395results about How to "Easy to optimize" patented technology

The invention provides an image processing method, device and equipment for scene reconstruction and rendering. The method comprises the following steps: initializing sparse point clouds corresponding to each scene image to be reconstructed to obtain a plurality of initial Gaussian surface elements; optimizing each initial Gaussian surface element based on an inverse depth map, a rendering scene image and a normal vector map obtained by rendering the initial Gaussian surface elements to obtain a plurality of first target Gaussian surface elements; based on the importance of the first target Gaussian surface elements, determining a second target Gaussian surface element, the importance being determined based on a plurality of sub-models and a target scene image to be reconstructed matched with each sub-model, the plurality of sub-models being obtained by performing mesh generation on the plurality of first target Gaussian surface elements, and the plurality of sub-models being obtained by performing mesh generation on the plurality of first target Gaussian surface elements; the target scene image to be reconstructed matched with each sub-model is determined based on the pose of the scene image to be reconstructed and the rendering result of the sub-model; and determining a third target Gaussian surface element for rendering the reconstructed scene image based on the spatial range of the grid.

The invention relates to the field of medical model training, and particularly discloses a medical treatment guide model training method and system, a terminal and a medium, and the method comprises the steps: carrying out the incremental pre-training of a selected large language model through priori knowledge, and obtaining a basic large language model; acquiring a public real doctor-patient dialogue data set; constructing a doctor-patient dialogue template, and generating and constructing a doctor-patient dialogue data set by using a third-party large language model based on the doctor-patient dialogue template; combining the real doctor-patient dialogue data set and the constructed doctor-patient dialogue data set into a data set, and recording the data set as an initial doctor-patient dialogue data set; performing duplicate removal preprocessing on the initial doctor-patient dialogue data set to obtain a doctor-patient dialogue training data set; and using the doctor-patient dialogue training data set to carry out supervision fine tuning training on the basic large language model to obtain a doctor-seeing and hospital-guiding model. According to the invention, the joint training data set is formed based on the public data set and the automatic construction data set, so that the accuracy of the medical treatment guide model is improved.

The invention discloses a hotspot mining system and method based on Internet finance and economics information and a medium, and relates to the field of Internet finance and economics hotspot mining systems, and the system comprises an information collection module which collects finance and economics information in a multi-source manner, determines a collection period according to source characteristics, and carries out the screening and storage; the text preprocessing module is used for cleaning and analyzing texts, standardizing terms and carrying out classification labeling; the hotspot evaluation index calculation module is used for calculating indexes such as classification weighting, TF-IDF and the like; the hotspot mining and analyzing module is used for setting threshold mining hotspots, analyzing emotion and the like; and the visual display module is used for visually displaying the hot spots and supporting interaction and pushing. According to the invention, financial information is integrated, and hotspots are mined accurately. The information acquisition efficiency is improved, and decision-making investment and research are assisted. Information spreading is promoted, and financial field development is promoted.

The invention discloses a GNN and GRU-based bridge structure time-varying fire response prediction method and system, and the method comprises the steps: building a to-be-detected bridge structure FEM model, randomly generating a fire curve, and generating a data set through the combination of the FEM model and the fire curve; establishing a graph structure according to the FEM model, and embedding static and dynamic characteristics for temperature, stress and displacement prediction in nodes of the graph structure; a message transmission layer is constructed, the GNN is responsible for space information transmission, and the GRU is responsible for time information transmission; simulating a heat conduction formula by using a message passing formula combining the two; receiving node input vectors including temperature, stress and displacement prediction characteristics from the graph structure; determining a loss function and separately training GNN + GRU networks for temperature, stress and displacement prediction; and when the to-be-detected bridge structure encounters a fire, acquiring a fire response condition of the to-be-detected bridge structure through the trained model. The problem that a traditional GNN model can only predict non-time-varying boundary fire response is solved.

The invention discloses a hospital internal network security isolation method based on an all-optical network architecture. The method comprises the following steps: S1, dividing a hospital internal network into a plurality of virtual optical slices by using an all-optical network slicing technology; s2, performing time domain isolation by using an optical time domain multiplexing technology, and transmitting data on different optical wavelengths by using a wavelength selective switching technology; s3, monitoring and managing bandwidth allocation through a software defined network control platform; s4, traffic prediction and resource optimization are carried out based on a decision tree integration algorithm; s5, detecting potential anomalies and security threats by using the graph neural network; s6, abnormal behaviors are detected through an isolation forest algorithm, and abnormal traffic is marked and isolated; s7, recording a network traffic log through a block chain technology; and S8, when a network fault occurs, using an intelligent self-healing mechanism to automatically switch redundant optical path resources and recover the optical path. According to the invention, all-optical network slicing and artificial intelligence technologies are fully utilized, and security isolation and resource optimization of a hospital internal network are realized.

The invention provides a distributed fault detection and dynamic task scheduling system for an unmanned ship cluster, and the system achieves the efficient cooperation of the unmanned ship cluster in a complex marine environment through the cooperation of a sensor data collection module, a data processing and feature extraction module, a fault state classification module, an information sharing module, a weighted voting decision module, a task scheduling module and the like. The system firstly collects state information of each unmanned ship and adjacent ships, performs feature extraction and classification on data, and judges whether a fault exists or not. Fault information is shared to each boat in a cluster through a multi-path redundancy communication mechanism, and weighted decision is carried out by a voting mechanism, so that the accuracy of fault detection is improved. After a fault is confirmed, the system dynamically adjusts task allocation according to task priorities, key tasks are transferred to the unmanned surface vehicle in a good state, and continuity and stability of cluster tasks are guaranteed. Different from traditional centralized control, the distributed control method disclosed by the invention enhances the damage resistance and the self-adaptability of the system.

The invention discloses a machining control method, device and system based on a five-axis machining machine tool and a medium. The method comprises the steps that geometrical characteristic parameters of a to-be-machined workpiece and tool characteristic information are obtained, a cutting force model is established according to the obtained geometrical characteristic parameters, and cutting force distribution conditions under different paths are calculated; in the cutting force model, based on the cutting force distribution condition and the geometric feature parameters, generating an optimal cutter path corresponding to the cutter feature information by using a path optimization algorithm; according to the generated tool path and tool feature information, establishing a simulation environment of the five-axis machining tool, and simulating motion states of the tool in different paths; in the simulation environment, setting a reward function including path smoothness and cutting force stability; using a reinforcement learning algorithm to optimize the fairness of the tool path, and generating a smooth tool path; verifying the generated smooth tool path in a simulation environment; and triggering a processing execution instruction. The machining precision and the surface quality are improved.

The invention provides a method and system for optimizing a commodity title and computing equipment, and the method comprises the steps: obtaining commodity information which comprises an original title of a commodity and a commodity attribute; obtaining related flow words associated with the commodity by using an algorithm recommendation system; based on the commodity information, first candidate traffic words used for being added into the original title are selected from the related traffic words through a large language model; based on the commodity information and the first recommended traffic word, utilizing a large language model to generate a second candidate traffic word used for being added into the original title; based on the commodity information, the first candidate traffic word and the second candidate traffic word, determining excluded risk traffic words by using a large language model to obtain a third candidate traffic word; and selecting an optimized traffic word from the third candidate traffic words by utilizing a large language model based on the commodity information, and adding the optimized traffic word into the original title, thereby generating a new title. According to the technical scheme, merchants can be better helped to screen drainage keywords, commodity titles can be more conveniently optimized, and commodity competitiveness is improved.

The invention discloses a 2D-CNN network wake flow electromagnetic field detection method based on wavelet transform time-frequency spectrum characteristics. The method comprises the steps of (1) generating an underwater vehicle wake flow electromagnetic field calculation data set through a multi-physics field coupling method of hydrodynamics and electromagnetism, (2) extracting time-frequency spectrum features of a wake flow electromagnetic field through wavelet transform, and (3) developing a wake flow electromagnetic field detection model based on a 2D-CNN deep neural network. And training the model by using the data after the wavelet transform for wake flow electromagnetic field detection. According to the 2D-CNN network wake flow electromagnetic field detection method based on the wavelet transform time-frequency spectrum characteristics, the detection capability of the wake flow electromagnetic field can be effectively improved, a new technical means is provided for detection of the wake flow electromagnetic field of an underwater vehicle, and the method has a good application prospect in the field of marine target electromagnetic field monitoring and early warning.

The invention discloses a power equipment communication transmission system based on a power wireless private network. The system comprises a path planning module, a communication parameter adjusting module and an execution module. According to the power equipment communication transmission system based on the power wireless private network, efficient, stable and safe data transmission of power equipment in a complex network environment is realized through a low-delay and high-reliability wireless private network communication technology; the real-time performance and the stability of communication are improved through the path planning module, the communication parameter adjusting module and the execution module, the self-adaptive adjusting function is achieved, the anti-interference capability is enhanced, maintenance and optimization are facilitated, and the operation efficiency, the reliability and the safety of the power equipment are remarkably improved.

The invention relates to the technical field of hydrogen storage performance calculation and evaluation, and discloses a method and a system for calculating and evaluating the hydrogen storage performance of a solid hydrogen storage material. Analyzing and designing the mechanical property, electron distribution, heat-conducting property, heat stability and the like of the material; the hydrogen adsorption performance of the newly designed hydrogen storage material is calculated by calculating the adsorption state and adsorption energy of hydrogen molecules on the hydrogen storage material and the hydrogen adsorption capacity of the material under different pressures and temperatures; and drawing a hydrogen adsorption dynamic curve of the solid hydrogen storage material so as to calculate and evaluate the hydrogen storage dynamic performance of the material. The system comprises a performance analysis module, a performance calculation module and a performance evaluation module. The research and development efficiency and hydrogen storage performance of the solid hydrogen storage material can be comprehensively improved in theory and practical application. Each step provides important theoretical support and data basis for solving technical challenges in different aspects.

The invention belongs to the technical field of power generation, and discloses a power generation gas turbine grid-connected power allocation technology and device which comprises a power detection module, a data processing module and a power regulation and control module. And the output power of the gas turbine is detected in real time through the power detection module, so that the real-time performance of power allocation is ensured. The power detection has the advantages of single current detection, simplicity, linearity and high efficiency. And accuracy: through the data processing module and a preset algorithm, a power regulation and control instruction can be accurately calculated, and accurate allocation of the grid-connected power of the gas turbine is realized. The power allocation effect is that any proportion can be adopted for adjustment, and the power error is reduced; and stability: the output power of the gas turbine is regulated and controlled through the power regulation and control module, so that the stability of the power grid can be effectively improved. According to the power allocation effect, the problem that power oscillates between the two gas turbines does not occur.

The invention provides a multi-level performance analysis and optimization method based on an RISC-V framework, and aims to position and optimize performance bottlenecks in application programs through efficient performance data acquisition and analysis means. The method comprises the following steps: firstly, sampling and monitoring all application programs by utilizing an eBPF, capturing applications or processes with abnormal performance, then deeply analyzing the abnormal applications by utilizing a perf tool, and obtaining performance indexes such as function call, instruction number, CPU period and the like. A hot function generated by perf is analyzed, a specific performance counter of an RISC-V architecture is further used, data of the performance counter is read from a user mode, the performance data in the function execution process are accurately monitored in combination with the uprobe technology, and the performance bottleneck is deeply analyzed and positioned. Finally, the performance problem is solved by means of code optimization, system architecture adjustment and the like, and the performance is continuously optimized in multiple iterations. According to the scheme, global performance monitoring can be converted into accurate function level analysis, dependence on a kernel module is reduced, the flexibility and accuracy of performance analysis are improved, and the method has wide application value and is particularly suitable for performance adjustment and optimization in the fields of high-performance calculation, embedded systems, cloud services and the like.

The invention, which relates to the automatic driving field, discloses a Transform-based networked automatic driving vehicle trajectory prediction method comprising the following steps: obtaining vehicle driving data and preprocessing the vehicle driving data to obtain time sequence information of each vehicle; a context vector of each vehicle is obtained by using a Transform encoder; inputting the context vector of each vehicle into a graph neural network to obtain an overall feature context vector I; obtaining a driving intention probability matrix of the current vehicle by using a random forest algorithm; splicing the driving intention probability matrix of the current vehicle and the overall feature context vector I, inputting the spliced driving intention probability matrix and the spliced overall feature context vector I into a decoder, and finally outputting the trajectory distribution probability of the vehicle; through combination of Transformer and GNN, not only can the dynamic state of a single vehicle be understood, but also the relative position and motion relation between vehicles can be understood, so that the vehicle trajectory can be predicted more accurately.

The invention discloses a named entity recognition method and system based on a knowledge enhancement pre-training model, relates to the technical field of named entity recognition, and aims to solve the problem that an existing training model cannot perform named entity recognition more accurately. According to the method, the knowledge enhancement pre-training model is introduced, so that the model can learn more priori knowledge and semantic information, the generalization ability of unseen text data is improved, the interpretability of the model is enhanced through the detailed performance evaluation and optimization process, the working mechanism of the model is beneficially understood, and the working efficiency of the model is improved. Therefore, the model is better adjusted and optimized, the generalization ability of the optimized model is also improved, the model can better adapt to different application scenes and data sets, the generation of the monitoring log enables performance analysis and problem tracking to be more convenient, the continuous optimization of the model performance is facilitated, and the optimization efficiency is improved. A standard knowledge enhancement training model can be improved according to user feedback on a target application terminal.

The invention provides an end-to-end automatic driving system and method. The system comprises an environment sensing module, a feature extraction module, a data fusion module, a decision generation model module and a decision analysis module. The environment sensing module comprises a plurality of types of sensors, and each sensor collects environment information data of different modes. The feature extraction module extracts features of each modal environment information data. And the data fusion module fuses the features of the modal environment information data to obtain fused features. The decision generation model module determines a driving decision by using a trained decision generation model based on the fusion features. And the decision analysis module converts the driving decision output by the decision generation model module into a specific vehicle control instruction. The method has higher adaptability to complex and dynamic driving scenes, reduces the cost of data annotation during training, and improves the generalization ability of the model in various driving scenes.

The invention relates to the technical field of capacitor testing, and provides an aluminum electrolytic capacitor testing and sorting machine which comprises a sorting rotary table, a rack and a plurality of U-shaped grooves, one U-shaped groove is used for feeding, the other U-shaped grooves are used for testing, a through groove is formed in the inner side face of each U-shaped groove, a placing plate penetrates through the through grooves, and the placing plates are arranged in the through grooves. A camera is arranged above the U-shaped groove used for feeding, mounting plates are arranged above the U-shaped groove used for testing, the mounting plates are used for mounting capacitor testing devices, each mounting plate is connected with a pair of electrode plates, the surface of the placing plate is rotatably connected with a movable circular plate, a second motor is arranged on the rack, and the movable circular plate is rotatably connected with the movable circular plate. An output shaft of the second motor is fixedly connected with an electromagnet, and a material collecting box is arranged below the U-shaped groove for testing; and the camera is used for collecting an image of the capacitor and identifying the positive electrode and the negative electrode of the capacitor, so that the positive electrode and the negative electrode on the capacitor correspond to the positions of the electrode plates, accurate alignment of the pins and the electrode plates is realized, and high efficiency and convenience are realized.

The invention discloses a method for detecting the hydrazine content in a medicine containing carbidopa. The invention provides a method for detecting hydrazine content in a medicine containing carbidopa, which comprises the following steps: (1) mixing a sample to be detected and an aldehyde substance in a solvent under the action of acid, deriving hydrazine in the sample to be detected and the aldehyde substance to obtain a test solution, and (2) detecting the content of hydrazine in the test solution. Compared with the prior art, the method has the advantages that the carbidopa does not need to be removed through pretreatment, hydrazine introduced in the determination process can be ignored, operation is simple, derivatization time and sample injection time do not need to be accurately controlled, repeatability is good, and quantification is accurate.

The invention discloses an invisible appliance based on a transparent piezoelectric film and a design method thereof. A perioral muscle pressure sensor is installed on the lip / cheek lingual surface of teeth of the invisible orthodontic appliance, an occlusal force sensor is installed on the occlusal surface, and a tooth surface counter-acting force sensor is installed on the inner side tissue surface, so that dynamic changes of perioral muscle pressure, occlusal force and orthodontic force can be monitored in real time. The invisible orthodontic appliance provided by the invention integrates a sensor with high piezoelectric coefficient and high flexibility, has high sensitivity and accuracy, can be used for testing dynamic changes of orthodontic mechanics and evaluating the influence of perioral muscle pressure, occlusal force and the like on treatment during physiological processes of chewing, breathing, swallowing, language and the like of a patient; a doctor optimizes an orthodontic scheme through the PC terminal and pays attention to the association between oral functional mechanics and malocclusion, bad oral habits, bruxism, temporomandibular joint disorder and other diseases, and the efficiency and accuracy of oral health management are improved.

The invention discloses a space neglect rehabilitation training system based on artificial intelligence. The system comprises a space neglect evaluation module used for evaluating the space neglect condition of a patient through a space neglect evaluation program; the rehabilitation program setting module is used for setting a rehabilitation program of the mobile wearable equipment by utilizing a rehabilitation program setting model according to the related data of the patient; the wearable equipment execution training command module is used for executing a corresponding rehabilitation task by the mobile wearable equipment according to the rehabilitation program set in the rehabilitation program setting module and recording the completion condition of the rehabilitation task; and the terminal collection feedback module is used for reading the training data of the patient, evaluating and judging the training compliance of the patient by utilizing the training data of the patient and constructing a training effect analysis model, and generating an adjustment strategy of a rehabilitation training scheme. Through the system provided by the invention, the training compliance and effect of the patient can be comprehensively and objectively evaluated, and the quality and efficiency of rehabilitation treatment are improved.

The invention discloses a standard component design effectiveness evaluation method. The method comprises the following steps: determining form design parameters, material characteristics and a production process flow according to a standard component design scheme; determining original morphological parameters and material attributes of the original processing material according to the production process flow, and applying the original morphological parameters and the material attributes to the finite element model; the production process flow is simulated through process simulation software, and state parameters of the standard part in the whole machining and forming period are obtained; driving the finite element model to enter processing and forming dynamic performance analysis of the standard component based on the state parameters to obtain multiple performance indexes of the standard component; and evaluating the plurality of performance indexes based on preset performance index requirements to obtain an evaluation result of the design effectiveness of the standard component. By physically simulating the production process of the standard component and performing finite element analysis on the whole cycle of the production process, the effectiveness of the design of the standard component is extracted and evaluated, and a designer can conveniently optimize the standard component in the design stage.

The invention provides a traditional Chinese medicine processing process monitoring system and method based on an intelligent sensor, and relates to the technical field of traditional Chinese medicine processing monitoring, and the system comprises a sensor module, a data acquisition and transmission module, a central control unit and data processing module, a monitoring and early warning module, and a tracing management module. The sensor module comprises a temperature sensor, a humidity sensor, a pressure sensor, a photoelectric sensor, a chemical component sensor and a gas sensor; and early warning measures in the monitoring and early warning module comprise threshold early warning, trend early warning and intelligent early warning. By arranging the monitoring and early warning module, various parameters of traditional Chinese medicine processing can be effectively and strictly controlled in the traditional Chinese medicine processing process, meanwhile, an early warning function is set, when the parameters reach or reach threshold values set in advance, alarm reminding can be conducted in time, the ingredients of medicinal materials can be well stabilized, and then the quality of medicinal material processing is guaranteed.

The invention provides a GPU cluster NLP task parallelization system and method based on a particle swarm optimization, and the method comprises the steps: carrying out the analysis of an input NLP model based on a task decomposition module, dividing the NLP model into a plurality of subtasks, and collecting and recording the GPU cluster resource state based on a GPU resource management module, a plurality of sub-tasks are mapped on a GPU cluster based on a task allocation module, a plurality of independent particle swarms are constructed, task allocation is optimized by using a particle swarm algorithm based on a particle swarm optimization module, task execution is scheduled according to an optimized task allocation scheme based on a task scheduling module, and task execution is completed. And based on a result processing module, collecting and integrating sub-task calculation results on each GPU, and outputting the sub-task calculation results. According to the method, the NLP training speed is remarkably increased, the resource utilization efficiency is effectively improved, and the method is suitable for being used in a compute-intensive NLP task and particularly has a remarkable effect in a scene where GPU resources are rich and highly parallel computing is needed.

The invention relates to a water quality detection technology, and discloses a water ion concentration measurement system, a water purification system and a measurement method, and the water ion concentration measurement system comprises a polarization circuit, an open-circuit potential measurement circuit, a current amplification circuit, a microcontroller circuit, a temperature measurement circuit and a data storage circuit; the polarization circuit polarizes the working electrode; the open-circuit potential measuring circuit measures a corresponding open-circuit potential and transmits measurement data to the current amplifying circuit; the current amplification circuit is used for performing current amplification and filtering processing on the measurement data; the temperature measuring circuit is used for collecting temperature data and transmitting the temperature data to the microcontroller circuit; the microcontroller circuit is used for generating a polarization signal corresponding to the polarization circuit, calculating the ion concentration of the water sample according to the measurement data and the temperature data, and calibrating the system according to a standard solution with known concentration; and the data storage circuit is used for storing related data transmitted by the microcontroller circuit. The invention aims to realize accurate measurement of the ion concentration in water.

The invention discloses a liver cancer post-transplantation muscle loss prediction method and device, and belongs to the technical field of biomedicine, and the method comprises the steps: constructing an unbalanced random forest model comprising a plurality of decision trees, independently predicting through the plurality of decision trees to obtain a plurality of high-risk or low-risk dichotomy prediction results, taking the proportion of the decision tree with the prediction result being high risk in all decision trees as the prediction probability of muscle loss after liver cancer liver transplantation finally output by the model; performing risk classification according to the prediction probability, and judging the postoperative muscle loss level of the patient as high risk, medium risk or low risk; constructing a decision tree model to layer prognosis of patients, and dividing the patients into a high-risk group, a medium-risk group and a low-risk group; and carrying out survival rate prediction in combination with liver transplantation standards and prognosis layering results of patients. According to the method, the muscle loss level after the liver transplantation can be accurately predicted, and a clinician can make a personalized treatment scheme and perform scientific management after the transplantation.

The invention discloses an automatic homework correcting system. Comprising a handwriting collection module, an OCR recognition module, a wrongly written character correction module and an intelligent scoring module. The handwriting acquisition module is used for acquiring handwriting; the OCR recognition module is used for recognizing the handwriting; the wrongly written character correction module is used for correcting wrongly written characters; the intelligent scoring module is used for comprehensive consideration; the test paper handwriting is collected and quickly converted into text data, the CR recognition module can accurately recognize the writing content of the test paper, the writing content can be accurately converted into texts even in the face of different writing styles and handwriting characteristics, and the wrongly written character correction module can ensure the text accuracy of examinee answers; the intelligent scoring module is combined with question requirements, a standard question solving process and an examinee answering process, deep analysis is carried out through an LLM large language model, objective and accurate scores and targeted comments can be given, and the accuracy and reliability of examination review are improved.

The invention belongs to the technical field of preparation of barium titanate, and particularly relates to nanoscale tetragonal-phase barium titanate and a confinement preparation method thereof. The preparation method comprises the following steps: pumping a solution of barium titanium salt and a solution of a precipitator into a mixing reactor of a microchannel reactor for confinement mixing, and aging the obtained mixed slurry to obtain a barium titanate precursor; the flow velocity of the solution of the barium titanium salt and the flow velocity of the solution of the precipitant are independently 20-70 mL / min; and mixing the barium titanate precursor, a barium source and water for hydrothermal reaction to obtain the nanoscale tetragonal-phase barium titanate. The method can be used for efficiently and stably preparing the nanoscale tetragonal barium titanate which is small in size, high in crystallinity, high in particle size uniformity, narrow in particle size distribution and high in tetragonal phase purity.

The invention belongs to the technical field of gene engineering and enzyme engineering, and particularly relates to a glucanase mutant and construction and application of a gene sequence of the glucanase mutant, and the glucanase mutant has an amino acid sequence as shown in SEQ ID NO.2. The glucanase mutant is obtained by carrying out G214A mutation, S249P mutation and D286L mutation on an amino acid sequence as shown in SEQ ID NO. 1. The glucanase mutant has the advantages that the glucanase mutant is obtained; compared with the non-mutated EGL, the glucanase mutant EGL62 provided by the invention has the advantages that the thermal stability is obviously improved; after incubation at 75 DEG C for 3 minutes, the enzyme activity of 60% or above can still be maintained; the enzyme activity is still more than 50% after the enzyme is treated for 1 hour under the condition that the pH value is 3.0-7.0. The glucanase mutant EGL62 provided by the invention has the optimal pH value of 5.0 and the optimal temperature of 50 DEG C, and is good in stability and wide in application prospect.

The utility model relates to the technical field of motors, and discloses a detachable intelligent driving synchronous motor, comprising a first casing, the outer wall of which is provided with a plurality of first thread grooves; a plurality of first connecting holes corresponding to the first threaded grooves are formed in the edge of the first end cover, and one end of the first end cover is detachably connected with one end of the first machine shell; the bottom end of the second machine shell is detachably connected with the other end of the first end cover; the second end cover is detachably connected with the other end of the second machine shell; according to the utility model, the motor is designed into a plurality of detachable parts, so that when the motor breaks down, maintenance personnel can find and replace the faulty parts more easily, and the whole motor does not need to be disassembled; therefore, the maintenance efficiency is greatly improved, and the maintenance cost is reduced.

The invention discloses a power grid planning method, system and equipment and a computer readable storage medium, and aims to greatly reduce manual operation and improve data processing accuracy and efficiency by automatically collecting and processing GIS map data, a power grid operation state and load prediction data. And path planning is performed by combining an A algorithm with a GIS technology, so that the path is optimal and feasible, and the planning period is remarkably shortened. The system comprises a data acquisition module, a problem definition module, a path planning module, a cost calculation module, a path optimization module, a scheme generation module, a visual display module and the like, all the modules work cooperatively, and systematization and standardization of the planning process are achieved. Through an intelligent optimization algorithm, the planning scheme quality is improved, multiple factors are comprehensively considered, and it is ensured that the scheme is scientific, reasonable and economical. The modular design enables the system to be easy to expand and optimize, adapts to power grid planning problems of different scales and complexities, improves the adaptability and flexibility of planning, and provides a comprehensive and efficient solution for power grid planning.