A control system and method for a lithium battery pruning shear

Abstract

A control system and method for a lithium battery pruning shear provided by the present application relate to the technical field of equipment control. In the present application, first, a target pruning shear semantic vector corresponding to the recorded data of the target pruning shear is mined; secondly, a historical pruning shear semantic vector having an associated relationship with the target pruning shear semantic vector is determined, and historical blade load state data corresponding to the historical pruning shear semantic vector is determined, and a blade load state semantic vector of the historical blade load state data is mined; then, based on the target pruning shear semantic vector, the historical pruning shear semantic vector, and the blade load state semantic vector, the blade load state data of the target lithium battery pruning shear is determined, and the target lithium battery pruning shear is controlled based on the blade load state data. Based on the above, the problem of relatively low reliability in controlling the lithium battery pruning shear in the prior art can be improved.

Description

Technical Field

[0001] This application relates to the technical field of equipment control. Specifically, it relates to a control system and method for a lithium - battery pruning shear. Background Art

[0002] A lithium - battery pruning shear is an electric pruning tool driven by a lithium - ion battery, which is widely used in fields such as gardening, agriculture, and forestry. This tool is designed to easily prune trees, shrubs, and plants, and has the characteristics of high efficiency and convenience. With the development of battery technology, lithium - battery pruning shears will become lighter and their battery life will be greatly improved. At the same time, the introduction of intelligent functions (such as automatic cutting, intelligent sensors, etc.) will also make their use more efficient and convenient. After research by the inventor, it is found that under different cutting conditions, the load on the blade will change. For example, when encountering harder materials, the load will increase. However, in the prior art, the intelligent control of the pruning shear does not refer to the load on the blade, resulting in a relatively low reliability problem in controlling the lithium - battery pruning shear. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a control system and method for a lithium - battery pruning shear to improve the relatively low reliability problem in controlling the lithium - battery pruning shear existing in the prior art.

[0004] To achieve the above purpose, this application adopts the following technical solutions:

[0005] A control method for a lithium - battery pruning shear includes:

[0006] Extracting target pruning shear record data and mining a target pruning shear semantic vector corresponding to the target pruning shear record data, where the target pruning shear record data includes pruning image data to be pruned and at least one pruning shear load data, and each pruning shear load data is collected for the blade load of the target lithium - battery pruning shear based on a corresponding load determination method;

[0007] Determining a historical pruning shear semantic vector having an association relationship with the target pruning shear semantic vector, determining historical blade load status data corresponding to the historical pruning shear semantic vector, and mining a blade load status semantic vector of the historical blade load status data, where the historical pruning shear record data corresponding to the historical pruning shear semantic vector belongs to the data formed by the target lithium - battery pruning shear during historical pruning operations;

[0008] Based on the target pruning shear semantic vector, the historical pruning shear semantic vector, and the blade load status semantic vector, determine the blade load status data of the target lithium - battery pruning shear based on the target pruning shear record data, and control the target lithium - battery pruning shear based on the blade load status data so that the blade load is managed, where the blade load status data is used to reflect whether the blade load of the target lithium - battery pruning shear is overloaded.

[0009] In a preferred option of the present application, in the above - mentioned control method of the lithium - battery pruning shear, the step of extracting the target pruning shear record data and mining the target pruning shear semantic vector corresponding to the target pruning shear record data includes:

[0010] Extract the target pruning shear record data and mine the local pruning shear semantic vectors respectively possessed by the local data of at least two dimensions corresponding to the target pruning shear record data, where the image data to be pruned is used as the local data of one dimension, and each type of pruning shear load data is used as the local data of one dimension;

[0011] Perform an aggregation process on the local pruning shear semantic vectors of at least two dimensions to form the target pruning shear semantic vector corresponding to the target pruning shear record data.

[0012] In a preferred option of the present application, in the above - mentioned control method of the lithium - battery pruning shear, the step of performing an aggregation process on the local pruning shear semantic vectors of at least two dimensions to form the target pruning shear semantic vector corresponding to the target pruning shear record data includes:

[0013] Use the aggregation units of the first layer in the aggregation model included in the load status analysis network to perform self - attention processing on the local pruning shear semantic vectors of at least two dimensions respectively, and output the first pruning shear semantic attention vector, the second pruning shear semantic attention vector, and the third pruning shear semantic attention vector corresponding to the first - layer aggregation unit, where the first pruning shear semantic attention vector corresponds to the image data to be pruned, the second pruning shear semantic attention vector corresponds to the first type of pruning shear load data, the third pruning shear semantic attention vector corresponds to the second type of pruning shear load data, the first type of pruning shear load data is collected based on the pressure sensor on the blade, the second type of pruning shear load data is obtained based on the change of the acceleration collected by the acceleration sensor, and the load status analysis network belongs to a neural network formed by training with sample data and corresponding blade load status labels;

[0014] For each aggregation unit at each level other than the aggregation unit at the first level in the aggregation model, splice the first pruning semantic attention vector, the second pruning semantic attention vector, and the third pruning semantic attention vector corresponding to the aggregation unit at the previous level to form a spliced attention vector, and perform pooling compression on the spliced attention vector to output a pooled compression vector. Then, perform cross-attention processing on the first pruning semantic attention vector, the second pruning semantic attention vector, and the third pruning semantic attention vector corresponding to the aggregation unit at the previous level based on the pooled compression vector, and output the first pruning semantic attention vector, the second pruning semantic attention vector, and the third pruning semantic attention vector corresponding to the aggregation unit at the current level;

[0015] Splice the first pruning semantic attention vector, the second pruning semantic attention vector, and the third pruning semantic attention vector corresponding to the aggregation unit at the last level to form the target pruning semantic vector corresponding to the target pruning record data.

[0016] In a preferred selection of the present application, in the above control method of the lithium battery pruning shear, the steps of determining the historical pruning semantic vector having an association relationship with the target pruning semantic vector, determining the historical blade load state data corresponding to the historical pruning semantic vector, and mining the blade load state semantic vector of the historical blade load state data include:

[0017] Determine each historical pruning record data formed by the target lithium battery pruning shear during historical pruning operations to obtain a corresponding plurality of historical pruning record data;

[0018] Extract a plurality of historical pruning semantic vectors corresponding to the plurality of historical pruning record data;

[0019] Among the plurality of historical pruning semantic vectors, determine a historical pruning semantic vector with the smallest vector distance from the target pruning semantic vector as the historical pruning semantic vector having an association relationship with the target pruning semantic vector;

[0020] Determine the historical blade load state data corresponding to the historical pruning semantic vector having an association relationship with the target pruning semantic vector, and perform semantic mining on the historical blade load state data to output a corresponding blade load state semantic vector.

[0021] In a preferred option of the present application, in the above control method of the lithium battery pruning shear, the step of determining the blade load state data of the target lithium battery pruning shear based on the target pruning shear semantic vector, the historical pruning shear semantic vector, and the blade load state semantic vector, and controlling the target lithium battery pruning shear based on the blade load state data to control the blade load includes:

[0022] Performing an aggregation process on the historical pruning shear semantic vector and the target pruning shear semantic vector to output an aggregated pruning shear semantic vector, and performing semantic association mining on the blade load state semantic vector and the aggregated pruning shear semantic vector to output an associated pruning shear semantic vector;

[0023] Performing a mapping process on the associated pruning shear semantic vector according to the first mapping matrix to form a pruning shear semantic mapping vector, and determining a pruning shear semantic enhancement vector according to the pruning shear semantic mapping vector and the blade load state semantic vector, and performing an activation process on the pruning shear semantic enhancement vector to output a pruning shear semantic activation vector;

[0024] Performing a mapping process on the pruning shear semantic activation vector according to the second mapping matrix to output a pruning shear semantic intermediate vector, and obtaining a pruning shear semantic output vector according to the pruning shear semantic intermediate vector and the pruning shear semantic enhancement vector, where the first mapping matrix and the second mapping matrix belong to the network parameters of a load state analysis network formed by training with sample data and corresponding blade load state labels, and the load state analysis network belongs to a neural network;

[0025] Determining the blade load state data of the target lithium battery pruning shear based on the target pruning shear record data according to the pruning shear semantic output vector, and controlling the blade load of the target lithium battery pruning shear based on the blade load state data.

[0026] In a preferred option of the present application, in the above control method of the lithium battery pruning shear, the step of performing an aggregation process on the historical pruning shear semantic vector and the target pruning shear semantic vector to output an aggregated pruning shear semantic vector, and performing semantic association mining on the blade load state semantic vector and the aggregated pruning shear semantic vector to output an associated pruning shear semantic vector includes:

[0027] Concatenating the historical pruning shear semantic vector and the target pruning shear semantic vector to form a corresponding pruning shear semantic concatenated vector as the corresponding aggregated pruning shear semantic vector;

[0028] Based on the first spatial transformation matrix, perform spatial transformation on the semantic vector of the blade load state to output a first spatial transformation vector, and based on the second spatial transformation matrix, perform spatial transformation on the semantic vector of the aggregate pruning shear to output a second spatial transformation vector, and further, based on the third spatial transformation matrix, perform spatial transformation on the semantic vector of the aggregate pruning shear to output a third spatial transformation vector;

[0029] Multiply the first spatial transformation vector and the second spatial transformation vector to output an association parameter matrix, and then perform noise application processing on the association parameter matrix to output an association parameter noise matrix, wherein during the noise application processing, the number of association parameters with noise applied is less than the number of association parameters without noise applied;

[0030] Multiply the association parameter matrix and the third spatial transformation vector to output a first associated semantic vector, and multiply the association parameter noise matrix and the third spatial transformation vector to output a second associated semantic vector, and further, based on the first associated semantic vector and the second associated semantic vector, output an associated pruning shear semantic vector.

[0031] In a preferred selection of the present application, in the above control method of the lithium - battery pruning shear, the step of multiplying the association parameter matrix and the third spatial transformation vector to output a first associated semantic vector, multiplying the association parameter noise matrix and the third spatial transformation vector to output a second associated semantic vector, and further, based on the first associated semantic vector and the second associated semantic vector, outputting an associated pruning shear semantic vector includes:

[0032] Multiply the association parameter matrix and the third spatial transformation vector to output a corresponding first associated semantic vector, and multiply the association parameter noise matrix and the third spatial transformation vector to output a corresponding second associated semantic vector;

[0033] Determine a first weighting coefficient and a second weighting coefficient, wherein the first weighting coefficient is greater than the second weighting coefficient, and the sum of the first weighting coefficient and the second weighting coefficient is equal to 1;

[0034] Multiply the first weighting coefficient and the first associated semantic vector to obtain a corresponding first multiplication result, multiply the second weighting coefficient and the second associated semantic vector to obtain a corresponding second multiplication result, and then perform a superposition operation on the first multiplication result and the second multiplication result to output a corresponding associated pruning shear semantic vector.

[0035] In a preferred option of the present application, in the above control method of the lithium battery pruning shear, the step of mapping the associated pruning shear semantic vector according to the first mapping matrix to form a pruning shear semantic mapping vector, and then determining a pruning shear semantic enhancement vector based on the pruning shear semantic mapping vector and the blade load state semantic vector, and activating the pruning shear semantic enhancement vector to output a pruning shear semantic activation vector includes:

[0036] Performing a multiplication operation on the first mapping matrix and the associated pruning shear semantic vector to form a pruning shear semantic mapping vector, and performing a superposition operation on the pruning shear semantic mapping vector and the blade load state semantic vector to form a pruning shear semantic enhancement vector;

[0037] Activating the pruning shear semantic enhancement vector through a non-linear activation function to output a pruning shear semantic activation vector.

[0038] In a preferred option of the present application, in the above control method of the lithium battery pruning shear, the step of mapping the pruning shear semantic activation vector according to the second mapping matrix to output a pruning shear semantic intermediate vector, and then obtaining a pruning shear semantic output vector based on the pruning shear semantic intermediate vector and the pruning shear semantic enhancement vector includes:

[0039] Performing a mapping multiplication operation on the second mapping matrix and the pruning shear semantic activation vector to output a pruning shear semantic intermediate vector;

[0040] Performing a superposition operation on the pruning shear semantic intermediate vector and the pruning shear semantic enhancement vector to obtain a pruning shear semantic output vector.

[0041] The present application also provides a control system for a lithium battery pruning shear, including:

[0042] A target data mining module, configured to extract target pruning shear record data and mine a target pruning shear semantic vector corresponding to the target pruning shear record data, where the target pruning shear record data includes to-be-pruned image data and at least one pruning shear load data, and each pruning shear load data is collected for the blade load of the target lithium battery pruning shear based on a corresponding load determination method;

[0043] A historical data mining module, configured to determine a historical pruning shear semantic vector having an association relationship with the target pruning shear semantic vector, determine historical blade load state data corresponding to the historical pruning shear semantic vector, and mine a blade load state semantic vector of the historical blade load state data, where the historical pruning shear record data corresponding to the historical pruning shear semantic vector belongs to the data formed by the target lithium battery pruning shear during historical pruning operations;

[0044] A pruning shear control module, configured to determine, based on the target pruning shear semantic vector, the historical pruning shear semantic vector, and the blade load status semantic vector, the blade load status data of the target lithium battery pruning shear based on the target pruning shear record data, and control the target lithium battery pruning shear based on the blade load status data, so as to control the blade load, where the blade load status data is used to reflect whether the blade load of the target lithium battery pruning shear is overloaded.

[0045] A control system and method for a lithium battery pruning shear provided in this application. First, a target pruning shear semantic vector corresponding to the target pruning shear record data is mined. Secondly, a historical pruning shear semantic vector having an associated relationship with the target pruning shear semantic vector is determined, and historical blade load status data corresponding to the historical pruning shear semantic vector is determined, and a blade load status semantic vector of the historical blade load status data is mined. Then, based on the target pruning shear semantic vector, the historical pruning shear semantic vector, and the blade load status semantic vector, the blade load status data of the target lithium battery pruning shear is determined, and the target lithium battery pruning shear is controlled based on the blade load status data. Based on the above content, since the target pruning shear record data includes pruning image data to be pruned and at least one pruning shear load data, the target pruning shear semantic vector not only carries semantic information of the blade load, but also carries semantic information of objects such as the branches to be pruned (different branch objects have different hardnesses, which affect the blade load). Therefore, the target pruning shear semantic vector can have relatively rich and reliable semantic information, making the reliability of the analyzed blade load status data higher. In addition, since the historical pruning shear semantic vector and the blade load status semantic vector are also combined, the reliability of the determined blade load status data can be further improved, thereby ensuring the reliability of the control of the target lithium battery pruning shear based on the blade load status data, and further improving the problem of relatively low reliability in controlling the lithium battery pruning shear in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] To make the above objects, features, and advantages of this application more obvious and understandable, the following specifically gives preferred embodiments and cooperates with the accompanying drawings for detailed description as follows;

[0047] Figure 1 It is a structural block diagram of an electronic device provided in an embodiment of this application;

[0048] Figure 2 It is a schematic flowchart of a control method for a lithium battery pruning shear provided in an embodiment of this application;

[0049] Figure 3Schematic diagram for aggregating local pruning shear semantic vectors provided by an embodiment of the present application;

[0050] Figure 4 Block diagram of the control system of a lithium - battery pruning shear provided by an embodiment of the present application. Detailed implementation manners

[0051] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Usually, the components of the embodiments of the present application described and shown in the accompanying drawings here can be arranged and designed in various different configurations.

[0052] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but merely represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the present application.

[0053] As Figure 1 shown, an embodiment of the present application provides an electronic device. Among them, the electronic device may include a memory, a processor, and a control system of a lithium - battery pruning shear.

[0054] Specifically, the memory and the processor are electrically connected directly or indirectly to achieve data transmission or interaction. For example, the memory and the processor may be electrically connected through one or more communication buses or signal lines. The control system of the lithium - battery pruning shear includes at least one software function module stored in the memory in the form of software or firmware (firmware). The processor is used to execute the executable computer programs stored in the memory, such as the software function modules and computer programs included in the control system of the lithium - battery pruning shear, etc., to implement the control method of the lithium - battery pruning shear provided by the embodiment of the present application.

[0055] Optionally, the memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electric Erasable Programmable Read-Only Memory (EEPROM), etc.

[0056] Moreover, the processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), a System on Chip (SoC), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0057] It can be understood that Figure 1 the structure shown is only schematic, and the electronic device may further include more or fewer components than those shown Figure 1 in it, or have a different configuration from that shown Figure 1 For example, it may further include a communication unit for information interaction with other devices (such as a lithium battery pruning shear).

[0058] Combined with Figure 2 , an embodiment of the present application further provides a control method for a lithium battery pruning shear applicable to the above-mentioned electronic device. Among them, the method steps defined by the process related to the control method of the lithium battery pruning shear can be implemented by the electronic device.

[0059] Next, the Figure 2 specific process shown will be elaborated in detail.

[0060] Step S110, extract the target pruning shear record data, and mine the target pruning shear semantic vector corresponding to the target pruning shear record data.

[0061] In an embodiment of the present application, the electronic device can extract target pruning shear record data and mine a target pruning shear semantic vector corresponding to the target pruning shear record data. Among them, the target pruning shear record data includes to-be-pruned image data and at least one pruning shear load data, and each type of pruning shear load data is obtained by collecting the blade load of the target lithium battery pruning shear based on a corresponding load determination method. Exemplarily, the to-be-pruned image data can be formed by collecting an image of a corresponding pruning object (such as a tree branch, etc.) before the target lithium battery pruning shear performs work (which can be used to reflect the type, thickness, etc. of the pruning object). The pruning shear load data can be collected during the pruning process of the target lithium battery pruning shear. The target pruning shear semantic vector refers to the semantic information (or semantic features) mined from the target pruning shear record data and is represented in the form of a vector for subsequent processing.

[0062] Step S120: Determine a historical pruning shear semantic vector having an associated relationship with the target pruning shear semantic vector, determine historical blade load status data corresponding to the historical pruning shear semantic vector, and mine a blade load status semantic vector of the historical blade load status data.

[0063] In an embodiment of the present application, the electronic device can determine a historical pruning shear semantic vector having an associated relationship with the target pruning shear semantic vector, determine historical blade load status data corresponding to the historical pruning shear semantic vector, and mine a blade load status semantic vector of the historical blade load status data. Among them, the historical pruning shear record data corresponding to the historical pruning shear semantic vector belongs to the data formed by the target lithium battery pruning shear during historical pruning operations. For example, it can be the data formed by the most recent pruning operation in history. The historical blade load status data can be the status of the blade load determined most recently in history.

[0064] Step S130: Based on the target pruning shear semantic vector, the historical pruning shear semantic vector, and the blade load status semantic vector, determine the blade load status data of the target lithium battery pruning shear based on the target pruning shear record data, and control the target lithium battery pruning shear based on the blade load status data so that the blade load is managed.

[0065] In an embodiment of the present application, the electronic device may determine, based on the target pruning shear semantic vector, the historical pruning shear semantic vector, and the blade load status semantic vector, the blade load status data of the target lithium - ion pruning shear based on the target pruning shear record data, and control the target lithium - ion pruning shear based on the blade load status data so that the blade load is managed. Among them, the blade load status data is used to reflect whether there is an overload in the blade load of the target lithium - ion pruning shear. For example, when there is an overload, the target lithium - ion pruning shear can be controlled to reduce the cutting speed, and the user can also be prompted to replace the blade, etc., so that the blade load can be reduced to ensure the safe and reliable operation of the target lithium - ion pruning shear.

[0066] Based on the above content, since the target pruning shear record data includes the to - be - pruned image data and at least one pruning shear load data, the target pruning shear semantic vector not only carries the semantic information of the blade load, but also carries the semantic information of objects such as the pruned branches (different branch objects have different hardnesses, which affect the blade load). Therefore, the target pruning shear semantic vector can have relatively rich and reliable semantic information, making the reliability of the analyzed blade load status data higher. In addition, since the historical pruning shear semantic vector and the blade load status semantic vector are also combined, the reliability of the determined blade load status data can be further improved, thereby ensuring the reliability of the control of the target lithium - ion pruning shear based on the blade load status data, and further improving the problem of relatively low reliability in controlling lithium - ion pruning shears in the existing technology.

[0067] In the first aspect, regarding step S110, it should be noted that the specific method for mining the target pruning shear semantic vector corresponding to the target pruning shear record data is not limited and can be selected and configured according to actual needs.

[0068] For example, in an alternative embodiment, in order to improve the reliability of the mined target pruning shear semantic vector, step S110 described above may further include step S111 and step S112, and the specific content of each step is described as follows.

[0069] Step S111, extract the target pruning shear record data, and mine the local pruning shear semantic vectors respectively possessed by at least two - dimensional local data corresponding to the target pruning shear record data.

[0070] In the embodiments of the present application, target pruning shear record data can be extracted, and local pruning shear semantic vectors respectively possessed by at least two dimensions of local data corresponding to the target pruning shear record data can be mined. Among them, the image data of the to-be-pruned is used as local data of one dimension, and each type of pruning shear load data is used as local data of one dimension. In this way, the local pruning shear semantic vector corresponding to the image data of the to-be-pruned and the local pruning shear semantic vector corresponding to the pruning shear load data can be obtained, that is, at least two local pruning shear semantic vectors can be obtained. Specifically, for the image data of the to-be-pruned, convolutional processing can be performed through a convolutional neural network to obtain the corresponding local pruning shear semantic vector. For the pruning shear load data (such as the force sequence), word embedding processing can be performed through a word embedding model to obtain the corresponding local pruning shear semantic vector. Exemplarily, strain gauges can be installed on the blade, and the strain applied during the cutting process can be monitored, so as to deduce the load. For example, the load time sequence can be "0 (time / second): 0 (load / N); 1 second: 2.5N; 2 seconds: 5.0N; 3 seconds: 7.5N; 4 seconds: 10.0N; 5 seconds: 8.0N; 6 seconds: 12.0N; 7 seconds: 15.0N; 8 seconds: 13.0N; 9 seconds: 10.0N". Through word embedding processing, the following word embedding vectors can be obtained:

[0071] 0 seconds: [0.1, 0.2, 0.3, 0.0, 0.1, 0.0, 0.1, 0.2, 0.0, 0.0, 0.0, 0.0,...];

[0072] 0N: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...];

[0073] 1 second: [0.15, 0.25, 0.35, 0.05, 0.1, 0.05, 0.15, 0.25, 0.05, 0.05, 0.05,0.05,...];

[0074] 2.5N: [0.1, 0.2, 0.3, 0.05, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,0.0,...];

[0075] 2 seconds: [0.2, 0.3, 0.4, 0.1, 0.15, 0.1, 0.2, 0.3, 0.1, 0.1, 0.1,0.1,...];

[0076] 5.0N: [0.2, 0.3, 0.4, 0.1, 0.15, 0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...];

[0077] 3 seconds: [0.25, 0.35, 0.45, 0.15, 0.2, 0.15, 0.25, 0.35, 0.15, 0.15, 0.15, 0.15,...];

[0078] 7.5N: [0.3, 0.4, 0.5, 0.15, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...];

[0079] 4 seconds: [0.3, 0.4, 0.5, 0.2, 0.25, 0.2, 0.3, 0.4, 0.2, 0.2, 0.2, 0.2,...];

[0080] 10.0N: [0.4, 0.5, 0.6, 0.2, 0.25, 0.15, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...];

[0081] 5 seconds: [0.28, 0.38, 0.48, 0.18, 0.23, 0.18, 0.28, 0.38, 0.18, 0.18, 0.18, 0.18,...];

[0082] 8.0N: [0.35, 0.45, 0.55, 0.18, 0.23, 0.12, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...];

[0083] 6 seconds: [0.35, 0.45, 0.55, 0.25, 0.3, 0.25, 0.35, 0.45, 0.25, 0.25, 0.25, 0.25,...];

[0084] 12.0N: [0.5, 0.6, 0.7, 0.3, 0.35, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...];

[0085] 7 seconds: [0.4, 0.5, 0.6, 0.3, 0.35, 0.3, 0.4, 0.5, 0.3, 0.3, 0.3, 0.3,...];

[0086] 15.0N: [0.6, 0.7, 0.8, 0.4, 0.45, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...];

[0087] 8 seconds: [0.38, 0.48, 0.58, 0.28, 0.33, 0.28, 0.38, 0.48, 0.28, 0.28, 0.28, 0.28,...];

[0088] 13.0N: [0.55, 0.65, 0.75, 0.35, 0.4, 0.22, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...];

[0089] 9 seconds: [0.3, 0.4, 0.5, 0.2, 0.25, 0.2, 0.3, 0.4, 0.2, 0.2, 0.2, 0.2,...];

[0090] 10.0N: [0.4, 0.5, 0.6, 0.3, 0.35, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...].

[0091] Step S112: Aggregate the local pruning shear semantic vectors in at least two dimensions to form a target pruning shear semantic vector corresponding to the target pruning shear record data.

[0092] In the embodiments of the present application, after obtaining the local pruning shear semantic vectors, the local pruning shear semantic vectors in at least two dimensions can be aggregated to form a target pruning shear semantic vector corresponding to the target pruning shear record data. In this way, the target pruning shear semantic vector can have the semantic information carried by each local pruning shear semantic vector.

[0093] It can be understood that in the above step S112, the specific manner of aggregating the local pruning shear semantic vectors in at least two dimensions is not limited and can be selected according to actual needs. For example, in an alternative embodiment, in order to fully aggregate the local pruning shear semantic vectors so that the obtained target pruning shear semantic vector has better semantic representation ability, combined with Figure 3 , the above step S112 can further include the following content:

[0094] First, use the aggregation units in the first layer of the aggregation model included in the load status analysis network to perform self-attention processing on the local pruning semantic vectors of at least two dimensions, and output the first pruning semantic attention vector, the second pruning semantic attention vector, and the third pruning semantic attention vector corresponding to the first layer aggregation unit. Among them, the first pruning semantic attention vector corresponds to the image data to be pruned, the second pruning semantic attention vector corresponds to the first type of pruning shear load data, and the third pruning semantic attention vector corresponds to the second type of pruning shear load data. That is, perform self-attention processing on the local pruning semantic vector corresponding to the image data to be pruned to obtain the first pruning semantic attention vector, perform self-attention processing on the first type of pruning shear load data to obtain the second pruning semantic attention vector, and perform self-attention processing on the second type of pruning shear load data to obtain the third pruning semantic attention vector. The first type of pruning shear load data is collected based on the pressure sensor on the blade, and the second type of pruning shear load data is obtained based on the change in the acceleration collected by the acceleration sensor (for example, an increase in acceleration indicates an increase in load, and a decrease in acceleration indicates a decrease in load). The load status analysis network belongs to a neural network trained with sample data and corresponding blade load status labels (for example, the sample data is processed according to steps S110 - S130 to obtain the corresponding sample blade load status data, then calculate the error between the sample blade load status data and the blade load status label, and finally, update the network parameters of the load status analysis network in the direction of reducing this error until the error converges and then end the training); it can be understood that considering that the possible dimensions of the local data corresponding to the local pruning semantic vectors of at least two dimensions may be different (such as images and text sequences), therefore, before performing self-attention processing on the local pruning semantic vector of the image dimension, corresponding mapping processing can be performed first to obtain the corresponding local mapping vector, and then corresponding self-attention processing is performed. Among them, the mapping processing can be multiplying the local pruning semantic vector by the mapping parameters in the aggregation unit of the first layer to obtain the corresponding local mapping vector. During the training process, the initial mapping parameters can be a randomly generated vector, and then they are continuously iteratively updated during the training process;

[0095] Secondly, for each level of aggregation units in the aggregation model except for the aggregation units at the first level (i.e., from the second level to the last level), the first pruning semantic attention vector, the second pruning semantic attention vector, and the third pruning semantic attention vector corresponding to the aggregation units at the previous level are concatenated to form a concatenated attention vector. Moreover, the concatenated attention vector is pooled and compressed (such as average pooling or max pooling, etc.) to output a pooled and compressed vector, and cross-attention processing is performed on the first pruning semantic attention vector, the second pruning semantic attention vector, and the third pruning semantic attention vector corresponding to the aggregation units at the previous level based on the pooled and compressed vector to output the first pruning semantic attention vector, the second pruning semantic attention vector, and the third pruning semantic attention vector corresponding to the aggregation units at the current level. Exemplarily, the pooled and compressed vector can be first multiplied by the transposed vector of the first pruning semantic attention vector corresponding to the aggregation units at the first level to obtain the corresponding correlation parameter, and then the correlation parameter is multiplied by the first pruning semantic attention vector corresponding to the aggregation units at the first level to obtain the first pruning semantic attention vector corresponding to the aggregation units at the second level. Correspondingly, the second pruning semantic attention vector and the third pruning semantic attention vector can be obtained.

[0096] Finally, the first pruning semantic attention vector, the second pruning semantic attention vector, and the third pruning semantic attention vector corresponding to the aggregation units at the last level can be concatenated to form the target pruning semantic vector corresponding to the target pruning record data. Exemplarily, the target pruning semantic vector can be = concat (the first pruning semantic attention vector; the second pruning semantic attention vector; the third pruning semantic attention vector).

[0097] In the second aspect, regarding step S120, it should be noted that the specific manner of mining the blade load state semantic vector of the historical blade load state data is not limited and can be selected and configured accordingly according to actual needs.

[0098] For example, in an alternative implementation manner, in order to determine a blade load state semantic vector with a closer correlation, so that the reliability of subsequent processing can be higher, step S120 described above can further include the following content:

[0099] First, the respective historical pruning record data formed by the target lithium battery pruning shears during historical pruning operations can be determined to obtain a corresponding plurality of historical pruning record data. Each time a pruning operation is performed, a corresponding historical pruning record data can be formed.

[0100] Secondly, multiple historical pruning shear semantic vectors corresponding to the multiple historical pruning shear record data can be extracted. It can be directly extracted, such as the target pruning shear semantic vector mined from the historical pruning shear record data as the target pruning shear record data;

[0101] Then, among the multiple historical pruning shear semantic vectors, the smallest historical pruning shear semantic vector between the target pruning shear semantic vector can be determined as the historical pruning shear semantic vector having an association relationship with the target pruning shear semantic vector; Exemplarily, the vector distance can be the cosine distance between vectors, etc.;

[0102] Finally, the historical blade load state data corresponding to the historical pruning shear semantic vector having an association relationship with the target pruning shear semantic vector (i.e., the blade load state data determined based on the historical pruning shear semantic vector in history) can be determined, and semantic mining is performed on the historical blade load state data to output the corresponding blade load state semantic vector (such as the word embedding vector obtained by performing word embedding processing on the historical blade load state data).

[0103] In the third aspect, regarding step S130, it should be noted that the specific manner of determining the blade load state data of the target lithium battery pruning shear based on the target pruning shear record data is not limited and can be selected and configured accordingly according to actual needs.

[0104] For example, in an alternative implementation, in order to ensure the reliability of the determined blade load state data and ensure the reliability of the control of the target lithium battery pruning shear, the above step S130 may further include step S131, step S132, step S133, and step S134, and the specific content of each step is as follows.

[0105] Step S131, perform aggregation processing on the historical pruning shear semantic vector and the target pruning shear semantic vector to output an aggregated pruning shear semantic vector, and perform semantic association mining on the blade load state semantic vector and the aggregated pruning shear semantic vector to output an associated pruning shear semantic vector.

[0106] In the embodiment of the present application, aggregation processing can be performed on the historical pruning shear semantic vector and the target pruning shear semantic vector to output an aggregated pruning shear semantic vector, and semantic association mining can be performed on the blade load state semantic vector and the aggregated pruning shear semantic vector to output an associated pruning shear semantic vector. That is, the historical pruning shear semantic vector can be first incorporated into the target pruning shear semantic vector, and then the blade load state semantic vector can be incorporated, so that the semantic representation ability of the obtained associated pruning shear semantic vector is better and the semantic information carried is richer.

[0107] Step S132: Map the associated pruning shear semantic vector according to the first mapping matrix to form a pruning shear semantic mapping vector. Then, determine a pruning shear semantic enhancement vector based on the pruning shear semantic mapping vector and the blade load status semantic vector, and perform an activation process on the pruning shear semantic enhancement vector to output a pruning shear semantic activation vector.

[0108] In the embodiment of the present application, the associated pruning shear semantic vector can be mapped according to the first mapping matrix (to enhance the expression ability of the semantic vector) to form a pruning shear semantic mapping vector. Then, determine a pruning shear semantic enhancement vector based on the pruning shear semantic mapping vector and the blade load status semantic vector, and perform an activation process on the pruning shear semantic enhancement vector to output a pruning shear semantic activation vector.

[0109] Step S133: Map the pruning shear semantic activation vector according to the second mapping matrix to output a pruning shear semantic intermediate vector, and obtain a pruning shear semantic output vector based on the pruning shear semantic intermediate vector and the pruning shear semantic enhancement vector.

[0110] In the embodiment of the present application, the pruning shear semantic activation vector can be mapped according to the second mapping matrix to output a pruning shear semantic intermediate vector, and obtain a pruning shear semantic output vector based on the pruning shear semantic intermediate vector and the pruning shear semantic enhancement vector. Among them, the first mapping matrix and the second mapping matrix belong to the network parameters of the load status analysis network formed by training with sample data and corresponding blade load status labels (as described above), and the load status analysis network belongs to a neural network.

[0111] Step S134: Determine the blade load status data of the target lithium - ion pruning shear based on the target pruning shear record data according to the pruning shear semantic output vector, and control the blade load of the target lithium - ion pruning shear based on the blade load status data.

[0112] In the embodiments of the present application, the blade load status data of the target lithium battery pruning shear based on the target pruning shear record data can be determined according to the pruning shear semantic output vector, and the blade load of the target lithium battery pruning shear can be controlled based on the blade load status data. Exemplarily, the load status analysis network may further include a fully connected model and an output function (such as a classification function such as softmax). The fully connected model can perform a fully connected process on the pruning shear semantic output vector to obtain a corresponding pruning shear fully connected vector. Then, the pruning shear fully connected vector is processed by the output function to obtain the probability of overload and the probability of non-overload, thereby forming the blade load status data, and then the blade load is controlled.

[0113] It can be understood that in the above step S131, the specific methods for performing the aggregation process and semantic association mining are not limited and can be selected according to actual needs. For example, in an alternative embodiment, in order to output an associated pruning shear semantic vector with higher semantic representation accuracy, the above step S131 may further include step S131a, step S131b, step S131c, and step S131d. The specific contents of each step are described as follows.

[0114] Step S131a: Concatenate the historical pruning shear semantic vector and the target pruning shear semantic vector to form a corresponding pruning shear semantic concatenation vector as the corresponding aggregated pruning shear semantic vector.

[0115] In the embodiments of the present application, the historical pruning shear semantic vector and the target pruning shear semantic vector can be concatenated to form a corresponding pruning shear semantic concatenation vector as the corresponding aggregated pruning shear semantic vector (i.e., the pruning shear semantic concatenation vector).

[0116] Step S131b: Perform a space transformation on the blade load status semantic vector based on the first space transformation matrix to output a first space transformation vector, perform a space transformation on the aggregated pruning shear semantic vector based on the second space transformation matrix to output a second space transformation vector, and perform a space transformation on the aggregated pruning shear semantic vector based on the third space transformation matrix to output a third space transformation vector.

[0117] In the embodiment of the present application, the spatial transformation of the blade load state semantic vector can be performed based on the first spatial transformation matrix (i.e., multiplying the blade load state semantic vector by the first spatial transformation matrix) to output the first spatial transformation vector, and the spatial transformation of the aggregate pruning shear semantic vector can be performed based on the second spatial transformation matrix to output the second spatial transformation vector (i.e., multiplying the aggregate pruning shear semantic vector by the second spatial transformation matrix), and, the spatial transformation of the aggregate pruning shear semantic vector can be performed based on the third spatial transformation matrix to output the third spatial transformation vector (i.e., multiplying the aggregate pruning shear semantic vector by the third spatial transformation matrix); in addition, the first spatial transformation matrix, the second spatial transformation matrix, and the third spatial transformation matrix can be network parameters in the load state analysis network and are formed during the training process;

[0118] Step S131c: Multiply the first spatial transformation vector and the second spatial transformation vector to output an association parameter matrix, and perform noise application processing on the association parameter matrix to output an association parameter noise matrix.

[0119] In the embodiment of the present application, the first spatial transformation vector and the second spatial transformation vector can be multiplied to output an association parameter matrix (which can be multiplied by the transposed vector of the second spatial transformation vector. For example, the first spatial transformation vector can be a 1*N row vector, and the transposed vector of the second spatial transformation vector can be an M*1 column vector. Multiplying them can obtain an M*N matrix, and then, the mean value of each column of this matrix is calculated to obtain a 1*N row vector, that is, the association parameter matrix), and, perform noise application processing on the association parameter matrix to output an association parameter noise matrix. Wherein, during the noise application processing, the number of association parameters with noise applied is less than the number of association parameters without noise applied; exemplarily, the association parameter matrix can be superimposed with a random vector to obtain the association parameter noise matrix.

[0120] Step S131d: Multiply the association parameter matrix and the third spatial transformation vector to output a first associated semantic vector, multiply the association parameter noise matrix and the third spatial transformation vector to output a second associated semantic vector, and output an associated pruning shear semantic vector based on the first associated semantic vector and the second associated semantic vector.

[0121] In the embodiments of the present application, the associated parameter matrix and the third space conversion vector can be multiplied to output a first associated semantic vector, and the associated parameter noise matrix and the third space conversion vector can be multiplied to output a second associated semantic vector. Moreover, based on the first associated semantic vector and the second associated semantic vector, an associated pruning shear semantic vector is output. Based on this, by adding noise in the process of semantic association mining, the problem of over-focusing on associated semantic information can be avoided to a certain extent, and the representation ability of the associated pruning shear semantic vector can be improved.

[0122] It can be understood that in the above step S131d, the specific manner of outputting the associated pruning shear semantic vector is not limited and can be selected according to actual needs. For example, in an alternative embodiment, in order to enable the associated pruning shear semantic vector to reliably fuse the first associated semantic vector and the second associated semantic vector, the above step S131d may further include the following content:

[0123] First, the associated parameter matrix and the third space conversion vector can be multiplied to output the corresponding first associated semantic vector, and the associated parameter noise matrix and the third space conversion vector can be multiplied to output the corresponding second associated semantic vector;

[0124] Second, a first weighting coefficient and a second weighting coefficient can be determined, where the first weighting coefficient is greater than the second weighting coefficient, and the sum of the first weighting coefficient and the second weighting coefficient is equal to 1;

[0125] Finally, the first weighting coefficient and the first associated semantic vector are multiplied to obtain the corresponding first multiplication result, and the second weighting coefficient and the second associated semantic vector are multiplied to obtain the corresponding second multiplication result. Then, the first multiplication result and the second multiplication result are subjected to a superposition operation to output the corresponding associated pruning shear semantic vector, that is, weighted mean calculation.

[0126] It can be understood that in the above step S132, the specific manner of outputting the pruning shear semantic activation vector is not limited and can be selected according to actual needs. For example, in an alternative embodiment, the above step S132 may further include the following content:

[0127] First, the first mapping matrix and the associated pruning shear semantic vector can be multiplied to form a pruning shear semantic mapping vector, and the pruning shear semantic mapping vector and the blade load state semantic vector are subjected to a superposition operation to form a pruning shear semantic enhancement vector;

[0128] Secondly, the pruning shear semantic enhancement vector can be activated through a non-linear activation function (such as Tanh (hyperbolic tangent function)) to output a pruning shear semantic activation vector.

[0129] It can be understood that in the above step S133, the specific manner of obtaining the pruning shear semantic output vector is not limited and can be selected according to actual needs. For example, in an alternative embodiment, the above step S133 may further include the following:

[0130] First, the second mapping matrix and the pruning shear semantic activation vector can be subjected to a mapping multiplication operation to output a pruning shear semantic intermediate vector;

[0131] Secondly, the pruning shear semantic intermediate vector and the pruning shear semantic enhancement vector can be subjected to a superposition operation to obtain a pruning shear semantic output vector.

[0132] Combined Figure 4 , the embodiment of the present application further provides a control system for a lithium battery pruning shear applicable to the above electronic device. Among them, the control system of the lithium battery pruning shear may include a target data mining module, a historical data mining module, and a pruning shear control module.

[0133] The target data mining module is used to extract target pruning shear record data and mine the target pruning shear semantic vector corresponding to the target pruning shear record data. Among them, the target pruning shear record data includes to-be-pruned image data and at least one pruning shear load data, and each type of pruning shear load data is collected based on a corresponding load determination method for the blade load of the target lithium battery pruning shear. In the embodiment of the present application, the target data mining module can be used to execute Figure 2 the step S110 shown. For the relevant content of the target data mining module, reference can be made to the description of step S110 above.

[0134] The historical data mining module is used to determine the historical pruning shear semantic vector having an association relationship with the target pruning shear semantic vector, determine the historical blade load status data corresponding to the historical pruning shear semantic vector, and mine the blade load status semantic vector of the historical blade load status data. Among them, the historical pruning shear record data corresponding to the historical pruning shear semantic vector belongs to the data formed by the target lithium battery pruning shear during historical pruning operations. In the embodiment of the present application, the historical data mining module can be used to execute Figure 2 the step S120 shown. For the relevant content of the historical data mining module, reference can be made to the description of step S120 above.

[0135] The pruning shear control module is configured to determine the blade load status data of the target lithium - battery pruning shear based on the target pruning shear semantic vector, the historical pruning shear semantic vector, and the blade load status semantic vector, and to control the target lithium - battery pruning shear based on the blade load status data, so as to control the blade load, where the blade load status data is used to reflect whether the blade load of the target lithium - battery pruning shear is overloaded. In an embodiment of the present application, the pruning shear control module can be used to execute Figure 2 the steps shown in S130. For the relevant content of the pruning shear control module, reference can be made to the description of step S130 above.

[0136] In an embodiment of the present application, corresponding to the above - mentioned control method for a lithium - battery pruning shear applied to the electronic device, a computer - readable storage medium is further provided. A computer program is stored in the computer - readable storage medium, and when the computer program runs, it executes each step of the control method for the lithium - battery pruning shear. Among them, the steps executed when the foregoing computer program runs will not be elaborated here one by one, and reference can be made to the explanation of the control method for the lithium - battery pruning shear above.

[0137] In summary, for the control system and method of a lithium - battery pruning shear provided in the present application, first, the target pruning shear semantic vector corresponding to the target pruning shear record data is mined; second, the historical pruning shear semantic vector associated with the target pruning shear semantic vector is determined, and the historical blade load status data corresponding to the historical pruning shear semantic vector is determined, and the blade load status semantic vector of the historical blade load status data is mined; then, based on the target pruning shear semantic vector, the historical pruning shear semantic vector, and the blade load status semantic vector, the blade load status data of the target lithium - battery pruning shear is determined, and the target lithium - battery pruning shear is controlled based on the blade load status data. Based on the above, since the target pruning shear record data includes pruning - to - be image data and at least one pruning shear load data, the target pruning shear semantic vector not only carries the semantic information of the blade load, but also carries the semantic information of objects such as the branches to be pruned (different branch objects have different hardnesses, which affect the blade load). Therefore, the target pruning shear semantic vector can have relatively rich and reliable semantic information, making the reliability of the analyzed blade load status data higher. In addition, since the historical pruning shear semantic vector and the blade load status semantic vector are also combined, the reliability of the determined blade load status data can be further improved, thereby ensuring the reliability of the control of the target lithium - battery pruning shear based on the blade load status data, and further improving the problem of relatively low reliability in controlling the lithium - battery pruning shear in the prior art.

[0138] In several embodiments provided by the embodiments of the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0139] In addition, in each embodiment of the present application, the various functional modules can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0140] If the described functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes. It should be noted that in this article, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or device. Without further limitations, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article, or device including the said element.

[0141] The above are only the preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A control method for a lithium battery pruning shear, characterized in that, Including: Extracting target pruning shear record data and mining a target pruning shear semantic vector corresponding to the target pruning shear record data, wherein the target pruning shear record data includes to-be-pruned image data and at least one pruning shear load data, and each pruning shear load data is obtained by collecting the blade load of the target lithium battery pruning shear based on a corresponding load determination method; Determining a historical pruning shear semantic vector having an association relationship with the target pruning shear semantic vector, determining historical blade load state data corresponding to the historical pruning shear semantic vector, and mining a blade load state semantic vector of the historical blade load state data, wherein the historical pruning shear record data corresponding to the historical pruning shear semantic vector belongs to data formed by the target lithium battery pruning shear during historical pruning operations; Concatenate the historical pruning shear semantic vector and the target pruning shear semantic vector to form a corresponding pruning shear semantic concatenated vector as the corresponding aggregated pruning shear semantic vector; perform a spatial transformation on the blade load status semantic vector based on the first spatial transformation matrix to output a first spatially transformed vector, and perform a spatial transformation on the aggregated pruning shear semantic vector based on the second spatial transformation matrix to output a second spatially transformed vector, and, perform a spatial transformation on the aggregated pruning shear semantic vector based on the third spatial transformation matrix to output a third spatially transformed vector; multiply the first spatially transformed vector and the second spatially transformed vector to output an association parameter matrix, and perform a noise application process on the association parameter matrix to output an association parameter noise matrix, where, during the noise application process, the number of association parameters with noise applied is less than the number of association parameters without noise applied; multiply the association parameter matrix and the third spatially transformed vector to output a first associated semantic vector, and multiply the association parameter noise matrix and the third spatially transformed vector to output a second associated semantic vector, and, based on the first associated semantic vector and the second associated semantic vector, output an associated pruning shear semantic vector; perform a mapping process on the associated pruning shear semantic vector according to the first mapping matrix to form a pruning shear semantic mapping vector, and, based on the pruning shear semantic mapping vector and the blade load status semantic vector, determine a pruning shear semantic enhancement vector, and perform an activation process on the pruning shear semantic enhancement vector to output a pruning shear semantic activation vector; perform a mapping process on the pruning shear semantic activation vector according to the second mapping matrix to output a pruning shear semantic intermediate vector, and based on the pruning shear semantic intermediate vector and the pruning shear semantic enhancement vector, obtain a pruning shear semantic output vector, where, the first mapping matrix and the second mapping matrix belong to the network parameters of a load status analysis network formed by training with sample data and corresponding blade load status labels, and the load status analysis network belongs to a neural network; determine the blade load status data of the target lithium battery pruning shear based on the target pruning shear record data according to the pruning shear semantic output vector, and control the blade load of the target lithium battery pruning shear based on the blade load status data, where, the blade load status data is used to reflect whether there is an overload in the blade load of the target lithium battery pruning shear.

2. The control method of the lithium battery pruning shear according to claim 1, wherein, The steps of extracting the target pruning shear record data and mining the target pruning shear semantic vector corresponding to the target pruning shear record data include: Extract the target pruning shear record data, and mine the local pruning shear semantic vectors respectively possessed by at least two dimensions of local data corresponding to the target pruning shear record data, where, the pruning image data to be pruned is used as one dimension of local data, and each type of pruning shear load data is used as one dimension of local data; Aggregate the local pruning shear semantic vectors of at least two dimensions to form the target pruning shear semantic vector corresponding to the target pruning shear record data.

3. The control method of the lithium battery pruning shear according to claim 2, wherein The step of aggregating the local pruning shear semantic vectors of at least two dimensions to form the target pruning shear semantic vector corresponding to the target pruning shear record data includes: Using the aggregation units of the first layer in the aggregation model included in the load state analysis network, perform self-attention processing on the local pruning shear semantic vectors of at least two dimensions respectively, and output the first pruning shear semantic attention vector, the second pruning shear semantic attention vector, and the third pruning shear semantic attention vector corresponding to the aggregation unit of the first layer. Among them, the at least two dimensions are three dimensions, which respectively correspond to the image data to be pruned, the first pruning shear load data, and the second pruning shear load data. The first pruning shear semantic attention vector corresponds to the image data to be pruned, the second pruning shear semantic attention vector corresponds to the first pruning shear load data, and the third pruning shear semantic attention vector corresponds to the second pruning shear load data. The first pruning shear load data is collected based on the pressure sensor on the blade, and the second pruning shear load data is obtained based on the change of the acceleration collected by the acceleration sensor. The load state analysis network belongs to a neural network trained by sample data and corresponding blade load state labels; For each layer of aggregation unit other than the aggregation unit of the first layer in the aggregation model, splice the first pruning shear semantic attention vector, the second pruning shear semantic attention vector, and the third pruning shear semantic attention vector corresponding to the aggregation unit of the previous layer to form a spliced attention vector, and perform pooling compression on the spliced attention vector to output a pooled compression vector. And based on the pooled compression vector, perform cross-attention processing on the first pruning shear semantic attention vector, the second pruning shear semantic attention vector, and the third pruning shear semantic attention vector corresponding to the aggregation unit of the previous layer, and output the first pruning shear semantic attention vector, the second pruning shear semantic attention vector, and the third pruning shear semantic attention vector corresponding to the aggregation unit of the current layer; Splice the first pruning shear semantic attention vector, the second pruning shear semantic attention vector, and the third pruning shear semantic attention vector corresponding to the aggregation unit of the last layer to form the target pruning shear semantic vector corresponding to the target pruning shear record data.

4. The control method of the lithium battery pruning shear according to claim 1, characterized in that, The steps of determining the historical pruning shear semantic vector having an association relationship with the target pruning shear semantic vector, determining the historical blade load state data corresponding to the historical pruning shear semantic vector, and mining the blade load state semantic vector of the historical blade load state data include: Determine each historical pruning shear record data formed by the target lithium battery pruning shear during historical pruning operations to obtain a corresponding plurality of historical pruning shear record data; Extract a plurality of historical pruning shear semantic vectors corresponding to the plurality of historical pruning shear record data; Among the multiple historical pruning shear semantic vectors, determine a historical pruning shear semantic vector with the smallest vector distance from the target pruning shear semantic vector as the historical pruning shear semantic vector associated with the target pruning shear semantic vector; Determine the historical blade load status data corresponding to the historical pruning shear semantic vector associated with the target pruning shear semantic vector, and perform semantic mining on the historical blade load status data to output the corresponding blade load status semantic vector.

5. The control method of the lithium battery pruning shear according to claim 1, characterized in that, The steps of multiplying the association parameter matrix by the third space conversion vector to output a first association semantic vector, multiplying the association parameter noise matrix by the third space conversion vector to output a second association semantic vector, and outputting an associated pruning shear semantic vector based on the first association semantic vector and the second association semantic vector include: Multiply the association parameter matrix by the third space conversion vector to output the corresponding first association semantic vector, and multiply the association parameter noise matrix by the third space conversion vector to output the corresponding second association semantic vector; Determine a first weighting coefficient and a second weighting coefficient, where the first weighting coefficient is greater than the second weighting coefficient, and the sum of the first weighting coefficient and the second weighting coefficient is equal to 1; Multiply the first weighting coefficient by the first association semantic vector to obtain the corresponding first multiplication result, multiply the second weighting coefficient by the second association semantic vector to obtain the corresponding second multiplication result, and perform a superposition operation on the first multiplication result and the second multiplication result to output the corresponding associated pruning shear semantic vector.

6. The control method of the lithium battery pruning shear according to claim 1, characterized in that, The steps of performing a mapping process on the associated pruning shear semantic vector according to the first mapping matrix to form a pruning shear semantic mapping vector, determining a pruning shear semantic enhancement vector based on the pruning shear semantic mapping vector and the blade load status semantic vector, and performing an activation process on the pruning shear semantic enhancement vector to output a pruning shear semantic activation vector include: Perform a multiplication operation on the first mapping matrix and the associated pruning shear semantic vector to form a pruning shear semantic mapping vector, and perform a superposition operation on the pruning shear semantic mapping vector and the blade load status semantic vector to form a pruning shear semantic enhancement vector; Perform an activation process on the pruning shear semantic enhancement vector through a non-linear activation function to output a pruning shear semantic activation vector.

7. The control method of the lithium battery pruning shear according to claim 1, characterized in that The steps of performing a mapping process on the pruning shear semantic activation vector according to the second mapping matrix to output a pruning shear semantic intermediate vector, and obtaining a pruning shear semantic output vector based on the pruning shear semantic intermediate vector and the pruning shear semantic enhancement vector include: Perform a mapping multiplication operation on the second mapping matrix and the pruning shear semantic activation vector to output a pruning shear semantic intermediate vector; Perform a superposition operation on the pruning shear semantic intermediate vector and the pruning shear semantic enhancement vector to obtain a pruning shear semantic output vector.

8. A control system for a lithium battery pruning shear, characterized in that, Include: A target data mining module is used to extract target pruning shear record data and mine a target pruning shear semantic vector corresponding to the target pruning shear record data. Among them, the target pruning shear record data includes to-be-pruned image data and at least one pruning shear load data, and each pruning shear load data is obtained by collecting the blade load of the target lithium battery pruning shear based on a corresponding load determination method; A historical data mining module is used to determine a historical pruning shear semantic vector having an association relationship with the target pruning shear semantic vector, determine historical blade load state data corresponding to the historical pruning shear semantic vector, and mine a blade load state semantic vector of the historical blade load state data. Among them, the historical pruning shear record data corresponding to the historical pruning shear semantic vector belongs to the data formed by the target lithium battery pruning shear during historical pruning operations; The pruning shear control module is used to splice the historical pruning shear semantic vector and the target pruning shear semantic vector to form a corresponding pruning shear semantic splicing vector as the corresponding aggregated pruning shear semantic vector; based on the first space transformation matrix, perform space transformation on the blade load state semantic vector to output a first space transformation vector, and based on the second space transformation matrix, perform space transformation on the aggregated pruning shear semantic vector to output a second space transformation vector, and, based on the third space transformation matrix, perform space transformation on the aggregated pruning shear semantic vector to output a third space transformation vector; multiply the first space transformation vector and the second space transformation vector to output an associated parameter matrix, and perform noise application processing on the associated parameter matrix to output an associated parameter noise matrix, where, during the noise application processing, the number of associated parameters with noise applied is less than the number of associated parameters without noise applied; multiply the associated parameter matrix and the third space transformation vector to output a first associated semantic vector, and multiply the associated parameter noise matrix and the third space transformation vector to output a second associated semantic vector, and, based on the first associated semantic vector and the second associated semantic vector, output an associated pruning shear semantic vector; perform mapping processing on the associated pruning shear semantic vector according to the first mapping matrix to form a pruning shear semantic mapping vector, and, based on the pruning shear semantic mapping vector and the blade load state semantic vector, determine a pruning shear semantic enhancement vector, and perform activation processing on the pruning shear semantic enhancement vector to output a pruning shear semantic activation vector; perform mapping processing on the pruning shear semantic activation vector according to the second mapping matrix to output a pruning shear semantic intermediate vector, and based on the pruning shear semantic intermediate vector and the pruning shear semantic enhancement vector, obtain a pruning shear semantic output vector, where, the first mapping matrix and the second mapping matrix belong to the network parameters of a load state analysis network formed by training with sample data and corresponding blade load state labels, and the load state analysis network belongs to a neural network; determine the blade load state data of the target lithium battery pruning shear based on the target pruning shear record data according to the pruning shear semantic output vector, and control the blade load of the target lithium battery pruning shear based on the blade load state data, where, the blade load state data is used to reflect whether the blade load of the target lithium battery pruning shear is overloaded.

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