Electric meter intelligent turnover method and device based on mechanical arm

Abstract

This invention relates to the field of electricity meter turnover technology, and discloses a method and device for intelligent electricity meter turnover based on a robotic arm. The method includes acquiring instruction types for electricity meter turnover commands, including storage instructions and retrieval instructions; determining an optimal position based on the instruction type and a preset position selection algorithm; and performing path planning and motion control on the robotic arm based on the optimal position to achieve electricity meter turnover control. The optimal position includes an optimal storage position and an optimal retrieval position. This invention improves storage and retrieval efficiency while optimizing storage layout through a multi-dimensional scoring model in the warehousing stage and multi-model collaborative decision-making in the retrieval stage. Through adaptive adjustment of path planning and motion modes, it enhances the stability and control accuracy of electricity meter storage and retrieval movements. This invention effectively improves the intelligence level of electricity meter turnover control through precise matching in warehousing and global optimization in retrieval.

Description

Technical Field

[0001] This invention relates to the field of electricity meter turnover technology, and in particular to an intelligent electricity meter turnover method and device based on a robotic arm. Background Technology

[0002] Currently, most electricity meter access processes are handled manually, making it impossible to achieve intelligent management of meter turnover. In particular, during emergency repairs, the use of meters often results in the need to issue work orders after the meters have been used, and there is often no supervision of the meter usage. It is also impossible to effectively manage and count electricity meters that have been issued but not installed on-site, which brings difficulties to electricity meter inventory management.

[0003] To improve the intelligence level of electricity meter inventory management, the commonly used method is to use turnover cabinets or turnover warehouses for electricity meter inventory control and to use robotic arms for intelligent storage and retrieval. However, the current turnover devices based on robotic arms can only control the robotic arms to grab the corresponding electricity meters for entry and exit from the warehouse. They lack the verification and validation of the electricity meters entering and leaving the warehouse, and cannot realize the automatic optimization of storage and retrieval location, storage and retrieval path and storage layout. Therefore, they cannot meet the current intelligent management and control needs of electricity meter turnover. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method and apparatus for intelligent meter turnover based on a robotic arm, which can solve the problem of low intelligence in existing meter turnover systems, thereby improving meter turnover efficiency, optimizing storage layout, and enhancing the level of intelligence in meter turnover.

[0005] In a first aspect, the present invention provides a smart meter turnover method based on a robotic arm, the method comprising:

[0006] The instruction types for obtaining meter turnover instructions include storage instructions and retrieval instructions;

[0007] Based on the instruction type and the preset position selection algorithm, the optimal position is determined, and based on the optimal position, the robot arm is used for path planning and motion control to realize the turnover control of the electricity meter. The optimal position includes the optimal storage position and the optimal retrieval position.

[0008] The step of determining the optimal position based on the instruction type and a preset position selection algorithm includes:

[0009] In response to an instruction type of storage instruction, the location matching degree is calculated based on the first meter parameter of the meter to be stored and the first storage position parameter of the available storage location, and the optimal storage location is determined based on the location matching degree.

[0010] In response to an instruction of type "extraction instruction", a set of candidate extraction locations is obtained based on the second meter parameters of the meters to be extracted. The location priority is calculated based on the number of meters to be extracted and the second storage location parameters of each candidate extraction location in the set of candidate extraction locations. The optimal extraction location is then determined based on the location priority.

[0011] Furthermore, before the step of calculating the location matching degree based on the first meter parameters of the meter to be stored and the first storage location parameters of the vacant storage location, the method further includes:

[0012] Collect images of the electricity meters to be stored in the warehouse and perform image preprocessing on the electricity meter images;

[0013] Barcode parsing and type matching are performed on the preprocessed meter images to obtain the identification parameters of the meters entering the warehouse;

[0014] The instruction meter parameters of the meter to be stored are obtained from the storage instructions, and the instruction meter parameters are compared with the identification meter parameters. If the comparison results are consistent, the instruction meter parameters are used as the first meter parameters; otherwise, an early warning reminder is issued.

[0015] Furthermore, the step of calculating the location matching degree based on the first meter parameters of the electricity meter to be stored and the first storage location parameters of the vacant storage location includes:

[0016] Based on the first meter parameters of the meter to be put into storage, the meter physical parameters and meter type of the meter to be put into storage are obtained, and the first storage location parameters of the vacant storage location are extracted from the database. The first storage location parameters include the storage location physical parameters, the surrounding storage type and the location convenience.

[0017] Calculate the size matching degree and weight matching degree based on the physical parameters of the electricity meter and the physical parameters of the storage location;

[0018] Calculate the type matching degree based on the meter type and the surrounding storage type;

[0019] The location matching degree of each vacant storage location is obtained based on the size matching degree, the weight matching degree, the type matching degree, and the location convenience.

[0020] Furthermore, the step of determining the optimal storage location based on the location matching degree includes:

[0021] Based on the comparison results of the location matching degree and the matching degree threshold, a set of candidate storage locations is obtained;

[0022] Calculate the movement efficiency score of each candidate storage location based on the distance between each candidate storage location and the storage location in the candidate storage location set.

[0023] Based on the location matching degree and the motion efficiency score, a comprehensive location score is obtained, and based on the comprehensive location score, the optimal storage location is determined.

[0024] Further, the step of calculating the motion efficiency score of each candidate storage location based on the distance between each candidate storage location in the candidate storage location set and the storage location includes:

[0025] Based on the location coordinates of each candidate storage location in the candidate storage location set and the location coordinates of the inbound location, calculate the straight-line distance, horizontal distance, and vertical distance between the candidate storage location and the inbound location;

[0026] Calculate the time consumption score based on the straight-line distance and the preset speed, and calculate the energy consumption score based on the horizontal distance and the vertical distance;

[0027] Based on the time consumption score and the energy consumption score, the motion efficiency score of each candidate storage location is obtained.

[0028] Further, the steps of obtaining a set of candidate extraction locations based on the second meter parameters of the meters to be released, calculating the location priority based on the number of meters to be released and the second storage location parameters of each candidate extraction location in the set of candidate extraction locations, and determining the optimal extraction location based on the location priority include:

[0029] Based on the second meter parameters of the meter to be dispatched, the meter model of the meter to be dispatched is obtained, and based on the meter model, a set of candidate extraction locations is obtained;

[0030] If the number of meters to be dispatched is one, the location priority is calculated based on the second storage location parameter of each candidate location in the candidate location set, and the optimal location is determined based on the location priority. Otherwise, it is determined whether the meter models of the multiple meters to be dispatched are the same.

[0031] If the meter models are the same, the location priority is calculated based on the second storage location parameter of each candidate extraction location in the candidate extraction location set, and the optimal extraction location is determined based on the outbound quantity and the location priority.

[0032] If the meter models are different, the dominant model is selected from multiple meter models according to the model selection rules. The position priority is calculated based on the second storage parameters of each candidate extraction position of the dominant model. The optimal extraction position of the dominant model is determined based on the position priority.

[0033] Using the optimal extraction position of the dominant model as the anchor point, and based on the principle of the nearest anchor point position, the corresponding optimal extraction position is selected from the set of candidate extraction positions of non-dominant models.

[0034] Furthermore, the calculation steps for the position priority include:

[0035] Extract the location coordinates, storage time of stored meters, and surrounding storage type of each candidate extraction location from the database;

[0036] Based on the location coordinates of each candidate extraction location and the location coordinates of the outbound location, calculate the distance value between each candidate extraction location and the outbound location, and calculate the distance index based on the distance value;

[0037] Calculate the time index based on the storage time of the stored electricity meters, and calculate the compatibility index based on the surrounding storage type.

[0038] The location priority is obtained based on the distance index, the time index, and the compatibility index.

[0039] Furthermore, the step of performing path planning and motion control of the robotic arm based on the optimal position includes:

[0040] Based on the optimal position, a path planning algorithm is used to plan the path of the robot arm and obtain the motion trajectory of the robot arm.

[0041] Based on the physical parameters of the turnover meter, the corresponding motion mode is extracted from the preset motion mode library. The turnover meter includes meters to be put into storage and meters to be taken out of storage. The motion mode contains the reference values ​​of multiple motion parameters, including the maximum motion speed, motion acceleration and buffer trigger distance.

[0042] The robotic arm is motion controlled according to the motion trajectory and the motion pattern.

[0043] Furthermore, the step of extracting the corresponding motion mode from the preset motion mode library based on the physical parameters of the turnover meter includes:

[0044] The physical parameter type of the turnover meter is determined by comparing the physical parameters of the turnover meter with the preset threshold ranges of multiple physical parameters.

[0045] Based on the physical parameter type, the corresponding motion mode is extracted from the preset motion mode library;

[0046] Obtain the median of the threshold range of the physical parameters corresponding to the physical parameters of the electricity meter, and obtain the speed correction coefficient based on the comparison relationship between the physical parameters of the electricity meter and the median;

[0047] The reference value of the maximum motion speed in the motion mode is corrected according to the speed correction coefficient;

[0048] In response to the fact that the turnover meter is a meter to be put into storage, the location convenience of the optimal storage location is obtained from the database, and the reference value of the buffer trigger distance in the movement mode is corrected according to the location convenience.

[0049] Secondly, the present invention provides an intelligent turnover device for electricity meters based on a robotic arm, the device including a controller that performs the steps of the method described above.

[0050] This invention provides a method and device for intelligent meter turnover based on a robotic arm. Through a multi-dimensional scoring model in the warehousing stage, this invention achieves precise matching between meters and storage areas, improving storage efficiency while optimizing the storage layout. In the outbound stage, dynamic collaborative decision-making across multiple models effectively improves outbound efficiency and further optimizes the storage layout. Furthermore, through path planning and adaptive adjustment of motion modes, it enhances the stability and control accuracy of meter access. This invention, through precise matching in warehousing and global optimization in outbound processes, improves meter access efficiency and optimizes meter storage layout, thereby effectively enhancing the intelligence level of meter turnover control. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the intelligent meter turnover method based on a robotic arm in an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figure 1 The first embodiment of the present invention proposes a smart meter turnover method based on a robotic arm, comprising steps S10 to S20:

[0054] Step S10: Obtain the instruction type of the electricity meter turnover instruction, wherein the instruction type includes storage instruction and retrieval instruction;

[0055] Step S20: Determine the optimal position according to the instruction type and the preset position selection algorithm, and perform path planning and motion control on the robot arm according to the optimal position to realize the turnover control of the electricity meter. The optimal position includes the optimal storage position and the optimal retrieval position.

[0056] This invention provides an intelligent control method for electricity meter turnover. An electricity meter is an instrument used to measure electrical energy, also known as a kilowatt-hour meter, or simply an electricity meter. Electricity meters can be classified into DC electricity meters and AC electricity meters according to the type of current they use. AC electricity meters can be further classified into single-phase electricity meters and three-phase electricity meters according to their phase lines. They can also be classified into mechanical, electronic, induction, and smart meters according to their working principle. This invention is applied in scenarios such as electricity meter turnover cabinets or warehouses. A robotic arm is installed in the cabinet or warehouse to grasp electricity meters and perform inbound and outbound operations. For inbound storage operations, the robotic arm grasps the electricity meter placed in the inbound position and moves it to the corresponding storage position. For outbound retrieval operations, the robotic arm grasps the electricity meter in the storage position and moves it to the outbound position. This invention achieves intelligent and efficient electricity meter turnover through accurate analysis of the storage and retrieval positions and motion control of the robotic arm.

[0057] In this embodiment, corresponding operations are performed according to the received meter turnover instructions. The meter turnover instructions include storage instructions for meter entry into the warehouse and retrieval instructions for meter exit from the warehouse. The execution process of the storage instructions and retrieval instructions will be described in detail below.

[0058] Upon receiving a storage instruction, the system first verifies the identity of the operator. A high-definition camera captures a facial image, extracts facial feature points to generate a facial feature vector, and matches this vector against feature vectors in a pre-defined access control database to verify the operator's permissions. Only after successful verification will the storage operation begin to prevent unauthorized access. Once identity verification is successful, the storage module automatically pops out and automatically retracts after the operator places the meter to be stored in it. Then, parameters are extracted from the meter placed at the storage location on the module. Specific steps include:

[0059] Collect images of the electricity meters to be stored in the warehouse and perform image preprocessing on the electricity meter images;

[0060] Barcode parsing and type matching are performed on the preprocessed meter images to obtain the identification parameters of the meters entering the warehouse;

[0061] The instruction meter parameters of the meter to be stored are obtained from the storage instructions, and the instruction meter parameters are compared with the identification meter parameters. If the comparison results are consistent, the instruction meter parameters are used as the first meter parameters; otherwise, an early warning reminder is issued.

[0062] In this embodiment, firstly, a high-definition camera captures images of the electricity meters placed at the storage location, and the captured meter images are preprocessed, including grayscale conversion, Gaussian filtering, and histogram equalization, to eliminate noise and enhance contrast. Then, the preprocessed meter images undergo barcode parsing and type matching. Barcode parsing uses edge detection and connected component analysis to determine the barcode region, followed by character matching and encoding parsing to identify the meter's barcode. The barcode contains information such as the meter's serial number, unit code, manufacturer code, meter type, and meter model. Furthermore, type matching is performed on the meter images by extracting shape features through contour detection and matching these features with feature templates to further determine the meter type. Barcode parsing and type matching yield the meter's identification parameters, including meter type, serial number, unit code, and meter model.

[0063] Then, the instruction meter parameters of the meter to be stored are extracted from the received storage instruction, and the identified meter parameters are compared with the instruction meter parameters. If they match, it means that the meter to be stored is the meter in the storage instruction, and the subsequent storage operation can be carried out. If they do not match, it means that the wrong meter has been placed, and an early warning will be issued.

[0064] After verifying that the meter parameters and the command meter parameters match, the first meter parameter is used as the first meter parameter. Then, the location matching degree is calculated based on the first meter parameter and the first storage location parameter of the vacant storage location. Based on the location matching degree, the optimal storage location is determined. For example, a preset storage area is matched according to the meter type. The location matching degree of each vacant storage location is calculated according to the distance between each vacant location and the storage location within the storage area. Finally, the location with the highest location matching degree, that is, the closest location, is taken as the optimal storage location.

[0065] In a preferred embodiment, to improve the efficiency of electricity meter storage and optimize the storage layout, the present invention performs a matching analysis of electricity meters and storage locations using multi-dimensional indicators to select the most suitable storage location. Specific steps include:

[0066] Based on the first meter parameters of the meter to be put into storage, the meter physical parameters and meter type of the meter to be put into storage are obtained, and the first storage location parameters of the vacant storage location are extracted from the database. The first storage location parameters include the storage location physical parameters, the surrounding storage type and the location convenience.

[0067] Calculate the size matching degree and weight matching degree based on the physical parameters of the electricity meter and the physical parameters of the storage location;

[0068] Calculate the type matching degree based on the meter type and the surrounding storage type;

[0069] The location matching degree of each vacant storage location is obtained based on the size matching degree, the weight matching degree, the type matching degree, and the location convenience.

[0070] In this embodiment, the meter type and model are first extracted from the first meter parameters. Based on the meter model, the physical parameters of the meter are extracted from the database. The physical parameters include the meter size and the meter weight. The physical parameters of the available storage location are also extracted from the database, including length, width, height, and maximum weight limit. Then, the physical compatibility is calculated based on the physical parameters of the meter and the physical parameters of the available storage location. The physical compatibility includes size compatibility and weight compatibility. Specifically, size compatibility is represented by a size matching degree, which is calculated by comparing the size of the meter with the size of the storage space. Assuming each matching degree has a score of 10, if any of the meter's length, width, or height is greater than the corresponding size of the storage space, the size matching degree is 0. If the meter's length, width, and height are all smaller than the length, width, and height of the available storage space, and the corresponding differences between the two are all within the preset size difference threshold range, the size matching degree is 10. If any of the corresponding differences between the two exceed the preset size difference threshold, the difference is subtracted from the difference threshold, and a size correction coefficient is set based on the subtraction result. The coefficient is then multiplied by 10 to obtain the size matching degree. For example, if the difference between the length of the storage space and the length of the meter is 30cm, which is greater than the length difference threshold of 25cm, then 30-25=5, and the coefficient is set to 0.95. If there are multiple size parameters that exceed the size difference threshold, the coefficient is set according to the maximum value in the subtraction result. Of course, the size matching degree of storage locations that exceed the size difference threshold can also be set to 0. The specific size matching degree calculation method can be flexibly set according to the actual situation.

[0071] Weight compatibility is represented by a weight matching degree. If the weight of the meter is greater than the maximum weight limit of the available storage space, the weight matching degree is 0. If the weight of the meter is less than the maximum weight limit of the available storage space, and the weight difference is less than or equal to the preset weight difference threshold, the weight matching degree is 10. If the weight difference is greater than the preset weight difference threshold, the weight matching degree can be set to 0. Alternatively, a weight correction coefficient can be set according to the degree of the difference to calculate the weight matching degree. The setting method of the weight correction coefficient is the same as that of the size correction coefficient mentioned above, and will not be described in detail here.

[0072] Then, based on the surrounding storage types of the available storage locations and the meter types of the meters to be stored, the type matching degree is calculated. The surrounding storage types refer to the types of meters stored within a certain range around the storage location. That is, the types of meters stored in the locations within a preset range around the storage location are counted. If there are meters stored in these surrounding locations and the meter type is the same as the type of the meter to be stored, the type matching degree is 10. If the meter type is completely different from the type of the meter to be stored, the type matching degree is 0. If there are multiple types of meters, the percentage of the meter type to be stored in these locations is counted, and the product of the percentage and 10 is used as the type matching degree. For example, if the percentage is 30%, the type matching degree is 10 * 30% = 3. If there are no meters stored in these surrounding locations, the default type matching degree is 5.

[0073] In addition, this embodiment also pre-sets the location convenience of each storage location. Location convenience refers to the ease of access to the storage location. For example, the location convenience is 10 for a location adjacent to the passage, 7 for a location far from the passage, and 5 for a corner location. The specific values ​​can be set according to the actual situation.

[0074] Then, according to preset weights, such as size matching degree weight of 0.4, weight matching degree weight of 0.3, type matching degree weight of 0.2, and location convenience degree weight of 0.1, the above size matching degree, weight matching degree, type matching degree and location convenience degree are weighted and summed to obtain the location matching degree of each available storage location, and the location with the highest location matching degree is selected as the optimal storage location.

[0075] In a preferred embodiment, to further improve storage efficiency, the present invention, based on position matching degree, also considers the impact of different storage locations on the robot's motion efficiency. The storage locations are further screened using position matching degree and motion efficiency score. Specific steps include:

[0076] Based on the comparison results of the location matching degree and the matching degree threshold, a set of candidate storage locations is obtained;

[0077] Calculate the movement efficiency score of each candidate storage location based on the distance between each candidate storage location and the storage location in the candidate storage location set.

[0078] Based on the location matching degree and the motion efficiency score, a comprehensive location score is obtained, and based on the comprehensive location score, the optimal storage location is determined.

[0079] In this embodiment, the location matching degree is first compared with a matching degree threshold. Storage locations with a matching degree greater than the matching degree threshold are selected as candidate storage locations, thus obtaining a set of candidate storage locations. Then, based on the coordinates of the candidate storage locations, the motion efficiency score of the location is calculated. For example, the straight-line distance between each candidate storage location and the entry location is counted. Based on the magnitude of the straight-line distance, the motion efficiency score of each candidate storage location is determined. For example, the minimum straight-line distance is used as the baseline value. The baseline value is divided by the straight-line distance value and multiplied by 10 to obtain the motion efficiency score. Assuming the minimum straight-line distance value is 2m, the motion efficiency score of the location corresponding to this distance value is 10. If the straight-line distance between a location and the entry location is 2.5m, the motion efficiency score of that location is 2 / 2.5*10=8.

[0080] Considering the impact of straight-line distance on running time, and the impact of horizontal and vertical distance on the energy consumption of the robotic arm, in a preferred embodiment, the present invention also provides another method for calculating motion efficiency scores, the specific steps of which include:

[0081] Based on the location coordinates of each candidate storage location in the candidate storage location set and the location coordinates of the inbound location, calculate the straight-line distance, horizontal distance, and vertical distance between the candidate storage location and the inbound location;

[0082] Calculate the time consumption score based on the straight-line distance and the preset speed, and calculate the energy consumption score based on the horizontal distance and the vertical distance;

[0083] Based on the time consumption score and the energy consumption score, the motion efficiency score of each candidate storage location is obtained.

[0084] In this embodiment, the straight-line distance, horizontal distance, and vertical distance between the two locations are first calculated based on the coordinates of the entry location and the coordinates of the candidate storage location. The coordinates of the entry location can be understood as the initial position coordinates of the robot. To simplify the calculation, the coordinates of the entry location can be used as the origin to define the reference coordinate system. At this time, the straight-line distance, horizontal distance, and vertical distance can be easily calculated using the three-dimensional coordinates of the candidate storage location.

[0085] Then, based on the straight-line distance and the preset speed, the time consumption value is calculated, and a time consumption score is determined based on the time consumption value. Here, the preset speed is the preset movement speed of the robot arm. Preferably, considering the influence of different storage locations on the robot arm's movement speed, for example, the robot arm needs to control its speed to approach a corner position, while it can approach a position near the channel at a faster speed. In this embodiment, a correction coefficient is set to correct the preset speed. For example, the correction coefficient for a position near the channel is 1, and the correction coefficient for a position far from the channel or a corner is 0.7. The preset speed is corrected based on the correction coefficient, and then the time consumption value of the straight-line distance for the robot arm's movement is calculated. After obtaining the time consumption value for each position, the minimum time consumption value is used as the baseline value. The quotient obtained by dividing the baseline value by the time consumption value is multiplied by 10 to obtain the time consumption score corresponding to that position.

[0086] According to mechanical dynamics, due to the influence of gravity, the vertical movement of a robotic arm typically consumes more energy than its horizontal movement. To simplify calculations, a weight value is designed to represent the ratio of horizontal to vertical energy consumption per unit. Since movement distance is directly related to energy consumption, the energy consumption value of a candidate storage location can be equivalently represented by the horizontal distance. That is, the weight value is first multiplied by the vertical distance to convert the vertical distance to a horizontal distance, and then the converted horizontal distance is added to the original horizontal distance to obtain the energy consumption value. Then, an energy consumption score is calculated for each location based on the energy consumption value. Specifically, the minimum energy consumption value is used as the baseline value, the baseline value is divided by the energy consumption value, and the quotient is multiplied by 10 to obtain the energy consumption score. Finally, the time consumption score and the energy consumption score are weighted and summed to obtain the motion efficiency score for that location.

[0087] After obtaining the motion efficiency scores for each candidate storage location, the optimal storage location can be selected based on these scores. For example, the optimal storage location can be selected from the candidate storage location set based on the maximum motion efficiency score to ensure storage efficiency. Preferably, in this embodiment, the location matching degree and motion efficiency score are weighted and summed, for example, with weights set to 0.7 and 0.3 respectively, to obtain a comprehensive score for each location. Then, the optimal storage location is selected based on the comprehensive score, thereby achieving a balance between optimizing storage layout and improving storage efficiency. This embodiment selects the optimal storage location through location matching degree and motion efficiency score, improving the efficiency of meter storage while also optimizing the storage layout.

[0088] When a retrieval instruction is received, this embodiment obtains a set of candidate retrieval locations based on the second meter parameters of the meters to be retrievaled. Based on the quantity of meters to be retrievaled and the second storage parameters of each candidate retrieval location in the set, the location priority is calculated, and the optimal retrieval location is determined according to the location priority. Specific steps include:

[0089] Based on the second meter parameters of the meter to be dispatched, the meter model of the meter to be dispatched is obtained, and based on the meter model, a set of candidate extraction locations is obtained;

[0090] If the number of meters to be dispatched is one, the location priority is calculated based on the second storage location parameter of each candidate location in the candidate location set, and the optimal location is determined based on the location priority. Otherwise, it is determined whether the meter models of the multiple meters to be dispatched are the same.

[0091] If the meter models are the same, the location priority is calculated based on the second storage location parameter of each candidate extraction location in the candidate extraction location set, and the optimal extraction location is determined based on the outbound quantity and the location priority.

[0092] If the meter models are different, the dominant model is selected from multiple meter models according to the model selection rules. The position priority is calculated based on the second storage parameters of each candidate extraction position of the dominant model. The optimal extraction position of the dominant model is determined based on the position priority.

[0093] Using the optimal extraction position of the dominant model as the anchor point, and based on the principle of the nearest anchor point position, the corresponding optimal extraction position is selected from the set of candidate extraction positions of non-dominant models.

[0094] In this embodiment, firstly, based on the model of the meter to be dispatched, the location where the meter of that model is stored is found in the database, thus obtaining a set of candidate retrieval locations for that type of meter. Then, depending on the number of meters to be dispatched, different methods are used to find the optimal retrieval location from the candidate retrieval location set. It should be noted that although both warehousing and dispatching are based on warehousing and dispatching locations sequentially, the meters to be dispatched are placed sequentially by the operator, and the optimal storage location is selected based on the placed meter. In other words, the warehousing operation can be understood as a single meter operation; therefore, only the location selection and storage control need to be performed based on the meters placed at the warehousing location. For dispatching commands, the grasping and moving of the meters to be dispatched are all done by a robotic arm. When there are multiple dispatching quantities, single path planning and combined path planning will have a significant impact on dispatching efficiency. Therefore, this embodiment adopts corresponding optimal retrieval location selection methods according to different dispatching quantities and meter models.

[0095] First, determine whether the outbound quantity is one or multiple. If the outbound quantity is one, calculate the position priority of each candidate extraction position in the candidate extraction position set corresponding to the meter model according to preset indicators. For example, calculate the position priority according to the distance between the candidate extraction position and the outbound position, and then select the position with the highest position priority as the optimal extraction position.

[0096] In a preferred embodiment, in order to improve outbound efficiency while optimizing warehouse layout, the present invention also provides another method for calculating location priority, the specific steps of which include:

[0097] Extract the location coordinates, storage time of stored meters, and surrounding storage type of each candidate extraction location from the database;

[0098] Based on the location coordinates of each candidate extraction location and the location coordinates of the outbound location, calculate the distance value between each candidate extraction location and the outbound location, and calculate the distance index based on the distance value;

[0099] Calculate the time index based on the storage time of the stored electricity meters, and calculate the compatibility index based on the surrounding storage type.

[0100] The location priority is obtained based on the distance index, the time index, and the compatibility index.

[0101] In this embodiment, the second storage location parameters of each candidate extraction location are first obtained, including location coordinates, the entry time of the stored electricity meter, and the surrounding storage type. Then, the distance between the candidate extraction location and the exit location is calculated based on their location coordinates. The distance index is calculated based on the distance value. Specifically, the straight-line distance between the candidate extraction location and the exit location is calculated, the minimum straight-line distance is used as the benchmark value, the benchmark value is divided by the straight-line distance, and the quotient is multiplied by 10 to obtain the distance index.

[0102] Then, based on the storage time of the stored meters at each candidate extraction location, the time index is calculated. Since meters also have a shelf life, in order to avoid storing meters that were stored earlier without being used, this embodiment calculates the time index based on the first-in, first-out rule. Specifically, in days, the storage time of the meters at each location is calculated based on the storage time of the stored meters. The longest storage time is used as the benchmark value. Then, the storage time is divided by the benchmark value and multiplied by 10 to obtain the time index.

[0103] Then, based on the surrounding storage types of each candidate extraction location, a compatibility index is calculated. This compatibility index can be understood as having the same purpose as the type matching degree during optimal storage location selection, but calculated in the opposite way. Both type matching degree and compatibility index aim to cluster meters of the same type for storage as much as possible to increase the storage density of that type of meter and reduce fragmented inventory. However, one is used in the storage stage and the other in the extraction stage; therefore, their calculation methods are opposite. It should be noted that for large warehouses, to optimize warehouse layout, when calculating the priority or matching degree for location selection, compatibility indexes or type matching degrees can be calculated based on a lower-level meter model. However, for smaller warehouses, there may be multiple meter models for one meter type, but each meter model has a small quantity. Therefore, lower-level parameters cannot achieve a good regional clustering effect. Therefore, in this embodiment, the layout is optimized according to meter type. For large warehouses, meter model can also be used as a calculation parameter; no specific limitation is made here. Furthermore, in this embodiment and subsequent embodiments, the values ​​of matching degree, score, or index are dimensionless.

[0104] For compatibility metrics, the types of electricity meters stored within a predetermined radius of the storage location are statistically analyzed. If electricity meters are stored in these surrounding locations and their types match the type of the meter to be shipped, the type matching degree is 0. If the meter types are completely different from the type of the meter to be shipped, or if no electricity meters are stored nearby, the type matching degree is 10. If there are multiple meter types, the percentage of the type of the meter to be shipped in these locations is calculated. The percentage is then subtracted from 1 and multiplied by 10 to obtain the compatibility metric. By using the compatibility metric, meter types with fewer quantities in the area can be prioritized, reducing fragmented inventory and increasing the storage density of the same type of meter in the area, thereby optimizing the storage layout.

[0105] Finally, the time, distance, and compatibility metrics are weighted and summed according to preset weights to obtain the location priority. As can be seen, the location priority in this embodiment not only considers movement efficiency but also combines meter turnover efficiency and storage layout, effectively improving meter issuance efficiency and optimizing storage layout.

[0106] When there are multiple outbound quantities, the outbound quantities can be divided into two cases based on the meter model: those with the same meter model and those with different meter models. For the case where there are multiple outbound quantities and the meter models are the same, a set of candidate extraction locations can be selected according to the meter model, and the location priority of each candidate extraction location can be calculated. The processing steps in this case are the same as those for the case where there is only one outbound quantity. The difference is that in this case, multiple optimal extraction locations that match the outbound quantity need to be selected from the set of candidate extraction locations in descending order of location priority.

[0107] For cases where there are multiple outgoing quantities and different meter models, there are multiple candidate extraction location sets, each corresponding to a different meter model. It should be noted that if there are other restrictions besides the meter model, the location sets can be selected based on these restrictions.

[0108] Then, according to the model selection rules, select the dominant model from multiple meter models. These model selection rules include the rule of the largest quantity, the rule of the largest weight, or the rule of the highest urgency. For example, select the model with the largest quantity or the model with the largest weight as the dominant model. If there is an urgency requirement, then select the model that needs to be extracted urgently as the dominant model. The specific rules can be flexibly set according to the actual situation.

[0109] After selecting the dominant model, the candidate extraction locations for that model are prioritized. The optimal extraction location for that model is determined based on this priority and then used as an anchor point. If there are multiple meters of that dominant model awaiting delivery, there will be multiple optimal extraction locations. In this case, the location with the highest priority is chosen as the anchor point. Then, based on the rule of proximity, the optimal extraction location for each non-dominant model is selected from the candidate extraction locations. This can be understood as follows: for non-dominant models, the straight-line distance between each candidate extraction location and the anchor point is calculated, and the location with the shortest straight-line distance is chosen as the optimal extraction location for that non-dominant model. If there are multiple non-dominant models, they are selected sequentially according to the length of the straight-line distance. The optimal extraction location for each meter model can be obtained through this method.

[0110] This embodiment employs different extraction rules to select the optimal extraction location for different quantities and meter models. By designing location priorities, it can improve outbound efficiency and optimize the storage layout. Based on location priorities, for multiple outbound meters, it also considers the location association between each outbound meter. Through regional centralization and multi-model collaborative selection, it facilitates subsequent multi-objective path planning, thereby further improving outbound efficiency.

[0111] Through the above embodiments, the optimal position under different instruction types can be obtained. Combining the optimal position, the outbound position, or the inbound position, a path planning algorithm can be used to plan the robot's path for a single or multiple objectives, thereby obtaining the robot's motion trajectory. The motion trajectory is then converted into control commands for the robot to control its motion. It should be noted that conventional path planning algorithms can be used in this embodiment, such as breadth-first search, multi-objective A* algorithm, fast random tree algorithm, or ant colony algorithm. The specific algorithm can be flexibly selected according to the actual situation, and no specific limitation is imposed here.

[0112] To ensure the safety and stability of the meter access process, in a preferred embodiment, the present invention also provides a method for planning the motion pattern and trajectory of a robotic arm, the specific steps of which include:

[0113] Based on the optimal position, a path planning algorithm is used to plan the path of the robot arm and obtain the motion trajectory of the robot arm.

[0114] Based on the physical parameters of the turnover meter, the corresponding motion mode is extracted from the preset motion mode library. The turnover meter includes meters to be put into storage and meters to be taken out of storage. The motion mode contains the reference values ​​of multiple motion parameters, including the maximum motion speed, motion acceleration and buffer trigger distance.

[0115] The robotic arm is motion controlled according to the motion trajectory and the motion pattern.

[0116] In this embodiment, once the optimal extraction location and outbound location or the optimal storage location and inbound location are determined, path planning can be performed based on the coordinates of the starting point. Therefore, the path planning steps for outbound and inbound operations are the same. Here, the path planning method of this embodiment is described using the inbound stage as an example.

[0117] First, based on the optimal storage location and a pre-defined path planning algorithm, the robotic arm's path is planned to obtain its movement trajectory. Then, the physical parameters of the meters to be stored are acquired, namely their weight and size. Since there are various types and models of meters, different types or even different models of the same type may have different dimensions and weights. For example, three-phase meters are larger and heavier than single-phase meters. These different sizes and weights will inevitably affect the robotic arm's movement. For lighter and smaller meters, the robotic arm can maintain a relatively high movement speed while ensuring stability during movement and placement. For heavier and larger meters, the robotic arm needs to control its speed to ensure stability during movement and placement. Therefore, this embodiment pre-sets multiple motion modes in the motion mode library, each corresponding to different physical parameters, such as the weight and size classification of the electricity meter. Based on the classification combination of the physical parameters of the electricity meter to be stored, the corresponding motion mode is extracted from the motion mode library. This motion mode includes baseline values ​​for multiple motion parameters, such as maximum motion speed, motion acceleration, and buffer trigger distance. The maximum motion speed refers to the maximum movement speed of the robotic arm's end effector; motion acceleration refers to the rate of change from 0 to the maximum motion speed; and the buffer trigger distance refers to the straight-line distance between the robotic arm's end effector used to trigger deceleration and the optimal storage position. That is, when the buffer trigger distance is reached, the robotic arm needs to decelerate and reach the target placement speed when reaching the optimal storage position to avoid rigid impact and ensure the safe and stable placement of the electricity meter. These baseline values ​​can be obtained through historical data and simulation of electricity meter movement under different classification combinations.

[0118] Then, based on the baseline values ​​of each motion parameter in the motion mode and the planned motion trajectory, a sequence of discrete joint angles executable by the robotic arm is generated and converted into control commands to control the robotic arm to grasp, move, and place the electricity meter, thereby realizing the storage of the electricity meter in the warehouse. This embodiment adds a limitation on the baseline values ​​of the operating parameters for the motion control of the robotic arm. Conventional control methods can be used for the control of the robotic arm, and the control steps will not be described in detail here.

[0119] For path planning during the outbound phase, a multi-objective path planning algorithm can be used for multiple meters to be outbound. For each meter, the corresponding motion mode is selected according to its physical parameters. The specific path planning for the outbound phase can be executed according to the steps described above, and will not be repeated here. Furthermore, during the outbound phase, the robotic arm will grab the corresponding meter and place it at the outbound position. At this time, the same identification operation as for the meters to be received is used, that is, to determine whether the meter parameters of the meter at the outbound position are consistent with the meter parameters in the extraction command. If they are consistent, the outbound module corresponding to the outbound position will retract and move to the outbound port to automatically open, so that the staff can retrieve the meter, thus completing the meter outbound extraction operation.

[0120] In a preferred embodiment, the present invention also provides an adaptive adjustment method for motion modes to further improve the stability and accuracy of meter movement control, the specific steps of which include:

[0121] The physical parameter type of the turnover meter is determined by comparing the physical parameters of the turnover meter with the preset threshold ranges of multiple physical parameters.

[0122] Based on the physical parameter type, the corresponding motion mode is extracted from the preset motion mode library;

[0123] Obtain the median of the threshold range of the physical parameters corresponding to the physical parameters of the electricity meter, and obtain the speed correction coefficient based on the comparison relationship between the physical parameters of the electricity meter and the median;

[0124] The reference value of the maximum motion speed in the motion mode is corrected according to the speed correction coefficient;

[0125] In response to the fact that the turnover meter is a meter to be put into storage, the location convenience of the optimal storage location is obtained from the database, and the reference value of the buffer trigger distance in the movement mode is corrected according to the location convenience.

[0126] In this embodiment, firstly, based on the size and weight of the meter, a matching threshold range is found from multiple pre-set size threshold ranges and weight threshold ranges to determine the size and weight type of the meter. The size type includes three types: large, medium, and small, where size is represented by volume. The weight type is also divided into three types: light load, medium load, and heavy load. Based on different combinations of size and weight types, the baseline values ​​of motion parameters under that combination are extracted from a pre-set motion mode library. The baseline values ​​in the motion mode library are obtained based on historical data analysis and simulation.

[0127] The baseline values ​​of motion parameters in the motion mode library are the initial settings for motion parameters. When designing these initial parameters, the best matching physical parameters are the median values ​​of their corresponding threshold ranges. The closer the actual physical parameters are to the median values ​​of the threshold range, the higher the matching degree with the motion mode. If the physical parameters are close to the endpoints of the threshold, the matching degree with the motion mode will decrease. In order to make the motion parameters better match the current actual situation, this embodiment corrects the initial baseline values ​​based on the comparison relationship between the actual physical parameters and the median of the threshold range, thereby realizing the adaptive adjustment of motion parameters.

[0128] Specifically, first, the median of the physical parameter threshold range corresponding to the current meter's physical parameter type is obtained. Then, the actual physical parameter is compared with the median. If the actual physical parameter equals the median, the baseline value remains unchanged. If the actual physical parameter does not equal the median, the ratio obtained by dividing the median by the actual physical parameter is used as the speed correction coefficient. Then, the speed correction coefficient is multiplied by the baseline value of the maximum movement speed to obtain the corrected baseline value. This is because the larger the weight or size, the greater the accuracy of motion control of the robot arm. Therefore, it is necessary to increase or decrease the movement speed and acceleration accordingly based on changes in weight and size to improve the accuracy and stability of the robot arm's motion control. For example, if a meter weighs 1.5kg, the median of its corresponding threshold range (1.2, 2.2) is 1.7. Since 1.5 is not equal to 1.7, the speed correction factor is 1.7 / 1.5 = 1.13. Then, 1.13 is multiplied by the reference value of the maximum movement speed to obtain the correction reference value. If the meter weighs 1.7kg and the median is 1.5, the speed correction factor is 1.5 / 1.7 = 0.88. It can be seen that the reference value of the maximum movement speed will be adaptively adjusted according to the ratio of the median to the weight in order to improve the control accuracy of the robot. Since physical parameters include both weight and size, the correction factor can be calculated based on either weight or size. Alternatively, the maximum or average of the correction factors calculated from both parameters can be used as the final correction factor. It should be noted that since size and weight are linearly proportional, generally the larger the size, the greater the weight. Therefore, it is unlikely that one parameter will be greater than the median while the other is less than the median. If such a situation occurs, the current baseline value can be kept unchanged, or the average of the correction factors calculated from both parameters can be used as the final correction factor.

[0129] Besides weight and size, the location of the optimal position also affects the control of the robotic arm. Here, the optimal position refers to the optimal storage position. This is because during the warehousing stage, the robotic arm will grab the meter to be stored at the storage location and quickly move it to the vicinity of the optimal storage position. Then, it will reduce its speed to ensure the accuracy of placement control. The difficulty of placement control will be different depending on whether the optimal storage position is close to the aisle or far away from the aisle or in a corner. For positions that are not convenient for placement, due to the large number of obstacles around, the robotic arm should reduce its movement speed to ensure safe passage through the obstacles. Therefore, the buffer trigger distance of the robotic arm needs to be appropriately extended. Specifically, as shown in the above embodiment for calculating position matching degree, each position is pre-set with a position convenience score. Position convenience score refers to the ease of access for the operation. For example, a position adjacent to the channel has a convenience score of 10, a position far from the channel has a convenience score of 7, and a corner position has a convenience score of 5. In this embodiment, the position convenience score is used to calculate the distance correction coefficient. 10 is divided by the position convenience score to obtain the distance correction coefficient. Then, the distance correction coefficient is multiplied by the baseline value of the buffer trigger distance. Of course, to avoid the buffer trigger distance being too long and affecting storage efficiency, upper and lower limits for the distance correction coefficient can be set as coefficient constraints, such as a distance correction coefficient greater than or equal to 1 and less than or equal to 1.8. Through the motion mode adaptive adjustment mechanism provided in this embodiment, the stability and accuracy of the robot's motion control can be ensured, thereby ensuring the safety and stability of the meter turnover control.

[0130] This embodiment provides a smart meter turnover method based on a robotic arm. Through a multi-dimensional scoring model in the warehousing stage, this invention achieves precise matching between meters and storage areas, improving storage efficiency while optimizing the storage layout. In the outbound stage, dynamic collaborative decision-making across multiple models effectively improves outbound efficiency and further optimizes the storage layout. Furthermore, through path planning and adaptive adjustment of motion modes, it enhances the stability and control accuracy of meter access. This invention, through precise matching in warehousing and global optimization in outbound processes, improves meter access efficiency, optimizes meter storage layout, and thus effectively enhances the intelligence level of meter turnover control.

[0131] Based on the same inventive concept, the second embodiment of the present invention proposes an intelligent turnover device for electricity meters based on a robotic arm, which includes a controller that executes the steps of the method described above.

[0132] The technical features and effects of the smart meter turnover device based on a robotic arm proposed in this embodiment of the invention are the same as those of the method proposed in this embodiment of the invention, and will not be repeated here. The controller of the aforementioned smart meter turnover device based on a robotic arm can be set inside the turnover device or outside the device, and the turnover control is performed through wireless communication. In this embodiment, the setting of the controller can refer to the conventional setting method, and is not specifically limited thereto.

[0133] In summary, the present invention proposes a method and apparatus for intelligent meter turnover based on a robotic arm. The method obtains the instruction type of the meter turnover command, which includes storage instructions and retrieval instructions; determines the optimal position based on the instruction type and a preset position selection algorithm; and performs path planning and motion control on the robotic arm based on the optimal position to achieve meter turnover control. The optimal position includes an optimal storage position and an optimal retrieval position. The step of determining the optimal position based on the instruction type and the preset position selection algorithm includes: responding to a storage instruction, calculating a position matching degree based on the first meter parameters of the meter to be stored and the first storage position parameters of the available storage location, and determining the optimal storage position based on the position matching degree; responding to a retrieval instruction, obtaining a set of candidate retrieval positions based on the second meter parameters of the meter to be retrieved, calculating the position priority based on the number of meters to be retrieved and the second storage position parameters of each candidate retrieval position in the candidate retrieval position set, and determining the optimal retrieval position based on the position priority. This invention achieves precise matching between electricity meters and storage areas through a multi-dimensional scoring model in the warehousing stage, improving storage efficiency while optimizing the storage layout. In the warehousing stage, dynamic collaborative decision-making among multiple models effectively improves warehousing efficiency and further optimizes the storage layout. Furthermore, through path planning and adaptive adjustment of movement modes, it enhances the stability and control accuracy of electricity meter access. This invention improves electricity meter access efficiency and optimizes the electricity meter storage layout through precise matching in warehousing and global optimization in warehousing, effectively enhancing the intelligence level of electricity meter turnover control.

[0134] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0135] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A smart meter turnover method based on a robotic arm, characterized in that, include: The instruction types for obtaining meter turnover instructions include storage instructions and retrieval instructions; Based on the instruction type and the preset position selection algorithm, the optimal position is determined, and based on the optimal position, the robot arm is used for path planning and motion control to realize the turnover control of the electricity meter. The optimal position includes the optimal storage position and the optimal retrieval position. The step of determining the optimal position based on the instruction type and a preset position selection algorithm includes: In response to an instruction type of storage instruction, the location matching degree is calculated based on the first meter parameter of the meter to be stored and the first storage position parameter of the available storage location, and the optimal storage location is determined based on the location matching degree. In response to an instruction type of extraction instruction, a set of candidate extraction locations is obtained based on the second meter parameters of the meter to be extracted. The location priority is calculated based on the number of meters to be extracted and the second storage location parameters of each candidate extraction location in the set of candidate extraction locations. The optimal extraction location is then determined based on the location priority. The step of calculating the location matching degree based on the first meter parameters of the electricity meter to be stored and the first storage location parameters of the vacant storage location includes: Based on the first meter parameters of the meter to be put into storage, the meter physical parameters and meter type of the meter to be put into storage are obtained, and the first storage location parameters of the vacant storage location are extracted from the database. The first storage location parameters include the storage location physical parameters, the surrounding storage type and the location convenience. Calculate the size matching degree and weight matching degree based on the physical parameters of the electricity meter and the physical parameters of the storage location; Calculate the type matching degree based on the meter type and the surrounding storage type; The location matching degree of each vacant storage location is obtained based on the size matching degree, the weight matching degree, the type matching degree, and the location convenience degree. The steps of obtaining a set of candidate extraction locations based on the second meter parameters of the meters to be released, calculating the location priority based on the number of meters to be released and the second storage location parameters of each candidate extraction location in the set of candidate extraction locations, and determining the optimal extraction location based on the location priority include: Based on the second meter parameters of the meter to be dispatched, the meter model of the meter to be dispatched is obtained, and based on the meter model, a set of candidate extraction locations is obtained; If the number of meters to be dispatched is one, the location priority is calculated based on the second storage location parameter of each candidate location in the candidate location set, and the optimal location is determined based on the location priority. Otherwise, it is determined whether the meter models of the multiple meters to be dispatched are the same. If the meter models are the same, the location priority is calculated based on the second storage location parameter of each candidate extraction location in the candidate extraction location set, and the optimal extraction location is determined based on the outbound quantity and the location priority. If the meter models are different, the dominant model is selected from multiple meter models according to the model selection rules. The position priority is calculated based on the second storage parameters of each candidate extraction position of the dominant model. The optimal extraction position of the dominant model is determined based on the position priority. Using the optimal extraction position of the dominant model as the anchor point, and based on the principle of the nearest anchor point position, the corresponding optimal extraction position is selected from the set of candidate extraction positions of non-dominant models.

2. The intelligent meter turnover method based on a robotic arm according to claim 1, characterized in that, Before the step of calculating the location matching degree based on the first meter parameters of the meter to be stored and the first storage location parameters of the vacant storage location, the method further includes: Collect images of the electricity meters to be stored in the warehouse and perform image preprocessing on the electricity meter images; Barcode parsing and type matching are performed on the preprocessed meter images to obtain the identification parameters of the meters entering the warehouse; The instruction meter parameters of the meter to be stored are obtained from the storage instructions, and the instruction meter parameters are compared with the identification meter parameters. If the comparison results are consistent, the instruction meter parameters are used as the first meter parameters; otherwise, an early warning reminder is issued.

3. The intelligent meter turnover method based on a robotic arm according to claim 1, characterized in that, The step of determining the optimal storage location based on the location matching degree includes: Based on the comparison results of the location matching degree and the matching degree threshold, a set of candidate storage locations is obtained; Calculate the movement efficiency score of each candidate storage location based on the distance between each candidate storage location and the storage location in the candidate storage location set. Based on the location matching degree and the motion efficiency score, a comprehensive location score is obtained, and based on the comprehensive location score, the optimal storage location is determined.

4. The intelligent meter turnover method based on a robotic arm according to claim 3, characterized in that, The step of calculating the movement efficiency score of each candidate storage location based on the distance between each candidate storage location in the candidate storage location set and the storage location includes: Based on the location coordinates of each candidate storage location in the candidate storage location set and the location coordinates of the inbound location, calculate the straight-line distance, horizontal distance, and vertical distance between the candidate storage location and the inbound location; Calculate the time consumption score based on the straight-line distance and the preset speed, and calculate the energy consumption score based on the horizontal distance and the vertical distance; Based on the time consumption score and the energy consumption score, the motion efficiency score of each candidate storage location is obtained.

5. The intelligent meter turnover method based on a robotic arm according to claim 1, characterized in that, The calculation steps for the position priority include: Extract the location coordinates, storage time of stored meters, and surrounding storage type of each candidate extraction location from the database; Based on the location coordinates of each candidate extraction location and the location coordinates of the outbound location, calculate the distance value between each candidate extraction location and the outbound location, and calculate the distance index based on the distance value; Calculate the time index based on the storage time of the stored electricity meters, and calculate the compatibility index based on the surrounding storage type. The location priority is obtained based on the distance index, the time index, and the compatibility index.

6. The intelligent meter turnover method based on a robotic arm according to claim 1, characterized in that, The steps of path planning and motion control of the robotic arm based on the optimal position include: Based on the optimal position, a path planning algorithm is used to plan the path of the robot arm and obtain the motion trajectory of the robot arm. Based on the physical parameters of the turnover meter, the corresponding motion mode is extracted from the preset motion mode library. The turnover meter includes meters to be put into storage and meters to be taken out of storage. The motion mode contains the reference values ​​of multiple motion parameters, including the maximum motion speed, motion acceleration and buffer trigger distance. The robotic arm is motion controlled according to the motion trajectory and the motion pattern.

7. The intelligent meter turnover method based on a robotic arm according to claim 6, characterized in that, The step of extracting the corresponding motion mode from the preset motion mode library based on the physical parameters of the turnover meter includes: The physical parameter type of the turnover meter is determined by comparing the physical parameters of the turnover meter with the preset threshold ranges of multiple physical parameters. Based on the physical parameter type, the corresponding motion mode is extracted from the preset motion mode library; Obtain the median of the threshold range of the physical parameters corresponding to the physical parameters of the electricity meter, and obtain the speed correction coefficient based on the comparison relationship between the physical parameters of the electricity meter and the median; The reference value of the maximum motion speed in the motion mode is corrected according to the speed correction coefficient; In response to the fact that the turnover meter is a meter to be put into storage, the location convenience of the optimal storage location is obtained from the database, and the reference value of the buffer trigger distance in the movement mode is corrected according to the location convenience.

8. A smart meter turnover device based on a robotic arm, characterized in that, Includes a controller that performs the steps of the method as described in any one of claims 1 to 7.

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