Data monitoring methods and systems for electricity meter sorting and identification platforms

Abstract

This invention discloses a data monitoring method and system for an electricity meter sorting and identification platform, relating to the field of power data technology. It primarily addresses the problem of low effectiveness and accuracy in data monitoring of existing electricity meter sorting and identification platforms. The method mainly includes: determining target data processing objects and target image processing objects based on the identification list of the electricity meter sorting and identification platform; acquiring target data corresponding to the target data processing objects and target images corresponding to the target image processing objects according to acquisition nodes, where acquisition nodes are determined based on the sorting operation time and duration of the mechanical equipment; extracting features from the target data and target images using the feature types corresponding to the acquisition nodes to obtain monitoring features; and comparing the monitoring features according to the monitoring strategy matched to the identification list to obtain the data monitoring results for the target data processing objects and target image processing objects. This method is mainly used for monitoring data from electricity meter sorting and identification platforms.

Description

Technical Field

[0001] This invention relates to the field of power data technology, and in particular to a data monitoring method and system for an electricity meter sorting and identification platform. Background Technology

[0002] With the development of technologies such as intelligent layout and intelligent identification of electricity meters in power systems, electricity meter sorting and identification platforms identify various electricity meters in the power transmission network to ensure the safety and effectiveness of electricity meters.

[0003] Currently, existing electricity meter sorting and identification platforms typically monitor the data collected during the identification process using video recording. This involves installing cameras in various transmission areas of the sorting and identification system and using the captured video data as the monitoring basis. However, the parsing and processing of video data requires the electricity meter sorting and identification platform to process a large number of frame images, resulting in monitoring latency and increasing the platform's data processing load. This significantly impacts the effectiveness and accuracy of the data monitoring provided by the electricity meter sorting and identification platform. Summary of the Invention

[0004] In view of this, the present invention provides a data monitoring method and system for an electricity meter sorting and identification platform, the main purpose of which is to solve the problem of low effectiveness and accuracy of data monitoring in existing electricity meter sorting and identification platforms.

[0005] According to one aspect of the present invention, a data monitoring method for an electricity meter sorting and identification platform is provided, comprising:

[0006] The identification list of the electricity meter sorting and identification platform determines the target data processing object and the target image processing object. The identification list stores identification task information corresponding to different transmission identification pipelines.

[0007] The target data corresponding to the target data processing object and the target image corresponding to the target image processing object are acquired according to the acquisition node. The acquisition node is determined according to the sorting operation time and sorting operation duration of the mechanical equipment.

[0008] By utilizing the feature types corresponding to the acquisition nodes, feature extraction is performed on the target data and the target image to obtain monitoring features. The monitoring features are then compared according to the monitoring strategy matched by the identification list to obtain the data monitoring results of the target data processing object and the target image processing object.

[0009] Furthermore, the identification list based on the electricity meter sorting and identification platform determines the target data processing object and the target image processing object, including:

[0010] Analyze the identification objects, identification intervals, identification quantities, and identification equipment information of different transmission identification pipelines in the identification task information;

[0011] Obtain the updated identification and monitoring conditions, and based on the identification and monitoring conditions, filter out the target data processing object and the target image processing object from the identification object, identification interval, identification quantity, and identification equipment information;

[0012] The identification and monitoring conditions include at least one of quantity conditions, time conditions, and characteristic conditions.

[0013] Furthermore, before acquiring the target data corresponding to the target data processing object and the target image corresponding to the target image processing object according to the acquisition node, the method further includes:

[0014] The process duration of the transmission identification pipeline is obtained, and the number of nodes is determined by performing a ratio calculation based on the sorting operation time and the sorting operation runtime.

[0015] The data collection nodes are determined based on the flow duration and the number of nodes.

[0016] Further, the step of acquiring the target data corresponding to the target data processing object and the target image corresponding to the target image processing object according to the acquisition node includes:

[0017] The first acquisition device sends a first acquisition instruction to the first acquisition device of the target data processing object according to the acquisition node, and receives the transmitted target data. The first acquisition device is configured on the mechanical equipment for acquiring the target data.

[0018] The acquisition node sends a second acquisition command to the second acquisition device of the target image processing object and receives the transmitted target image. The second acquisition device is configured in the identification area of ​​the target image.

[0019] Furthermore, the step of extracting features from the target data and the target image using the feature type corresponding to the acquisition node to obtain monitoring features includes:

[0020] Obtain the feature types pre-bound to the collection node, including extreme value feature types and blacklist / whitelist feature types;

[0021] Numerical extraction is performed on the target data according to the feature types, and the identifier is extracted from the target image to obtain identifier monitoring features and numerical monitoring features.

[0022] Further, the step of performing feature comparison on the monitoring features according to the monitoring strategy matching the identification list to obtain the data monitoring results of the target data processing object and the target image processing object includes:

[0023] Retrieve the monitoring strategy of the identification list, wherein the monitoring strategy includes a single monitoring strategy or a combination of monitoring strategies;

[0024] Based on the monitoring strategy, the identification monitoring strategy and the numerical monitoring features are compared to obtain the data monitoring results.

[0025] Furthermore, the method also includes:

[0026] When the data monitoring result indicates data anomaly, an abnormal operation warning message is generated to drive warning operations and identify abnormal operations based on the abnormal operation warning message.

[0027] According to another aspect of the present invention, a data monitoring system for an electricity meter sorting and identification platform is provided, comprising:

[0028] The determination module is used to determine the target data processing object and the target image processing object based on the identification list of the electricity meter sorting and identification platform. The identification list stores identification task information corresponding to different transmission identification pipelines.

[0029] The acquisition module is used to acquire target data corresponding to the target data processing object and target image corresponding to the target image processing object according to the acquisition node. The acquisition node is determined according to the sorting operation time and sorting operation duration of the mechanical equipment.

[0030] The feature processing module is used to extract features from the target data and the target image using the feature type corresponding to the acquisition node, obtain monitoring features, and perform feature comparison on the monitoring features according to the monitoring strategy matched by the identification list, so as to obtain the data monitoring results of the target data processing object and the target image processing object.

[0031] Furthermore, the determining module includes:

[0032] The parsing unit is used to parse the identification objects, identification intervals, identification quantities, and identification equipment information of different transmission identification pipelines in the identification task information;

[0033] The filtering unit is used to obtain the updated identification and monitoring conditions, and to filter out the target data processing object and the target image processing object from the identification object, identification interval, identification quantity and identification equipment information based on the identification and monitoring conditions.

[0034] The identification and monitoring conditions include at least one of quantity conditions, time conditions, and characteristic conditions.

[0035] Furthermore, the system also includes:

[0036] The calculation module is used to obtain the flow time of the transmission identification pipeline, and to perform a ratio calculation based on the sorting operation time and the sorting operation runtime to determine the number of nodes;

[0037] The determining module is also used to determine the acquisition node based on the flow duration and the number of nodes.

[0038] Furthermore, the acquisition module includes:

[0039] The first acquisition unit is used to send a first acquisition instruction to the first acquisition device of the target data processing object according to the acquisition node, and to receive the transmitted target data. The first acquisition device is configured on the mechanical equipment for acquiring the target data.

[0040] The second acquisition unit is used to send a second acquisition instruction to the second acquisition device of the target image processing object according to the acquisition node, and to receive the transmitted target image, wherein the second acquisition device is configured in the identification area of ​​the target image.

[0041] Furthermore, the feature processing module includes:

[0042] The acquisition unit is used to acquire the feature types pre-bound to the acquisition node, including extreme value feature types and blacklist / whitelist feature types;

[0043] The feature extraction unit is used to extract numerical values ​​from the target data according to the feature type, and to extract identifiers from the target image to obtain identifier monitoring features and numerical monitoring features.

[0044] Furthermore, the feature processing module further includes:

[0045] The retrieval unit is used to retrieve the monitoring strategy of the identification list, wherein the monitoring strategy includes a single monitoring strategy or a combination of monitoring strategies;

[0046] The feature comparison unit is used to compare the identification monitoring strategy and the numerical monitoring features based on the monitoring strategy to obtain the data monitoring results.

[0047] Furthermore, the system also includes:

[0048] The generation module is used to generate abnormal operation warning information when the data monitoring result is abnormal, so as to drive warning operation and identify abnormal operation based on the abnormal operation warning information.

[0049] According to another aspect of the present invention, a storage medium is provided, wherein at least one executable instruction is stored therein, the executable instruction causing a processor to perform an operation corresponding to the data monitoring method of the above-described electricity meter sorting and identification platform.

[0050] According to another aspect of the present invention, a terminal is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0051] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the data monitoring method of the above-mentioned electricity meter sorting and identification platform.

[0052] By employing the above-described technical solutions, the technical solutions provided by the embodiments of the present invention have at least the following advantages:

[0053] This invention provides a data monitoring method and system for an electricity meter sorting and identification platform. Compared with existing technologies, this invention determines target data processing objects and target image processing objects based on an identification list of the electricity meter sorting and identification platform. The identification list stores identification task information corresponding to different transmission identification pipelines. Target data corresponding to the target data processing object and target images corresponding to the target image processing object are acquired according to acquisition nodes, which are determined based on the sorting operation time and duration of the mechanical equipment. Features are extracted from the target data and target images using the feature types corresponding to the acquisition nodes to obtain monitoring features. These features are then compared according to the monitoring strategy matched to the identification list to obtain the data monitoring results for the target data processing object and the target image processing object. Specifically, filtering target data processing objects and target image processing objects based on the identification list allows for data filtering from both equipment and electricity meter type dimensions, while data extraction according to acquisition nodes is performed from a time dimension. Through filtering and extraction, the data processing volume and pressure of the electricity meter sorting and identification platform are significantly reduced, while monitoring delays are greatly avoided, thereby ensuring the effectiveness and accuracy of data monitoring for the electricity meter sorting and identification platform.

[0054] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0055] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0056] Figure 1 This invention provides a flowchart of a data monitoring method for an energy meter sorting and identification platform.

[0057] Figure 2 This invention provides a flowchart of a data monitoring method for an energy meter sorting and identification platform according to an embodiment of the present invention.

[0058] Figure 3 This diagram illustrates a data monitoring system for an energy meter sorting and identification platform according to an embodiment of the present invention.

[0059] Figure 4 A schematic diagram of the structure of a terminal provided in an embodiment of the present invention is shown. Detailed Implementation

[0060] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0061] This invention provides a data monitoring method for an electricity meter sorting and identification platform, such as... Figure 1 As shown, the method includes:

[0062] 101. Determine the target data processing object and the target image processing object based on the identification list of the electricity meter sorting and identification platform.

[0063] In this embodiment of the invention, the current execution entity is the server of the electricity meter sorting and identification platform. The server type can be a local server or a cloud server; this embodiment does not specifically limit the type. The identification tasks of the electricity meter sorting and identification platform are constantly changing and updated according to the identification production schedule. Correspondingly, the monitoring objects of the electricity meter sorting and identification platform also need to be confirmed before each monitoring. For example, on the morning of December 3, the identification task of the No. 1 transmission identification line is to test the voltage output function, current output function, and fee control function of batch A, 1000 electricity meters, and label them; on the afternoon of December 3, the identification task of the No. 2 transmission identification line is to test the communication function, current output function, and fee control function of batch B, 500 electricity meters, and identify the electricity meter model. The identification list stores the identification task information corresponding to different transmission identification lines. Before monitoring the identification data of the electricity meter sorting and identification platform, the processing object of the currently monitored identification task can be determined according to the identification list. Since the identification data includes image form and data form, it is necessary to determine the target data processing object and the target image processing object separately. The identification list can be used to identify production scheduling plans.

[0064] It should be noted that the monitoring of the electricity meter sorting and identification platform includes not only the electricity meter data collected during the identification process, but also the operational data of the relevant equipment performing the identification operations within the identification system. Therefore, the target data processing objects and target image processing objects include both the operational items related to electricity and the mechanical equipment performing those operations. Regarding the example above, on the afternoon of December 3rd, the target data processing objects for the #2 transmission and identification pipeline were the communication function parameters, current and fee control function parameters, and the operational data of the corresponding mechanical equipment; the target image processing objects were the electricity meter tag images, and the operational images of the mechanical equipment corresponding to the communication function, current output function, and fee control function.

[0065] In one embodiment of the present invention, for further illustration and limitation, such as Figure 2 As shown, the step of determining the target data processing object and the target image processing object based on the identification list of the electricity meter sorting and identification platform includes:

[0066] 201. Analyze the identification objects, identification intervals, identification quantities, and identification equipment information of different transmission identification pipelines in the identification task information.

[0067] 202. Obtain the updated identification and monitoring conditions, and based on the identification and monitoring conditions, filter out the target data processing object and the target image processing object from the identification object, identification interval, identification quantity, and identification equipment information.

[0068] In this embodiment of the invention, multiple operating transmission and authentication pipelines of the authentication system can be monitored simultaneously. The target data processing objects and target image processing objects include the data processing objects and image processing objects that need to be monitored on different transmission and authentication pipelines. The authentication task information for different transmission and authentication pipelines is determined from the authentication list. The authentication task information for each transmission and authentication pipeline (hereinafter referred to as the pipeline) is parsed to obtain the authentication object, authentication interval, authentication quantity, and authentication equipment information for the current pipeline. The authentication object refers to the models of the electricity meters being authenticated by the current pipeline. Different models and batches of electricity meters require different authentication items, i.e., different data processing objects and image processing objects. Different authentication items are operated by corresponding mechanical equipment. The authentication equipment information refers to the operations performed by the mechanical equipment used to operate the corresponding authentication item. The authentication quantity refers to the number of mechanical equipment participating in the operation of the corresponding authentication item. Since the mechanical equipment used to perform all authentication operations is sequentially configured on the transmission and authentication pipeline, and each batch of electricity meters may only require a few of these mechanical equipment, the applied mechanical equipment is also discontinuous. Therefore, based on the operating time of each piece of machinery, the conveyor speed, and the distance between different pieces of machinery, the time interval for the same energy meter to be calibrated at different workstations can be calculated, i.e., the calibration interval. For example, if the current conveyor line requires machinery A and machinery B, with machinery A operating for 10 seconds, a distance of 5 meters between the two pieces of machinery, and a conveyor speed of 0.5 m / s, the calibration interval between machinery A and machinery B can be calculated as 5 ÷ 0.5 + 10 = 20 seconds.

[0069] To identify target data processing objects and target image processing objects, updated identification and monitoring conditions are obtained. These conditions include at least one of quantity, time, and feature conditions, and can be updated based on the number of electricity meters currently in the identification batch and the monitoring accuracy requirements of that batch. The quantity condition specifies the number of pieces of equipment to be sampled. The time condition is the time interval between data extractions; for example, if the time condition is less than 50 seconds, and the identification interval for equipment A and equipment B is 20 seconds, then the data processing objects and image processing objects corresponding to equipment A and equipment B are the target data processing objects and target image processing objects, respectively. The feature conditions include the model characteristics of the electricity meters and the operational characteristics of the equipment. For example, if the feature conditions include equipment used for voltage detection and a model H electricity meter, then the data processing objects and image processing objects corresponding to the model H electricity meter and the equipment used for voltage detection are the target data processing objects and target image processing objects, respectively. In other words, during the screening process, quantity conditions serve as constraints on the number of identifications, time conditions serve as constraints on the identification interval, and characteristic conditions serve as constraints on the information of the identification objects and identification equipment.

[0070] It should be noted that quantity, time, and characteristic conditions can be used individually or in combination as identification and monitoring conditions. The combination method and the specific content of each condition can be customized according to monitoring needs, and this embodiment of the invention does not impose specific limitations. The target data processing object and the target image processing object are determined through the updated identification and monitoring conditions. Since the identification and monitoring conditions are updated based on the number of electricity meters in the current identification batch and the monitoring accuracy requirements of the current identification batch, they can more accurately match the number and accuracy requirements of the electricity meters currently being identified, and also give users greater freedom and flexibility in configuring the identification and monitoring conditions.

[0071] 102. Acquire the target data corresponding to the target data processing object and the target image corresponding to the target image processing object according to the acquisition node.

[0072] In this embodiment of the invention, to reduce the amount of data collected, target data corresponding to the target data processing object and target images corresponding to the target image processing object are obtained by extracting data from the full dataset according to the collection nodes. The collection nodes are the time points at which data and images are extracted, and can be determined according to the sorting operation time and runtime of the mechanical equipment. Since the mechanical and testing equipment in the identification system continuously generate data during the identification process, acquiring all the data would require processing a large amount of data, significantly increasing the platform's data processing pressure and unnecessarily consuming platform resources. Furthermore, since the collection nodes are determined by the sorting operation time and runtime of the mechanical equipment, matching the current distribution pattern of data and image generation, the target data and target images obtained according to the collection nodes can meet the monitoring accuracy requirements. Therefore, acquiring data and images according to the collection nodes can reduce the amount of monitoring data processed while ensuring monitoring accuracy, thereby reducing the platform's data processing pressure.

[0073] In one embodiment of the present invention, for further illustration and limitation, such as Figure 2 As shown, before the steps of acquiring the target data corresponding to the target data processing object and the target image corresponding to the target image processing object according to the acquisition node, the method further includes:

[0074] 203. Obtain the flow time of the transmission identification pipeline, and perform a ratio calculation based on the sorting operation time and the sorting operation runtime to determine the number of nodes.

[0075] 204. Determine the acquisition nodes based on the flow duration and the number of nodes.

[0076] In this embodiment of the invention, after filtering by energy meter, model, and mechanical equipment dimensions to obtain target data processing objects and target image processing objects, it is also necessary to filter the data corresponding to the target data processing objects and target image processing objects, that is, to acquire target data and target images according to the acquisition nodes. The number of acquisition nodes is calculated based on the sorting operation time and sorting operation runtime. The sorting operation time is the duration for the mechanical equipment to sort the energy meters, and the sorting operation runtime is the time required to complete the sorting operation for all energy meters in the current batch. The quotient of the sorting operation runtime and the sorting operation time is taken as the number of nodes. For example, if the sorting operation runtime is 1 hour and the sorting operation time is 6 minutes, then the number of nodes is 10. Then, the acquisition nodes, i.e., the acquisition time points, are calculated based on the flow duration and the number of nodes. The flow duration is the time it takes for the energy meter to go from being put into the transmission and identification flow line to leaving the line. The time interval between data collection nodes is obtained by dividing the data flow duration by the number of nodes. Then, the data collection time point, i.e. the data collection node, is determined based on this time interval and the current system time.

[0077] It should be noted that by extracting target data and target images according to the acquisition nodes, the amount of data and images collected and processed can be reduced while ensuring a balanced distribution of monitoring data, thereby achieving the goal of accurate monitoring based on a small amount of data processing.

[0078] In one embodiment of the present invention, for further explanation and limitation, the step of acquiring target data corresponding to the target data processing object and target image corresponding to the target image processing object according to the acquisition node includes:

[0079] According to the acquisition node, a first acquisition instruction is sent to the first acquisition device of the target data processing object, and the target data transmitted is received.

[0080] The acquisition node sends a second acquisition command to the second acquisition device of the target image processing object and receives the transmitted target image.

[0081] In this embodiment of the invention, to achieve data acquisition, when a data acquisition node is triggered, a data acquisition command is sent to the first acquisition device corresponding to the target data processing object, so that the first acquisition device acquires the target data of the corresponding mechanical equipment and forwards it to the current execution entity. The first acquisition device is configured on the mechanical equipment acquiring the target data, and may be an electrical data acquisition device, a mechanical equipment operation data acquisition device, etc. Similarly, to achieve image acquisition, a data acquisition command is also sent to the second acquisition device corresponding to the target image processing object, so that the second acquisition device acquires the target image of the corresponding mechanical equipment and forwards it to the current execution entity. The second acquisition device is configured in the identification area of ​​the acquired target image, and one identification area can correspond to multiple second acquisition devices.

[0082] It should be noted that when the second acquisition device is a device with a variable image acquisition area, that is, when the image acquisition device corresponds to multiple identification areas, the second acquisition device can adjust the image acquisition angle according to the acquisition command to find the identification area to be captured. Since one identification area corresponds to multiple second acquisition devices, and not every second acquisition device's current shooting position corresponds to the identification area pointed to by the acquisition command, before sending the acquisition command to the second acquisition device, images captured by each second acquisition device at its current position are retrieved to determine the second acquisition device pointing to the required identification area, and to confirm that the selected second acquisition device can capture the image of the identification area.

[0083] 103. Using the feature type corresponding to the acquisition node, feature extraction is performed on the target data and the target image to obtain monitoring features. The monitoring features are then compared according to the monitoring strategy matched by the identification list to obtain the data monitoring results of the target data processing object and the target image processing object.

[0084] In this embodiment of the invention, after determining the acquisition nodes, the feature types of the monitoring data and images can be matched according to the density or number of acquisition nodes. Feature extraction is then performed using the matching feature types to obtain the features in the target data and target images that satisfy the feature types, i.e., the monitoring features. After extracting the monitoring features from the target data and target images, feature comparison is performed on the monitoring features according to the monitoring strategy. The monitoring strategy is pre-configured, and different monitoring strategies are matched with different identification tasks in the identification list. For example, the monitoring strategy for the electricity meter function test task includes preset current extreme values, voltage extreme values, and standard test image features. The maximum current and maximum voltage in the target data are extracted, and the positional relationship between the mechanical equipment and the electricity meter in the target image is extracted. The maximum current is compared with the current extreme value, and the maximum voltage is compared with the voltage extreme value. The positional relationship between the mechanical equipment and the electricity meter in the target image is compared with the standard positional relationship between the mechanical equipment and the electricity meter in the standard test image features. If the maximum current and maximum voltage do not exceed the corresponding extreme values, the data monitoring result of the target data processing object is normal; otherwise, it is abnormal. If the positional relationship between the mechanical equipment and the electricity meter in the target image is consistent with the positional relationship in the standard test image, then the data monitoring result of the target image processing object is normal; otherwise, it is abnormal.

[0085] It should be noted that since the target data and target images are data corresponding to the target data processing objects and target image processing objects screened based on the identification list, and the data is extracted according to the collection nodes, the data processing volume and data processing pressure of the electricity meter sorting and identification platform are greatly reduced. At the same time, the monitoring delay is greatly avoided, thereby ensuring the effectiveness and accuracy of the data monitoring of the electricity meter sorting and identification platform.

[0086] In one embodiment of the present invention, for further explanation and limitation, the step of extracting features from the target data and the target image using the feature type corresponding to the acquisition node to obtain monitoring features includes:

[0087] Obtain the feature type pre-bound to the acquisition node;

[0088] Numerical extraction is performed on the target data according to the feature types, and the identifier is extracted from the target image to obtain identifier monitoring features and numerical monitoring features.

[0089] In this embodiment of the invention, the acquisition nodes are bound to feature types, including extreme value feature types or blacklist / whitelist feature types. The binding relationship between feature types and acquisition nodes can be determined based on the time interval between acquisition nodes. For example, if the time interval between acquisition nodes is smaller than the historical average, i.e., the acquisition nodes are relatively dense, then the feature types are configured as extreme value feature types and blacklist / whitelist feature types. If the time interval between acquisition nodes is larger than the historical average, i.e., the acquisition nodes are relatively sparse, then the feature type is configured as a blacklist / whitelist feature type. If the time interval between acquisition nodes is close to the historical average, then the feature type is configured as an extreme value feature type. After determining the feature type, numerical extraction is performed on the target data according to the feature type, and identifier extraction is performed on the target image. Extreme value feature types can be used to judge target data and target images; similarly, blacklist / whitelist feature types can also be used to judge target data and target images. For example, if the feature type is a maximum value, then the maximum value of the target data is extracted; if the feature type is a minimum value, then the minimum value of the target data is extracted; if the feature type is the marginal extreme value of the target detection box, then the marginal maximum and minimum values ​​of the target detection boxes of the same object in the target image are extracted. For example, if the feature type is a blacklist or whitelist feature type, then the target detection box is marked for the corresponding features in the target image according to the target features recorded in the blacklist or whitelist; and the data value coverage range is extracted from the target data according to the numerical range recorded in the blacklist or whitelist.

[0090] In one embodiment of the present invention, for further explanation and limitation, the step of performing feature comparison on the monitoring features according to the monitoring strategy of the identification list to obtain the data monitoring results of the target data processing object and the target image processing object includes:

[0091] The monitoring strategy for retrieving the aforementioned identification list;

[0092] Based on the monitoring strategy, the identification monitoring strategy and the numerical monitoring features are compared to obtain the data monitoring results.

[0093] In this embodiment of the invention, the monitoring strategy includes a single monitoring strategy or a combination of monitoring strategies. The monitoring strategy is matched to the content of different identification tasks in the identification list. For example, if the identification task for the current batch of electricity meters is relatively simple, only measuring values ​​such as voltage and current, then the monitoring strategy can be a single extreme value comparison strategy. That is, if the voltage and current values ​​are less than or equal to the corresponding monitoring extreme values, it is normal; if they are greater than the corresponding monitoring extreme values, it is abnormal. If the current batch of electricity meters requires comprehensive identification, and the identification tasks in the identification list include multiple tests such as prepaid function detection, voltage, current and power detection, and electricity meter type identification, then the monitoring strategy includes a combination of sub-monitoring strategies corresponding to each monitoring item. After obtaining the sub-monitoring results based on each sub-monitoring strategy, if all sub-monitoring results are normal, the obtained monitoring result is normal; otherwise, it is abnormal. For example, a logical judgment combination algorithm can be used as the sub-monitoring strategy for the corresponding prepaid function detection, monitoring relevant parameters (remaining amount, tripping threshold, etc.) and events (recharge, tripping, etc.) of the prepaid function, and determining whether the prepaid function is normal based on a preset logical relationship. For example, machine learning algorithms (such as decision trees, neural networks, etc.) can be used to train and learn the characteristic data of electricity meters (such as model, specifications, manufacturer, etc.) to build a classification model. If the electricity meter type matches the blacklist, the monitoring result is abnormal; if the electricity meter type matches the whitelist, the monitoring result is normal.

[0094] In one embodiment of the present invention, for further explanation and limitation, the method further includes:

[0095] When the data monitoring result indicates data anomaly, an abnormal operation warning message is generated to drive warning operations and identify abnormal operations based on the abnormal operation warning message.

[0096] In this embodiment of the invention, when the data monitoring result is abnormal, it indicates that there is an abnormal energy meter or malfunctioning mechanical equipment used for identification during the current identification process. To address the abnormal situation or notify relevant personnel for handling, an abnormal operation warning message corresponding to the cause of the data abnormality is generated. This allows the control terminal to control audible, visual, and electrical warning devices to perform warning operations based on the abnormal operation warning message, i.e., drive the warning operation. Simultaneously, it controls the mechanical equipment and the transmission identification pipeline to perform corresponding identification abnormality operations. For example, if an abnormal energy meter is identified, the abnormal energy meter is removed from the transmission identification pipeline; if the identification mechanical equipment is malfunctioning, the transmission identification pipeline and the corresponding mechanical equipment are stopped. By driving the warning operation and the identification abnormality operation, timely handling and feedback of abnormal situations can be achieved, avoiding derivative impacts caused by abnormalities, thereby improving the effectiveness of monitoring.

[0097] This invention provides a data monitoring method for an electricity meter sorting and identification platform. Compared with existing technologies, this invention determines target data processing objects and target image processing objects based on an identification list of the electricity meter sorting and identification platform. The identification list stores identification task information corresponding to different transmission identification pipelines. Target data corresponding to the target data processing object and target images corresponding to the target image processing object are acquired according to acquisition nodes, which are determined based on the sorting operation time and duration of the mechanical equipment. Features are extracted from the target data and target images using the feature types corresponding to the acquisition nodes to obtain monitoring features. These monitoring features are then compared according to the monitoring strategy matched to the identification list to obtain the data monitoring results for the target data processing object and the target image processing object. Specifically, filtering target data processing objects and target image processing objects based on the identification list allows for data filtering from both equipment and electricity meter type dimensions, while data extraction according to acquisition nodes is performed from a time dimension. Through filtering and extraction, the data processing volume and pressure of the electricity meter sorting and identification platform are significantly reduced, while monitoring delays are greatly avoided, thereby ensuring the effectiveness and accuracy of data monitoring for the electricity meter sorting and identification platform.

[0098] Furthermore, as a response to the above Figure 1 The implementation of the method shown in this invention provides a data monitoring system for an electricity meter sorting and identification platform, such as... Figure 3 As shown, the system includes:

[0099] The determination module 31 is used to determine the target data processing object and the target image processing object based on the identification list of the electricity meter sorting and identification platform. The identification list stores identification task information corresponding to different transmission identification pipelines.

[0100] The acquisition module 32 is used to acquire target data corresponding to the target data processing object and target image corresponding to the target image processing object according to the acquisition node. The acquisition node is determined according to the sorting operation time and sorting operation duration of the mechanical equipment.

[0101] The feature processing module 33 is used to extract features from the target data and the target image using the feature type corresponding to the acquisition node, obtain monitoring features, and compare the monitoring features according to the monitoring strategy matched by the identification list, so as to obtain the data monitoring results of the target data processing object and the target image processing object.

[0102] Furthermore, the determining module 31 includes:

[0103] The parsing unit is used to parse the identification objects, identification intervals, identification quantities, and identification equipment information of different transmission identification pipelines in the identification task information;

[0104] The filtering unit is used to obtain the updated identification and monitoring conditions, and to filter out the target data processing object and the target image processing object from the identification object, identification interval, identification quantity and identification equipment information based on the identification and monitoring conditions.

[0105] The identification and monitoring conditions include at least one of quantity conditions, time conditions, and characteristic conditions.

[0106] Furthermore, the system also includes:

[0107] The calculation module is used to obtain the flow time of the transmission identification pipeline, and to perform a ratio calculation based on the sorting operation time and the sorting operation runtime to determine the number of nodes;

[0108] The determining module is also used to determine the acquisition node based on the flow duration and the number of nodes.

[0109] Furthermore, the acquisition module 32 includes:

[0110] The first acquisition unit is used to send a first acquisition instruction to the first acquisition device of the target data processing object according to the acquisition node, and to receive the transmitted target data. The first acquisition device is configured on the mechanical equipment for acquiring the target data.

[0111] The second acquisition unit is used to send a second acquisition instruction to the second acquisition device of the target image processing object according to the acquisition node, and to receive the transmitted target image, wherein the second acquisition device is configured in the identification area of ​​the target image.

[0112] Furthermore, the feature processing module 33 includes:

[0113] The acquisition unit is used to acquire the feature types pre-bound to the acquisition node, including extreme value feature types and blacklist / whitelist feature types;

[0114] The feature extraction unit is used to extract numerical values ​​from the target data according to the feature type, and to extract identifiers from the target image to obtain identifier monitoring features and numerical monitoring features.

[0115] Furthermore, the feature processing module 33 also includes:

[0116] The retrieval unit is used to retrieve the monitoring strategy of the identification list, wherein the monitoring strategy includes a single monitoring strategy or a combination of monitoring strategies;

[0117] The feature comparison unit is used to compare the identification monitoring strategy and the numerical monitoring features based on the monitoring strategy to obtain the data monitoring results.

[0118] Furthermore, the system also includes:

[0119] The generation module is used to generate abnormal operation warning information when the data monitoring result is abnormal, so as to drive warning operation and identify abnormal operation based on the abnormal operation warning information.

[0120] This invention provides a data monitoring system for an electricity meter sorting and identification platform. Compared with existing technologies, this invention determines target data processing objects and target image processing objects based on an identification list of the electricity meter sorting and identification platform. The identification list stores identification task information corresponding to different transmission identification pipelines. Target data corresponding to the target data processing object and target images corresponding to the target image processing object are acquired according to acquisition nodes, which are determined based on the sorting operation time and duration of the mechanical equipment. Features are extracted from the target data and target images using the feature types corresponding to the acquisition nodes to obtain monitoring features. These monitoring features are then compared according to the monitoring strategy matched to the identification list to obtain the data monitoring results for the target data processing object and the target image processing object. Specifically, filtering target data processing objects and target image processing objects based on the identification list allows for data filtering from both equipment and electricity meter type dimensions, while data extraction according to acquisition nodes is performed from a time dimension. Through filtering and extraction, the data processing volume and pressure of the electricity meter sorting and identification platform are significantly reduced, while monitoring delays are greatly avoided, thereby ensuring the effectiveness and accuracy of data monitoring for the electricity meter sorting and identification platform.

[0121] According to one embodiment of the present invention, a storage medium is provided, the storage medium storing at least one executable instruction, which can execute the data monitoring method of the electricity meter sorting and identification platform in any of the above method embodiments.

[0122] Figure 4 The diagram shows a structural schematic of a terminal according to an embodiment of the present invention. The specific implementation of the terminal is not limited by the specific embodiment of the present invention.

[0123] like Figure 4 As shown, the terminal may include: a processor 402, a communications interface 404, a memory 406, and a communications bus 408.

[0124] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408.

[0125] Communication interface 404 is used to communicate with other network elements such as clients or other servers.

[0126] The processor 402 is used to execute program 410, specifically to execute the relevant steps in the above-described embodiment of the data monitoring method for the electricity meter sorting and identification platform.

[0127] Specifically, program 410 may include program code that includes computer operation instructions.

[0128] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The terminal may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0129] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0130] Specifically, program 410 can be used to cause processor 402 to perform the following operations:

[0131] The identification list of the electricity meter sorting and identification platform determines the target data processing object and the target image processing object. The identification list stores identification task information corresponding to different transmission identification pipelines.

[0132] The target data corresponding to the target data processing object and the target image corresponding to the target image processing object are acquired according to the acquisition node. The acquisition node is determined according to the sorting operation time and sorting operation duration of the mechanical equipment.

[0133] By utilizing the feature types corresponding to the acquisition nodes, feature extraction is performed on the target data and the target image to obtain monitoring features. The monitoring features are then compared according to the monitoring strategy matched by the identification list to obtain the data monitoring results of the target data processing object and the target image processing object.

[0134] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing systems. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Optionally, they can be implemented using program code executable by a computing system, thereby storing them in a storage system for execution by the computing system. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0135] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A data monitoring method for an electricity meter sorting and identification platform, characterized in that, include: The identification list of the electricity meter sorting and identification platform determines the target data processing object and the target image processing object. The identification list stores identification task information corresponding to different transmission identification pipelines. The target data corresponding to the target data processing object and the target image corresponding to the target image processing object are acquired according to the acquisition node. The acquisition node is determined according to the sorting operation time and sorting operation duration of the mechanical equipment. The target data and the target image are feature extracted using the feature type corresponding to the acquisition node to obtain monitoring features. The monitoring features are then compared according to the monitoring strategy matched by the identification list to obtain the data monitoring results of the target data processing object and the target image processing object. The identification list based on the electricity meter sorting and identification platform determines the target data processing object and the target image processing object, including: Analyze the identification objects, identification intervals, identification quantities, and identification equipment information of different transmission identification pipelines in the identification task information; Obtain the updated identification and monitoring conditions, and based on the identification and monitoring conditions, filter out the target data processing object and the target image processing object from the identification object, identification interval, identification quantity, and identification equipment information; The identification and monitoring conditions include at least one of quantity conditions, time conditions, and characteristic conditions.

2. The method according to claim 1, characterized in that, Before acquiring the target data corresponding to the target data processing object and the target image corresponding to the target image processing object according to the acquisition node, the method further includes: The process duration of the transmission identification pipeline is obtained, and the number of nodes is determined by performing a ratio calculation based on the sorting operation time and the sorting operation runtime. The data collection nodes are determined based on the flow duration and the number of nodes.

3. The method according to claim 2, characterized in that, The step of acquiring the target data corresponding to the target data processing object and the target image corresponding to the target image processing object according to the acquisition node includes: The first acquisition device sends a first acquisition instruction to the first acquisition device of the target data processing object according to the acquisition node, and receives the transmitted target data. The first acquisition device is configured on the mechanical equipment for acquiring the target data. The acquisition node sends a second acquisition command to the second acquisition device of the target image processing object and receives the transmitted target image. The second acquisition device is configured in the identification area of ​​the target image.

4. The method according to claim 3, characterized in that, The step of extracting features from the target data and the target image using the feature type corresponding to the acquisition node to obtain the monitoring features includes: Obtain the feature types pre-bound to the collection node, including extreme value feature types and blacklist / whitelist feature types; Numerical extraction is performed on the target data according to the feature types, and the identifier is extracted from the target image to obtain identifier monitoring features and numerical monitoring features.

5. The method according to claim 4, characterized in that, The step of performing feature comparison on the monitoring features according to the monitoring strategy of matching the identification list to obtain the data monitoring results of the target data processing object and the target image processing object includes: Retrieve the monitoring strategy of the identification list, wherein the monitoring strategy includes a single monitoring strategy or a combination of monitoring strategies; Based on the monitoring strategy, the identification monitoring features and the numerical monitoring features are compared to obtain the data monitoring results.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: When the data monitoring result indicates data anomaly, an abnormal operation warning message is generated to drive warning operations and identify abnormal operations based on the abnormal operation warning message.

7. A data monitoring system for an electricity meter sorting and identification platform, characterized in that, include: The determination module is used to determine target data processing objects and target image processing objects based on the identification list of the electricity meter sorting and identification platform. The identification list stores identification task information corresponding to different transmission and identification pipelines. The determination of target data processing objects and target image processing objects based on the identification list of the electricity meter sorting and identification platform includes: parsing the identification objects, identification intervals, identification quantities, and identification equipment information of different transmission and identification pipelines in the identification task information; obtaining updated identification monitoring conditions; and filtering target data processing objects and target image processing objects from the identification objects, identification intervals, identification quantities, and identification equipment information based on the identification monitoring conditions. The identification monitoring conditions include at least one of quantity conditions, time conditions, and feature conditions. The acquisition module is used to acquire target data corresponding to the target data processing object and target image corresponding to the target image processing object according to the acquisition node. The acquisition node is determined according to the sorting operation time and sorting operation duration of the mechanical equipment. The feature processing module is used to extract features from the target data and the target image using the feature type corresponding to the acquisition node, obtain monitoring features, and perform feature comparison on the monitoring features according to the monitoring strategy matched by the identification list, so as to obtain the data monitoring results of the target data processing object and the target image processing object.

8. A storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the data monitoring method of the electricity meter sorting and identification platform as described in any one of claims 1-6.

9. A terminal, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the data monitoring method of the electricity meter sorting and identification platform as described in any one of claims 1-6.

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