Control system and method for lithium battery pruning shears

Abstract

The invention provides a control system and method for lithium battery pruning shears, and relates to the technical field of equipment control. The method comprises the following steps: firstly, mining a target pruning shear semantic vector corresponding to target pruning shear record data; secondly, 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; and then, based on the semantic vector of the target pruning shears, the semantic vector of the historical pruning shears and the semantic vector of the blade load state, determining the blade load state data of the target lithium-ion pruning shears, and based on the blade load state data, controlling the target lithium-ion pruning shears. On the basis of the content, the problem that in the prior art, the reliability of controlling the lithium battery pruning shears is relatively low can be solved.

Description

Technical Field

[0001] The present application relates to the field of equipment control technology, and in particular to a control system and method for lithium-ion pruning shears. Background Art

[0002] Lithium-ion pruning shears are electric pruning tools powered by lithium-ion batteries and are widely used in gardening, agriculture, forestry and other fields. This tool is designed to easily prune trees, shrubs and plants, and is efficient and convenient. With the development of battery technology, lithium-ion pruning shears will become lighter and lighter, and their battery life will be greatly improved. At the same time, the introduction of intelligent functions (such as automatic cutting, smart sensors, etc.) will also make their use more efficient and convenient. The inventors have 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 shears does not refer to the load on the blade, which makes the reliability of the control of the lithium-ion pruning shears relatively low. Summary of the invention

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

[0004] To achieve the above objectives, this application adopts the following technical solutions: A control method for a lithium-ion pruning shear, comprising: Extracting target pruning shears record data, and mining the target pruning shears semantic vector corresponding to the target pruning shears record data, wherein the target pruning shears record data includes to-be-pruned image data and at least one type of pruning shears load data, each type of pruning shears load data is obtained by collecting the blade load of the target lithium-ion pruning shears based on a corresponding load determination method; Determine a historical pruning shears semantic vector associated with the target pruning shears semantic vector, determine historical blade load state data corresponding to the historical pruning shears semantic vector, and mine the blade load state semantic vector of the historical blade load state data, wherein the historical pruning shears record data corresponding to the historical pruning shears semantic vector belongs to data generated by the target lithium-ion pruning shears during historical pruning operations; Based on the target pruning shears semantic vector, the historical pruning shears semantic vector and the blade load status semantic vector, the blade load status data of the target lithium-electric pruning shears based on the target pruning shears record data is determined, and the target lithium-electric pruning shears is controlled based on the blade load status data so that the blade load is managed, wherein the blade load status data is used to reflect whether the blade load of the target lithium-electric pruning shears is overloaded.

[0005] In a preferred embodiment of the present application, in the control method of the lithium-ion pruning shears, the step of extracting the target pruning shears record data and mining the target pruning shears semantic vector corresponding to the target pruning shears record data includes: Extract target pruning shears record data, and mine local pruning shears semantic vectors respectively possessed by local data of at least two dimensions corresponding to the target pruning shears record data, wherein the image data to be pruned is local data of one dimension, and each type of pruning shears load data is local data of one dimension; Aggregation processing is performed on the local pruning shears semantic vectors of at least two dimensions to form a target pruning shears semantic vector corresponding to the target pruning shears record data.

[0006] In a preferred embodiment of the present application, in the control method of the lithium-ion pruning shears, the step of aggregating the local pruning shears semantic vectors of at least two dimensions to form a target pruning shears semantic vector corresponding to the target pruning shears record data includes: Utilizing the aggregation unit of the first level in the aggregation model included in the load state analysis network, the local pruning shears semantic vectors of at least two dimensions are subjected to self-attention processing respectively, and the first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the first level aggregation unit are output, wherein the first pruning shears semantic attention vector corresponds to the image data to be pruned, the second pruning shears semantic attention vector corresponds to the first type of pruning shears load data, the third pruning shears semantic attention vector corresponds to the second type of pruning shears load data, the first type of pruning shears load data is formed based on the pressure sensor on the blade, the second type of pruning shears load data is obtained based on the change of acceleration collected by the acceleration sensor, and the load state analysis network is a neural network formed by training with sample data and corresponding blade load state labels; For each level of aggregation units other than the first level of aggregation units in the aggregation model, the first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the aggregation units of the previous level are spliced ​​to form a spliced ​​attention vector, and the spliced ​​attention vector is pooled and compressed to output a pooled compressed vector, and based on the pooled compressed vector, the first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the aggregation units of the previous level are cross-attention processed to output the first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the aggregation units of the current level; The first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the aggregation unit of the last level are concatenated to form a target pruning shears semantic vector corresponding to the target pruning shears record data.

[0007] In a preferred embodiment of the present application, in the control method of the lithium-ion pruning shears, the steps of determining a historical pruning shears semantic vector associated with the target pruning shears semantic vector, determining historical blade load state data corresponding to the historical pruning shears 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-ion pruning shears during the pruning operation in history, and obtain corresponding multiple historical pruning shear record data; Extracting multiple historical pruning shear semantic vectors corresponding to the multiple historical pruning shear record data; Determine, among the multiple historical pruning shears semantic vectors, a historical pruning shears semantic vector having the smallest vector distance with the target pruning shears semantic vector as the historical pruning shears semantic vector associated with the target pruning shears semantic vector; Determine the historical blade load state data corresponding to the historical pruning shears semantic vector associated with the target pruning shears semantic vector, and perform semantic mining on the historical blade load state data to output the corresponding blade load state semantic vector.

[0008] In a preferred selection of the present application, in the control method of the above-mentioned lithium-electric pruning shears, the step of determining the blade load status data of the target lithium-electric pruning shears based on the target pruning shears record data based on the target pruning shears semantic vector, the historical pruning shears semantic vector and the blade load status semantic vector, and controlling the target lithium-electric pruning shears based on the blade load status data so that the blade load is controlled, includes: Aggregating the historical pruning shears semantic vector and the target pruning shears semantic vector to output an aggregated pruning shears semantic vector, and performing semantic association mining on the blade load state semantic vector and the aggregated pruning shears semantic vector to output an associated pruning shears semantic vector; According to the first mapping matrix, mapping processing is performed on the associated pruning shears semantic vector to form a pruning shears semantic mapping vector, and according to the pruning shears semantic mapping vector and the blade load state semantic vector, a pruning shears semantic reinforcement vector is determined, and the pruning shears semantic reinforcement vector is activated to output a pruning shears semantic activation vector; According to the second mapping matrix, the pruning shears semantic activation vector is mapped, and the pruning shears semantic intermediate vector is output, and the pruning shears semantic output vector is obtained according to the pruning shears semantic intermediate vector and the pruning shears semantic reinforcement vector, wherein the first mapping matrix and the second mapping matrix belong to 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; The blade load status data of the target lithium-electric pruning shears based on the target pruning shears record data is determined according to the pruning shears semantic output vector, and the blade load of the target lithium-electric pruning shears is controlled based on the blade load status data.

[0009] In a preferred embodiment of the present application, in the control method of the lithium-ion pruning shears, the steps of aggregating the historical pruning shears semantic vector and the target pruning shears semantic vector to output the aggregated pruning shears semantic vector, and performing semantic association mining on the blade load state semantic vector and the aggregated pruning shears semantic vector to output the associated pruning shears semantic vector include: splicing the historical pruning shears semantic vector and the target pruning shears semantic vector to form a corresponding pruning shears semantic splicing vector as a corresponding aggregated pruning shears semantic vector; Based on the first spatial transformation matrix, the blade load state semantic vector is spatially transformed to output a first spatial transformation vector, and based on the second spatial transformation matrix, the aggregated pruning shears semantic vector is spatially transformed to output a second spatial transformation vector, and, based on the third spatial transformation matrix, the aggregated pruning shears semantic vector is spatially transformed to output a third spatial transformation vector; Multiplying the first spatial transformation vector and the second spatial transformation vector to output an associated parameter matrix, and performing noise application processing on the associated parameter matrix to output an associated parameter noise matrix, wherein in the process of performing the noise application processing, the number of associated parameters to which noise is applied is less than the number of associated parameters to which noise is not applied; The association parameter matrix and the third spatial transformation vector are multiplied to output a first association semantic vector, and the association parameter noise matrix and the third spatial transformation vector are multiplied to output a second association semantic vector, and, based on the first association semantic vector and the second association semantic vector, an association pruning shears semantic vector is output.

[0010] In a preferred embodiment of the present application, in the control method of the lithium-ion pruning shears, the step of multiplying the association parameter matrix and the third space conversion vector to output a first association semantic vector, and multiplying the association parameter noise matrix and the third space conversion vector to output a second association semantic vector, and outputting an association pruning shears semantic vector based on the first association semantic vector and the second association semantic vector includes: Multiplying the association parameter matrix and the third spatial conversion vector to output a corresponding first association semantic vector, and multiplying the association parameter noise matrix and the third spatial conversion vector to output a corresponding second association semantic vector; Determine a first weighting coefficient and a second weighting coefficient, wherein the first weighting coefficient is greater than the second weighting coefficient, and a sum of the first weighting coefficient and the second weighting coefficient is equal to 1; The first weighting coefficient and the first associated semantic vector are multiplied to obtain a corresponding first multiplication result, and the second weighting coefficient and the second associated semantic vector are multiplied to obtain a corresponding second multiplication result, and the first multiplication result and the second multiplication result are superimposed to output a corresponding associated pruning shears semantic vector.

[0011] In a preferred embodiment of the present application, in the control method of the lithium-ion pruning shears, the associated pruning shears semantic vector is mapped according to the first mapping matrix to form a pruning shears semantic mapping vector, and the pruning shears semantic enhancement vector is determined according to the pruning shears semantic mapping vector and the blade load state semantic vector, and the pruning shears semantic enhancement vector is activated to output the pruning shears semantic activation vector, including: Performing a multiplication operation on the first mapping matrix and the associated pruning shears semantic vector to form a pruning shears semantic mapping vector, and superimposing the pruning shears semantic mapping vector and the blade load state semantic vector to form a pruning shears semantic enhancement vector; The pruning shears semantic enhancement vector is activated by a nonlinear activation function, and a pruning shears semantic activation vector is output.

[0012] In a preferred embodiment of the present application, in the control method of the lithium-ion pruning shears, the step of mapping the pruning shears semantic activation vector according to the second mapping matrix, outputting the pruning shears semantic intermediate vector, and obtaining the pruning shears semantic output vector according to the pruning shears semantic intermediate vector and the pruning shears semantic reinforcement vector includes: Performing a mapping multiplication operation on the second mapping matrix and the pruning shears semantic activation vector, and outputting a pruning shears semantic intermediate vector; The pruning shears semantic intermediate vector and the pruning shears semantic enhancement vector are superimposed to obtain a pruning shears semantic output vector.

[0013] The present application also provides a control system for a lithium-ion pruning shear, comprising: a target data mining module, for extracting target pruning shears record data and mining target pruning shears semantic vectors corresponding to the target pruning shears record data, wherein the target pruning shears record data includes to-be-pruned image data and at least one type of pruning shears load data, each type of pruning shears load data is obtained by collecting the blade load of the target lithium-ion pruning shears based on a corresponding load determination method; A historical data mining module, for determining a historical pruning shears semantic vector associated with the target pruning shears semantic vector, and determining historical blade load state data corresponding to the historical pruning shears semantic vector, and mining a blade load state semantic vector of the historical blade load state data, wherein the historical pruning shears record data corresponding to the historical pruning shears semantic vector belongs to data formed by the target lithium-ion pruning shears during historical pruning operations; A pruning shears control module is used to determine the blade load status data of the target lithium-ion pruning shears based on the target pruning shears record data based on the target pruning shears semantic vector, the historical pruning shears semantic vector and the blade load status semantic vector, and to control the target lithium-ion pruning shears based on the blade load status data so that the blade load is managed and controlled, wherein the blade load status data is used to reflect whether the blade load of the target lithium-ion pruning shears is overloaded.

[0014] The present application provides a control system and method for lithium-ion pruning shears. First, a target pruning shears semantic vector corresponding to the target pruning shears record data is mined; second, a historical pruning shears semantic vector associated with the target pruning shears semantic vector is determined, and the historical blade load state data corresponding to the historical pruning shears semantic vector is determined, and the blade load state semantic vector of the historical blade load state data is mined; then, based on the target pruning shears semantic vector, the historical pruning shears semantic vector and the blade load state semantic vector, the blade load state data of the target lithium-ion pruning shears is determined, and the target lithium-ion pruning shears is controlled based on the blade load state data. Based on the above content, since the target pruning shears record data includes the image data to be pruned and at least one pruning shears load data, the target pruning shears semantic vector carries not only the semantic information of the blade load, but also the semantic information of objects such as pruned branches (different branch objects have different hardnesses, which have an impact on the blade load). Therefore, the target pruning shears semantic vector can have relatively rich and reliable semantic information, so that the reliability of the analyzed blade load state data can be higher. In addition, since the historical pruning shears semantic vector and the blade load state semantic vector are also combined, the reliability of the determined blade load state data can be further improved, thereby ensuring the reliability of the control of the target lithium-electric pruning shears based on the blade load state data, thereby improving the relatively low reliability of the control of lithium-electric pruning shears in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to make the above-mentioned objects, features and advantages of the present application more clearly understood, preferred embodiments are specifically described below in detail with reference to the accompanying drawings; Figure 1 A structural block diagram of an electronic device provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of a control method for a lithium-ion pruning shear provided in an embodiment of the present application; Figure 3 A schematic diagram of aggregating the semantic vectors of local pruning shears provided in an embodiment of the present application; Figure 4 A block diagram of a control system for a lithium-ion pruning shear provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0017] 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 present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0018] like Figure 1 As shown, an embodiment of the present application provides an electronic device, wherein the electronic device may include a memory, a processor, and a control system of a lithium-powered pruning shear.

[0019] In detail, the memory and the processor are electrically connected directly or indirectly to achieve data transmission or interaction. For example, the memory and the processor can be electrically connected through one or more communication buses or signal lines. The control system of the lithium-electric pruning shears includes at least one software function module stored in the memory in the form of software or firmware. The processor is used to execute an executable computer program stored in the memory, for example, the software function module and computer program included in the control system of the lithium-electric pruning shears, so as to implement the control method of the lithium-electric pruning shears provided in the embodiment of the present application.

[0020] 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 electrically erasable read-only memory (EEPROM), etc.

[0021] Furthermore, 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.

[0022] Understandably, Figure 1The structure shown is for illustration only, and the electronic device may also include Figure 1 More or fewer components as shown, or with Figure 1 The different configurations shown, for example, may also include a communication unit for exchanging information with other devices (such as lithium-electric pruning shears).

[0023] Combination Figure 2 The present application also provides a control method for a lithium-ion pruning shear that can be applied to the above electronic device. The method steps defined in the process related to the control method for the lithium-ion pruning shear can be implemented by the electronic device.

[0024] The following will Figure 2 The specific process shown is explained in detail.

[0025] Step S110: extracting target pruning shears record data, and mining the target pruning shears semantic vector corresponding to the target pruning shears record data.

[0026] In an embodiment of the present application, the electronic device can extract the target pruning shears record data and mine the target pruning shears semantic vector corresponding to the target pruning shears record data. The target pruning shears record data includes image data of branches to be pruned and at least one type of pruning shears load data, and each type of pruning shears load data is obtained by collecting the blade load of the target lithium-ion pruning shears based on a corresponding load determination method. Exemplarily, the image data of branches to be pruned can be formed by collecting images of corresponding pruning objects (such as branches, etc.) before the target lithium-ion pruning shears work (which can be used to reflect the type, thickness, etc. of the pruning object). The pruning shears load data can be collected during the pruning process of the target lithium-ion pruning shears. The target pruning shears semantic vector refers to the semantic information (or semantic features) mined from the target pruning shears record data, and is represented in the form of a vector for subsequent processing.

[0027] Step S120, determining a historical pruning shears semantic vector associated with the target pruning shears semantic vector, determining historical blade load state data corresponding to the historical pruning shears semantic vector, and mining a blade load state semantic vector of the historical blade load state data.

[0028] In an embodiment of the present application, the electronic device can determine a historical pruning shears semantic vector associated with the target pruning shears semantic vector, determine the historical blade load status data corresponding to the historical pruning shears semantic vector, and mine the blade load status semantic vector of the historical blade load status data. The historical pruning shears record data corresponding to the historical pruning shears semantic vector belongs to the data formed by the target lithium-ion pruning shears during the historical pruning operation, for example, it can be the data formed by the most recent pruning operation in history, and the historical blade load status data can be the status of the blade load most recently determined in history.

[0029] Step S130, based on the target pruning shears semantic vector, the historical pruning shears semantic vector and the blade load state semantic vector, determine the blade load state data of the target lithium-ion pruning shears based on the target pruning shears record data, and control the target lithium-ion pruning shears based on the blade load state data so that the blade load is controlled.

[0030] In an embodiment of the present application, the electronic device can determine the blade load status data of the target lithium-electric pruning shears based on the target pruning shears record data based on the target pruning shears semantic vector, the historical pruning shears semantic vector and the blade load status semantic vector, and control the target lithium-electric pruning shears based on the blade load status data so that the blade load is managed and controlled. The blade load status data is used to reflect whether the blade load of the target lithium-electric pruning shears is overloaded. For example, when there is an overload, the target lithium-electric pruning shears can be controlled to reduce the cutting speed, and the user can 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-electric pruning shears.

[0031] Based on the above content, since the target pruning shears record data includes the image data of the branches to be pruned and at least one pruning shears load data, the target pruning shears semantic vector carries not only the semantic information of the blade load, but also the semantic information of objects such as pruned branches (different branch objects have different hardnesses, which have an impact on the blade load). Therefore, the target pruning shears semantic vector can have relatively rich and reliable semantic information, so that the reliability of the analyzed blade load status data can be higher. In addition, since the historical pruning shears 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-electric pruning shears based on the blade load status data, thereby improving the problem of relatively low reliability of the control of lithium-electric pruning shears in the prior art.

[0032] First, it should be noted that for step S110, the specific method of mining the target pruning shears semantic vector corresponding to the target pruning shears record data is not limited, and can be selected and configured accordingly according to actual needs.

[0033] For example, in an alternative implementation, in order to improve the reliability of the mined semantic vector of the target pruning shears, the above step S110 may further include step S111 and step S112, and the specific content of each step is as follows.

[0034] Step S111: extract target pruning shears record data, and mine out local pruning shears semantic vectors respectively possessed by local data of at least two dimensions corresponding to the target pruning shears record data.

[0035] In an embodiment of the present application, the target pruning shears record data can be extracted, and the local pruning shears semantic vectors respectively possessed by the local data of at least two dimensions corresponding to the target pruning shears record data can be mined. Among them, the image data to be pruned is used as the local data of one dimension, and each type of pruning shears load data is used as the local data of one dimension. In this way, the local pruning shears semantic vector corresponding to the image data to be pruned and the local pruning shears semantic vector corresponding to the pruning shears load data can be obtained, that is, at least two local pruning shears semantic vectors can be obtained. Specifically, for the image data to be pruned, convolution processing can be performed through a convolutional neural network to obtain the corresponding local pruning shears semantic vector. For the pruning shears load data (such as a force sequence), word embedding processing can be performed through a word embedding model to obtain the corresponding local pruning shears semantic vector. For example, a strain gauge can be installed on the blade to monitor the strain applied during the cutting process, so as to deduce the load. For example, the load time series 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". By performing word embedding processing, the following word embedding vector can be obtained: 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...]; 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,...].

[0036] Step S112, performing aggregation processing on the local pruning shears semantic vectors of at least two dimensions to form a target pruning shears semantic vector corresponding to the target pruning shears record data.

[0037] In an embodiment of the present application, after obtaining the local pruning shears semantic vector, the local pruning shears semantic vectors of at least two dimensions may be aggregated to form a target pruning shears semantic vector corresponding to the target pruning shears record data. In this way, the target pruning shears semantic vector may contain the semantic information carried by each local pruning shears semantic vector.

[0038] It is understandable that, in the above step S112, the specific manner of aggregating the local pruning shears semantic vectors of 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 shears semantic vectors so that the obtained target pruning shears semantic vector has better semantic representation ability, Figure 3 , the above step S112 may further include the following contents: First, using the first-level aggregation unit in the aggregation model included in the load state analysis network, self-attention processing is performed on the local pruning shears semantic vectors of at least two dimensions respectively, and a first pruning shears semantic attention vector, a second pruning shears semantic attention vector and a third pruning shears semantic attention vector corresponding to the first-level aggregation unit are output, wherein the first pruning shears semantic attention vector corresponds to the image data to be pruned, the second pruning shears semantic attention vector corresponds to the first type of pruning shears load data, and the third pruning shears semantic attention vector corresponds to the second type of pruning shears load data. In other words, self-attention processing is performed on the local pruning shears semantic vector corresponding to the image data to be pruned to obtain the first pruning shears semantic attention vector, self-attention processing is performed on the first type of pruning shears load data to obtain the second pruning shears semantic attention vector, and self-attention processing is performed on the second type of pruning shears load data to obtain the third pruning shears semantic attention vector. The first type of pruning shears load data is acquired based on the pressure sensor on the blade, and the second type of pruning shears load data is acquired based on the change of acceleration acquired by the acceleration sensor (such as an increase in acceleration, indicating an increase in load, and a decrease in acceleration, indicating a decrease in load). The load state analysis network is a neural network formed by training sample data and corresponding blade load state labels (for example, the sample data is processed according to steps S110 to S130 to obtain corresponding sample blade load state data, and then the error between the sample blade load state data and the blade load state label is calculated. Finally, the network parameters of the load state analysis network can be updated in the direction of reducing the error). New, until the error converges, and then the training ends); It can be understood that, considering that the possible dimensions of the local data corresponding to the local pruning shears semantic vectors of at least two dimensions are different (such as images and text sequences), therefore, before the local pruning shears semantic vectors of the image dimension are self-attention processed, corresponding mapping processing can be performed first to obtain the corresponding local mapping vector, and then the corresponding self-attention processing can be performed, wherein the mapping processing can be multiplying the local pruning shears semantic vector with the mapping parameters in the aggregation unit of the first level to obtain the corresponding local mapping vector. During the training process, the initial mapping parameter can be a randomly generated vector, and then iteratively updated during the training process; Secondly, for each level of aggregation units other than the first level of aggregation units in the aggregation model (i.e., from the second level to the last level), the first pruning shears semantic attention vector, the second pruning shears semantic attention vector, and the third pruning shears semantic attention vector corresponding to the aggregation unit of the previous level are spliced ​​to form a spliced ​​attention vector, and the spliced ​​attention vector is pooled and compressed (such as mean pooling or maximum pooling, etc.), and a pooled compressed vector is output, and the first pruning shears semantic attention vector, the second pruning shears semantic attention vector, and the third pruning shears semantic attention vector corresponding to the aggregation unit of the previous level are respectively compressed based on the pooled compressed vector. The vector is cross-attended to output the first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the aggregation unit of the current level; illustratively, the pooled compression vector and the transposed vector of the first pruning shears semantic attention vector corresponding to the aggregation unit of the first level can be multiplied to obtain the corresponding association parameter, and the association parameter and the first pruning shears semantic attention vector corresponding to the aggregation unit of the first level can be multiplied to obtain the first pruning shears semantic attention vector corresponding to the aggregation unit of the second level, and accordingly, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector can be obtained; Finally, the first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the aggregation unit of the last level can be spliced ​​to form a target pruning shears semantic vector corresponding to the target pruning shears record data. Exemplarily, the target pruning shears semantic vector can be =concat(first pruning shears semantic attention vector; second pruning shears semantic attention vector; third pruning shears semantic attention vector).

[0039] Secondly, it should be noted that for step S120, the specific method of mining the blade load status semantic vector of the historical blade load status data is not limited and can be selected and configured accordingly according to actual needs.

[0040] For example, in an alternative implementation, in order to determine a blade load state semantic vector with a closer correlation so that the reliability of subsequent processing can be higher, the above step S120 may further include the following contents: First, each historical pruning shear record data formed by the target lithium-ion pruning shears in the pruning operation in the past can be determined to obtain a corresponding plurality of historical pruning shear record data. Each time a pruning operation is performed, a corresponding historical pruning shear record data can be formed. Secondly, multiple historical pruning shears semantic vectors corresponding to the multiple historical pruning shears record data may be extracted, which may be directly extracted, such as the target pruning shears semantic vector mined from the historical pruning shears record data as the target pruning shears record data; Then, the smallest historical pruning shears semantic vector between the historical pruning shears semantic vector and the target pruning shears semantic vector may be determined from the multiple historical pruning shears semantic vectors as the historical pruning shears semantic vector associated with the target pruning shears semantic vector; illustratively, the vector distance may be a cosine distance between vectors, etc.; Finally, the historical blade load status data corresponding to the historical pruning shears semantic vector that has an association relationship with the target pruning shears semantic vector (i.e., the blade load status data historically determined based on the historical pruning shears semantic vector) can be determined, and semantic mining can be performed on the historical blade load status data to output the corresponding blade load status semantic vector (such as a word embedding vector obtained by performing word embedding processing on the historical blade load status data).

[0041] In the third aspect, it should be noted that for step S130, the specific method of determining the blade load status data of the target lithium-ion pruning shears based on the recorded data of the target pruning shears is not limited and can be selected and configured accordingly according to actual needs.

[0042] For example, in an alternative embodiment, in order to ensure the reliability of the determined blade load status data, so that the reliability of the control of the target lithium-electric pruning shears can be guaranteed, the above-mentioned step S130 can further include step S131, step S132, step S133 and step S134, and the specific content of each step is described as follows.

[0043] Step S131, aggregate the historical pruning shears semantic vector and the target pruning shears semantic vector to output an aggregated pruning shears semantic vector, and perform semantic association mining on the blade load state semantic vector and the aggregated pruning shears semantic vector to output an associated pruning shears semantic vector.

[0044] In an embodiment of the present application, the historical pruning shears semantic vector and the target pruning shears semantic vector may be aggregated to output an aggregated pruning shears semantic vector, and the blade load state semantic vector and the aggregated pruning shears semantic vector may be semantically associated with each other to output an associated pruning shears semantic vector. In other words, the historical pruning shears semantic vector may be first integrated into the target pruning shears semantic vector, and then the blade load state semantic vector may be integrated, so that the obtained associated pruning shears semantic vector has better semantic representation ability and carries richer semantic information.

[0045] Step S132, mapping the associated pruning shears semantic vector according to the first mapping matrix to form a pruning shears semantic mapping vector, and determining a pruning shears semantic enhancement vector according to the pruning shears semantic mapping vector and the blade load state semantic vector, and activating the pruning shears semantic enhancement vector to output a pruning shears semantic activation vector.

[0046] In an embodiment of the present application, the associated pruning shears semantic vector can be mapped according to the first mapping matrix (to increase the expressive power of the semantic vector) to form a pruning shears semantic mapping vector, and, based on the pruning shears semantic mapping vector and the blade load state semantic vector, a pruning shears semantic enhancement vector can be determined, and the pruning shears semantic enhancement vector can be activated to output a pruning shears semantic activation vector.

[0047] Step S133, mapping the pruning shears semantic activation vector according to the second mapping matrix, outputting a pruning shears semantic intermediate vector, and obtaining a pruning shears semantic output vector according to the pruning shears semantic intermediate vector and the pruning shears semantic reinforcement vector.

[0048] In an embodiment of the present application, the pruning shears semantic activation vector can be mapped according to the second mapping matrix, and the pruning shears semantic intermediate vector can be output, and the pruning shears semantic output vector can be obtained according to the pruning shears semantic intermediate vector and the pruning shears semantic reinforcement vector. The first mapping matrix and the second mapping matrix belong to the network parameters of the load state analysis network formed by training with sample data and corresponding blade load state labels (as described above), and the load state analysis network belongs to a neural network.

[0049] Step S134, determining the blade load status data of the target lithium-ion pruning shears based on the target pruning shears record data according to the pruning shears semantic output vector, and controlling the blade load of the target lithium-ion pruning shears based on the blade load status data.

[0050] In an embodiment of the present application, the blade load status data of the target lithium-ion pruning shears based on the target pruning shears record data can be determined according to the pruning shears semantic output vector, and the blade load of the target lithium-ion pruning shears can be controlled based on the blade load status data. Exemplarily, the load status analysis network can also include a fully connected model and an output function (such as a classification function such as softmax), and the fully connected model can perform fully connected processing on the pruning shears semantic output vector to obtain a corresponding pruning shears fully connected vector, and then the pruning shears fully connected vector is processed by the output function to obtain the probability of overload and the probability of not overload, thereby forming blade load status data, and then the blade load is controlled.

[0051] It can be understood that, in the above-mentioned step S131, the specific method of performing aggregation processing and semantic association mining is not limited and can be selected according to actual needs. For example, in an alternative embodiment, in order to be able to output an associated pruning shears semantic vector with higher semantic representation accuracy, the above-mentioned step S131 can further include step S131a, step S131b, step S131c and step S131d, and the specific content of each step is described as follows.

[0052] Step S131a, concatenating the historical pruning shears semantic vector and the target pruning shears semantic vector to form a corresponding pruning shears semantic concatenation vector as a corresponding aggregated pruning shears semantic vector.

[0053] In an embodiment of the present application, the historical pruning shears semantic vector and the target pruning shears semantic vector may be spliced ​​to form a corresponding pruning shears semantic splicing vector as a corresponding aggregated pruning shears semantic vector (ie, a pruning shears semantic splicing vector).

[0054] Step S131b, based on the first spatial transformation matrix, spatially transform the blade load state semantic vector, output the first spatial transformation vector, and based on the second spatial transformation matrix, spatially transform the aggregated pruning shears semantic vector, output the second spatial transformation vector, and, based on the third spatial transformation matrix, spatially transform the aggregated pruning shears semantic vector, output the third spatial transformation vector.

[0055] In an embodiment of the present application, the blade load state semantic vector may be spatially transformed based on a first spatial transformation matrix (i.e., the blade load state semantic vector is multiplied by the first spatial transformation matrix), and the first spatial transformation vector is output; and based on a second spatial transformation matrix, the aggregated pruning shears semantic vector may be spatially transformed, and the second spatial transformation vector may be output (i.e., the aggregated pruning shears semantic vector is multiplied by the second spatial transformation matrix); and, based on a third spatial transformation matrix, the aggregated pruning shears semantic vector may be spatially transformed, and the third spatial transformation vector may be output (i.e., the aggregated pruning shears semantic vector is multiplied by the third spatial transformation matrix); in addition, the first spatial transformation matrix, the second spatial transformation matrix, and the third spatial transformation matrix may be network parameters in the load state analysis network, which are formed during the training process; Step S131c: multiply the first spatial transformation vector and the second spatial transformation vector to output a correlation parameter matrix, and perform noise processing on the correlation parameter matrix to output a correlation parameter noise matrix.

[0056] In an embodiment of the present application, the first spatial conversion vector and the second spatial conversion vector may be multiplied to output an associated parameter matrix (which may be multiplied with the transposed vector of the second spatial conversion vector. For example, the first spatial conversion vector may be a 1*N row vector, and the transposed vector of the second spatial conversion vector may be an M*1 column vector. The multiplication may result in an M*N matrix. Then, the mean value of each column of the matrix is ​​calculated to obtain a 1*N row vector, i.e., an associated parameter matrix), and the associated parameter matrix is ​​subjected to noise application processing to output an associated parameter noise matrix. In the process of applying noise, the number of associated parameters to which noise is applied is less than the number of associated parameters to which noise is not applied; illustratively, the associated parameter matrix may be superimposed with a random vector to obtain an associated parameter noise matrix.

[0057] Step S131d, multiply the association parameter matrix and the third spatial transformation vector to output a first association semantic vector, multiply the association parameter noise matrix and the third spatial transformation vector to output a second association semantic vector, and, based on the first association semantic vector and the second association semantic vector, output an association pruning shears semantic vector.

[0058] In an embodiment of the present application, the association parameter matrix and the third space conversion vector may be multiplied to output a first association semantic vector, and the association parameter noise matrix and the third space conversion vector may be multiplied to output a second association semantic vector, and, based on the first association semantic vector and the second association semantic vector, an association pruning shears semantic vector may be output. Based on this, by adding noise in the process of semantic association mining, the problem of excessive attention to association semantic information can be avoided to a certain extent, and the representation ability of the association pruning shears semantic vector can be improved.

[0059] It can be understood that, in the above step S131d, the specific method of outputting the associated pruning shears semantic vector is not limited and can be selected according to actual needs. For example, in an alternative implementation, in order to enable the associated pruning shears semantic vector to reliably fuse the first associated semantic vector and the second associated semantic vector, the above step S131d can further include the following contents: First, the association parameter matrix and the third space conversion vector may be multiplied to output a corresponding first association semantic vector, and the association parameter noise matrix and the third space conversion vector may be multiplied to output a corresponding second association semantic vector; Secondly, a first weighting coefficient and a second weighting coefficient may be determined, 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; Finally, the first weighting coefficient and the first associated semantic vector are multiplied to obtain a corresponding first multiplication result, and the second weighting coefficient and the second associated semantic vector are multiplied to obtain a corresponding second multiplication result, and the first multiplication result and the second multiplication result are superimposed to output the corresponding associated pruning shears semantic vector, that is, the weighted mean calculation.

[0060] It is understandable that, in the above step S132, the specific method of outputting the pruning shears semantic activation vector is not limited and can be selected according to actual needs. For example, in an alternative implementation, the above step S132 may further include the following contents: First, the first mapping matrix and the associated pruning shears semantic vector may be multiplied to form a pruning shears semantic mapping vector, and the pruning shears semantic mapping vector and the blade load state semantic vector may be superimposed to form a pruning shears semantic enhancement vector; Secondly, the pruning shears semantic enhancement vector may be activated by a nonlinear activation function (such as Tanh (hyperbolic tangent function)) to output a pruning shears semantic activation vector.

[0061] It is understandable that, in the above step S133, the specific method of obtaining the pruning shears semantic output vector is not limited and can be selected according to actual needs. For example, in an alternative implementation, the above step S133 may further include the following contents: Firstly, the second mapping matrix and the pruning shears semantic activation vector may be mapped and multiplied to output a pruning shears semantic intermediate vector; Secondly, the pruning shears semantic intermediate vector and the pruning shears semantic enhancement vector may be superimposed to obtain a pruning shears semantic output vector.

[0062] Combination Figure 4 The embodiment of the present application also provides a control system of a lithium-electric pruning shears applicable to the above electronic device. The control system of the lithium-electric pruning shears may include a target data mining module, a historical data mining module and a pruning shears control module.

[0063] The target data mining module is used to extract the target pruning shears record data and mine the target pruning shears semantic vector corresponding to the target pruning shears record data, wherein the target pruning shears record data includes the image data of the branches to be pruned and at least one type of pruning shears load data, and each type of pruning shears load data is obtained by collecting the blade load of the target lithium-ion pruning shears based on a corresponding load determination method. In the embodiment of the present application, the target data mining module can be used to perform Figure 2As shown in step S110, the relevant contents of the target data mining module can refer to the above description of step S110.

[0064] The historical data mining module is used to determine a historical pruning shears semantic vector associated with the target pruning shears semantic vector, determine the historical blade load state data corresponding to the historical pruning shears semantic vector, and mine the blade load state semantic vector of the historical blade load state data, wherein the historical pruning shears record data corresponding to the historical pruning shears semantic vector belongs to the data generated by the target lithium-ion pruning shears during the pruning operation in history. In the embodiment of the present application, the historical data mining module can be used to perform Figure 2 As shown in step S120, the relevant contents of the historical data mining module can refer to the above description of step S120.

[0065] The pruning shears control module is used to determine the blade load status data of the target lithium-ion pruning shears based on the target pruning shears record data based on the target pruning shears semantic vector, the historical pruning shears semantic vector and the blade load status semantic vector, and to control the target lithium-ion pruning shears based on the blade load status data so that the blade load is controlled, wherein the blade load status data is used to reflect whether the blade load of the target lithium-ion pruning shears is overloaded. In an embodiment of the present application, the pruning shears control module can be used to execute Figure 2 As shown in step S130, the relevant contents of the pruning shear control module can refer to the above description of step S130.

[0066] In the embodiment of the present application, corresponding to the control method of the lithium-electric pruning shears applied to the electronic device, a computer-readable storage medium is also provided, in which a computer program is stored, and when the computer program is run, each step of the control method of the lithium-electric pruning shears is executed. Among them, each step executed when the aforementioned computer program is run will not be repeated here one by one, and reference can be made to the explanation of the control method of the lithium-electric pruning shears in the previous text.

[0067] In summary, the present application provides a control system and method for lithium-ion pruning shears. First, a target pruning shears semantic vector corresponding to the target pruning shears record data is mined; second, a historical pruning shears semantic vector associated with the target pruning shears semantic vector is determined, and the historical blade load state data corresponding to the historical pruning shears semantic vector is determined, and the blade load state semantic vector of the historical blade load state data is mined; then, based on the target pruning shears semantic vector, the historical pruning shears semantic vector and the blade load state semantic vector, the blade load state data of the target lithium-ion pruning shears is determined, and the target lithium-ion pruning shears is controlled based on the blade load state data. Based on the above content, since the target pruning shears record data includes the image data to be pruned and at least one pruning shears load data, the target pruning shears semantic vector carries not only the semantic information of the blade load, but also the semantic information of objects such as pruned branches (different branch objects have different hardnesses, which have an impact on the blade load). Therefore, the target pruning shears semantic vector can have relatively rich and reliable semantic information, so that the reliability of the analyzed blade load state data can be higher. In addition, since the historical pruning shears semantic vector and the blade load state semantic vector are also combined, the reliability of the determined blade load state data can be further improved, thereby ensuring the reliability of the control of the target lithium-electric pruning shears based on the blade load state data, thereby improving the relatively low reliability of the control of lithium-electric pruning shears in the prior art.

[0068] In several embodiments provided in the embodiments of the present application, it should be understood that the disclosed device and method can also be implemented in other ways. The device and method embodiments described above are merely schematic, for example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the device, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0069] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0070] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, electronic device, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code. It should be noted that in this article, the term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0071] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A control method for lithium-ion pruning shears, characterized in that: include: Extracting target pruning shears record data, and mining the target pruning shears semantic vector corresponding to the target pruning shears record data, wherein the target pruning shears record data includes to-be-pruned image data and at least one type of pruning shears load data, each type of pruning shears load data is obtained by collecting the blade load of the target lithium-ion pruning shears based on a corresponding load determination method; Determine a historical pruning shears semantic vector associated with the target pruning shears semantic vector, determine historical blade load state data corresponding to the historical pruning shears semantic vector, and mine the blade load state semantic vector of the historical blade load state data, wherein the historical pruning shears record data corresponding to the historical pruning shears semantic vector belongs to data generated by the target lithium-ion pruning shears during historical pruning operations; Based on the target pruning shears semantic vector, the historical pruning shears semantic vector and the blade load status semantic vector, the blade load status data of the target lithium-electric pruning shears based on the target pruning shears record data is determined, and the target lithium-electric pruning shears is controlled based on the blade load status data so that the blade load is managed, wherein the blade load status data is used to reflect whether the blade load of the target lithium-electric pruning shears is overloaded.

2. The control method of the lithium-electric pruning shears according to claim 1, characterized in that: The step of extracting the target pruning shears record data and mining the target pruning shears semantic vector corresponding to the target pruning shears record data includes: Extract target pruning shears record data, and mine local pruning shears semantic vectors respectively possessed by local data of at least two dimensions corresponding to the target pruning shears record data, wherein the image data to be pruned is local data of one dimension, and each type of pruning shears load data is local data of one dimension; Aggregation processing is performed on the local pruning shears semantic vectors of at least two dimensions to form a target pruning shears semantic vector corresponding to the target pruning shears record data.

3. The control method of the lithium-electric pruning shears according to claim 2, characterized in that: The step of aggregating the local pruning shears semantic vectors of at least two dimensions to form a target pruning shears semantic vector corresponding to the target pruning shears record data includes: Utilizing the aggregation unit of the first level in the aggregation model included in the load state analysis network, the local pruning shears semantic vectors of at least two dimensions are subjected to self-attention processing respectively, and the first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the first level aggregation unit are output, wherein the first pruning shears semantic attention vector corresponds to the image data to be pruned, the second pruning shears semantic attention vector corresponds to the first type of pruning shears load data, the third pruning shears semantic attention vector corresponds to the second type of pruning shears load data, the first type of pruning shears load data is formed based on the pressure sensor on the blade, the second type of pruning shears load data is obtained based on the change of acceleration collected by the acceleration sensor, and the load state analysis network is a neural network formed by training with sample data and corresponding blade load state labels; For each level of aggregation units other than the first level of aggregation units in the aggregation model, the first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the aggregation units of the previous level are spliced ​​to form a spliced ​​attention vector, and the spliced ​​attention vector is pooled and compressed to output a pooled compressed vector, and based on the pooled compressed vector, the first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the aggregation units of the previous level are cross-attention processed to output the first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the aggregation units of the current level; The first pruning shears semantic attention vector, the second pruning shears semantic attention vector and the third pruning shears semantic attention vector corresponding to the aggregation unit of the last level are concatenated to form a target pruning shears semantic vector corresponding to the target pruning shears record data.

4. The control method of the lithium-electric pruning shears according to claim 1, characterized in that: The steps of determining a historical pruning shears semantic vector associated with the target pruning shears semantic vector, determining historical blade load state data corresponding to the historical pruning shears 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-ion pruning shears during the pruning operation in history, and obtain corresponding multiple historical pruning shear record data; Extracting multiple historical pruning shear semantic vectors corresponding to the multiple historical pruning shear record data; Determine, among the multiple historical pruning shears semantic vectors, a historical pruning shears semantic vector having the smallest vector distance with the target pruning shears semantic vector as the historical pruning shears semantic vector associated with the target pruning shears semantic vector; Determine the historical blade load state data corresponding to the historical pruning shears semantic vector associated with the target pruning shears semantic vector, and perform semantic mining on the historical blade load state data to output the corresponding blade load state semantic vector.

5. The control method of the lithium-electric pruning shears according to any one of claims 1 to 4, characterized in that: The step of determining the blade load status data of the target lithium-ion pruning shears based on the target pruning shears record data based on the target pruning shears semantic vector, the historical pruning shears semantic vector and the blade load status semantic vector, and controlling the target lithium-ion pruning shears based on the blade load status data so that the blade load is controlled, comprises: Aggregating the historical pruning shears semantic vector and the target pruning shears semantic vector to output an aggregated pruning shears semantic vector, and performing semantic association mining on the blade load state semantic vector and the aggregated pruning shears semantic vector to output an associated pruning shears semantic vector; According to the first mapping matrix, mapping processing is performed on the associated pruning shears semantic vector to form a pruning shears semantic mapping vector, and according to the pruning shears semantic mapping vector and the blade load state semantic vector, a pruning shears semantic reinforcement vector is determined, and the pruning shears semantic reinforcement vector is activated to output a pruning shears semantic activation vector; According to the second mapping matrix, the pruning shears semantic activation vector is mapped, and the pruning shears semantic intermediate vector is output, and the pruning shears semantic output vector is obtained according to the pruning shears semantic intermediate vector and the pruning shears semantic reinforcement vector, wherein the first mapping matrix and the second mapping matrix belong to 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; The blade load status data of the target lithium-electric pruning shears based on the target pruning shears record data is determined according to the pruning shears semantic output vector, and the blade load of the target lithium-electric pruning shears is controlled based on the blade load status data.

6. The control method of the lithium-electric pruning shears according to claim 5, characterized in that: The steps of aggregating the historical pruning shears semantic vector and the target pruning shears semantic vector to output an aggregated pruning shears semantic vector, and performing semantic association mining on the blade load state semantic vector and the aggregated pruning shears semantic vector to output an associated pruning shears semantic vector include: splicing the historical pruning shears semantic vector and the target pruning shears semantic vector to form a corresponding pruning shears semantic splicing vector as a corresponding aggregated pruning shears semantic vector; Based on the first spatial transformation matrix, the blade load state semantic vector is spatially transformed to output a first spatial transformation vector, and based on the second spatial transformation matrix, the aggregated pruning shears semantic vector is spatially transformed to output a second spatial transformation vector, and, based on the third spatial transformation matrix, the aggregated pruning shears semantic vector is spatially transformed to output a third spatial transformation vector; Multiplying the first spatial transformation vector and the second spatial transformation vector to output an associated parameter matrix, and performing noise application processing on the associated parameter matrix to output an associated parameter noise matrix, wherein in the process of performing the noise application processing, the number of associated parameters to which noise is applied is less than the number of associated parameters to which noise is not applied; The association parameter matrix and the third spatial transformation vector are multiplied to output a first association semantic vector, and the association parameter noise matrix and the third spatial transformation vector are multiplied to output a second association semantic vector, and, based on the first association semantic vector and the second association semantic vector, an association pruning shears semantic vector is output.

7. The control method of the lithium-electric pruning shears according to claim 6, characterized in that: The step of multiplying the association parameter matrix and the third spatial conversion vector to output a first association semantic vector, multiplying the association parameter noise matrix and the third spatial conversion vector to output a second association semantic vector, and outputting an association pruning shears semantic vector based on the first association semantic vector and the second association semantic vector comprises: Multiplying the association parameter matrix and the third spatial conversion vector to output a corresponding first association semantic vector, and multiplying the association parameter noise matrix and the third spatial conversion vector to output a corresponding second association semantic vector; Determine a first weighting coefficient and a second weighting coefficient, wherein the first weighting coefficient is greater than the second weighting coefficient, and a sum of the first weighting coefficient and the second weighting coefficient is equal to 1; The first weighting coefficient and the first associated semantic vector are multiplied to obtain a corresponding first multiplication result, and the second weighting coefficient and the second associated semantic vector are multiplied to obtain a corresponding second multiplication result, and the first multiplication result and the second multiplication result are superimposed to output a corresponding associated pruning shears semantic vector.

8. The control method of the lithium-electric pruning shears according to claim 5, characterized in that: The steps of mapping the associated pruning shears semantic vector according to the first mapping matrix to form a pruning shears semantic mapping vector, determining a pruning shears semantic enhancement vector according to the pruning shears semantic mapping vector and the blade load state semantic vector, activating the pruning shears semantic enhancement vector, and outputting a pruning shears semantic activation vector include: Performing a multiplication operation on the first mapping matrix and the associated pruning shears semantic vector to form a pruning shears semantic mapping vector, and superimposing the pruning shears semantic mapping vector and the blade load state semantic vector to form a pruning shears semantic enhancement vector; The pruning shears semantic enhancement vector is activated by a nonlinear activation function, and a pruning shears semantic activation vector is output.

9. The control method of the lithium-electric pruning shears according to claim 5, characterized in that: The step of mapping the pruning shears semantic activation vector according to the second mapping matrix, outputting a pruning shears semantic intermediate vector, and obtaining a pruning shears semantic output vector according to the pruning shears semantic intermediate vector and the pruning shears semantic reinforcement vector comprises: Performing a mapping multiplication operation on the second mapping matrix and the pruning shears semantic activation vector, and outputting a pruning shears semantic intermediate vector; The pruning shears semantic intermediate vector and the pruning shears semantic enhancement vector are superimposed to obtain a pruning shears semantic output vector.

10. A control system for lithium-ion pruning shears, characterized in that: include: a target data mining module, for extracting target pruning shears record data and mining target pruning shears semantic vectors corresponding to the target pruning shears record data, wherein the target pruning shears record data includes to-be-pruned image data and at least one type of pruning shears load data, each type of pruning shears load data is obtained by collecting the blade load of the target lithium-ion pruning shears based on a corresponding load determination method; A historical data mining module, for determining a historical pruning shears semantic vector associated with the target pruning shears semantic vector, and determining historical blade load state data corresponding to the historical pruning shears semantic vector, and mining a blade load state semantic vector of the historical blade load state data, wherein the historical pruning shears record data corresponding to the historical pruning shears semantic vector belongs to data formed by the target lithium-ion pruning shears during historical pruning operations; A pruning shears control module is used to determine the blade load status data of the target lithium-ion pruning shears based on the target pruning shears record data based on the target pruning shears semantic vector, the historical pruning shears semantic vector and the blade load status semantic vector, and to control the target lithium-ion pruning shears based on the blade load status data so that the blade load is managed and controlled, wherein the blade load status data is used to reflect whether the blade load of the target lithium-ion pruning shears is overloaded.

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