Data integration method and device based on virtual electric meter, medium and electronic equipment

Abstract

This application relates to a data integration method, apparatus, medium, and electronic device based on virtual electricity meters, belonging to the field of electricity consumption monitoring technology. The method includes: acquiring at least one historical electricity consumption data dimension and at least one historical time period in a single zone of a product processing plant where anomalies have occurred; determining a target zone from each of the zones based on the historical electricity consumption data dimensions and the historical time periods; acquiring a corresponding actual electricity consumption data set through multiple branch meters corresponding to the target zone, the actual electricity consumption data set including actual electricity consumption data of different dimensions; and integrating the actual electricity consumption data of the same dimension in each set of actual electricity consumption data to obtain the final electricity consumption data set of the virtual meter corresponding to the target zone. This application improves the convenience of monitoring electricity consumption data for an overall electricity consumption area.

Description

Technical Field

[0001] This application relates to the field of electricity monitoring technology, specifically to a data integration method, device, medium, and electronic equipment based on a virtual electricity meter. Background Technology

[0002] A virtual electricity meter is a device or system based on software algorithms and data analysis technology used to simulate and calculate electricity consumption. It is not a traditional physical electricity meter, but rather achieves metering and monitoring of electricity consumption by collecting, processing, and integrating relevant data. Specifically, it collects data from multiple data sources, commonly including smart meters, sensors, and power monitoring systems. Using virtual meters eliminates the need for complex wiring modifications and hardware installations required by traditional meters. Only the corresponding software and data acquisition equipment need to be deployed within the existing power system, enabling real-time monitoring of overall electricity consumption or usage data for a specific area.

[0003] Currently, the common method for monitoring overall electricity consumption data in areas with monitoring needs is to install a main electricity meter or master meter in the area to achieve real-time monitoring of overall electricity consumption data. However, when the area has complex wiring layouts or limited construction space, it is difficult to install the master meter, making it less convenient to monitor overall electricity consumption data. Summary of the Invention

[0004] To improve the convenience of monitoring overall electricity consumption data in an area, this application provides a data integration method, apparatus, medium, and electronic device based on a virtual meter.

[0005] The first aspect of this application provides a data integration method based on a virtual electricity meter, specifically including:

[0006] Obtain at least one historical electricity consumption data dimension and at least one historical time period in which an anomaly occurred in a single partition of the product processing plant;

[0007] Based on each of the aforementioned historical electricity consumption data dimensions and each of the aforementioned historical time periods, the target partition is determined from each of the aforementioned partitions;

[0008] By using multiple branch meters corresponding to the target partition, the corresponding actual electricity consumption data set is obtained, and the actual electricity consumption data set includes actual electricity consumption data in different dimensions;

[0009] The actual electricity consumption data of the same dimension in each group's actual electricity consumption data set are integrated to obtain the final electricity consumption data set of the virtual meter corresponding to the target partition.

[0010] By adopting the above technical solution, based on the dimensions of historical electricity consumption data showing anomalies in a single partition and the historical time periods during which the anomalies occurred, the target partition with current electricity consumption data monitoring needs can be determined relatively accurately, i.e., the partition where electricity consumption data may currently be abnormal. Then, multiple sets of actual electricity consumption data are collected from the branch meters within the target partition (i.e., the branch meters installed and deployed in the target partition). Finally, the actual electricity consumption data of the same dimension in each actual electricity consumption data set is integrated to obtain the result of integrating the actual electricity consumption data collected from multiple branch meters, i.e., the final electricity consumption data set. This data integration method achieves the purpose of monitoring the overall electricity consumption data of the target partition through virtual meters, achieving the same effect as monitoring the overall electricity consumption data of the target partition by deploying main meters (real meters) in the target partition. This avoids the high difficulty of deploying real meters and improves the convenience of monitoring the overall electricity consumption data of the electricity consumption area.

[0011] In one implementation, determining the target partition from each of the partitions based on each of the historical electricity consumption data dimensions and each of the historical time periods specifically includes:

[0012] At least one target electricity consumption data dimension is determined from each of the aforementioned historical electricity consumption data dimensions, wherein the target electricity consumption data dimension is a historical electricity consumption data dimension in which electricity consumption data is prone to anomalies;

[0013] From each of the historical time periods, a target time period corresponding to a single target electricity consumption data dimension is determined. The target time period is the historical time period in which the electricity consumption data of a single target electricity consumption data dimension is likely to be abnormal.

[0014] A first weight is determined for each of the target electricity consumption data dimensions, and a second weight is determined for each target time period corresponding to each target electricity consumption data dimension. The first weight represents the probability that the electricity consumption data of the target electricity consumption data dimension is abnormal, and the second weight represents the probability that the abnormal electricity consumption data of the target electricity consumption data dimension occurs within the corresponding target time period.

[0015] The target partition is determined from each of the partitions based on the current time, the first weight, and the second weight corresponding to each partition.

[0016] In one implementation, determining the target partition from the partitions based on the current time, a first weight, and a second weight corresponding to a single partition specifically includes:

[0017] The target time period in which the current time is located is determined as the key time period. If the key time period exists in each target time period corresponding to the target electricity consumption data dimension, then the corresponding target electricity consumption data dimension is determined as the key dimension.

[0018] For a single partition, calculate the first product of the first weight of each key dimension and the second weight of the corresponding key time period, and sum the first products to obtain the corresponding first summation result;

[0019] Based on the first summation result corresponding to each partition, the target partition is determined from each partition.

[0020] In one implementation, determining the target partition from each of the partitions based on the first summation result corresponding to each of the partitions specifically includes:

[0021] Each of the first summation results is compared with a preset first threshold. If the first summation result is greater than the first threshold, the corresponding partition is determined as a candidate partition.

[0022] Determine the product processing stage corresponding to each of the candidate partitions, select the largest first product from the first products corresponding to each candidate partition, and determine the key dimension corresponding to the largest first product as the reference dimension;

[0023] Determine the impact coefficient of abnormal power consumption data for each candidate partition on the corresponding product processing stage, and obtain the remaining delivery time of the current production batch of products in the product processing plant.

[0024] If the remaining delivery time is less than the preset second threshold, the influence coefficient corresponding to the same candidate partition is multiplied by the largest first product to obtain the target result, and the candidate partition corresponding to the largest target result is determined as the target partition.

[0025] If the remaining delivery time is not less than the second threshold, then the candidate partition with the largest summation result is determined as the target partition.

[0026] In one embodiment, the method further includes:

[0027] If the electricity consumption data of a single dimension in the final electricity consumption data set exceeds the corresponding anomaly judgment threshold, then the corresponding dimension will be determined as the actual anomaly dimension.

[0028] Based on the historical fault types that have occurred in a single electrical device in the target partition, at least one target fault type is determined, wherein the target fault type is a historical fault type that is prone to occur.

[0029] After a single target fault type occurs, obtain the historical anomaly dimension of the electricity consumption data that has been abnormal, and determine at least one target anomaly dimension corresponding to a single target fault type based on the historical anomaly dimension. The target anomaly dimension is the historical anomaly dimension of the single target fault type that is likely to induce abnormal electricity consumption data.

[0030] Determine a first weight for each of the target fault types, and determine a second weight for the target anomaly dimension corresponding to each of the target fault types;

[0031] Based on the actual anomaly dimension and the first and second weights corresponding to a single electrical device, the equipment maintenance reminder corresponding to the target partition is determined, and the equipment maintenance reminder is sent to the terminal of the maintenance personnel.

[0032] In one implementation, determining the equipment maintenance reminder corresponding to the target partition based on the actual anomaly dimension and the first and second weights corresponding to a single electrical device specifically includes:

[0033] For a single electrical device, calculate the second product of the first weight of each target fault type and the second weight of the corresponding actual anomaly dimension, and sum the second products to obtain the second summation result of the corresponding electrical device;

[0034] Based on the second summation result, the first maintenance sequence of the corresponding electrical equipment is determined. The larger the second summation result, the earlier the corresponding first maintenance sequence is.

[0035] Select at least one target product from the second products corresponding to each of the electrical devices, and determine the second maintenance sequence of the faults in the corresponding electrical devices based on the target product. The larger the target product, the earlier the second maintenance sequence of the corresponding target fault type is. The target product is a second product that exceeds a preset third threshold.

[0036] Each of the first maintenance sequences and the corresponding second maintenance sequences is determined as the equipment maintenance reminder for the target partition.

[0037] In one embodiment, the method further includes:

[0038] When a corresponding target fault type actually exists in the electrical equipment, the corresponding electrical equipment is identified as a key electrical equipment, and the third product of the first weight of the target fault type actually existing in each key electrical equipment and the second weight of the corresponding actual anomaly dimension is calculated.

[0039] Summing each of the third products yields the corresponding third summation result. If the third summation result exceeds a preset fourth threshold, then the anomaly judgment threshold corresponding to the actual anomaly dimension is verified to be correct.

[0040] If the third summation result does not exceed the fourth threshold, then the anomaly judgment threshold corresponding to the actual anomaly dimension is incorrect.

[0041] A second aspect of this application provides a data integration device based on a virtual electricity meter, specifically comprising:

[0042] The information acquisition module is used to acquire at least one historical electricity consumption data dimension and at least one historical time period in which an anomaly occurred in a single partition of the product processing plant.

[0043] The partition determination module is used to determine the target partition from each of the partitions based on each of the historical electricity consumption data dimensions and each of the historical time periods;

[0044] The data acquisition module is used to obtain the corresponding actual electricity consumption data set through multiple branch meters corresponding to the target partition. The actual electricity consumption data set includes actual electricity consumption data in different dimensions.

[0045] The data integration module is used to integrate the actual electricity consumption data of the same dimension in each group of actual electricity consumption data sets to obtain the final electricity consumption data set of the virtual meter corresponding to the target partition.

[0046] By adopting the above technical solution, after the information acquisition module obtains the historical electricity consumption data dimensions and historical time periods, the partition determination module determines the target partition from each partition based on the historical electricity consumption data dimensions and historical time periods. Then, the data acquisition module obtains the actual electricity consumption data set corresponding to the branch electricity meter. Finally, the data integration module integrates the actual electricity consumption data of the same dimension in each set of actual electricity consumption data to obtain the final electricity consumption data set of the virtual electricity meter corresponding to the target partition.

[0047] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when loaded and executed by a processor, performs the steps of the method described in any one of the first aspects.

[0048] A fourth aspect of this application provides an electronic device, specifically comprising:

[0049] A processor, a memory, and a computer program stored in the memory and capable of running on the processor, the processor being configured to load and execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0050] In summary, this application includes at least one of the following beneficial technical effects: Based on the dimensions of historical electricity consumption data showing anomalies in a single partition and the historical time periods during which the electricity consumption data showed anomalies, the target partition with current electricity consumption data monitoring needs can be determined relatively accurately, i.e., the partition where electricity consumption data anomalies may currently exist. Then, multiple sets of actual electricity consumption data are collected through multiple branch meters within the target partition (i.e., branch meters installed and deployed in the target partition). Finally, the actual electricity consumption data of the same dimension in each actual electricity consumption data set is integrated to obtain the result of integrating the actual electricity consumption data collected from multiple branch meters, i.e., the final electricity consumption data set. This data integration method achieves the purpose of monitoring the overall electricity consumption data of the target partition through virtual meters, achieving the same effect as monitoring the overall electricity consumption data of the target partition by deploying a main meter (real meter) in the target partition. This avoids the high difficulty of deploying real meters and improves the convenience of monitoring the overall electricity consumption data of the electricity consumption area. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a data integration method based on a virtual electricity meter provided in an embodiment of this application;

[0052] Figure 2 This is a schematic diagram of the structure of a data integration device based on a virtual electricity meter provided in an embodiment of this application;

[0053] Figure 3 This is a schematic diagram of another data integration device based on a virtual electricity meter provided in an embodiment of this application.

[0054] Explanation of reference numerals in the attached diagram: 11. Information acquisition module; 12. Partition determination module; 13. Data acquisition module; 14. Data integration module; 15. Maintenance reminder module; 16. Threshold verification module. Detailed Implementation

[0055] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0056] In the description of the embodiments of this application, words such as "exemplarily," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0057] In the description of the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, or A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0058] See Figure 1 This application discloses a flowchart of a data integration method based on a virtual electricity meter, which can be implemented using a computer program or run on a data integration device based on a virtual electricity meter using a von Neumann architecture. The computer program can be integrated into an application or run as a standalone utility application, specifically including:

[0059] S101: Obtain at least one historical electricity consumption data dimension and at least one historical time period in which an anomaly occurred in a single partition of the product processing plant.

[0060] Specifically, in this embodiment, a product processing plant is an industrial building where raw materials or semi-finished products are processed, assembled, manufactured, or handled. A product processing plant can be a machinery processing plant, or in other embodiments, a food processing plant. The product processing plant includes product processing stages. A zoning is a different type of area obtained by dividing the processing area of ​​the product processing plant according to the product processing stages. For example, if the product processing plant is an automobile manufacturing plant, and the product processing stage is the car body painting stage, then the zoning can be a painting workshop or a painting area. In addition, each zoning includes multiple branch meters. A branch meter refers to an energy metering device installed on a branch line or branch of the zoning's power system, and each branch meter covers at least one electrical device. Electricity consumption data refers to the collection of all data related to power consumption and grid parameters within the zoning, reflecting the zoning's electricity usage. Electricity consumption data includes, but is not limited to, data on electricity consumption, actual power, current, voltage, power factor, and other dimensions. It should be noted that each zoning does not have a corresponding main meter or master meter.

[0061] Furthermore, in this application embodiment, the execution entity of a data integration method based on virtual meters is a server. The server is wirelessly connected to a user's terminal, which is a personal computer or tablet computer. The terminal has a data integration-related client installed, and the server is the client's backend server, specifically a standalone physical server or a cluster of multiple physical servers. When a user needs to monitor the overall electricity consumption of each zone, they send a monitoring start command to the server through the client on the terminal. Based on the command, the server obtains at least one historical electricity consumption data dimension and at least one historical time period in which the electricity consumption data is abnormal in a single zone. Specifically, it is the historical electricity consumption data dimension and historical time period within a preset time period, which is within the past year or within the past six months. One feasible acquisition method is to obtain the historical electricity consumption data dimension and historical time period of the corresponding zone based on the abnormal monitoring records of electricity consumption data of each branch meter in a single zone. The abnormal monitoring records include, but are not limited to, information such as the dimensions and time periods in which the electricity consumption data is abnormal.

[0062] S102: Determine the target partition from each partition based on each dimension of historical electricity consumption data and each historical time period.

[0063] Specifically, after determining the historical electricity consumption data dimensions and historical time periods for a single partition, the frequency of recurrence of a single historical electricity consumption data dimension is counted across all historical electricity consumption data dimensions. If the frequency exceeds a corresponding threshold, the corresponding historical electricity consumption data dimension is designated as the target electricity consumption data dimension, i.e., the historical electricity consumption data dimension where anomalies are likely to occur. Next, the specific historical time periods corresponding to the anomalies in the target electricity consumption data dimension are selected from each historical time period. The frequency of recurrence of a single specific historical time period is counted across all specific historical time periods. If the frequency exceeds a corresponding threshold, the corresponding specific historical time period is designated as the target time period, i.e., the historical time period where anomalies in the electricity consumption data of a single target electricity consumption data dimension are likely to occur.

[0064] Further, a first weight is determined for each target electricity consumption data dimension, and a second weight is determined for the target time period corresponding to each target electricity consumption data dimension. The first weight is the ratio of the frequency of each target electricity consumption data dimension to the sum of the frequencies of all target electricity consumption data dimensions. The second weight is the ratio of the frequency of a single target time period corresponding to a target electricity consumption data dimension to the sum of the frequencies of all corresponding target time periods. Finally, based on the current time, the first weight, and the second weight corresponding to a single partition, target partitions are determined from each partition. One possible implementation is as follows:

[0065] The current time period is identified as the key time period. If a key time period exists among the target time periods corresponding to the target electricity consumption data dimension, then the corresponding target electricity consumption data dimension is identified as the key dimension. Next, for a single partition, the first product of the first weight of each key dimension and the second weight of the corresponding key time period is calculated. The larger the first product, the greater the probability that the electricity consumption data of the corresponding key dimension is abnormal at the current time. The first products are summed to obtain the first summation result. The larger the first summation result, the greater the overall probability that the corresponding partition has abnormal electricity consumption data at the current time. Finally, the largest first summation result is selected from the various first summation results, and the partition corresponding to the largest first summation result is identified as the target partition. Here, the target partition is the partition where the overall electricity consumption data needs to be monitored. In other embodiments, a feasible way to determine the target partition based on the various first summation results is to compare the various first summation results with a preset first threshold. If the first summation result is greater than the first threshold, it indicates that the corresponding partition is more likely to have abnormal electricity consumption data at the current time, and then the corresponding partition is identified as a candidate partition. Next, determine the product processing stage corresponding to each candidate partition. For example, if the candidate partition is a painting workshop or painting area, then the corresponding product processing stage is the paint painting stage. Then, select the largest first product from all the first products corresponding to each candidate partition, and determine the key dimension corresponding to the largest first product as the reference dimension, that is, the dimension with the highest probability of abnormal electricity consumption data in that candidate partition.

[0066] Furthermore, the impact coefficient of abnormal electricity consumption data in the reference dimension of a single candidate partition on the corresponding product processing stage is determined. Specifically, the corresponding impact coefficient can be matched from a preset coefficient matching table. The larger the impact coefficient, the more easily the abnormal electricity consumption data in the reference dimension will affect the corresponding product processing stage, and the more easily it will interfere with product processing, thereby affecting the product production schedule. The coefficient matching table includes different electricity consumption data dimensions and their impact coefficients on different product processing stages. Simultaneously, the remaining delivery time for the current production batch of products in the product processing plant is obtained. The remaining delivery time is sent to the server by personnel via a terminal.

[0067] The remaining delivery time is compared with a preset second threshold. If the remaining delivery time is less than the second threshold, it indicates that the delivery time for the current production batch is relatively tight. In this case, the influence coefficient corresponding to the same candidate partition is multiplied by the largest first product to obtain the target result. The larger the target result, the greater the possibility that the product processing in the corresponding candidate partition will be disturbed, and the easier it is to delay the delivery of the current production batch. In this case, the candidate partition corresponding to the largest target result among all target results is determined as the target partition. Conversely, if the remaining delivery time is not less than the second threshold, it indicates that the delivery time for the current production batch is relatively ample. In this case, the candidate partition with the largest first summation result is determined as the target partition.

[0068] S103: Obtain the corresponding actual electricity consumption data set through multiple branch meters corresponding to the target partition.

[0069] Specifically, the actual electricity consumption data set includes actual electricity consumption data from different dimensions. After the target partition is determined, actual electricity consumption data from different dimensions of the target partition is collected through each branch meter corresponding to the target partition to obtain the corresponding actual electricity consumption data set. It should be noted that, in the embodiments of this application, the actual electricity consumption data set corresponding to each branch meter includes, but is not limited to, actual electricity consumption data from different dimensions such as active power of phase A, active power of phase B, active power of phase C, total power factor, voltage of phase A, voltage of phase B, and voltage of phase C.

[0070] S104: Integrate the actual electricity consumption data of the same dimension in each set of actual electricity consumption data to obtain the final set of electricity consumption data of the virtual meter corresponding to the target partition.

[0071] Specifically, after obtaining the actual electricity consumption data sets collected by each branch meter, the actual electricity consumption data of the same dimension in each actual electricity consumption data set is integrated to obtain the final electricity consumption data set corresponding to the target partition. For example, there are two branch meters, namely branch meter 1 and branch meter 2. The actual electricity consumption data sets corresponding to branch meter 1 and branch meter 2 both contain actual electricity consumption data of dimensions such as active power of phase A, active power of phase B, active power of phase C, total power factor, power factor of phase A, power factor of phase B, and power factor of phase C. The specific integration process is as follows: the actual electricity consumption data of the dimensions of total power factor, power factor of phase A, power factor of phase B, and power factor of phase C in branch meter 1 and branch meter 2 are averaged respectively to obtain the integrated electricity consumption data of the corresponding dimension. The actual electricity consumption data for phase A, phase B, and phase C active power in branch meters 1 and 2 are summed separately to obtain the integrated electricity consumption data for each dimension. Finally, the integrated electricity consumption data for each dimension are aggregated to obtain the final electricity consumption data set. Therefore, there is no need to set a master meter for the target zone. Based on the form of virtual meters, the electricity consumption data of multiple branch meters (real meters) are integrated to achieve effective monitoring of the overall electricity consumption data of the target zone.

[0072] In other embodiments, after the final power consumption data set corresponding to the target partition is determined, the power consumption data of a single dimension in the final power consumption data set is compared with the corresponding anomaly judgment threshold. If the power consumption data of a single dimension exceeds the corresponding anomaly judgment threshold, then the corresponding dimension is determined as the actual anomaly dimension. Next, based on the historical monitoring records of the target partition, the historical fault types that have occurred in a single power device in the target partition are obtained, and the first frequency of recurrence of a single historical fault type among all historical fault types is counted. If the first frequency exceeds the corresponding frequency threshold, then the corresponding historical fault type is determined as the target fault type, that is, the historical fault type that a single power device is prone to occur in. Then, based on the above historical monitoring records, the historical anomaly dimension that has occurred in the power consumption data of the target partition after the occurrence of a single target fault type is obtained, and the second frequency of recurrence of a single historical anomaly dimension among all historical anomaly dimensions is counted. If the second frequency exceeds the corresponding frequency threshold, then the corresponding historical anomaly dimension is determined as the target anomaly dimension corresponding to the target fault type, that is, the historical anomaly dimension that a single target fault type is prone to induce power consumption data anomalies. Historical monitoring records include, but are not limited to, different types of historical faults that have occurred in different electrical equipment and dimensions of abnormalities that have accompanied power consumption data.

[0073] Furthermore, for a single electrical device, a first weight is determined for each target fault type, and a second weight is determined for the target anomaly dimension corresponding to each target fault type. The first weight is the ratio of the first frequency of each target fault type to the sum of the first frequencies of all target fault types, and the second weight is the ratio of the second frequency of the single target anomaly dimension corresponding to the target fault type to the sum of the second frequencies of all corresponding target anomaly dimensions. Finally, based on the actual anomaly dimensions existing in the target partition and the first and second weights corresponding to a single electrical device, a maintenance reminder for the corresponding target partition is determined. One feasible implementation is as follows:

[0074] For a single electrical device, a second product is calculated between the first weight of the single target fault type and the second weight of the corresponding actual anomaly dimension. The larger the second product, the more likely the occurrence of the corresponding target fault type in a single electrical device will trigger anomalies in the actual anomaly dimension of power consumption data. The second products are summed to obtain the second summation result for the corresponding electrical device. The larger the second summation result, the greater the overall probability that a fault in the corresponding electrical device will trigger anomalies in the actual anomaly dimension of power consumption data. Next, based on the second summation result, the first maintenance sequence for the corresponding electrical device is determined. The larger the second summation result, the earlier the corresponding first maintenance sequence, and the higher the priority for maintenance of the corresponding electrical device, thereby eliminating the safety risk of anomalies in the actual anomaly dimension of power consumption data in the target partition.

[0075] Furthermore, at least one target product is selected from each second product corresponding to each electrical device, and the second maintenance order of the corresponding electrical device is determined according to the target product. The larger the target product, the more likely the corresponding target fault type is to cause abnormal power consumption data in the actual abnormal dimension, and the earlier the corresponding second maintenance order is, thereby improving the efficiency of fault diagnosis of electrical devices. The target product is a second product that exceeds a preset third threshold. Finally, the first maintenance order and the corresponding second maintenance order of each electrical device are determined as the equipment maintenance reminder for the target partition, and this equipment maintenance reminder is sent to the maintenance personnel's terminal, enabling maintenance personnel to more effectively eliminate the safety risks of abnormal power consumption data in the actual abnormal dimension, while efficiently diagnosing faults in electrical devices. In other embodiments, the results of each second summation are summed to obtain a final summation result. If the final summation result is greater than a preset fourth threshold, it indicates that the possibility of abnormal power consumption data in the actual abnormal dimension in the target partition is relatively high, and further verification of the determination of the actual abnormal dimension is more reasonable.

[0076] In another embodiment, after maintenance personnel go to the target area for maintenance, if the corresponding target fault type actually exists in the electrical equipment, then the corresponding electrical equipment is identified as key electrical equipment. The third product of the first weight of the actual target fault type in each key electrical equipment and the second weight of the corresponding actual anomaly dimension is calculated. The third products are summed to obtain the corresponding third summation result. If this third summation result exceeds the fourth threshold, it indicates that the electrical data of the actual anomaly dimension in the target area is likely to be abnormal, and the anomaly judgment threshold corresponding to the actual anomaly dimension is correct. Conversely, if the third summation result does not exceed the fourth threshold, it indicates that the electrical data of the actual anomaly dimension in the target area is unlikely to be abnormal, and the anomaly judgment threshold corresponding to the actual anomaly dimension is incorrect. In another embodiment, the fourth product of the first weight of the actual target fault type in each key electrical equipment and the second weight of the corresponding target anomaly dimension is calculated. The fourth products corresponding to the same target anomaly dimension are summed to obtain the fourth summation result for the corresponding target anomaly dimension. If the fourth summation result exceeds a fourth threshold, the corresponding target anomaly dimension is determined as a dimension of concern. If this dimension of concern is not an actual anomaly dimension, the anomaly judgment threshold corresponding to this dimension of concern is determined to be incorrect. The electricity consumption data of the dimension of concern is highly likely to be abnormal, but there is a problem of missed detection. Further, the fourth products corresponding to this dimension of concern are determined as products of concern, the key electrical equipment where the target fault type corresponding to the products of concern is distributed is determined as important electrical equipment, and the branch meters covering important electrical equipment are determined as important branch meters. If the actual electricity consumption data of the dimension of concern in the actual electricity consumption data set corresponding to each important branch meter all exceed the corresponding electricity consumption data threshold, then the verification of each electricity consumption data threshold is correct.

[0077] The implementation principle of the data integration method based on virtual meters in this application embodiment is as follows: Based on the dimensions of historical electricity consumption data that show anomalies in a single partition and the historical time periods during which the electricity consumption data shows anomalies, the target partition with current electricity consumption data monitoring needs can be determined relatively accurately, that is, the partition where there may be current electricity consumption data anomalies. Then, multiple sets of actual electricity consumption data are collected through multiple branch meters covered in the target partition, that is, branch meters installed and deployed in the target partition. Finally, the actual electricity consumption data of the same dimension in each actual electricity consumption data set is integrated to obtain the result of integrating the actual electricity consumption data collected from multiple branch meters, that is, the final electricity consumption data set. This data integration method achieves the purpose of monitoring the overall electricity consumption data of the target partition through virtual meters, achieving the same effect as monitoring the overall electricity consumption data of the target partition by deploying main meters (real meters) in the target partition. This avoids the high difficulty of deploying real meters and improves the convenience of monitoring the overall electricity consumption data of the electricity consumption area.

[0078] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0079] Please see Figure 2 This is a schematic diagram of the structure of a data integration device based on a virtual electricity meter provided in an embodiment of this application. This data integration device based on a virtual electricity meter can be implemented as all or part of the device through software, hardware, or a combination of both. The device includes...

[0080] Information acquisition module 11 is used to acquire at least one historical power consumption data dimension and at least one historical time period in which an anomaly occurs in a single partition of the product processing plant.

[0081] The partition determination module 12 is used to determine the target partition from each partition based on each dimension of historical electricity consumption data and each historical time period;

[0082] Data acquisition module 13 is used to obtain the corresponding actual electricity consumption data set through multiple branch meters corresponding to the target partition. The actual electricity consumption data set includes actual electricity consumption data in different dimensions.

[0083] The data integration module 14 is used to integrate the actual electricity consumption data of the same dimension in each set of actual electricity consumption data to obtain the final electricity consumption data set of the virtual meter corresponding to the target partition.

[0084] Optional, the partition determination module 12 is specifically used for:

[0085] From each historical electricity consumption data dimension, at least one target electricity consumption data dimension is determined. The target electricity consumption data dimension is the historical electricity consumption data dimension in which anomalies are likely to occur.

[0086] The target time period corresponding to a single target electricity consumption data dimension is determined from each historical time period. The target time period is the historical time period in which the electricity consumption data of a single target electricity consumption data dimension is likely to be abnormal.

[0087] Determine the first weight for each target electricity consumption data dimension and the second weight for the target time period corresponding to each target electricity consumption data dimension. The first weight represents the probability of an anomaly in the electricity consumption data of the target electricity consumption data dimension, and the second weight represents the probability that the anomaly in the electricity consumption data of the target electricity consumption data dimension occurs within the corresponding target time period.

[0088] The target partition is determined from the partitions based on the current time, the first weight, and the second weight corresponding to a single partition.

[0089] Optional, the partition determination module 12 is specifically used for:

[0090] The target time period in which the current time is located is identified as the key time period. If there is a key time period among the target time periods corresponding to the target electricity consumption data dimension, then the corresponding target electricity consumption data dimension is identified as the key dimension.

[0091] For a single partition, calculate the first product of the first weight of each key dimension and the second weight of the corresponding key time period, and sum the first products to obtain the corresponding first summation result;

[0092] Based on the first summation result of each partition, the target partition is determined from each partition.

[0093] Optional, the partition determination module 12 is specifically used for:

[0094] The first summation result is compared with the preset first threshold. If the first summation result is greater than the first threshold, the corresponding partition is determined as the candidate partition.

[0095] Determine the product processing stage corresponding to each candidate partition, select the largest first product from the first products corresponding to each candidate partition, and determine the key dimension corresponding to the largest first product as the reference dimension;

[0096] Determine the impact coefficient of abnormal electricity consumption data for each candidate partition on the corresponding product processing stage, and obtain the remaining delivery time of the current production batch of products in the product processing plant.

[0097] If the remaining delivery time is less than the preset second threshold, the influence coefficient corresponding to the same candidate partition is multiplied by the largest first product to obtain the target result, and the candidate partition corresponding to the largest target result is determined as the target partition.

[0098] If the remaining delivery time is not less than the second threshold, then the candidate partition with the largest summation result is determined as the target partition.

[0099] Optional, such as Figure 3 As shown, the device also includes a maintenance reminder module 15, specifically used for:

[0100] If the electricity consumption data of a single dimension in the final electricity consumption data set exceeds the corresponding anomaly judgment threshold, then the corresponding dimension will be determined as the actual anomaly dimension.

[0101] Based on the historical fault types that have occurred in a single electrical device in the target partition, at least one target fault type is determined. The target fault type is a historical fault type that is likely to occur.

[0102] After a single target fault type occurs, obtain the historical anomaly dimensions of the electricity consumption data that have been abnormal. Based on the historical anomaly dimensions, determine at least one target anomaly dimension corresponding to the single target fault type. The target anomaly dimension is the historical anomaly dimension of the single target fault type that is likely to induce abnormal electricity consumption data.

[0103] Determine the first weight for each target fault type, and determine the second weight for the target anomaly dimension corresponding to each target fault type;

[0104] Based on the actual anomaly dimension and the first and second weights corresponding to a single electrical device, the equipment maintenance reminders for the target zone are determined and sent to the maintenance personnel's terminals.

[0105] Optional, maintenance reminder module 15, specifically used for:

[0106] For a single electrical device, calculate the second product of the first weight of each target fault type and the second weight of the corresponding actual anomaly dimension, and sum the second products to obtain the second summation result of the corresponding electrical device;

[0107] Based on the second summation result, determine the first maintenance sequence of the corresponding electrical equipment. The larger the second summation result, the earlier the corresponding first maintenance sequence is.

[0108] Select at least one target product from the second products corresponding to each electrical device, and determine the second maintenance sequence of the fault in the corresponding electrical device based on the target product. The larger the target product, the earlier the second maintenance sequence of the corresponding target fault type is. The target product is the second product that exceeds the preset third threshold.

[0109] Each first maintenance sequence and its corresponding second maintenance sequence are determined as the equipment maintenance reminder for the target zone.

[0110] Optionally, the device also includes a threshold verification module 16, specifically used for:

[0111] When a corresponding target fault type actually exists in the electrical equipment, the corresponding electrical equipment is identified as a key electrical equipment, and the third product of the first weight of the target fault type actually existing in each key electrical equipment and the second weight of the corresponding actual anomaly dimension is calculated.

[0112] Summing each third product yields the corresponding third summation result. If the third summation result exceeds the preset fourth threshold, then the anomaly judgment threshold corresponding to the actual anomaly dimension is verified to be correct.

[0113] If the third summation result does not exceed the fourth threshold, then the anomaly judgment threshold corresponding to the actual anomaly dimension is incorrect.

[0114] It should be noted that the data integration device based on a virtual electricity meter provided in the above embodiments is only illustrated by the division of the above functional modules when executing the data integration method based on a virtual electricity meter. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the data integration device based on a virtual electricity meter and the data integration method based on a virtual electricity meter provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0115] This application also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements a data integration method based on a virtual electricity meter as described in the above embodiments.

[0116] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0117] The above-described data integration method based on a virtual electricity meter is stored in the computer-readable storage medium and loaded and executed on a processor to facilitate the storage and application of the method.

[0118] This application also discloses an electronic device in which a computer program is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, it implements the above-mentioned data integration method based on a virtual electricity meter.

[0119] The electronic device can be a desktop computer, a laptop computer, or a cloud server, and includes, but is not limited to, a processor and a memory. For example, the electronic device may also include input / output devices, network access devices, and buses.

[0120] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0121] The memory can be an internal storage unit of an electronic device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the electronic device. Furthermore, the memory can be a combination of an internal storage unit and an external storage device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0122] In this electronic device, a data integration method based on a virtual electricity meter, as described in the above embodiment, is stored in the memory of the electronic device and loaded and executed on the processor of the electronic device for convenient use.

[0123] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A data integration method based on virtual electricity meters, characterized in that, The method includes: Obtain at least one historical electricity consumption data dimension and at least one historical time period in which an anomaly occurred in a single partition of the product processing plant; Based on each of the aforementioned historical electricity consumption data dimensions and each of the aforementioned historical time periods, the target partition is determined from each of the aforementioned partitions; By using multiple branch meters corresponding to the target partition, the corresponding actual electricity consumption data set is obtained, and the actual electricity consumption data set includes actual electricity consumption data in different dimensions; The actual electricity consumption data of the same dimension in each group of actual electricity consumption data sets are integrated to obtain the final electricity consumption data set of the virtual meter corresponding to the target partition; If the electricity consumption data of a single dimension in the final electricity consumption data set exceeds the corresponding anomaly judgment threshold, then the corresponding dimension is determined as the actual anomaly dimension; based on the historical fault types that have occurred in a single electrical device in the target partition, at least one target fault type is determined, and the target fault type is a historical fault type that is prone to occur; the historical anomaly dimensions in which the electricity consumption data has been abnormal after the occurrence of a single target fault type are obtained, and based on the historical anomaly dimensions, at least one target anomaly dimension corresponding to a single target fault type is determined, and the target anomaly dimension is a historical anomaly dimension in which a single target fault type is prone to induce electricity consumption data anomalies; a first weight is determined for each target fault type, and a second weight is determined for the target anomaly dimension corresponding to each target fault type; based on the actual anomaly dimension, the first weight and the second weight corresponding to a single electrical device, the device corresponding to the target partition is determined. The process of issuing maintenance reminders and sending these reminders to maintenance personnel's terminals includes: for a single piece of electrical equipment, calculating the second product of a first weight for each target fault type and a second weight for the corresponding actual anomaly dimension, and summing each second product to obtain a second summation result for the corresponding electrical equipment; determining a first maintenance order for the corresponding electrical equipment based on the second summation result, where a larger second summation result indicates a higher first maintenance order; selecting at least one target product from the second products corresponding to each piece of electrical equipment, and determining a second maintenance order for the faults in the corresponding electrical equipment based on the target product, where a larger target product indicates a higher second maintenance order for the corresponding target fault type, and the target product being a second product exceeding a preset third threshold; and determining each first maintenance order and corresponding second maintenance order as the equipment maintenance reminder for the target partition. Calculate the fourth product of the first weight of the actual target fault type in each key electrical equipment and the second weight of the corresponding target anomaly dimension. Summate the fourth products corresponding to the same target anomaly dimension to obtain the fourth summation result of the corresponding target anomaly dimension. If the fourth summation result exceeds the fourth threshold, then the corresponding target anomaly dimension is determined as a dimension to be monitored. If this dimension to be monitored is not an actual anomaly dimension, then the anomaly judgment threshold corresponding to this dimension to be monitored is determined to be incorrect. The fourth products corresponding to this dimension to be monitored are determined as products to be monitored. The key electrical equipment where the target fault type corresponding to the products to be monitored is distributed is determined as important electrical equipment. The branch meters covering important electrical equipment are determined as important branch meters. If the actual electricity consumption data of the dimension to be monitored in the actual electricity consumption data set corresponding to each important branch meter all exceed the corresponding electricity consumption data threshold, then the verification of each electricity consumption data threshold is correct; the key electrical equipment is the electrical equipment that actually has the corresponding target fault type.

2. The data integration method based on virtual meters according to claim 1, characterized in that, The step of determining the target partition from each of the partitions based on each of the historical electricity consumption data dimensions and each of the historical time periods specifically includes: At least one target electricity consumption data dimension is determined from each of the aforementioned historical electricity consumption data dimensions, wherein the target electricity consumption data dimension is a historical electricity consumption data dimension in which electricity consumption data is prone to anomalies; From each of the historical time periods, a target time period corresponding to a single target electricity consumption data dimension is determined. The target time period is the historical time period in which the electricity consumption data of a single target electricity consumption data dimension is likely to be abnormal. A first weight is determined for each of the target electricity consumption data dimensions, and a second weight is determined for each target time period corresponding to each target electricity consumption data dimension. The first weight represents the probability that the electricity consumption data of the target electricity consumption data dimension is abnormal, and the second weight represents the probability that the abnormal electricity consumption data of the target electricity consumption data dimension occurs within the corresponding target time period. The target partition is determined from each of the partitions based on the current time, the first weight, and the second weight corresponding to each partition.

3. The data integration method based on virtual meters according to claim 2, characterized in that, The step of determining the target partition from each of the partitions based on the current time, the first weight, and the second weight corresponding to a single partition specifically includes: The target time period in which the current time is located is determined as the key time period. If the key time period exists in each target time period corresponding to the target electricity consumption data dimension, then the corresponding target electricity consumption data dimension is determined as the key dimension. For a single partition, calculate the first product of the first weight of each key dimension and the second weight of the corresponding key time period, and sum the first products to obtain the corresponding first summation result; Based on the first summation result corresponding to each partition, the target partition is determined from each partition.

4. The data integration method based on virtual meters according to claim 3, characterized in that, The step of determining the target partition from each of the partitions based on the first summation result corresponding to each of the partitions specifically includes: Each of the first summation results is compared with a preset first threshold. If the first summation result is greater than the first threshold, the corresponding partition is determined as a candidate partition. Determine the product processing stage corresponding to each of the candidate partitions, select the largest first product from the first products corresponding to each candidate partition, and determine the key dimension corresponding to the largest first product as the reference dimension; Determine the impact coefficient of abnormal power consumption data for each candidate partition on the corresponding product processing stage, and obtain the remaining delivery time of the current production batch of products in the product processing plant. If the remaining delivery time is less than the preset second threshold, the influence coefficient corresponding to the same candidate partition is multiplied by the largest first product to obtain the target result, and the candidate partition corresponding to the largest target result is determined as the target partition. If the remaining delivery time is not less than the second threshold, then the candidate partition with the largest summation result is determined as the target partition.

5. The data integration method based on virtual meters according to claim 1, characterized in that, The method further includes: When a corresponding target fault type actually exists in the electrical equipment, the corresponding electrical equipment is identified as a key electrical equipment, and the third product of the first weight of the target fault type actually existing in each key electrical equipment and the second weight of the corresponding actual anomaly dimension is calculated. Summing each of the third products yields the corresponding third summation result. If the third summation result exceeds a preset fourth threshold, then the anomaly judgment threshold corresponding to the actual anomaly dimension is verified to be correct. If the third summation result does not exceed the fourth threshold, then the anomaly judgment threshold corresponding to the actual anomaly dimension is incorrect.

6. A data integration device based on a virtual electricity meter, used to implement the data integration method based on a virtual electricity meter as described in any one of claims 1 to 5, characterized in that, include: The information acquisition module (11) is used to acquire at least one historical power consumption data dimension and at least one historical time period in which a single partition in the product processing plant has an anomaly. The partition determination module (12) is used to determine the target partition from each of the partitions based on each of the historical electricity consumption data dimensions and each of the historical time periods; The data acquisition module (13) is used to obtain the corresponding actual electricity consumption data set through multiple branch meters corresponding to the target partition, wherein the actual electricity consumption data set includes actual electricity consumption data of different dimensions; The data integration module (14) is used to integrate the actual electricity consumption data of the same dimension in each group of actual electricity consumption data sets to obtain the final electricity consumption data set of the virtual meter corresponding to the target partition.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the method of any one of claims 1-5.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor loads and executes the computer program, it implements the method of any one of claims 1-5.

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