Data monitoring method and system of electric energy meter sorting and identifying platform

Abstract

The invention discloses a data monitoring method and system for an electric energy meter sorting and identifying platform, and relates to the technical field of electric power data. The problem that an existing electric energy meter sorting and identifying platform is low in data monitoring effectiveness and accuracy is mainly solved. The method mainly comprises the steps of determining a target data processing object and a target image processing object based on an identification list of an electric energy meter sorting and identification platform; acquiring target data corresponding to the target data processing object and a target image corresponding to the target image processing object according to an acquisition node, wherein the acquisition node is determined according to the sorting operation time and the sorting operation running duration of the mechanical equipment; and performing feature extraction on the target data and the target image by using the feature types corresponding to the acquisition nodes to obtain monitoring features, and performing feature comparison on the monitoring features according to a monitoring strategy matched with the identification list to obtain data monitoring results of the target data processing object and the target image processing object. The method is mainly used for monitoring data of the electric energy meter sorting and identifying platform.

Description

Technical Field

[0001] The present invention relates to the field of power data technology, and in particular to a data monitoring method and system for an electric energy meter sorting and identification platform. Background Art

[0002] With the development of intelligent layout and intelligent identification technologies of electricity meters in power systems, the electricity meter sorting and identification platform identifies each electricity meter in the power transmission network to ensure the safety and effectiveness of the electricity meters.

[0003] Currently, existing electricity meter sorting and verification platforms typically monitor data during meter verification using video recording. Specifically, cameras are installed in each transmission area of ​​the sorting and verification system, and the captured video data serves as the basis for monitoring. However, because the parsing and processing of video data requires the platform to process large amounts of frame image data, this introduces monitoring delays and increases the platform's data processing pressure, significantly impacting the effectiveness and accuracy of the platform's data monitoring. Summary of the Invention

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

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

[0006] Determine the target data processing object and the target image processing object based on the identification list of the electric energy meter sorting and identification platform, wherein the identification list stores identification task information corresponding to different transmission identification pipelines;

[0007] Acquiring target data corresponding to the target data processing object and a target image corresponding to the target image processing object according to an acquisition node, wherein the acquisition node is determined according to a sorting operation time and a running time of the sorting operation of the mechanical equipment;

[0008] The target data and the target image are subjected to feature extraction using the feature type corresponding to the acquisition node to obtain monitoring features, and the monitoring features are subjected to feature comparison according to the monitoring strategy matched by the identification list to obtain data monitoring results of the target data processing object and the target image processing object.

[0009] Furthermore, the determination of the target data processing object and the target image processing object based on the identification list of the electric energy meter sorting and identification platform includes:

[0010] Parsing the identification object, identification interval, identification quantity, and identification equipment information of different transmission identification pipelines in the identification task information;

[0011] Acquire updated identification monitoring conditions, and select 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;

[0012] Wherein, 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] Obtaining the flow length of the transmission and identification pipeline, and performing a ratio calculation based on the sorting operation time and the sorting operation running time to determine the number of nodes;

[0015] The collection node is determined based on the flow time and the number of nodes.

[0016] Furthermore, 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:

[0017] sending a first acquisition instruction to a first acquisition device of the target data processing object according to the acquisition node, and receiving the transmitted target data, wherein the first acquisition device is configured on a mechanical device that collects the target data;

[0018] According to the acquisition node, a second acquisition instruction is sent to a second acquisition device of the target image processing object, and the transmitted target image is received. The second acquisition device is configured to acquire an identification area of ​​the target image.

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

[0020] Acquire the feature type pre-bound to the acquisition node, the feature type including extreme value feature type and blacklist and whitelist feature type;

[0021] According to the feature types, numerical extraction is performed on the target data, and identification extraction is performed on the target image to obtain identification monitoring features and numerical monitoring features.

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

[0023] Retrieving the monitoring strategy of the identification list, wherein the monitoring strategy includes a single monitoring strategy or a combined monitoring strategy;

[0024] Based on the monitoring strategy, feature comparison is performed on the identification monitoring strategy and the numerical monitoring feature to obtain a data monitoring result.

[0025] Furthermore, the method further comprises:

[0026] When the data monitoring result is data abnormality, abnormal operation warning information is generated to drive warning operation and identify abnormal operation based on the abnormal operation warning information.

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

[0028] A determination module, configured to determine a target data processing object and a target image processing object based on an identification list of an electric energy meter sorting and identification platform, wherein the identification list stores identification task information corresponding to different transmission identification pipelines;

[0029] an acquisition module, configured to acquire target data corresponding to the target data processing object and a target image corresponding to the target image processing object according to an acquisition node, wherein the acquisition node is determined according to a sorting operation time and a running time of the sorting operation of the mechanical equipment;

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

[0031] Furthermore, the determining module includes:

[0032] a parsing unit, configured to parse the identification object, identification interval, identification quantity, and identification equipment information of different transmission identification pipelines in the identification task information;

[0033] a screening unit, configured to obtain updated identification monitoring conditions, and screen out target data processing objects and target image processing objects from the identification objects, identification intervals, identification quantities, and identification device information based on the identification monitoring conditions;

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

[0035] Furthermore, the system further comprises:

[0036] A calculation module, configured to obtain the flow time of the transmission and identification pipeline, and perform a ratio calculation based on the sorting operation time and the sorting operation running time to determine the number of nodes;

[0037] The determination module is further configured to determine a collection node based on the flow duration and the number of nodes.

[0038] Furthermore, the acquisition module includes:

[0039] a first acquisition unit, configured to send a first acquisition instruction to a first acquisition device of the target data processing object according to the acquisition node, and receive the transmitted target data, wherein the first acquisition device is configured on a mechanical device that collects 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 receive the transmitted target image, and the second acquisition device is configured to acquire the identification area of ​​the target image.

[0041] Furthermore, the feature processing module includes:

[0042] An acquiring unit, configured to acquire a feature type pre-bound to the acquisition node, wherein the feature type includes an extreme value feature type and a blacklist and whitelist feature type;

[0043] The feature extraction unit is used to perform numerical extraction on the target data and identifier extraction on the target image according to the feature type, so as to obtain identifier monitoring features and numerical monitoring features.

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

[0045] A retrieving unit, configured to retrieve the monitoring strategy of the identification list, wherein the monitoring strategy includes a single monitoring strategy or a combined monitoring strategy;

[0046] A feature comparison unit is used to perform feature comparison on the identification monitoring strategy and the numerical monitoring feature based on the monitoring strategy to obtain a data monitoring result.

[0047] Furthermore, the system further comprises:

[0048] A generation module is used to generate abnormal operation warning information when the data monitoring result is data abnormality, 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 the storage medium stores at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the data monitoring method of the above-mentioned electric energy meter sorting and identification platform.

[0050] According to another aspect of the present invention, there is provided a terminal, 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 via the communication bus;

[0051] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the data monitoring method of the above-mentioned electric energy meter sorting and identification platform.

[0052] By means of the above technical solution, the technical solution provided by the embodiment of the present invention has at least the following advantages:

[0053] The present invention provides a data monitoring method and system for an electric energy meter sorting and identification platform. Compared with the prior art, the present invention determines target data processing objects and target image processing objects based on an identification list of the electric energy meter sorting and identification platform. The identification list stores identification task information corresponding to different transmission identification pipelines; obtains target data corresponding to the target data processing object and target image corresponding to the target image processing object according to an acquisition node, where the acquisition node is determined according to the sorting operation time and the sorting operation running time of the mechanical equipment; extracts features from the target data and target image using the feature type corresponding to the acquisition node to obtain monitoring features, and compares the monitoring features according to the monitoring strategy matched with the identification list to obtain data monitoring results for the target data processing object and target image processing object. The screening of target data processing objects and target image processing objects based on the identification list can filter data from the device dimension and the electric energy meter type dimension, while the extraction based on the acquisition node extracts data from the time dimension. Through screening and extraction, the data processing volume and pressure of the electric energy meter sorting and identification platform are greatly reduced, while monitoring delays are greatly avoided, thereby ensuring the effectiveness and accuracy of data monitoring of the electric energy meter sorting and identification platform.

[0054] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0056] Figure 1 A flow chart of a data monitoring method for an electric energy meter sorting and identification platform provided by an embodiment of the present invention is shown;

[0057] Figure 2 A flow chart of a data monitoring method for another electric energy meter sorting and identification platform provided by an embodiment of the present invention is shown;

[0058] Figure 3 A block diagram showing the composition of a data monitoring system for an electric energy meter sorting and identification platform provided by an embodiment of the present invention is shown;

[0059] Figure 4 A schematic structural diagram of a terminal provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0060] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0061] The embodiment of the present invention provides a data monitoring method for an electric energy 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 present 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, and this is not specifically limited in this embodiment. The identification tasks of the electricity meter sorting and identification platform are constantly changing and updated based on the identification production schedule. Accordingly, the monitoring targets of the electricity meter sorting and identification platform must be confirmed before each monitoring. For example, on the morning of December 3rd, the identification task of transmission identification line 1 was to inspect the voltage output function, current output function, and fee control function of 1,000 electricity meters in batch A and apply labels. On the afternoon of December 3rd, the identification task of transmission identification line 2 was to inspect the communication function, current output function, and fee control function of 500 electricity meters in batch B and identify the meter models. The identification list stores identification task information corresponding to different transmission identification lines. Before monitoring the identification data of the electricity meter sorting and identification platform, the processing target of the currently monitored identification task can be determined based on the identification list. Given that the identification data is in both image and data form, the target data processing target and the target image processing target must be determined separately. Among them, the appraisal list can be an appraisal production schedule.

[0064] It should be noted that the monitoring of the electricity meter sorting and identification platform includes both the electricity meter data collected during the identification process and the operating data of the relevant equipment performing the identification operations in the identification system. Therefore, the target data processing objects and target image processing objects include both the operation items for electricity and the mechanical equipment performing the operation items. Regarding the content described in the above example, on the afternoon of December 3rd, the target data processing objects of the 2# transmission identification pipeline were the communication function parameters, current and fee control function parameters, and the operating data of the mechanical equipment corresponding to these parameters; the target image processing objects were the electricity meter label image and the operating 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 explanation and limitation, 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 electric energy meter sorting and identification platform includes:

[0066] 201. Parse the identification task information to determine the identification object, identification interval, identification quantity, and identification equipment information of different transmission identification pipelines.

[0067] 202. Obtain updated identification monitoring conditions, and filter out 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.

[0068] In an embodiment of the present invention, multiple active transmission and identification pipelines in an identification 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 identification pipelines. Identification task information for each transmission and identification pipeline (hereinafter referred to as a pipeline) is determined from an identification list. The identification task information for each transmission and identification pipeline (hereinafter referred to as a pipeline) is parsed to obtain the identification object, identification interval, identification quantity, and identification equipment information for the current pipeline. The identification object refers to the types of electric energy meters being identified by the current pipeline. Different models and batches of electric energy meters require different identification items, i.e., different data processing objects and image processing objects. Different identification items are performed by corresponding mechanical equipment. The identification equipment information refers to the operations performed by the mechanical equipment used to perform the corresponding identification items. The identification quantity refers to the number of mechanical equipment involved in performing the corresponding identification items. Since the mechanical equipment used to perform all identification operations is sequentially configured on the transmission and identification pipeline, each batch of electric energy meters may require only a few of these mechanical equipment, and the mechanical equipment used is also discontinuous. Therefore, based on the operating time of each mechanical device, the line transmission speed, and the distance between different mechanical devices, we can calculate the time interval between verifications for the same energy meter at different workstations, known as the verification interval. For example, if the current assembly line requires two mechanical devices, Mechanical Device A and Mechanical Device B, the operating time of Mechanical Device A is 10 seconds, the distance between the two devices is 5 meters, and the line transmission speed is 0.5 m / s, the verification interval between Mechanical Device A and Mechanical Device B can be calculated as 5 ÷ 0.5 + 10 = 20 seconds.

[0069] To screen out target data processing objects and target image processing objects, updated identification and monitoring conditions are obtained. The identification and monitoring conditions include at least one of quantity, time, and feature conditions, and can be updated based on the number of electricity meters in the current identification batch and the monitoring accuracy requirements for the current identification batch. The quantity condition is the condition for screening mechanical equipment, namely, the number of mechanical equipment data to be extracted. 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 Mechanical Equipment A and Mechanical Equipment B is 20 seconds, the data processing objects and image processing objects corresponding to Mechanical Equipment A and Mechanical Equipment B are the target data processing objects and target image processing objects. The feature conditions include the model characteristics of the electricity meter and the operating characteristics of the mechanical equipment. For example, if the feature conditions include mechanical equipment for voltage detection and an H-type electricity meter, the data processing objects and image processing objects corresponding to the H-type electricity meter and the mechanical equipment for voltage detection are the target data processing objects and target image processing objects. That is to say, in the screening process, the quantity condition serves as a constraint on the number of identifications, the time condition serves as a constraint on the identification interval, and the feature condition serves as a constraint on the identification object and identification equipment information.

[0070] It should be noted that the quantity condition, time condition, and feature condition can be used alone as identification and monitoring conditions, or they can be combined as identification and monitoring conditions. The condition combination method and the specific content of each condition can be customized according to the monitoring requirements, and the embodiment of the present invention does not make specific limitations. The target data processing object and the target image processing object are determined by the updated identification and monitoring conditions. Since the identification and monitoring conditions are updated based on the number of electric energy meters currently in the identification batch and the monitoring accuracy requirements of the current identification batch, they can more closely match the number and accuracy requirements of the electric energy meters currently in the identification, and also allow users to have a higher degree of freedom and flexibility in configuring the identification and monitoring conditions.

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

[0072] In an embodiment of the present 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 data according to acquisition nodes. The acquisition node is the time point at which data and images are extracted, and can be determined based on the sorting operation time and the duration of the sorting operation. Since the mechanical equipment and testing equipment in the identification system continuously generate data during the identification process, if all data were acquired, a large amount of data would need to be processed, which would significantly increase the platform's data processing pressure and cause unnecessary consumption of platform operating resources. Furthermore, since the acquisition node is determined by the sorting operation time and the duration of the sorting operation, the target data and target images acquired according to the acquisition node can meet the monitoring accuracy requirements by matching the distribution pattern of the current data and image generation. Therefore, acquiring data and images according to the acquisition node can reduce the monitoring data processing volume while ensuring monitoring accuracy, thereby reducing the platform's data processing pressure.

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

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

[0075] 204. Determine a collection node based on the flow duration and the number of nodes.

[0076] In this embodiment of the present invention, after obtaining the target data processing objects and target image processing objects by filtering the electricity meter model and mechanical equipment dimensions, the data corresponding to these objects must be filtered, specifically, the target data and target images must be obtained according to the acquisition nodes. The number of acquisition nodes is calculated based on the sorting operation time and the sorting operation runtime. The sorting operation time is the duration of the mechanical equipment sorting the electricity meters, and the sorting operation runtime is the duration required to complete the sorting operation for all electricity meters in the current batch. The quotient of the sorting operation runtime and the sorting operation time is used as the number of nodes. For example, if the sorting operation runtime is 1 hour and the sorting operation time is 6 minutes, the number of nodes is 10. The acquisition nodes, i.e., the time points of acquisition, are then calculated based on the flow time and the number of nodes. The flow time is the time it takes for an electricity meter to enter the transmission and identification line and leave the line. Based on the flow length divided by the number of nodes, the time interval of the collection nodes is obtained, and then the collection time point, that is, the 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 acquisition nodes, it is possible to reduce the amount of data and image acquisition and processing while ensuring the balanced distribution of monitoring data, thereby achieving the purpose 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 the target image corresponding to the target image processing object according to the acquisition node includes:

[0079] Sending a first acquisition instruction to a first acquisition device of the target data processing object according to the acquisition node, and receiving the transmitted target data;

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

[0081] In an embodiment of the present invention, in order to achieve data acquisition, when the acquisition node is triggered, an acquisition instruction 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 device and forwards it to the current execution entity. The first acquisition device is configured on the mechanical device that acquires the target data, and can be an electrical data acquisition device, a mechanical device operation data acquisition device, etc. Similarly, in order to achieve image acquisition, an acquisition instruction 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 device and forwards it to the current execution entity. The second acquisition device is configured in the identification area for acquiring the 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 transformable 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 instruction to find the identification area that needs to be photographed. Since one identification area corresponds to multiple second acquisition devices, and not the current shooting position of each second acquisition device corresponds to the identification area pointed to by the acquisition instruction. Before sending the acquisition instruction to the second acquisition device, the images captured by the current positions of each second acquisition device are retrieved to determine the second acquisition device pointing to the required identification area, and to determine whether the selected second acquisition device can capture the identification area image.

[0083] 103. Extract features of the target data and the target image using the feature type corresponding to the acquisition node to obtain monitoring features, and perform feature comparison on the monitoring features according to the monitoring strategy matched by the identification list to obtain data monitoring results of the target data processing object and the target image processing object.

[0084] In an embodiment of the present invention, after determining the acquisition nodes, the feature types of the monitoring data and images can be matched based on the density or number of acquisition nodes. Feature extraction is then performed using the matching feature types to obtain features in the target data and target image that meet the feature types, namely, monitoring features. After extracting the monitoring features from the target data and target image, feature comparison is performed on the monitoring features according to a monitoring strategy. The monitoring strategies are preconfigured, and different monitoring strategies are matched to different identification tasks in the identification list. For example, the monitoring strategy for an electric energy meter functional 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 electric energy meter in the target image is extracted. The maximum current is compared with the current extreme value, the maximum voltage is compared with the voltage extreme value, and the positional relationship between the mechanical equipment and the electric energy meter in the target image is compared with the standard positional relationship between the mechanical equipment and the electric energy 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 electric energy meter in the target image is consistent with the positional relationship in the standard test image, 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 the data corresponding to the target data processing objects and target image processing objects screened based on the identification list, and are data extracted according to the acquisition nodes, the data processing volume and data processing pressure of the electricity meter sorting and identification platform are greatly reduced. At the same time, monitoring delays are greatly avoided, thereby ensuring the data monitoring effectiveness and accuracy 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] According to the feature types, numerical extraction is performed on the target data, and identification extraction is performed on the target image to obtain identification monitoring features and numerical monitoring features.

[0089] In an embodiment of the present invention, a collection node is bound to a feature type, wherein the feature type includes an extreme value feature type or a blacklist or whitelist feature type. The binding relationship between the feature type and the collection node can be determined based on the time interval between the collection nodes. For example, if the time interval between the collection nodes is smaller than the historical average, i.e., the collection nodes are densely populated, the feature type is configured as an extreme value feature type or a blacklist or whitelist feature type. If the time interval between the collection nodes is larger than the historical average, i.e., the collection nodes are sparse, the feature type is configured as a blacklist or whitelist feature type. If the time interval between the collection nodes is close to the historical average, the feature type is configured as an extreme value feature type. After determining the feature type, numerical extraction is performed on the target data and identification extraction is performed on the target image based on the feature type. The extreme value feature type can be used to determine the target data and target image, and similarly, the blacklist or whitelist feature type can also be used to determine the target data and target image. For example, if the feature type is a maximum value, the maximum value of the target data is extracted; if the feature type is a minimum value, the minimum value of the target data is extracted; and if the feature type is a marginal extreme value of the target detection box, the marginal maximum and minimum values ​​of the target detection box of the same object in the target image are extracted. For another example, if the feature type is a black and white list feature type, the target detection frame is marked for the corresponding features in the target image according to the target features recorded in the black and white list; and the data value coverage range is extracted for the target data according to the value range recorded in the black and white list.

[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 matched by the identification list to obtain data monitoring results of the target data processing object and the target image processing object includes:

[0091] Retrieving the monitoring strategy for the identified list;

[0092] Based on the monitoring strategy, feature comparison is performed on the identification monitoring strategy and the numerical monitoring feature to obtain a data monitoring result.

[0093] In embodiments of the present invention, monitoring strategies include single monitoring strategies or combined monitoring strategies. The monitoring strategies are matched to the content of the different identification tasks in the identification list. For example, if the identification tasks for the current batch of electricity meters are relatively simple, measuring only values ​​such as voltage and current, the monitoring strategy can be a single extreme value comparison strategy. Specifically, if the voltage and current values ​​are less than or equal to the corresponding monitoring extreme values, the meter is considered normal; if they are greater than the corresponding monitoring extreme values, the meter is considered abnormal. If the current batch of electricity meters requires comprehensive identification, and the identification tasks in the identification list include multiple tests such as fee control function testing, voltage, current, and power testing, and meter type identification, the monitoring strategy can include a combination of sub-monitoring strategies corresponding to each monitoring item. After obtaining sub-monitoring results based on each sub-monitoring strategy, if all sub-monitoring results are normal, the resulting monitoring result is considered normal; otherwise, the result is considered abnormal. For example, a logical judgment combination algorithm can be used as the sub-monitoring strategy for fee control function testing. This algorithm monitors relevant parameters of the fee control function (remaining balance, trip threshold, etc.) and events (recharge, trip, etc.), and determines whether the fee control function is functioning properly based on pre-set logical relationships. For example, machine learning algorithms (such as decision trees and neural networks) can be used to train and learn the characteristic data of electricity meters (such as model, specifications, manufacturer, etc.) to establish 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 illustration and limitation, the method further includes:

[0095] When the data monitoring result is data abnormality, abnormal operation warning information is generated to drive warning operation and identify abnormal operation based on the abnormal operation warning information.

[0096] In an embodiment of the present invention, when the data monitoring result is a data anomaly, it indicates that there is an electric energy meter with an abnormal identification result or the mechanical equipment used for identification is operating abnormally in the current identification process. In order to deal with the abnormal situation or notify relevant personnel to handle the abnormality, abnormal operation warning information corresponding to the cause of the data anomaly is generated. Based on the abnormal operation warning information, the control end can control the warning equipment that emits sound, light, electricity, etc. to perform a warning operation, that is, drive the warning operation. At the same time, the mechanical equipment and the transmission identification pipeline are controlled to perform the corresponding identification abnormal operation. For example, in the case where the electric energy meter is identified as abnormal, the abnormal electric energy meter is removed from the transmission identification pipeline; in the case where the mechanical equipment is identified as operating abnormally, the transmission identification pipeline and the corresponding mechanical equipment are stopped. By driving the warning operation and the identification abnormal operation, it is possible to achieve timely processing and timely feedback of abnormal situations, avoid derivative effects caused by abnormalities, and thus improve the effectiveness of monitoring.

[0097] The present invention provides a data monitoring method for an electric energy meter sorting and identification platform. Compared with the prior art, the present invention determines target data processing objects and target image processing objects based on an identification list of the electric energy 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 image corresponding to the target image processing object are acquired according to an acquisition node. The acquisition node is determined according to the sorting operation time and the sorting operation running time of the mechanical equipment. Feature extraction is performed on the target data and target image 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 with the identification list to obtain data monitoring results for the target data processing object and the target image processing object. Filtering the target data processing object and target image processing object based on the identification list can filter data from the device dimension and the electric energy meter type dimension, while extracting data based on the acquisition node is performed from the time dimension. Through filtering and extraction, the data processing volume and pressure of the electric energy meter sorting and identification platform are greatly reduced. At the same time, monitoring delays are greatly avoided, thereby ensuring the effectiveness and accuracy of data monitoring on the electric energy meter sorting and identification platform.

[0098] Furthermore, as a response to the above Figure 1 The embodiment of the present invention provides a data monitoring system for an electric energy meter sorting and identification platform, such as Figure 3 As shown, the system includes:

[0099] A determination module 31 is configured to determine a target data processing object and a target image processing object based on an identification list of an electric energy meter sorting and identification platform, wherein the identification list stores identification task information corresponding to different transmission identification pipelines;

[0100] an acquisition module 32 for acquiring target data corresponding to the target data processing object and a target image corresponding to the target image processing object according to an acquisition node, wherein the acquisition node is determined according to the sorting operation time and the running time of the sorting operation of the mechanical equipment;

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

[0102] Furthermore, the determining module 31 includes:

[0103] a parsing unit, configured to parse the identification object, identification interval, identification quantity, and identification equipment information of different transmission identification pipelines in the identification task information;

[0104] a screening unit, configured to obtain updated identification monitoring conditions, and screen out target data processing objects and target image processing objects from the identification objects, identification intervals, identification quantities, and identification device information based on the identification monitoring conditions;

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

[0106] Furthermore, the system further comprises:

[0107] A calculation module, configured to obtain the flow time of the transmission and identification pipeline, and perform a ratio calculation based on the sorting operation time and the sorting operation running time to determine the number of nodes;

[0108] The determination module is further configured to determine a collection node based on the flow duration and the number of nodes.

[0109] Furthermore, the acquisition module 32 includes:

[0110] a first acquisition unit, configured to send a first acquisition instruction to a first acquisition device of the target data processing object according to the acquisition node, and receive the transmitted target data, wherein the first acquisition device is configured on a mechanical device that collects 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 receive the transmitted target image, and the second acquisition device is configured to acquire the identification area of ​​the target image.

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

[0113] An acquiring unit, configured to acquire a feature type pre-bound to the acquisition node, wherein the feature type includes an extreme value feature type and a blacklist and whitelist feature type;

[0114] The feature extraction unit is used to perform numerical extraction on the target data and identifier extraction on the target image according to the feature type, so as to obtain identifier monitoring features and numerical monitoring features.

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

[0116] A retrieving unit, configured to retrieve the monitoring strategy of the identification list, wherein the monitoring strategy includes a single monitoring strategy or a combined monitoring strategy;

[0117] A feature comparison unit is used to perform feature comparison on the identification monitoring strategy and the numerical monitoring feature based on the monitoring strategy to obtain a data monitoring result.

[0118] Furthermore, the system further comprises:

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

[0120] The present invention provides a data monitoring system for an electric energy meter sorting and identification platform. Compared with the prior art, the present invention determines target data processing objects and target image processing objects based on an identification list of the electric energy 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 image corresponding to the target image processing object are acquired according to an acquisition node. The acquisition node is determined according to the sorting operation time and the sorting operation running time of the mechanical equipment. Feature extraction is performed on the target data and target image 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 to the identification list to obtain data monitoring results for the target data processing object and the target image processing object. Filtering the target data processing object and target image processing object based on the identification list can filter data from the device dimension and the electric energy meter type dimension, while extracting data based on the acquisition node is performed from the time dimension. Through filtering and extraction, the data processing volume and pressure of the electric energy meter sorting and identification platform are greatly reduced. At the same time, monitoring delays are greatly avoided, thereby ensuring the effectiveness and accuracy of data monitoring on the electric energy meter sorting and identification platform.

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

[0122] Figure 4 A schematic structural diagram of a terminal provided according to an embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the terminal.

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

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

[0125] The communication interface 404 is used to communicate with other devices such as clients or other servers.

[0126] The processor 402 is configured to execute the program 410 , and specifically to execute the relevant steps in the embodiment of the data monitoring method for the electric energy meter sorting and identification platform.

[0127] Specifically, the program 410 may include program codes, which include 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 one or more processors included in the terminal may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

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

[0130] The program 410 may be specifically configured to cause the processor 402 to perform the following operations:

[0131] Determine the target data processing object and the target image processing object based on the identification list of the electric energy meter sorting and identification platform, wherein the identification list stores identification task information corresponding to different transmission identification pipelines;

[0132] Acquiring target data corresponding to the target data processing object and a target image corresponding to the target image processing object according to an acquisition node, wherein the acquisition node is determined according to a sorting operation time and a running time of the sorting operation of the mechanical equipment;

[0133] The target data and the target image are subjected to feature extraction using the feature type corresponding to the acquisition node to obtain monitoring features, and the monitoring features are subjected to feature comparison according to the monitoring strategy matched by the identification list to obtain data monitoring results of the target data processing object and the target image processing object.

[0134] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computing system, and thus, they can be stored in a storage system and executed by the computing system. In some cases, the steps shown or described herein can be performed in a different order than that shown, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0135] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A data monitoring method for an electric energy meter sorting and identification platform, characterized in that: include: Determine the target data processing object and the target image processing object based on the identification list of the electric energy meter sorting and identification platform, wherein the identification list stores the identification task information corresponding to different transmission identification pipelines; Acquire target data corresponding to the target data processing object and target image corresponding to the target image processing object according to an acquisition node, wherein the acquisition node is determined according to the sorting operation time and the running time of the sorting operation 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, and the monitoring features are feature compared according to the monitoring strategy matched by the identification list to obtain data monitoring results of the target data processing object and the target image processing object.

2. The method according to claim 1, characterized in that The identification list based on the electric energy meter sorting and identification platform determines the target data processing object and the target image processing object, including: Parsing the identification object, identification interval, identification quantity, and identification equipment information of different transmission identification pipelines in the identification task information; Acquire updated identification monitoring conditions, and select 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; Wherein, the identification and monitoring conditions include at least one of quantity conditions, time conditions, and characteristic conditions.

3. 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: Obtaining the flow time of the transmission identification pipeline, and performing a ratio calculation based on the sorting operation time and the sorting operation running time to determine the number of nodes; The collection node is determined based on the flow time and the number of nodes.

4. The method according to claim 3, characterized in that: The acquiring of target data corresponding to the target data processing object and target image corresponding to the target image processing object according to the acquisition node comprises: According to the acquisition node, a first acquisition instruction is sent to a first acquisition device of the target data processing object, and the target data transmitted thereto is received, wherein the first acquisition device is configured on a mechanical device that collects the target data; The acquisition node sends a second acquisition instruction to a second acquisition device of the target image processing object, and receives the transmitted target image, and the second acquisition device is configured to acquire an identification area of ​​the target image.

5. The method according to claim 4, characterized in that The feature type corresponding to the acquisition node is used to extract features from the target data and the target image to obtain monitoring features, including: Acquire the feature type pre-bound to the acquisition node, the feature type including extreme value feature type and black and white list feature type; According to the feature types, numerical extraction is performed on the target data, and identification extraction is performed on the target image to obtain identification monitoring features and numerical monitoring features.

6. The method according to claim 5, characterized in that The monitoring strategy matched according to the identification list performs feature comparison on the monitoring features to obtain data monitoring results of the target data processing object and the target image processing object, including: Retrieving the monitoring strategy of the identification list, wherein the monitoring strategy includes a single monitoring strategy or a combined monitoring strategy; Based on the monitoring strategy, feature comparison is performed on the identification monitoring strategy and the numerical monitoring feature to obtain a data monitoring result.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: When the data monitoring result is data abnormality, abnormal operation warning information is generated to drive warning operation and identify abnormal operation based on the abnormal operation warning information.

8. A data monitoring system for an electric energy meter sorting and identification platform, characterized in that: include: A determination module, used to determine a target data processing object and a target image processing object based on an identification list of an electric energy meter sorting and identification platform, wherein the identification list stores identification task information corresponding to different transmission identification pipelines; An acquisition module, used for acquiring target data corresponding to the target data processing object and a target image corresponding to the target image processing object according to an acquisition node, wherein the acquisition node is determined according to a sorting operation time and a running time of a sorting operation of a mechanical device; The feature processing module is used to extract features of the target data and the target image using the feature type corresponding to the acquisition node to obtain monitoring features, and to compare the monitoring features according to the monitoring strategy matched by the identification list to obtain data monitoring results of the target data processing object and the target image processing object.

9. A storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to execute an operation corresponding to the data monitoring method of the electric energy meter sorting and identification platform as described in any one of claims 1 to 7.

10. A terminal, 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 via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the data monitoring method of the electric energy meter sorting and identification platform as described in any one of claims 1-7.

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