unbalance of positive and negative samples, the classifier threshold in the algorithm is changed to the actual ratio of the negative samples to the positive samples in the data. The accuracy of a training set in the model is improved by continuously training and parameter adjustment optimization of the algorithm model.
The machine learning electricity utilization identification model is a two-classification prediction task, and the accuracy of the model is mainly evaluated through the accuracy and the recall rate. In power usage forecasting scenarios, a high accuracy rate is required. The calculation formula of the accuracy rate and the recall rate is as follows:
the precision ratio is as follows: p is TP/(TP + FP)
The recall ratio is as follows: r is TP/(TP + FN)
Through model training and parameter tuning, the data of the random forest, the XGboost and the LightGBM on the test set are represented as follows:
(1) random forest model
(2) XGboost model
(3) LightGBM model
Model accuracy versus recall is as follows:
| model name | Rate of accuracy | Recall rate |
| Random forest | 81.62 | 69.87％ |
| XGBoost | 84.94 | 72.01％ |
| LightGBM | 82.97 | 71.14％ |
In the embodiment of the present invention, the second probability ═ random forest model output ═ weight 1+ XGBoost model output × > weight 2+ LightGBM model output × > weight 3.
In one embodiment, as shown in fig. 6, the step of determining the irregular power utilization probability according to the first probability and the second probability in step S208 may specifically include steps S602 to S604:
step S602, determining weights corresponding to the first probability and the second probability.
In the embodiment of the present invention, the weights corresponding to the first probability and the second probability may be empirical values, or may be determined by other algorithms, which is a specific implementation manner that may be selected, and the weights corresponding to the two probabilities in the embodiment of the present invention both take 0.5.
Step S604, determining the irregular power utilization probability according to the first probability, the second probability and the corresponding weight.
In the embodiment of the present invention, the electrical utilization probability is not normalized, i.e., the first probability is 0.5+ the second probability is 0.5. The irregular electricity usage may be further classified as extremely high (90% -100%), high (70% -90% contains 90%), general (50% -70% contains 70%), and low (0-50% contains 50%) according to the magnitude of the irregular electricity usage probability. As an optional processing mode, the risk early warning can be carried out on users with the type of the non-standard electricity utilization probability being extremely high every day and users with the non-standard electricity utilization probability being more than 60% for 4 continuous days.
According to the power consumption monitoring method provided by the embodiment of the invention, the power consumption monitoring data are divided, different processing modes are adopted for different data to determine the first probability and the second probability, and the first probability and the second probability finally determine the non-standard power consumption probability, so that whether a user does not use power in a standard manner can be more accurately judged, and the degree of automation is high. The method can be adapted to the existing power utilization system, and analysis processing is carried out on the basis of the existing power utilization monitoring data so as to determine whether the user is not in normal power utilization.
As shown in fig. 7, in an embodiment, there is provided an electricity consumption monitoring device, which may be integrated in the power supply monitoring system 120, and specifically may include:
the acquiring module 701 is configured to acquire power consumption monitoring data, where the power consumption monitoring data includes service data and operation data;
a first probability determining module 702, configured to filter the service data according to a preset rule, and determine a first probability according to the filtered service data;
a second probability determining module 703, configured to pre-process the operation data, process the pre-processed operation data by using a preset identification model, and output a second probability;
an irregular electricity utilization probability determining module 704 configured to determine an irregular electricity utilization probability according to the first probability and the second probability.
In the embodiment of the present invention, please refer to the content described in any one or combination of one or more of the foregoing embodiments for explaining specific steps executed by each module, which is not described herein again.
According to the power consumption monitoring device provided by the embodiment of the invention, the power consumption monitoring data are divided, different processing modes are adopted for different data to determine the first probability and the second probability, and the first probability and the second probability finally determine the non-standard power consumption probability, so that whether a user does not use power in a standard manner can be more accurately judged, and the degree of automation is high. The method can be adapted to the existing power utilization system, and analysis processing is carried out on the basis of the existing power utilization monitoring data so as to determine whether the user is not in normal power utilization.
FIG. 8 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may specifically be the power supply monitoring system 120 in fig. 1. As shown in fig. 8, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system, and may also store a computer program, and when the computer program is executed by the processor, the computer program may enable the processor to implement the power consumption monitoring method provided by the embodiment of the present invention. The internal memory may also store a computer program, and when the computer program is executed by the processor, the computer program may enable the processor to execute the power consumption monitoring method provided by the embodiment of the present invention. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the electricity monitoring apparatus provided herein may be implemented in the form of a computer program that is executable on a computer device such as that shown in fig. 8. The memory of the computer device may store various program modules constituting the electricity usage monitoring apparatus, such as an acquisition module 701, a first probability determination module 702, a second probability determination module 703, and an irregular electricity usage probability determination module 704 shown in fig. 7. The program modules constitute computer programs that cause the processor to execute the steps of the electricity usage monitoring method of the various embodiments of the present application described in the present specification.
For example, the computer device shown in fig. 8 may execute step S202 through the obtaining module 701 in the electricity consumption monitoring apparatus shown in fig. 7; the computer device may perform step S204 by the first probability determination module 702; the computer device may perform step S206 by the second probability determination module 703; the computer device may perform step S208 by the irregular power usage probability determination module 704.
In one embodiment, a computer device is proposed, the computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring power consumption monitoring data, wherein the power consumption monitoring data comprises service data and operation data;
screening the service data according to a preset rule, and determining a first probability according to the screened service data;
preprocessing the operation data, processing the preprocessed operation data by using a preset recognition model and outputting a second probability;
determining a probability for irregular power usage based on the first probability and the second probability.
In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the steps of: .
Acquiring power consumption monitoring data, wherein the power consumption monitoring data comprises service data and operation data;
screening the service data according to a preset rule, and determining a first probability according to the screened service data;
preprocessing the operation data, processing the preprocessed operation data by using a preset recognition model and outputting a second probability;
determining a probability for irregular power usage based on the first probability and the second probability.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A power consumption monitoring method, characterized in that the power consumption monitoring method comprises:
acquiring power consumption monitoring data, wherein the power consumption monitoring data comprises service data and operation data;
screening the service data according to a preset rule, and determining a first probability according to the screened service data;
preprocessing the operation data, processing the preprocessed operation data by using a preset recognition model and outputting a second probability;
determining a probability for irregular power usage based on the first probability and the second probability.
2. The electricity monitoring method according to claim 1, wherein the electricity monitoring data comprises electricity collection system, index data in marketing business system, meter collection data, event data, profile data, and historical electricity data.
3. The electricity consumption monitoring method according to claim 1, wherein the determining a first probability from the screened traffic data comprises:
determining an evaluation index;
determining the weight of each evaluation index by using an analytic hierarchy process;
and determining the first probability according to the screened business data, the evaluation index and the corresponding weight thereof.
4. The electricity consumption monitoring method according to claim 1, wherein the pre-processing the operational data comprises the steps of:
cleaning the operation data to remove abnormal values;
standardizing the cleaned operation data;
and converting the normalized operation data.
5. The electricity consumption monitoring method according to claim 1, wherein the recognition model comprises a random forest algorithm model, an XGBoost algorithm model, and a LightGBM algorithm model.
6. The electricity consumption monitoring method according to claim 1, wherein the processing the pre-processed operation data by using the preset recognition model and outputting the second probability comprises the following steps:
processing the operating data by using a random forest algorithm model to output a first result;
processing the operating data by utilizing an XGboost algorithm model to output a second result;
processing the operational data with a LightGBM algorithm model to output a third result;
and determining the second probability according to the first result, the second result, the third result and the respective corresponding preset weights.
7. The electricity consumption monitoring method according to claim 1, wherein said determining a probability for out-of-specification electricity consumption from the first probability and the second probability comprises the steps of:
determining weights corresponding to the first probability and the second probability;
and determining the irregular power utilization probability according to the first probability, the second probability and the respective corresponding weight.
8. An electricity monitoring device, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring power consumption monitoring data which comprises service data and operation data;
the first probability determination module is used for screening the service data according to a preset rule and determining a first probability according to the screened service data;
the second probability determination module is used for preprocessing the operation data, processing the preprocessed operation data by using a preset recognition model and outputting a second probability;
an irregular power utilization probability determination module for determining a probability for irregular power utilization based on the first probability and the second probability.
9. A computer arrangement, characterized by comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to carry out the steps of the electricity monitoring method according to any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, causes the processor to carry out the steps of the electricity usage monitoring method according to any one of claims 1 to 7.
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