CN118966813B

CN118966813B - A method, device, electronic device and storage medium for automatically locating high-loss transmission area

Info

Publication number: CN118966813B
Application number: CN202410890252.XA
Authority: CN
Inventors: 孙浩; 张冰; 王凡; 胡宽; 姜明杰; 董玉锡; 王新涛; 任鲁飞; 许崇杰; 李岩; 来勇; 李瑞青
Original assignee: Rizhao Power Supply Co of State Grid Shandong Electric Power Co Ltd
Current assignee: Rizhao Power Supply Co of State Grid Shandong Electric Power Co Ltd
Priority date: 2024-07-04
Filing date: 2024-07-04
Publication date: 2025-08-22
Anticipated expiration: 2044-07-04
Also published as: CN118966813A

Abstract

The present invention relates to the field of data processing, and provides a method, device, electronic device and storage medium for automatic positioning of high-loss areas in a substation. This application facilitates targeted processing of electricity consumption data by acquiring real-time electricity consumption data of each area in the substation and topological structure information of the substation, and determines high-loss areas in the substation based on correlation relationships between real-time electricity consumption data and topological structure information of each area, thereby solving the problem that the line loss rate in the substation is high but the high-loss area cannot be quickly located, and improving the working efficiency of the system; by acquiring relevant data of the substation and analyzing the relevant data to determine the target factors corresponding to the high-loss area, analyzing the causes of abnormal line loss rate, and avoiding affecting the inspection and optimization efficiency of the substation; by analyzing the correlation between the target factors to determine the target optimization strategy of the high-loss area, and optimizing the high-loss area according to the target optimization strategy, the optimization efficiency of the substation can be improved.

Description

Automatic positioning method and device for high loss of transformer area, electronic equipment and storage medium

Technical Field

The invention relates to the field of data processing, in particular to a method and a device for automatically positioning high loss of a transformer area, electronic equipment and a storage medium.

Background

In order to master the electricity consumption situation of users and make a proper management strategy, it is important for the power grid company to acquire the real-time electricity consumption data of the users in the transformer area. Under the normal condition, because the number of the users in the area is extremely large, the area terminal communicates with the master station front-end processor through the wireless public network, the data acquisition efficiency is slower, the integrity of the acquired data is difficult to ensure, and the processing difficulty is greatly increased if the acquired data is processed integrally.

The existing transformer area also has the problems that the line loss rate is high, but the high-loss area cannot be positioned quickly, and the working efficiency of the system is affected. In addition, the current area topology identification mainly depends on manual identification, so that the problems of low identification efficiency and easiness in error are solved, and meanwhile, the high-loss area positioning accuracy is low due to the fact that an effective method is lacked in analyzing causes of abnormality.

Disclosure of Invention

In view of the above, the embodiments of the present invention provide a method, an apparatus, an electronic device, and a storage medium for automatically locating a high-loss area, so as to solve the problems of low identification efficiency and easy error occurrence existing in the current method, and meanwhile, the problem of low accuracy in locating the high-loss area caused by the lack of an effective method for analyzing the cause of the abnormality.

In a first aspect, an embodiment of the present invention provides a method for automatically positioning a high loss of a station, where the method includes:

acquiring real-time electricity utilization data of each area in a platform area and topology structure information of the platform area;

based on the real-time electricity utilization data of each area and the topological structure information, carrying out association relation to determine a high-loss area in the platform area;

acquiring related data of the station area, and analyzing the related data to determine target factors corresponding to the high-loss area;

and analyzing the association relation between the target factors to determine a target optimization strategy of the high-loss area, and optimizing the high-loss area according to the target optimization strategy.

Further, the determining the high-loss area in the platform area based on the association relation between the real-time electricity consumption data of each area and the topology structure information includes:

And classifying each region by utilizing the real-time electricity utilization data to obtain category information corresponding to the region.

Constructing association relations between category information corresponding to each region and the topological structure information;

Searching target category information with occurrence times larger than preset times based on the association relation;

And calculating the electric energy loss corresponding to the target class information, and determining the area corresponding to the target class information as a high-loss area if the electric energy loss is larger than a preset loss.

Further, the clustering according to the real-time electricity consumption data of each region to obtain a clustering result, and determining the category information corresponding to each region based on the clustering result includes:

clustering is carried out according to the real-time electricity utilization data of each area, and a clustering result is obtained;

And determining category information corresponding to each region based on the clustering result.

Further, after determining the high-loss area in the transformer area based on the association relation between the real-time electricity consumption data of each area and the topology structure information, the method further includes:

obtaining a geographic position corresponding to the high-loss area and first loss data;

importing the geographic position corresponding to the high-loss area and the first loss data into a station area loss map, and determining a rendering style corresponding to the high-loss area by utilizing the first loss data;

And rendering the high-loss area in the area loss map according to the rendering style to obtain a rendered area loss map, and displaying the rendered area loss map.

Further, the obtaining the related data of the area, and analyzing the related data to determine the target factor corresponding to the high-loss area includes:

Acquiring preset indexes associated with a platform region, and acquiring related data corresponding to each preset index, wherein the related data comprises operation environment data, equipment state data and user type data;

Analyzing the influence degree of the related data corresponding to each preset index on the high-loss area;

And taking the preset index with the influence degree larger than the preset influence degree as the target factor.

Further, the analyzing the association relationship between the target factors determines a target optimization strategy of the high-loss region, including:

Analyzing the association degree among the target factors according to a preset association analysis method;

Constructing a candidate factor group by utilizing the two target factors with the association degree larger than a first threshold;

Determining a first factor group and a second factor group based on the candidate factor group, wherein the degree of association between two target factors in the first factor group is greater than the first threshold and smaller than a second threshold, and the degree of association between two target factors in the second factor group is greater than the second threshold;

Acquiring a first optimization strategy corresponding to the first factor group and a second optimization strategy corresponding to the second factor group;

calculating a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy;

And taking the first optimization strategy with the first evaluation value higher than a preset evaluation value and the second optimization strategy with the second evaluation value higher than the preset evaluation value as the target optimization strategy.

Further, after optimizing the high-loss region according to the target optimization strategy, the method further includes:

Detecting second loss data of the high loss region;

comparing the second loss data with the historical loss data of the high-loss area to obtain a comparison result;

And updating the target optimization strategy according to the comparison result.

In a second aspect, an embodiment of the present invention provides an automatic positioning device for high loss in a station, where the device includes:

The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring real-time electricity utilization data of each area in a platform area and topology structure information of the platform area;

the first analysis module is used for determining a high-loss area in the platform area based on the real-time electricity utilization data of each area and the topological structure information in an association relation;

the second analysis module is used for acquiring the related data of the station area and analyzing the related data to determine target factors corresponding to the high-loss area;

the processing module is used for analyzing the association relation between the target factors to determine a target optimization strategy of the high-loss area, and optimizing the high-loss area according to the target optimization strategy.

In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory and the processor are communicatively connected to each other, and the memory stores computer instructions, and the processor executes the computer instructions, thereby executing the method of the first aspect or any implementation manner corresponding to the first aspect.

In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of the first aspect or any of its corresponding embodiments.

The method and the system are convenient for processing the power consumption data in pertinence by acquiring the real-time power consumption data of each area in the platform area and the topological structure information of the platform area, determining the high-loss area in the platform area based on the real-time power consumption data of each area and the topological structure information in an association relation, solving the problem that the high-loss area cannot be positioned quickly due to high line loss rate of the platform area, improving the working efficiency of the system, determining the target factors corresponding to the high-loss area by acquiring the related data of the platform area and analyzing the related data, analyzing the reasons causing the abnormal line loss rate, avoiding influencing the checking and optimizing efficiency of the platform area, determining the target optimizing strategy of the high-loss area by analyzing the association relation between the target factors, optimizing the high-loss area according to the target optimizing strategy, and improving the optimizing efficiency of the platform area.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for automatically locating high loss in a cell according to some embodiments of the present invention;

FIG. 2 is a flow chart of a method for automatically locating high loss in a region according to other embodiments of the present invention;

FIG. 3 is a flow chart of a method for automatically locating high loss in a region according to still further embodiments of the present invention;

FIG. 4 is a block diagram of a high loss automatic positioning device for a transformer area according to an embodiment of the present invention;

Fig. 5 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

According to embodiments of the present invention, there is provided a method, apparatus, electronic device, and storage medium for high-loss automatic positioning of a region, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different from that illustrated herein.

In this embodiment, a method for automatically positioning high loss of a station is provided, and fig. 1 is a flowchart of a method for automatically positioning high loss of a station according to an embodiment of the present invention, as shown in fig. 1, where the flowchart includes the following steps:

And S11, acquiring real-time electricity utilization data of each area in the platform area and topology structure information of the platform area.

In the embodiment of the application, a transformer area refers to a power supply range of a transformer in a power system, and is generally composed of one or more transformers and auxiliary equipment thereof. And electric meters and related sensors are installed in each area of the platform area and are used for measuring real-time electricity utilization data. The ammeter can record information such as electricity consumption, power, and the sensor can monitor parameters such as current, voltage. The meter and sensor are connected to a data concentrator or gateway. The real-time electricity data is transmitted to the server through a wired or wireless communication mode. Such as ethernet, wireless communication (e.g., wi-Fi, loRaWAN, etc.), or Power Line Communication (PLC). And the stability and the safety of data transmission are ensured. Real-time electricity data is received at the server. The data may be organized and stored using a database management system and processed and analyzed using data analysis and processing tools. In addition, topology information of the transformer areas can be obtained through field investigation, wiring drawings or other relevant documents. Topology information can be combined with real-time electricity usage data for deeper analysis and management.

And step S12, carrying out association relation based on the real-time electricity utilization data and the topological structure information of each area, and determining the high-loss area in the platform area.

In the embodiment of the application, the high-loss area in the platform area is determined based on the association relation between the real-time electricity consumption data of each area and the topological structure information, as shown in fig. 2, and the method comprises the following steps of A1-A4:

and A1, classifying each region by using the real-time electricity utilization data to obtain category information corresponding to the region.

The method comprises the steps of clustering according to the real-time electricity utilization data of each area to obtain a clustering result, and determining category information corresponding to each area based on the clustering result.

The real-time electricity consumption data comprises information such as electricity consumption, electricity consumption time, electricity consumption peak value and the like. And processing the data by using a clustering algorithm, and gathering similar areas together to form different clustering results. The clustering algorithm may determine the manner and number of clusters based on the characteristics of the data and the similarity measure. After the clustering result is obtained, each cluster needs to be analyzed and interpreted to determine the category information corresponding to each area. For example, this can be done by observing the characteristics of the clusters, comparing the differences between different clusters, and combining business knowledge and experience. The category information may be defined according to factors such as electricity consumption mode, electricity consumption behavior, electricity consumption requirement, etc., for example, residential electricity consumption region, commercial electricity consumption region, industrial electricity consumption region, etc.

For example, the real-time electricity data is clustered by adopting a K-Means algorithm. First, the number of clusters (K value is selected) needs to be determined, and the data can be clustered into three categories, namely a peak electricity utilization area, a valley electricity utilization area and a stable electricity utilization area. Representative data points including peaks, valleys and plateau regions are selected as initial centroids. And according to Euclidean distance or other similarity measurement between the sample and the centroid, the sample is distributed into the categories to which the nearest centroid belongs, and the centroid is updated for each category. Whether convergence is achieved is determined by determining whether the centroid changes or whether the cluster error is less than a threshold. And analyzing the clustering result, and marking the real-time electricity consumption data corresponding to each clustering area as a peak electricity consumption area, a valley electricity consumption area or a stable electricity consumption area.

It should be noted that, the clustering result is that the regions with similar real-time electricity data are divided into the same group, and different groups have higher difference. Each group represents a kind of information, and can be judged and named according to the characteristics of the data in the group, such as peak electricity utilization area, valley electricity utilization area, stable electricity utilization area and the like. Taking electricity data of a city as an example, the clustering result may divide the city into different categories such as business, industrial and residential areas.

Through the two steps, each region can be classified according to the similarity of the real-time electricity utilization data, and the category information of each region is determined. Such analysis may help the power sector to better understand the power usage characteristics and needs of the different areas, thereby optimizing power supply and management strategies.

And A2, constructing an association relationship between category information and topology structure information corresponding to each region.

Specifically, an association relationship between category information and topology structure information corresponding to each region is constructed. The category information may include functions of the area, usage patterns, types of electrical devices, etc., and the topology information may include locations of the area in the power network, connection relationships, etc. By establishing the association relationship, the electricity utilization characteristics and the electricity flow condition of each area can be better known.

And A3, searching target category information with occurrence times larger than preset times based on the association relation.

Specifically, based on the established association relationship, target category information with the occurrence number greater than the preset number is searched. The description finds out category information that frequently occurs under specific conditions. Such frequently occurring category information may be associated with high losses because they represent some common power usage patterns or device types that may result in more power loss. For example, if a certain type of powered device is frequently used in a certain area and its number of occurrences is greater than a preset number of times compared to other areas, the device type may be regarded as target class information.

And A4, calculating the electric energy loss corresponding to the target class information, and determining the area corresponding to the target class information as a high-loss area if the electric energy loss is larger than the preset loss.

Specifically, for the determined target class information, the corresponding power consumption amount is calculated. The amount of power loss may be obtained by analyzing operational data of the power system, using power metering equipment, and the like. And if the electric energy loss corresponding to the target class information is larger than the preset loss, determining the corresponding area as a high-loss area.

According to the embodiment of the application, through analyzing the real-time electricity utilization data, each region is classified, and the association relation between the category information and the topological structure information is established. Therefore, the electricity consumption conditions of different areas can be better known, so that the electricity consumption management strategy is optimized, and the electric energy utilization efficiency is improved. And secondly, searching target category information with occurrence times larger than preset times, and calculating the corresponding electric energy loss, so that a high-loss area can be accurately identified.

And S13, acquiring relevant data of the station area, and analyzing the relevant data to determine target factors corresponding to the high-loss area.

In the embodiment of the application, the relevant data of the station area is obtained, and the relevant data is analyzed to determine the target factors corresponding to the high-loss area, as shown in fig. 3, comprising the following steps of B1-B3:

And step B1, acquiring preset indexes associated with the platform region, and acquiring related data corresponding to each preset index, wherein the related data comprise operation environment data, equipment state data and user type data.

Specifically, the preset indexes refer to specific performances or characteristics related to the platform area, and the indexes can be used for evaluating the operation condition, the energy efficiency, the reliability and the like of the platform area. For each preset index, data related to the preset index needs to be acquired for analysis and evaluation. The relevant data may include ① operating environment data regarding the environmental conditions in which the region is located, such as temperature, humidity, altitude, etc., which may affect the performance and reliability of the device. ② And equipment state data, namely running state information of various equipment in the transformer area, such as oil temperature, voltage, current and the like of the transformer, fault information of other equipment, maintenance records and the like. ③ User type data, namely the type and load characteristics of users in the platform region, such as industrial users, commercial users, residential users and the like, and different types of users can have different electricity utilization behaviors and requirements.

By collecting and analyzing the related data, the performance of the station area can be comprehensively evaluated, and corresponding measures are taken to optimize the operation management of the station area, so that the energy efficiency and the power supply reliability are improved. For example, if the voltage of a certain station is found to be unstable, the power quality can be improved by adjusting the tap of the transformer or installing a reactive compensation device, and if the load of a certain station is found to be excessive, measures such as load shifting or upgrading devices can be taken to relieve the load pressure.

And step B2, analyzing the influence degree of the related data corresponding to each preset index on the high-loss area.

Specifically, the influence degree of the related data corresponding to each preset index on the high-loss area is analyzed. In this step, the relevant data of each preset index needs to be deeply analyzed to determine the extent to which they affect the high-loss region. The degree of influence can be measured in a number of ways, such as statistical analysis, data mining, machine learning algorithms, etc.

For example, ① data correlation analysis, namely calculating correlation coefficients between each preset index and the high-loss region. The correlation coefficient may tell the strength of the linear relationship between the index and the high-loss region. A high correlation may mean that the index has a larger impact on the high-loss region. Wherein, when calculating the correlation coefficient between the preset index and the high-loss region, the pearson correlation coefficient may be used. The pearson correlation coefficient is a value between-1 and 1, which is used to measure the strength of the linear relationship between variables. The correlation coefficient is 1 if there is a complete positive correlation between the two variables, -1 if there is a complete negative correlation between the two variables, and 0 if there is no linear relationship between the two variables. If the correlation coefficient is positive, the positive correlation relationship between the preset index and the high-loss area is shown, namely, the probability of the high-loss area is increased along with the increase of the preset index, if the correlation coefficient is negative, the negative correlation relationship between the preset index and the high-loss area is shown, namely, the probability of the high-loss area is reduced along with the increase of the preset index, and if the correlation coefficient is close to 0, the linear relationship between the preset index and the high-loss area is not shown.

② Regression analysis, namely establishing a regression model, and taking each preset index as an independent variable and a high-loss area as a dependent variable. The influence degree of each index on the high-loss area can be determined through regression analysis, and corresponding regression coefficients are obtained. In the model, each preset index is taken as an independent variable, and a high-loss area is taken as the dependent variable. Specifically, first, relevant data needs to be collected, including the numerical value of each preset index and the information of the corresponding high-loss region. These data are then input into a regression model for data analysis and processing. The influence degree of each index on the high-loss region can be determined through regression analysis, and the influence degree can be represented by regression coefficients. The regression coefficient represents the amount of change in the independent variable for each unit of change in the independent variable. If the regression coefficient is positive, it is indicated that there is a positive correlation between the independent variable and the dependent variable, i.e., an increase in the independent variable results in an increase in the dependent variable, and if the regression coefficient is negative, it is indicated that there is a negative correlation between the independent variable and the dependent variable, i.e., an increase in the independent variable results in a decrease in the dependent variable.

And step B3, taking a preset index with the influence degree larger than the preset influence degree as a target factor.

Specifically, since the degree of influence of each preset index on the high-loss region has been determined. In this step, it is necessary to screen out of a plurality of preset indexes, according to a preset influence degree threshold value, those indexes having influence degrees greater than the threshold value. These indicators are considered as target factors having an important influence on the high-loss region. The method for determining the preset influence degree threshold can be selected according to specific situations. Comprehensive consideration can be performed according to experience, business field knowledge, data analysis results and the like. In general, the threshold should be set to distinguish between an index that has a significant impact on the high-loss region and an index that has a smaller impact.

And S14, analyzing the association relation between target factors to determine a target optimization strategy of the high-loss area, and optimizing the high-loss area according to the target optimization strategy.

The method comprises the steps of analyzing the association degree between target factors according to a preset association analysis method, constructing a candidate factor group by using two target factors with association degrees larger than a first threshold, determining a first factor group and a second factor group based on the candidate factor group, wherein the association degree between the two target factors in the first factor group is larger than the first threshold and smaller than the second threshold, the association degree between the two target factors in the second factor group is larger than the second threshold, acquiring a first optimization strategy corresponding to the first factor group and a second optimization strategy corresponding to the second factor group, calculating a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy, and taking the first optimization strategy with the first evaluation value higher than the preset evaluation value and the second optimization strategy with the second evaluation value higher than the preset evaluation value as target optimization strategies.

Specifically, the relevance analysis is a data analysis method for studying the relationship between variables. In this step, each target factor is analyzed using a preset correlation analysis method, such as correlation analysis, linear regression, etc., and the degree of correlation between them is calculated. The degree of association is generally represented by an index such as a correlation coefficient or a determination coefficient. Through the relevance analysis, the interrelationship among the target factors can be known, and a basis is provided for the construction and optimization strategy of the subsequent factor group.

Secondly, on the basis of relevance analysis, two target factors with the relevance degree larger than a first threshold value are selected, and a candidate factor group is constructed. The first threshold is a preset value for screening out target factor pairs with strong relevance. By constructing the candidate factor group, the optimization range can be further reduced, and the optimization efficiency is improved.

Further, among the candidate factor sets, a first factor set and a second factor set are further determined. The degree of association between the two target factors in the first factor group is greater than a first threshold but less than a second threshold, and the degree of association between the two target factors in the second factor group is greater than the second threshold. The second threshold is also a preset value for dividing the association relations of different degrees. By determining the set of factors, the target factors may be divided into different subsets in order to take different optimization strategies for the different subsets.

Next, corresponding optimization strategies are respectively obtained for the first factor group and the second factor group. The optimization strategy can be an optimization algorithm based on a mathematical model or a heuristic method based on experience or expert knowledge. By acquiring the optimization strategy, a basis can be provided for subsequent evaluation value calculation and target optimization strategy determination. And then, calculating a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy. The evaluation value is an index for evaluating the merits of the optimization strategy, and may be an evaluation value of an objective function, an evaluation value of a cost function, or the like. The effect of each optimization strategy can be quantified by calculating the evaluation value, and a basis is provided for determining the target optimization strategy.

And finally, taking a first optimization strategy with the first evaluation value higher than the preset evaluation value and a second optimization strategy with the second evaluation value higher than the preset evaluation value as target optimization strategies. The preset evaluation value is a preset value for judging whether the optimization strategy meets the requirement. By determining the target optimization strategy, an optimal optimization scheme can be screened out, and guidance is provided for practical application.

In the embodiment of the application, after the high-loss area is optimized according to the target optimization strategy, the method further comprises the steps of detecting second loss data of the high-loss area, comparing the second loss data with historical loss data of the high-loss area to obtain a comparison result, and updating the target optimization strategy according to the comparison result.

Specifically, the second loss data is data obtained by monitoring the high-loss region after optimizing the high-loss region according to the target optimization strategy. Including information on energy consumption, material consumption, production efficiency, etc. Detecting the second loss data may be through the use of sensors, monitoring devices, data analysis tools, and the like.

Historical wear data refers to wear records for areas of high wear over a period of time. Such data may provide information about wear patterns and trends, helping to identify if there is an opportunity for anomalies or improvement. The second loss data is compared to the historical loss data to determine if there is a significant difference or change.

The comparison may include several aspects, the tendency of loss to increase or decrease:

if the second loss data is above or below the historical average, the direction and magnitude of change in loss may be evaluated.

Loss fluctuation, namely comparing the fluctuation condition of the second loss data and the historical data to know the stability of loss.

Outliers-detecting if there are outliers that differ significantly from the historical data, these outliers may need further investigation and resolution.

Based on the comparison results, the target optimization strategy can be adjusted and updated. The target optimization strategy is a specific measure and action plan formulated to reduce the loss in the high-loss area. ① Specific improvements are determined for trends in loss increase or decrease. Including, for example, equipment maintenance, parameter adjustment, operational improvement, etc. ② For the case of large loss fluctuations, measures are considered to stabilize the operation of the plant, such as optimizing control algorithms, enhancing monitoring or adding backup equipment. ③ And (5) deeply analyzing the abnormal value, determining the root cause, and taking measures to solve the problem. Meanwhile, an abnormality monitoring and alarming mechanism is established so as to discover and process similar conditions in time.

Based on the comparison result and the updated strategy, new targets and indexes are set to monitor the implementation effect of the optimization strategy. And (3) periodically evaluating the execution condition of the optimization strategy, and carrying out necessary adjustment and improvement according to the actual effect.

In the embodiment of the application, after the association relation is carried out based on the real-time electricity consumption data and the topological structure information of each area, the method further comprises the steps of obtaining the geographic position corresponding to the high-loss area and the first loss data, importing the geographic position corresponding to the high-loss area and the first loss data into a platform area loss map, determining the rendering pattern corresponding to the high-loss area by utilizing the first loss data, rendering the high-loss area in the platform area loss map according to the rendering pattern, obtaining the rendered platform area loss map, and displaying the rendered platform area loss map.

Specifically, first, geographical location information of a high-loss area, such as longitude and latitude or an address, needs to be determined. At the same time, it is also necessary to obtain first loss data, possibly a value or an indicator of some energy loss, associated with the area. And importing the obtained geographic position and loss data into a station area loss map. This may be accomplished by associating or overlaying the data with a map. And then determining the rendering style of the high-loss region on the map according to the value or the range of the first loss data. For example, colors, icons, or other visualizations may be used to represent different degrees of wear. And rendering the high-loss area on the map according to the determined rendering style. This will cause the high-loss areas to be displayed in a specific manner on the map for easier identification and analysis by the user. And finally, displaying the rendered area loss map to a user. The user can intuitively know the position and loss condition of the high-loss area by viewing the map.

For example, there is loss data for a power station including the geographic location and loss of each area. It is desirable to make a zone wear map to highlight areas of higher wear. First, longitude and latitude coordinates and corresponding loss data of all high-loss areas are obtained. These data are then imported into the zone loss map. Next, a rendering style is determined according to the magnitude of the loss amount, for example, the loss amount is divided into three levels, a low loss area is indicated by green, a middle loss area is indicated by yellow, and a high loss area is indicated by red. And finally, rendering the high-loss area according to the determined rendering style to obtain a rendered area loss map, and displaying the area loss map to a user. The user can clearly see the distribution condition of the high-loss area through the map so as to take corresponding measures to reduce loss. The area loss map can help power management personnel to quickly locate a problem area, make a reasonable decision and improve the energy utilization efficiency and the system reliability.

In this embodiment, a device for automatically positioning high loss of a station is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, and is not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

The embodiment provides an automatic positioning device for high loss of a platform area, as shown in fig. 4, including:

An acquisition module 41, configured to acquire real-time electricity data of each area in the platform area and topology information of the platform area;

the first analysis module 42 is configured to determine a high-loss area in the transformer area based on the real-time electricity consumption data and the topology structure information of each area;

The second analysis module 43 is configured to obtain relevant data of the area, and analyze the relevant data to determine a target factor corresponding to the high-loss area;

And the processing module 44 is used for analyzing the association relation between the target factors to determine a target optimization strategy of the high-loss area, and optimizing the high-loss area according to the target optimization strategy.

In the embodiment of the present application, the first analysis module 42 is configured to classify each area by using the real-time electricity consumption data, so as to obtain category information corresponding to the area. The method comprises the steps of establishing association relations between category information corresponding to each area and topological structure information, searching target category information with occurrence times larger than preset times based on the association relations, calculating electric energy loss corresponding to the target category information, and determining the area corresponding to the target category information as a high-loss area if the electric energy loss is larger than the preset loss.

In the embodiment of the present application, the first analysis module 42 is configured to perform clustering according to the real-time electricity consumption data of each area to obtain a clustering result, and determine category information corresponding to each area based on the clustering result.

The device comprises a display module, a platform region loss map and a display module, wherein the display module is used for acquiring the geographic position corresponding to the high-loss region and first loss data, importing the geographic position corresponding to the high-loss region and the first loss data into the platform region loss map, determining a rendering mode corresponding to the high-loss region by using the first loss data, rendering the high-loss region in the platform region loss map according to the rendering mode to obtain a rendered platform region loss map, and displaying the rendered platform region loss map.

In the embodiment of the present application, the second analysis module 43 is configured to obtain preset indexes associated with a platform area, and obtain relevant data corresponding to each preset index, where the relevant data includes operation environment data, equipment status data, and user type data, analyze the influence degree of relevant data corresponding to each preset index on a high-loss area, and take preset indexes with influence degrees greater than the preset influence degree as target factors.

In the embodiment of the application, a processing module 44 is configured to analyze the association degree between each target factor according to a preset association analysis method, construct a candidate factor group by using two target factors with association degrees greater than a first threshold, determine a first factor group and a second factor group based on the candidate factor group, wherein the association degree between two target factors in the first factor group is greater than the first threshold and less than the second threshold, the association degree between two target factors in the second factor group is greater than the second threshold, acquire a first optimization strategy corresponding to the first factor group and a second optimization strategy corresponding to the second factor group, calculate a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy, and take the first optimization strategy with the first evaluation value higher than the preset evaluation value and the second optimization strategy with the second evaluation value higher than the preset evaluation value as target optimization strategies.

The device further comprises a detection module, a comparison result and a target optimization strategy updating module, wherein the detection module is used for detecting second loss data of the high-loss area, comparing the second loss data with historical loss data of the high-loss area to obtain the comparison result, and updating the target optimization strategy according to the comparison result.

Referring to fig. 5, fig. 5 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, and as shown in fig. 5, the computer device includes one or more processors 10, a memory 20, and interfaces for connecting components, including a high-speed interface and a low-speed interface. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 5.

The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.

Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods shown in implementing the above embodiments.

The memory 20 may include a storage program area that may store an operating system, an application program required for at least one function, and a storage data area that may store data created from the use of a computer device according to the presentation of an applet landing page, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The memory 20 may comprise volatile memory, such as random access memory, or nonvolatile memory, such as flash memory, hard disk or solid state disk, or the memory 20 may comprise a combination of the above types of memory.

The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random-access memory, a flash memory, a hard disk, a solid state disk, or the like, and further, the storage medium may further include a combination of the above types of memories. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.

Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.

Claims

1. The automatic positioning method for the high loss of the station area is characterized by comprising the following steps:

Analyzing the association relation between the target factors to determine a target optimization strategy of the high-loss area, and optimizing the high-loss area according to the target optimization strategy;

The method comprises the steps of analyzing the association degree between target factors according to a preset association analysis method, constructing a candidate factor group by utilizing two target factors with association degrees larger than a first threshold, wherein the first threshold is used for screening target factor pairs with strong association, determining a first factor group and a second factor group based on the candidate factor group, wherein the association degree between the two target factors in the first factor group is larger than the first threshold and smaller than a second threshold, the association degree between the two target factors in the second factor group is larger than a second threshold, the second threshold is used for dividing association relations of different degrees, acquiring a first optimization strategy corresponding to the first factor group and a second optimization strategy corresponding to the second factor group, calculating a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy, taking the first evaluation value higher than the first evaluation value and the second evaluation value higher than the first optimization strategy and the second optimization strategy as the first optimization strategy based on a mathematical optimization strategy or a heuristic optimization strategy, and taking the first evaluation value higher than the first evaluation value and the second evaluation value as the first optimization strategy based on a mathematical optimization strategy or the heuristic optimization strategy.

2. The method according to claim 1, wherein the determining the high-loss area in the area based on the association relation between the real-time electricity data of each area and the topology information includes:

3. The method according to claim 2, wherein the clustering according to the real-time electricity consumption data of each region to obtain a clustering result, and determining the category information corresponding to each region based on the clustering result comprises:

4. The method according to claim 1, wherein after determining the high-loss area in the area after performing association based on the real-time electricity data of each of the areas and the topology information, the method further comprises:

and rendering the high-loss area in the high-loss map according to the rendering style to obtain a rendered high-loss map, and displaying the rendered high-loss map.

5. The method of claim 1, wherein the acquiring the relevant data of the area and analyzing the relevant data to determine the target factor corresponding to the high-loss area comprises:

6. The method of claim 1, wherein after optimizing the high-loss region according to the target optimization strategy, the method further comprises:

Detecting second loss data of the high loss region;

7. An automatic positioning device for high loss of a station area, which is characterized by comprising:

the processing module is used for analyzing the association relation between the target factors to determine a target optimization strategy of the high-loss area, and optimizing the high-loss area according to the target optimization strategy;

The processing module is used for analyzing the association degree between the target factors according to a preset association analysis method, constructing a candidate factor group by utilizing two target factors with the association degree larger than a first threshold, wherein the first threshold is used for screening target factor pairs with strong association, determining a first factor group and a second factor group based on the candidate factor group, wherein the association degree between the two target factors in the first factor group is larger than the first threshold and smaller than the second threshold, the association degree between the two target factors in the second factor group is larger than the second threshold, the second threshold is used for dividing association relations of different degrees, acquiring a first optimization strategy corresponding to the first factor group and a second optimization strategy corresponding to the second factor group, calculating a first evaluation value corresponding to the first optimization strategy and a second evaluation value corresponding to the second optimization strategy, taking the first evaluation value higher than the preset evaluation value and the second evaluation value higher than the preset evaluation value as the first optimization strategy and taking the second optimization strategy higher than the preset evaluation value as the optimization strategy based on a heuristic optimization strategy or a heuristic optimization strategy based on a heuristic rule, and a mathematical optimization algorithm is adopted as the basis of a heuristic optimization algorithm.

8. A computer device, comprising:

A memory and a processor in communication with each other, the memory having stored therein computer instructions which, upon execution, cause the processor to perform the method of any of claims 1 to 6.

9. A computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1 to 6.