Methods and systems for sorting and detecting electricity meters in intelligent sorting platforms

Abstract

This invention discloses a method and system for sorting and detecting electricity meters on an intelligent sorting platform, relating to the field of power system technology. It primarily addresses the problem of low accuracy in existing electricity meter sorting and detection methods. The main steps include: acquiring all pre-configured detection nodes in the intelligent sorting platform and aligning the detection lag parameters, detection objects, and detection trigger conditions within these nodes to obtain standardized detection nodes; upon receiving a sorting task list, parsing the detection targets of the electricity meters in the list, where the detection targets characterize the extreme values ​​or numerical ranges through which the detection objects pass detection; after detecting that an electricity meter is placed on the sorting and transmission equipment, acquiring the sorting operation data and sorting image data of the detection nodes; and combining and identifying the sorting operation data and sorting image data according to the detection lag parameters, detection objects, and detection targets to obtain the electricity meter sorting and detection results. This method is used for electricity meter sorting and detection.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and system for sorting and detecting electricity meters using an intelligent sorting platform. Background Technology

[0002] With the development of technologies such as intelligent deployment and intelligent identification of electricity meters in power systems, electricity meter sorting and identification platforms are used to identify various electricity meters in the power transmission network to ensure the safety and effectiveness of meter operation. To ensure the accuracy and effectiveness of electricity meter sorting and identification, the entire sorting and identification process needs to be monitored.

[0003] Currently, the sorting and inspection of electricity meters typically relies on video data and collected operational data from the sorting equipment. However, the objects being inspected in video data and operational data are usually different, leading to inspection errors and significantly impacting the accuracy of electricity meter sorting and inspection. Summary of the Invention

[0004] In view of this, the present invention provides a method and system for sorting and detecting electricity meters on an intelligent sorting platform, the main purpose of which is to solve the problem of low accuracy in existing electricity meter sorting and detection.

[0005] According to one aspect of the present invention, a method for sorting and detecting electricity meters in an intelligent sorting platform is provided, comprising:

[0006] All pre-configured detection nodes in the intelligent sorting platform are obtained, and the detection lag parameters, detection objects, and detection trigger conditions in the detection nodes are aligned to obtain the standardized detection nodes.

[0007] When a sorting task list is received, the detection targets of the energy meters in the sorting task list are parsed. The detection targets are used to characterize the extreme values ​​or numerical ranges of the detection objects.

[0008] Once the electricity meter is detected to be placed on the sorting and transmission equipment, the sorting operation data and sorting image data of the detection node are acquired.

[0009] The sorting operation data and sorting image data are combined and identified according to the detection lag parameter, the detection object, and the detection target to obtain the sorting detection result of the electricity meter.

[0010] Based on the functional object mapping relationship, query the detection object that matches the detection function type, and retrieve the initial detection trigger condition that matches the detection object. The detection trigger condition includes at least one of time trigger condition, signal trigger condition, and identifier trigger condition.

[0011] The initial detection trigger conditions of each sorting device are aligned according to the detection hysteresis parameter to obtain the detection trigger conditions corresponding to different sorting devices.

[0012] Furthermore, the step of parsing the detection targets of the energy meters in the sorting task list includes:

[0013] The row identifier and column identifier in the sorting task list are parsed. The row identifier is used to identify the identity of the energy meter, and the column identifier is used to identify the functional parameters of the equipment.

[0014] Based on the numerical weight value between the row identifier and the column identifier, a detection object and its corresponding extreme value or numerical range are selected from the detection object library and determined as the detection target.

[0015] Furthermore, acquiring the sorting operation data and sorting image data of the detection node includes:

[0016] The target sensor of the detection node is determined, and the sorting operation data and sorting image data collected by the target sensor are obtained according to the detection trigger conditions in the detection node.

[0017] Further, the step of combining and identifying the sorting operation data and sorting image data according to the detection hysteresis parameter, the detection object, and the detection target to obtain the sorting detection result of the electricity meter includes:

[0018] Determine the acquisition time and acquisition order of the sorting operation data and the sorting image data, and generate a detection data sequence based on the acquisition time and acquisition order;

[0019] The detection data sequence is compared with the detection hysteresis parameter;

[0020] If the detection data sequence matches the detection lag parameter, then a one-to-one matching and identification is performed between the detection data sequence and the detection object and the detection target to generate a sorting detection result;

[0021] If the detection data sequence does not match the detection lag parameter, the detection data sequence is adjusted according to the detection lag parameter, and the adjusted detection data sequence is matched and identified one by one with the detection target to generate a sorting detection result.

[0022] Furthermore, the method also includes:

[0023] If the sorting and detection result is an anomaly, the anomaly type is determined, and a warning message matching the anomaly type is output.

[0024] According to another aspect of the present invention, an energy meter sorting and detection system for an intelligent sorting platform is provided, comprising:

[0025] The alignment module is used to obtain all the pre-configured detection nodes in the intelligent sorting platform, and to align the detection lag parameters, detection objects, and detection triggering conditions in the detection nodes to obtain the standardized detection nodes.

[0026] The parsing module is used to parse the detection targets of the energy meters in the sorting task list when a sorting task list is received. The detection targets are used to characterize the extreme values ​​or numerical ranges of the detection objects.

[0027] The acquisition module is used to acquire the sorting operation data and sorting image data of the detection node after the energy meter is detected to be placed on the sorting and transmission equipment.

[0028] The identification module is used to combine and identify the sorting operation data and sorting image data according to the detection hysteresis parameter, the detection object, and the detection target to obtain the sorting detection result of the electricity meter.

[0029] Furthermore, the alignment module includes:

[0030] The first determining unit is used to obtain the number of sorting devices of the same detection function type in the detection node and the sorting transmission distance, and to determine the detection lag parameter based on the number and the sorting transmission distance;

[0031] The query unit is used to query the detection object that matches the detection function type based on the functional object mapping relationship, and to retrieve the initial detection trigger condition that matches the detection object. The detection trigger condition includes at least one of time trigger condition, signal trigger condition, and identifier trigger condition.

[0032] The alignment unit is used to align the initial detection trigger conditions of each sorting device according to the detection hysteresis parameter to obtain the detection trigger conditions corresponding to different sorting devices.

[0033] Furthermore, the parsing module includes:

[0034] The parsing unit is used to parse the row identifiers and column identifiers in the sorting task list. The row identifiers are used to identify the identity of the energy meter, and the column identifiers are used to identify the functional parameters of the equipment.

[0035] The second determining unit is used to select a detection object and its corresponding extreme value or numerical range from the detection object library based on the numerical weight value between the row identifier and the column identifier, and determine it as the detection target.

[0036] Furthermore, the acquisition module includes:

[0037] The acquisition unit is used to determine the target sensor of the detection node and acquire the sorting operation data and sorting image data collected by the target sensor according to the detection trigger conditions in the detection node.

[0038] Furthermore, the identification module includes:

[0039] The third determining unit is used to determine the acquisition time and acquisition order of the sorting operation data and the sorting image data, and to generate a detection data sequence based on the acquisition time and the acquisition order.

[0040] The comparison unit is used to compare the detection data sequence with the detection hysteresis parameter;

[0041] The identification unit is used to perform a one-to-one matching identification between the detection data sequence and the detection object and the detection target if the detection data sequence matches the detection hysteresis parameter, and generate a sorting detection result.

[0042] An adjustment unit is used to adjust the detection data sequence according to the detection lag parameter if the detection data sequence does not match the detection lag parameter, and to perform one-to-one matching and identification with the detection target based on the adjusted detection data sequence to generate a sorting detection result.

[0043] Furthermore, the system also includes:

[0044] The early warning module is used to determine the anomaly type and output early warning information matching the anomaly type if the sorting detection result is an anomaly detection.

[0045] 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 electricity meter sorting and detection method of the above-described intelligent sorting platform.

[0046] 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;

[0047] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the electricity meter sorting and detection method of the above-mentioned intelligent sorting platform.

[0048] By employing the above-described technical solutions, the technical solutions provided by the embodiments of the present invention have at least the following advantages:

[0049] This invention provides a method and system for sorting and detecting electricity meters in an intelligent sorting platform. Compared with existing technologies, this invention obtains all pre-configured detection nodes in the intelligent sorting platform and aligns the detection lag parameters, detection objects, and detection trigger conditions in these nodes to obtain standardized detection nodes. When a sorting task list is received, the detection targets of the electricity meters in the sorting task list are parsed. The detection targets characterize the extreme values ​​or numerical ranges that the detection objects pass the detection. When the electricity meter is detected to be placed on the sorting and transmission device, the sorting operation data and sorting image data of the detection nodes are obtained. The sorting operation data and sorting image data are combined and identified according to the detection lag parameters, the detection objects, and the detection targets to obtain the sorting and detection results of the electricity meters. By aligning the detection lag parameters and detection trigger conditions in the detection nodes before sorting and detection, and by identifying the sorting operation data and sorting image data based on the detection lag parameters during the identification process, the matching of the sorting operation data and sorting image data can be ensured, thereby ensuring the accuracy of the sorting and detection results.

[0050] 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

[0051] 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:

[0052] Figure 1 This invention provides a flowchart of a smart sorting platform for sorting and detecting electricity meters according to an embodiment of the present invention.

[0053] Figure 2 This invention provides a flowchart of an energy meter sorting and detection method for another intelligent sorting platform.

[0054] Figure 3 This diagram illustrates the composition of an energy meter sorting and detection system for an intelligent sorting platform according to an embodiment of the present invention.

[0055] Figure 4 A schematic diagram of the structure of a terminal provided in an embodiment of the present invention is shown. Detailed Implementation

[0056] 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.

[0057] This invention provides a method for sorting and detecting electricity meters on an intelligent sorting platform, such as... Figure 1 As shown, the method includes:

[0058] 101. Obtain all pre-configured detection nodes in the intelligent sorting platform, and align the detection lag parameters, detection objects, and detection trigger conditions in the detection nodes to obtain the standardized detection nodes.

[0059] In this embodiment of the invention, multiple tests that the electricity meters are expected to undergo are assigned to different testing nodes. All testing nodes constitute the complete sorting and testing process for the current electricity meters, with each testing node being a node in the complete testing process. For example, if a batch of electricity meters needs to undergo testing, i.e., the testing objects include voltage testing, current testing, and prepaid function testing, then the complete testing process is to perform batch voltage testing on the electricity meters, then batch current testing on all the electricity meters, and finally batch prepaid function testing on the electricity meters. Each of the batch voltage testing, batch current testing, and batch prepaid function testing corresponds to a testing node. Each testing node contains multiple sorting devices that perform the same testing items. To ensure the consistency of the testing operations of each sorting device in the testing node, and to ensure that the collected sorting operation data and sorting image data can be accurately matched, before executing a specific testing task, the current executing entity needs to retrieve all testing nodes pre-configured by the operator through the intelligent sorting platform and parse the testing lag parameters, testing objects, and testing trigger conditions in each testing node. The current execution entity is the intelligent sorting platform server, which can be a local server or a cloud server. This embodiment of the invention does not impose specific limitations.

[0060] By analyzing the detection items that the sorting equipment in the detection node can perform, the detection objects can be obtained, i.e., the items that the sorting equipment needs to perform detection on, such as voltage detection, current detection, prepaid function detection, and electricity meter type identification. Since electricity meters are sequentially input into the sorting and transmission equipment and arrive at their corresponding sorting locations one after another, there is a time difference between the input time of different electricity meters into the sorting and transmission equipment and a time difference between the transmission time of different electricity meters to their corresponding sorting locations via the conveyor line. Since the input time difference is usually less than the transmission time difference, the electricity meters cannot arrive at their corresponding sorting locations simultaneously. When the first electricity meter arrives at its corresponding location, the remaining transmission distance or remaining transmission time of the other electricity meters relative to their corresponding sorting locations is calculated. The sequence of these remaining transmission distances or remaining transmission times constitutes the detection lag parameter. The detection trigger condition is the condition that triggers the corresponding sorting equipment to begin the detection operation, such as triggering a preset time point, triggering a preset identification target, or triggering upon receiving an execution command.

[0061] It should be noted that when there are relatively few electricity meters to be tested, but a large number of electricity meters need to be sorted and tested, multiple sorting devices can be configured to perform the same testing operation to improve sorting and testing efficiency, thus achieving batch testing of multiple electricity meters with the same testing object. For example, if there are 10 sorting devices distributed on a sorting conveyor, and each sorting device is configured to test voltage, then 10 electricity meters are sequentially fed into the input end of the sorting conveyor. When the first electricity meter is transported to the operating area of ​​the first sorting device, it is picked up by the first sorting device; when the second electricity meter is transported to the operating area of ​​the second sorting device, it is picked up by the second sorting device, and so on, until the tenth electricity meter is transported along the conveyor line from the position of the first sorting device to the operating area of ​​the tenth sorting device, where it is picked up by the tenth sorting device. There is a time mismatch between the collection of sorting operation data and the collection of sorting image data. By analyzing the detection lag parameter and adjusting it according to the detection trigger condition, the detection operation of each sorting device can be synchronized and aligned in time from the perspective of equipment control, thereby ensuring the accuracy of the subsequent collection and recognition.

[0062] In one embodiment of the present invention, for further illustration and limitation, such as Figure 2 As shown, the process of parsing the detection hysteresis parameters, detection objects, and detection triggering conditions in the detection node includes:

[0063] 201. Obtain the number of sorting devices belonging to the same detection function type in the detection node and the sorting transmission distance, and determine the detection hysteresis parameter based on the number and the sorting transmission distance;

[0064] 202. Based on the functional object mapping relationship, query the detection object that matches the detection function type, and retrieve the initial detection trigger condition that matches the detection object.

[0065] 203. Align the initial detection trigger conditions of each sorting device according to the detection hysteresis parameter to obtain the detection trigger conditions corresponding to different sorting devices.

[0066] In this embodiment of the invention, the sorting equipment includes a robotic arm configured with a sorting and conveying device and a detection device that works in conjunction with the robotic arm for detection. Each sorting device corresponds to one or more detection function types. The detection function type corresponding to the most sorting devices in a detection node is taken as the same detection function type for the current detection node, and the number of sorting devices capable of performing this same detection function type and the sorting and conveying distance between each sorting device, i.e., their relative positions on the assembly line, are determined.

[0067] The alignment method between the electricity meter placement order and the sorting equipment can be either to align the first-placed electricity meter with the farthest sorting device, or to align the first-placed electricity meter with the nearest sorting device. Different alignment methods require different approaches to determining the detection lag parameter based on the number of sorting devices and the sorting transmission distance. The following example uses the alignment detection of the first-placed electricity meter with the farthest sorting device to determine the detection lag parameter. In determining the detection lag parameter, it is necessary to assume the electricity meter transmission scenario. For example, five electricity meters (a, b, c, d, e) are placed sequentially. The sorting devices are arranged from nearest to farthest as 1, 2, 3, 4, 5. When electricity meter e reaches sorting device 1, the distance between electricity meter d and sorting device 2 is the difference between the transmission distance between sorting devices 1 and 2 and the product of the time interval between the placement of d and e and the transmission speed. The distance between c and sorting device 3 is the difference between the transmission distance between sorting devices 1 and 3 and the product of the time interval between the placement of c and e and the transmission speed. This process continues, obtaining the lag transmission distance for each electricity meter when the first meter arrives at its corresponding sorting device. This yields the detection lag parameter. In this sequence, the lag transmission distance corresponding to electricity meter e is 0. Of course, the lag transmission distance can also be converted to lag time based on the transmission speed; this embodiment of the invention does not impose specific limitations.

[0068] After identifying the same detection function type, the system queries the function object mapping relationship to find detection objects that have a mapping relationship with this same detection function type, and these are used as the detection objects for this detection node. The function object mapping relationship is pre-built, showing the mapping relationship between different detection function types and their corresponding detection objects. Different detection objects also have pre-configured corresponding detection trigger conditions. Therefore, after identifying the detection object, the detection trigger conditions of the matching detection object are retrieved as the initial detection trigger conditions for the current detection node. Taking the detection lag parameter as the lag time period as an example, for any sorting device, the initial detection trigger conditions are adjusted based on the lag time period corresponding to the sorting device in the detection lag parameter to obtain the detection trigger conditions for different sorting devices. The detection trigger conditions include at least one of time trigger conditions, signal trigger conditions, and identifier trigger conditions. The time trigger condition uses a preset time node as the instruction condition to start the detection operation; the sum of the initial preset time node and the lag time period can be used as the adjusted time trigger condition. The signal trigger condition uses a received specified trigger signal as the command to initiate the detection operation. The identifier trigger condition uses the recognition of a specified identifier, such as a green indicator light illuminating, as the command to initiate the detection operation. Both of these detection trigger conditions can be adjusted by delaying the issuance time of the detection trigger signal based on a lag time period. By identifying the detection lag parameters between sorting devices and aligning the detection trigger conditions with these lag parameters, it can be ensured that the collected sorting operation data matches the image data of the energy meter's transportation and detection process during detection.

[0069] 102. When a sorting task list is received, the detection targets of the energy meters in the sorting task list are parsed.

[0070] In this embodiment of the invention, the sorting task list is a data list entered or uploaded by operators through an intelligent sorting platform. It stores at least one detection object corresponding to different electricity meters, as well as conditional data for detecting and identifying the data of the detection objects, such as thresholds and ranges. Since the electricity meters being sorted and inspected are mostly recycled meters, their equipment models and types are quite diverse, their equipment conditions vary, and their corresponding equipment functional parameters are also different. Therefore, it is necessary to determine the detection objects and detection targets of the electricity meters based on their equipment conditions and functional parameters in the sorting task list. For example, if the sorting task list includes electricity meters 1 to 10, where the detection objects corresponding to the equipment functional parameters of electricity meters 1 to 5 are A, B, and C; the detection objects corresponding to the equipment functional parameters of electricity meters 6 to 8 are B, C, D, and E; and the detection objects corresponding to the equipment functional parameters of electricity meters 9 and 10 are B, D, and E, then B is determined as the detection object, and the detection conditions corresponding to the detection object are determined as the detection target. Among them, the detection target is used to characterize the extreme values ​​or numerical ranges of the detection object, that is, the standard used to determine whether the data corresponding to the detection object is normal or abnormal, qualified or unqualified.

[0071] It should be noted that when the equipment of electricity meters is relatively complex and the objects to be tested are difficult to standardize, taking the objects to be tested as the unified dimension for batch processing, extracting the common objects to be tested for electricity meters, and performing batch testing on each object can effectively improve the efficiency of electricity meter sorting and testing.

[0072] In one embodiment of the present invention, for further explanation and limitation, the step of parsing the detection targets of the energy meters in the sorting task list includes:

[0073] Parse the row and column identifiers in the sorting task list;

[0074] Based on the numerical weight value between the row identifier and the column identifier, a detection object and its corresponding extreme value or numerical range are selected from the detection object library and determined as the detection target.

[0075] In this embodiment of the invention, each row in the sorting task list corresponds to an identification identifier for an energy meter, i.e., the row identifier is used to characterize the energy meter's identity. The row identifier includes information such as the corresponding energy meter's asset type code, asset code, and verification code. Each column in the sorting task list corresponds to an energy meter's device function parameter, i.e., the column identifier is used to characterize the device function parameter. The intersection of the row and column information results in a data set representing the device function parameter corresponding to each energy meter. Each energy meter and each device function parameter is pre-configured with a corresponding weight. This weight characterizes the importance or urgency of detection for the corresponding energy meter or device function parameter. For example, if a row corresponds to many similar energy meters, its weight is higher; if a column corresponds to a device function parameter with a high probability of anomaly, its weight is higher; if a column corresponds to a device function parameter with a low probability of anomaly, its weight is lower. The weight of each row is multiplied by the weight of each column to obtain a numerical weight value between the row identifier and the column identifier. The device function parameter with the highest numerical weight value is selected to select a detection object from the detection object library, along with the corresponding detection target. The detection object database stores the correspondence between different detection objects, different detection ranges, and different electricity meter identification identifiers, along with their corresponding numerical weight values. By using these numerical weight values ​​and their corresponding electricity meter identification identifiers, the corresponding detection object and extreme values ​​or numerical ranges can be matched from the detection object database; the extreme values ​​or numerical ranges represent the corresponding detection targets. Based on the matching of the detection object with the detection objects corresponding to each of all detection nodes, the detection node that needs to be executed can be determined.

[0076] It should be noted that after the selection of the detection target is completed, the largest numerical weight value in the current sorting task list is deleted so that different detection objects can be identified in the next round of determining the detection objects and targets, and detection nodes that are different from the completed detection nodes can be matched from all detection nodes based on the detection objects.

[0077] 103. When the energy meter is detected to be placed on the sorting and transmission equipment, the sorting operation data and sorting image data of the detection node are acquired.

[0078] In this embodiment of the invention, after determining the target of the electricity meter inspection, the electricity meters of different inspection targets are placed on the sorting and transmission equipment (sorting and inspection production line) where the inspection node of the corresponding inspection target is located. After detecting that the electricity meter is placed on the sorting and transmission equipment, that is, the electricity meter that needs to be sorted and inspected has entered the sorting process of the current inspection node, sorting operation data and sorting image data are acquired from the data acquisition equipment and image acquisition equipment configured on the sorting and transmission equipment. The data acquisition equipment includes sensors for detecting the transmission data of the sorting and transmission equipment, sensors for collecting the operation data of the sorting equipment, and equipment for performing corresponding function detection on the electricity meter. The image acquisition equipment includes equipment for acquiring images of the corresponding operating areas of each sorting device. The acquired sorting image data is used to display the positional relationship between the electricity meter and the alignment mark on the sorting and transmission equipment, the connection relationship between the electricity meter and the inspection equipment, the activity trajectory and stopping position of the sorting equipment, and other information.

[0079] It should be noted that the sorting operation data and sorting image data can be data sequences collected in real time according to a preset data collection frequency. However, the current executing entity's acquisition of sorting operation data and sorting image data can be performed in response to detection trigger conditions. In other words, the current executing entity's acquisition of sorting operation data and sorting image data is a selective data acquisition process, avoiding excessive data transmission.

[0080] In one embodiment of the present invention, for further explanation and limitation, the step of obtaining the sorting operation data and sorting image data of the detection node includes:

[0081] The target sensor of the detection node is determined, and the sorting operation data and sorting image data collected by the target sensor are obtained according to the detection trigger conditions in the detection node.

[0082] In this embodiment of the invention, the sorting and conveying equipment is equipped with numerous sensors. However, not every sensor collects data relevant to the current detection node. Therefore, before acquiring sorting operation data and sorting image data, it is necessary to identify the sensors whose data is relevant to the current detection node, i.e., the target sensors. Target sensors can be determined through a list lookup. That is, a pre-built list of sensor-detection object correspondences is used to find sensors that match the detection object of the current detection node. For example, if the detection object of the current detection node is voltage detection, only the voltage detection sensor and the sensor used for acquiring voltage detection line wiring images need to be identified as target sensors; data from other sensors can be omitted. After identifying the target sensors, monitoring is initiated according to the detection trigger conditions. When the detection trigger conditions are met, sorting operation data and sorting image data are acquired from the target sensors. By identifying the target sensors, the scope of data acquisition can be further narrowed, reducing data transmission and processing volume.

[0083] 104. The sorting operation data and sorting image data are combined and identified according to the detection lag parameter, the detection object, and the detection target to obtain the sorting detection result of the electricity meter.

[0084] In this embodiment of the invention, after each detection node is completed, sorting operation data and sorting image data corresponding to all detected objects of each electricity meter are obtained. Then, the sorting operation data and sorting image data are judged according to the threshold or numerical range represented by the detection target to determine whether the sorting detection result of the corresponding electricity meter is normal or abnormal. If the sorting operation data and sorting image data meet the corresponding threshold or range, the sorting detection result of the corresponding electricity meter is normal; conversely, if at least one of the sorting operation data and sorting image data meets the corresponding threshold or range, the sorting detection result of the corresponding electricity meter is abnormal. Before judging according to the detection target, it is also necessary to judge the consistency of the detected objects corresponding to the sorting operation data and sorting image data. Specifically, the consistency of the sorting operation data and sorting image data can be verified by the detection lag parameter and the detected object. If the verification passes, the sorting operation data and sorting image data are then identified based on the detection target corresponding to the detected object. If the verification result fails, the result of the sorting detection can also be output. In other words, the abnormal sorting detection result can indicate both the abnormality of the detection data and the abnormality of the sorting detection equipment operation.

[0085] It should be noted that because the detection lag parameters and detection triggering conditions used to guide the acquisition of sorting operation data and sorting image data are aligned, the timing of acquiring sorting operation data and sorting image data is accurate. Furthermore, during the identification process of sorting detection results, the detection lag parameters, the detection object, and the detection target are used together as identification criteria, effectively ensuring the matching of sorting operation data and sorting image data, thereby ensuring the accuracy of sorting detection results.

[0086] In one embodiment of the present invention, for further explanation and limitation, the step of combining and identifying the sorting operation data and sorting image data according to the detection hysteresis parameter, the detection object, and the detection target to obtain the sorting detection result of the electricity meter includes:

[0087] Determine the acquisition time and acquisition order of the sorting operation data and the sorting image data, and generate a detection data sequence based on the acquisition time and acquisition order;

[0088] The detection data sequence is compared with the detection hysteresis parameter;

[0089] If the detection data sequence matches the detection lag parameter, then a one-to-one matching and identification is performed between the detection data sequence and the detection object and the detection target to generate a sorting detection result;

[0090] If the detection data sequence does not match the detection lag parameter, the detection data sequence is adjusted according to the detection lag parameter, and the adjusted detection data sequence is matched and identified one by one with the detection target to generate a sorting detection result.

[0091] In this embodiment of the invention, when the detection lag parameter is expressed as time, after acquiring sorting image data and sorting operation data, the data is sorted according to the acquisition time corresponding to the sorting image data and sorting operation data, and the acquisition order between each sorting image data and sorting operation data, to obtain a detection data sequence. The detection data sequence contains the acquisition time interval between each data point. This acquisition time interval is compared with the lag time interval in the detection lag parameter. If they match, it indicates that the adjustment of the detection triggering condition before performing sorting detection is effective, and the obtained sorting image data and sorting operation data are matched. Thresholds or range values ​​corresponding to the sorting image data and sorting operation data in the detection target can be used to judge data anomalies. If the detection data sequence does not match the detection lag parameter, it indicates that there is a deviation between the actual data acquisition process and the expected time of the detection triggering condition. In this case, the time intervals in the detection data sequence are adjusted according to the time interval of the detection lag parameter to obtain a detection data sequence that matches the detection lag parameter, i.e., the adjusted detection data sequence. The adjustment can be to shorten or lengthen the time interval. Preferably, the time interval can be adjusted in the direction of minimizing the difference from the lag time period. This embodiment of the invention does not impose specific limitations.

[0092] In one embodiment of the present invention, for further explanation and limitation, the method further includes:

[0093] If the sorting and detection result is an anomaly, the anomaly type is determined, and a warning message matching the anomaly type is output.

[0094] In this embodiment of the invention, if the sorting image data and sorting operation data exceed the corresponding threshold or range value, for example, if the distance between the voltage detection connector and the detection port of the energy meter in the sorting image data exceeds the corresponding distance threshold, or if the voltage value in the sorting operation data exceeds the rated voltage value of the voltmeter, then the detection result can be determined to be abnormal. For the above two types of abnormalities, they can be respectively identified as sorting image data abnormalities and sorting operation data abnormalities. Of course, abnormalities also include missing sorting image data and sorting operation data. After determining the abnormality type, the corresponding warning information for the current abnormality type is matched from the pre-constructed warning information corresponding to different abnormality types, and then the warning information is sent to the production line terminal to prompt the production line inspection personnel to confirm.

[0095] This invention provides a method for sorting and detecting electricity meters in an intelligent sorting platform. Compared with existing technologies, this invention obtains all pre-configured detection nodes in the intelligent sorting platform and aligns the detection lag parameters, detection objects, and detection trigger conditions in these nodes to obtain standardized detection nodes. When a sorting task list is received, the detection targets of the electricity meters in the sorting task list are parsed. The detection targets characterize the extreme values ​​or numerical ranges that the detection objects pass the detection. When the electricity meter is detected to be placed on the sorting and transmission device, the sorting operation data and sorting image data of the detection nodes are obtained. The sorting operation data and sorting image data are combined and identified according to the detection lag parameters, the detection objects, and the detection targets to obtain the sorting and detection results of the electricity meters. By aligning the detection lag parameters and detection trigger conditions in the detection nodes before sorting and detection, and by identifying the sorting operation data and sorting image data based on the detection lag parameters during the identification process, the matching of the sorting operation data and sorting image data can be ensured, thereby ensuring the accuracy of the sorting and detection results.

[0096] Furthermore, as a response to the above Figure 1 The implementation of the method shown in this invention provides an intelligent sorting platform for sorting and detecting electricity meters, as described in this embodiment. Figure 3 As shown, the system includes:

[0097] Alignment module 31 is used to obtain all the pre-configured detection nodes in the intelligent sorting platform, and to align the detection lag parameters, detection objects and detection trigger conditions in the detection nodes to obtain the standardized detection nodes.

[0098] The parsing module 32 is used to parse the detection targets of the energy meters in the sorting task list when a sorting task list is received. The detection targets are used to characterize the extreme values ​​or numerical ranges of the detection objects.

[0099] The acquisition module 33 is used to acquire the sorting operation data and sorting image data of the detection node after the energy meter is detected to be placed on the sorting and transmission device.

[0100] The identification module 34 is used to combine and identify the sorting operation data and sorting image data according to the detection hysteresis parameter, the detection object, and the detection target to obtain the sorting detection result of the electricity meter.

[0101] Furthermore, the alignment module 31 includes:

[0102] The first determining unit is used to obtain the number of sorting devices of the same detection function type in the detection node and the sorting transmission distance, and to determine the detection lag parameter based on the number and the sorting transmission distance;

[0103] The query unit is used to query the detection object that matches the detection function type based on the functional object mapping relationship, and to retrieve the initial detection trigger condition that matches the detection object. The detection trigger condition includes at least one of time trigger condition, signal trigger condition, and identifier trigger condition.

[0104] The alignment unit is used to align the initial detection trigger conditions of each sorting device according to the detection hysteresis parameter to obtain the detection trigger conditions corresponding to different sorting devices.

[0105] Furthermore, the parsing module 32 includes:

[0106] The parsing unit is used to parse the row identifiers and column identifiers in the sorting task list. The row identifiers are used to identify the identity of the energy meter, and the column identifiers are used to identify the functional parameters of the equipment.

[0107] The second determining unit is used to select a detection object and its corresponding extreme value or numerical range from the detection object library based on the numerical weight value between the row identifier and the column identifier, and determine it as the detection target.

[0108] Furthermore, the acquisition module 33 includes:

[0109] The acquisition unit is used to determine the target sensor of the detection node and acquire the sorting operation data and sorting image data collected by the target sensor according to the detection trigger conditions in the detection node.

[0110] Furthermore, the identification module 34 includes:

[0111] The third determining unit is used to determine the acquisition time and acquisition order of the sorting operation data and the sorting image data, and to generate a detection data sequence based on the acquisition time and the acquisition order.

[0112] The comparison unit is used to compare the detection data sequence with the detection hysteresis parameter;

[0113] The identification unit is used to perform a one-to-one matching identification between the detection data sequence and the detection object and the detection target if the detection data sequence matches the detection hysteresis parameter, and generate a sorting detection result.

[0114] An adjustment unit is used to adjust the detection data sequence according to the detection lag parameter if the detection data sequence does not match the detection lag parameter, and to perform one-to-one matching and identification with the detection target based on the adjusted detection data sequence to generate a sorting detection result.

[0115] Furthermore, the system also includes:

[0116] The early warning module is used to determine the anomaly type and output early warning information matching the anomaly type if the sorting detection result is an anomaly detection.

[0117] This invention provides a smart sorting platform for electricity meter sorting and detection. Compared with existing technologies, this invention obtains all pre-configured detection nodes in the smart sorting platform and aligns the detection lag parameters, detection objects, and detection trigger conditions in these nodes to obtain standardized detection nodes. When a sorting task list is received, the detection targets of the electricity meters in the list are parsed, where the detection targets characterize the extreme values ​​or numerical ranges that the detection objects pass the detection. When the electricity meter is detected to be placed on the sorting and transmission device, the sorting operation data and sorting image data of the detection nodes are obtained. The sorting operation data and sorting image data are combined and identified according to the detection lag parameters, the detection objects, and the detection targets to obtain the sorting and detection results of the electricity meters. By aligning the detection lag parameters and detection trigger conditions in the detection nodes before sorting and detection, and by identifying the sorting operation data and sorting image data based on the detection lag parameters during the identification process, the matching of the sorting operation data and sorting image data can be ensured, thereby ensuring the accuracy of the sorting and detection results.

[0118] According to one embodiment of the present invention, a storage medium is provided, the storage medium storing at least one executable instruction, the computer-executable instruction being able to execute the energy meter sorting and detection method of the intelligent sorting platform in any of the above method embodiments.

[0119] 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 embodiments of the present invention.

[0120] like Figure 4 As shown, the terminal may include: a processor 402, a communications interface 404, a memory 406, and a communications bus 408.

[0121] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408.

[0122] Communication interface 404 is used for network communication with other devices such as clients or other servers.

[0123] The processor 402 is used to execute program 410, specifically to execute the relevant steps in the above-described embodiment of the energy meter sorting and detection method of the intelligent sorting platform.

[0124] Specifically, program 410 may include program code that includes computer operation instructions.

[0125] 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.

[0126] 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.

[0127] Specifically, program 410 can be used to cause processor 402 to perform the following operations:

[0128] All pre-configured detection nodes in the intelligent sorting platform are obtained, and the detection lag parameters, detection objects, and detection trigger conditions in the detection nodes are aligned to obtain the standardized detection nodes.

[0129] When a sorting task list is received, the detection targets of the energy meters in the sorting task list are parsed. The detection targets are used to characterize the extreme values ​​or numerical ranges of the detection objects.

[0130] Once the electricity meter is detected to be placed on the sorting and transmission equipment, the sorting operation data and sorting image data of the detection node are acquired.

[0131] The sorting operation data and sorting image data are combined and identified according to the detection lag parameter, the detection object, and the detection target to obtain the sorting detection result of the electricity meter.

[0132] 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.

[0133] 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 method for sorting and detecting electricity meters on an intelligent sorting platform, characterized in that, include: All pre-configured detection nodes in the intelligent sorting platform are obtained, and the detection lag parameters, detection objects, and detection trigger conditions in the detection nodes are aligned to obtain standardized detection nodes. The alignment process includes: obtaining the number of sorting devices belonging to the same detection function type and the sorting transmission distance in the detection nodes, and determining the detection lag parameters based on the number and the sorting transmission distance; querying the detection objects matching the detection function type based on the function object mapping relationship, and retrieving the initial detection trigger conditions matching the detection objects, where the detection trigger conditions include at least one of time trigger conditions, signal trigger conditions, and identifier trigger conditions; and aligning the initial detection trigger conditions of each sorting device according to the detection lag parameters to obtain the detection trigger conditions corresponding to different sorting devices. When a sorting task list is received, the detection targets of the energy meters in the sorting task list are parsed. The detection targets are used to characterize the extreme values ​​or numerical ranges that the detection objects pass the test. The parsing of the detection targets of the energy meters in the sorting task list includes: parsing the row identifiers and column identifiers in the sorting task list. The row identifiers are used to characterize the energy meter identity, and the column identifiers are used to characterize the equipment functional parameters. Based on the numerical weight values ​​between the row identifiers and the column identifiers, the detection objects and their corresponding extreme values ​​or numerical ranges are selected from the detection object library and determined as the detection targets. Once the electricity meter is detected to be placed on the sorting and transmission equipment, the sorting operation data and sorting image data of the detection node are acquired. The sorting operation data and sorting image data are combined and identified according to the detection lag parameter, the detection object, and the detection target to obtain the sorting detection result of the energy meter. This includes: determining the acquisition time and order of the sorting operation data and the sorting image data, and generating a detection data sequence based on the acquisition time and order; comparing the detection data sequence with the detection lag parameter; if the detection data sequence matches the detection lag parameter, then a one-to-one matching identification is performed between the detection data sequence and the detection object and the detection target to generate a sorting detection result; if the detection data sequence does not match the detection lag parameter, then the detection data sequence is adjusted according to the detection lag parameter, and a one-to-one matching identification is performed between the adjusted detection data sequence and the detection target to generate a sorting detection result.

2. The method according to claim 1, characterized in that, The acquisition of the sorting operation data and sorting image data of the detection node includes: The target sensor of the detection node is determined, and the sorting operation data and sorting image data collected by the target sensor are obtained according to the detection trigger conditions in the detection node.

3. The method according to claim 1, characterized in that, The method further includes: If the sorting and detection result is an anomaly, the anomaly type is determined, and a warning message matching the anomaly type is output.

4. A smart sorting platform for electricity meter sorting and detection system, characterized in that, include: An alignment module is used to acquire all pre-configured detection nodes in the intelligent sorting platform and align the detection lag parameters, detection objects, and detection trigger conditions in the detection nodes to obtain standardized detection nodes. The parsing of the detection lag parameters, detection objects, and detection trigger conditions in the detection nodes includes: acquiring the number of sorting devices belonging to the same detection function type and the sorting transmission distance in the detection nodes, and determining the detection lag parameters based on the number and the sorting transmission distance; querying the detection objects matching the detection function type based on the function object mapping relationship, and retrieving the initial detection trigger conditions matching the detection objects, wherein the detection trigger conditions include at least one of time trigger conditions, signal trigger conditions, and identifier trigger conditions; and aligning the initial detection trigger conditions of each sorting device according to the detection lag parameters to obtain the detection trigger conditions corresponding to different sorting devices. The parsing module is used to parse the detection targets of the energy meters in the sorting task list when a sorting task list is received. The detection targets are used to characterize the extreme values ​​or numerical ranges that the detection objects pass the test. The parsing of the detection targets of the energy meters in the sorting task list includes: parsing the row identifier and column identifier in the sorting task list, where the row identifier is used to characterize the energy meter identity and the column identifier is used to characterize the device functional parameters; and selecting the detection objects and their corresponding extreme values ​​or numerical ranges from the detection object library based on the numerical weight values ​​between the row identifier and the column identifier to determine them as the detection targets. The acquisition module is used to acquire the sorting operation data and sorting image data of the detection node after the energy meter is detected to be placed on the sorting and transmission equipment. The identification module is used to combine and identify the sorting operation data and sorting image data according to the detection lag parameter, the detection object, and the detection target to obtain the sorting detection result of the electricity meter. This includes: determining the acquisition time and order of the sorting operation data and the sorting image data, and generating a detection data sequence based on the acquisition time and order; comparing the detection data sequence with the detection lag parameter; if the detection data sequence matches the detection lag parameter, then performing a one-to-one matching identification based on the detection data sequence with the detection object and the detection target to generate a sorting detection result; if the detection data sequence does not match the detection lag parameter, then adjusting the detection data sequence according to the detection lag parameter, and performing a one-to-one matching identification based on the adjusted detection data sequence with the detection target to generate a sorting detection result.

5. A storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the electricity meter sorting and detection method of the intelligent sorting platform as described in any one of claims 1-3.

6. 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 electricity meter sorting and detection method of the intelligent sorting platform as described in any one of claims 1-3.

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