641results about How to "Avoid confict" patented technology

The invention discloses a driving situation prediction and adaptive strategy generation system based on a cloud multi-mode large model. Comprising a multi-modal data acquisition and situation prediction module, a self-adaptive strategy generation and remote optimization module, a collaborative driving and multi-vehicle strategy coordination module, a cloud driving intelligent monitoring and feedback module and a dynamic risk assessment and path optimization module. Various input data from the vehicle and the environment are processed through the cloud, risk factors such as sudden obstacles, weather deterioration and complex traffic conditions in the automatic driving situation are predicted in advance, cloud computing provides powerful data processing capacity and storage resources, a large model deployed at the cloud can train data collected by the vehicle, and the risk factors can be predicted in advance. Therefore, the perception and prediction capabilities of the automatic driving system are improved, various coping strategies aiming at the risks are generated, and the vehicle is helped to make a safer and more intelligent driving decision in a complex scene through dynamic generation and remote optimization of an adaptive strategy.

The invention discloses a hierarchical multi-agent game confrontation and collaborative decision-making algorithm based on federated learning, and the algorithm comprises the following steps: setting a federated learning framework containing a plurality of agents, enabling the agents to be located in different regions, and carrying out the training of a local model through the data of the regions; in the federated learning framework, deploying a layered reinforcement learning model in each intelligent body; performing confrontation training on a plurality of agents in a game environment by using a hierarchical reinforcement learning model; on the basis of game confrontation, a collaborative decision algorithm is introduced to coordinate behaviors of multiple agents; and when a preset training round is reached or a specific condition is satisfied, uploading local model parameters to a central server, and sharing the model parameters through a federal learning framework to carry out global optimization. According to the method, efficient collaboration and strategy optimization among multiple agents can be realized on the premise that privacy is protected, and high adaptability and robustness are kept in a complex and changeable environment.

The invention provides a precise interference method and system for pilot signals of a remote controller of an unmanned aerial vehicle, and the method comprises the steps: receiving and processing an image transmission signal and a remote control signal of the unmanned aerial vehicle, and analyzing a starting time, a transmission period and different types of image transmission protocol classification results of the image transmission signal; extracting the time period of the remote control signal and the occurrence time of a pilot symbol in the remote control signal; generating optimized interference signal parameter configuration by utilizing a reinforcement learning algorithm, analyzing historical data by utilizing a long short-term memory network to obtain a historical analysis result, and predicting an optimal interference opportunity; monitoring a wireless communication environment around the unmanned aerial vehicle by using a real-time spectrum sensing technology so as to identify a potential conflict frequency band, and selecting a target interference frequency band by using a frequency agility technology; determining a precise interference method based on the historical analysis result, the optimal interference opportunity and the target interference frequency band; according to the invention, the interference precision and effect on the pilot signal of the remote controller of the unmanned aerial vehicle are improved.

The invention relates to the technical field of expressway unmanned aerial vehicle route planning, in particular to an intelligent route planning method and system for an expressway unmanned aerial vehicle, and the method specifically comprises the steps: planning unmanned aerial vehicle waypoints on an expressway based on a target three-dimensional model, and obtaining a waypoint set of the expressway; historical route data of the expressway are obtained, a deep learning algorithm is used for analyzing the historical route data, safe flight path features are extracted, a route safety evaluation model is constructed, and the route safety evaluation model is used for evaluating the route safety level of the route of the unmanned aerial vehicle; and determining route planning parameters according to the unmanned aerial vehicle task type, and determining an unmanned aerial vehicle route through the waypoint set, the route safety evaluation model and the waypoint planning parameters based on a multi-objective optimization algorithm. According to the invention, the safety and efficiency of the unmanned aerial vehicle in highway inspection and monitoring tasks are improved, and automatic planning and dynamic adjustment of the route of the unmanned aerial vehicle based on real-time data analysis are realized.

The invention provides a path planning method and device under multi-robot cooperative work, and relates to the technical field of path planning, and the method comprises the steps: constructing a two-dimensional layout map based on the structural layout feature information of a manufacturing workshop, and carrying out the rasterization processing through combining with the structural width of a predetermined robot, and obtaining a two-dimensional layout grid map; a task allocation evaluation mechanism is introduced to analyze the transportation task set to obtain a task list, a first task and a second task are extracted, the first task corresponds to the first robot, and the second task corresponds to the second robot; obtaining a first initial path and a second initial path; judging whether a first predetermined cooperative constraint is met or not, wherein the first predetermined cooperative constraint means that path intersection exists between the first initial path and the second initial path; and if yes, performing adjustment planning. According to the method, the technical problems of task delay and safety risk increase caused by the fact that multi-party path crossing and priority conflict cannot be processed when multi-robot cooperative work is coped with by traditional path planning are solved.

The invention provides a multi-robot collaborative task dynamic scheduling method and related equipment, which are applied to the technical field of robots, and the method comprises the following steps: collecting task scheduling scene information; the task scheduling scene information is used for constructing a multi-level graph structure, multi-dimensional input information is obtained based on the multi-level graph structure, the multi-level graph structure comprises a relation graph layer of multiple dimensions, and the relation graph layer comprises relation information between objects of the dimensions corresponding to the relation graph layer; determining a task allocation strategy based on the multi-dimensional input information; and based on the task allocation strategy, sending a task execution instruction to each target robot, and controlling the target robot to execute a task corresponding to the task execution instruction. Complicated relations among the robot, the environment and the tasks are expressed by using the multi-level graph structure, rich and complete relation information is provided for generating the task allocation strategy, the accuracy of the generated task allocation strategy is improved, and conflicts generated during task allocation are avoided.

The invention provides a chip production logistics network optimization method and system based on a graph neural network model, and relates to the technical field of chip production logistics optimizing.The method comprises the steps that logistics data of a plurality of process nodes of a chip production line are collected, node criticality scores are calculated, node types are determined, and the node types are determined; constructing a multi-level logistics network diagram comprising a material transmission edge and a priority-based resource competition edge; the method comprises the following steps: dividing logistics sub-networks, determining association strength among the sub-networks, constructing a sub-network state predictor and a cooperative scheduling module, and inputting a network topology structure and node features into a graph neural network for training to obtain an optimization model; and predicting a sub-network state change based on the optimization model to generate a resource allocation scheme and a node scheduling sequence, triggering an adjustment mechanism when a prediction state exceeds a threshold value, re-planning a material allocation and transmission path, and finally converting the scheme into an equipment control instruction and issuing the equipment control instruction to a process node.

The invention belongs to the technical field of humanoid double-arm robots, and particularly relates to an intelligent grabbing method of a humanoid double-arm robot, which comprises the following steps: acquiring an RGB image and a depth image of a target object through a global camera, performing alignment processing, detecting the target object by using a coarse positioning model, and determining pre-grabbing positions of double arms; global point cloud data in a working space are obtained in real time through a global camera, and path planning is performed by adopting an improved artificial potential field algorithm, so that two arms respectively reach pre-grabbing positions; acquiring an RGB image and a depth image of a target object through a double-arm tail end camera, performing alignment processing, detecting the RGB image of a target camera by using a fine positioning model, and finding a feasible target grabbing attitude by using a pre-trained 6D grabbing attitude prediction model; and the two arms are controlled to execute the grabbing action according to the target grabbing posture and placed at the preset position. The method can adapt to various complex scenes, and diversified grabbing is achieved.

The invention particularly relates to a document abstracting method based on domain knowledge and a multi-granularity graph network, which comprises the following steps: S1, constructing a knowledge coding module, carrying out domain knowledge graph transfer learning, extracting triple structure features in combination with a capsule network, and generating a text entity coding module; s2, constructing a knowledge fusion module, converting a document text into a semantic vector, and adaptively fusing the semantic vector with external knowledge; s3, constructing a word-level semantic graph, and aggregating neighborhood information through a semantic graph attention mechanism to obtain word embedding representation containing context; s4, constructing a sentence-level semantic graph, performing self-attention calculation on the word-level semantic vector in the S3 to obtain semantic node representation of the sentence-level semantic vector, and performing feature aggregation on sentence-level nodes to obtain global semantic information of the document; and S5, constructing an output layer, and predicting according to the document feature vector in the S4 to obtain a final extraction type abstract result. The method has the advantages that redundant information in the document is removed, and ambiguity of text semantics is eliminated.

The invention relates to a multi-AGV path planning method and device based on a time window and computer equipment. The method comprises the following steps: constructing a working environment map of AGVs by adopting a grid method; according to the working environment map, performing path planning on each AGV by adopting an improved Floyd bidirectional search JPS algorithm to obtain an initial global optimal path when each AGV executes a respective delivery task, constructing a time window model of the AGV, and according to the time window model and the initial global optimal paths when all the AGVs execute respective delivery tasks, constructing a time window model of the AGV, and according to the time window model and the initial global optimal paths when all the AGVs execute the respective delivery tasks, constructing a time window model of the AGV; and local path planning is carried out by adopting a conflict resolution strategy in which a translation time window algorithm and a re-planning strategy are parallel, and a conflict-free path of each AGV is obtained. According to the method, aiming at possible conflicts among multiple AGV paths, local path planning is carried out by adopting a time window and replanning mixed conflict resolution method, conflicts among the AGVs are avoided, and more reliable and efficient gains are achieved.

The invention relates to a satellite internet task planning method based on intention understanding and a large model, which belongs to the field of task planning and comprises the following steps: constructing a satellite internet intention library and a satellite internet knowledge tensor library based on RAG enhanced retrieval; receiving a communication intention demand report, and performing intention acceptance according to a user communication intention; performing RAG enhanced retrieval through the accepted communication intention and the knowledge tensor database to obtain a tensor retrieval result, and performing reasoning to obtain an intention analysis result; communication intention verification is carried out according to the intention analysis result and a satellite internet intention library, if legality is met, execution is continued, and otherwise, the process is ended and abnormal warning is carried out; task planning and scheduling are carried out through a satellite internet intention library and an equipment and resource library, and a network planning parameter result is obtained; and then network parameter configuration is carried out on the application layer to realize satellite planning, station planning and terminal planning. According to the method, the total resource occupancy rate and the service capacity can be effectively improved, and meanwhile load balancing is achieved.

The invention provides a multi-core AMP architecture inter-core communication method and system based on a high-security embedded real-time operating system, and the method comprises the steps: respectively operating an authentication version of a Quhua high-security embedded real-time operating system on an AMP heterogeneous multi-core platform, and guaranteeing the real-time performance of a task while providing functional security. And two driving interface mechanisms of IPC Not ify and IPC RPMsg are provided, so that the real-time performance of inter-core communication of the multi-core AMP architecture is improved. The IPC Not ify transmits messages based on the HW mailbox of the HW fifo and interrupts the receiving of core messages, and has low time delay; and a heterogeneous multi-core synchronization function is supported, so that a user does not need to concern the starting sequence of the AMP heterogeneous multi-core system and mainly focuses on development of inter-core communication application programs of the user. The IPC RPMsg provides a two-way communication link based on a channel between the cores, and supports large data volume communication interaction between the cores. A universal shared memory area is configured, and a spinlock technology is adopted to synchronize multi-core operation and prevent multi-core access conflicts. A method for sharing the same physical device by multiple cores is provided, and the problem of insufficient device hardware resources is solved.

The invention discloses an underground water level prediction method and system based on a space-time attention gating neural network. The method comprises the following steps: constructing a historical underground water level space-time sequence data set; performing principal component analysis on the space-time sequence data set to obtain a dimension-reduced space-time sequence data set; for the spatio-temporal sequence data set after dimension reduction, constructing a plurality of spatio-temporal characteristic matrixes through a sliding window method; training an underground water level prediction model by using the plurality of spatial-temporal characteristic matrixes to obtain a trained underground water level prediction model; obtaining time-space sequence data of historical underground water level of a to-be-measured position, constructing a time-space characteristic matrix of the underground water level, and predicting the underground water level of the to-be-measured position by using the trained underground water level prediction model; according to the method, rapid and accurate prediction of the underground water level is realized, and the generalization ability, the prediction precision and the interpretability of an underground water level prediction model are greatly improved while the modeling cost is reduced.

The invention provides a self-discovery ad-hoc network control method based on a swan-gap operating system, and relates to the technical field of data processing, and the method comprises the steps: based on the swan-gap operating system, extracting the identification information and communication parameters of equipment from all to-be-networked equipment; carrying out compatibility judgment on the communication protocol version and the maximum support bandwidth between the devices; determining a control relationship between the master device and the slave device; dynamically allocating communication resources to each device by adopting a distributed channel allocation algorithm in combination with task requirements of the devices and network bandwidth information; periodically synchronizing the resource state of each device; when the slave device sends a networking quitting request, the master device updates the networking candidate data and re-determines the networking control relationship after releasing the corresponding communication resources; when the master device exits, the device with the highest priority is selected from the slave devices to automatically take over the control role and complete the reconstruction of the networking control relation; according to the invention, the autonomy and accuracy of networking are improved.

The embodiment of the invention provides a vehicle control method, which is applied to the field of vehicle control, and comprises the following steps: determining a plurality of control instructions by responding to an input operation of a user, respectively determining urgency indexes and resource demand information of the plurality of control instructions according to the control instructions for different subsystems, and determining the urgency indexes and the resource demand information of the plurality of control instructions according to the urgency indexes and the resource demand information; according to the method, the urgency indexes are determined, resource use information of the subsystems is determined, target execution priorities corresponding to the control instructions are determined in combination with the urgency indexes, the resource demand information and the resource use information, the subsystems are scheduled to perform collaborative operation according to the control instructions according to the target execution priorities, and a unified and efficient instruction processing mechanism is provided. And complex coordination and conflicts in a plurality of subsystems in the vehicle are effectively avoided.

The invention relates to the technical field of equipment control, in particular to a self-adaptive switching method and system of a current transformer. The method comprises the following steps: collecting primary side parameters and secondary side parameters of a current transformer to obtain primary side collection parameters of the current transformer and secondary side collection parameters of the current transformer; performing current transformer state segmentation initialization according to the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters so as to generate a current transformer operation state classification result; switching result judgment is conducted on the current transformers according to the current transformer operation state classification result, and when the current transformer operation state classification result is a current transformer critical state section, parallel buffering is conducted on the current transformers, and critical current transformer switching data are generated. According to the invention, through accurate state classification, a dynamic switching mechanism, time sequence optimization and visual display, the switching sensitivity and stability of the current transformer are improved.

The invention discloses a multi-locomotive path planning method and system based on a Q-learning algorithm, and relates to the technical field of iron-steel interface locomotive path planning, and the method comprises the steps: obtaining environment data, building a transportation system environment model, and defining states and actions; performing path optimization on the vehicle path through a Q-learning algorithm to obtain an initial path plan; and the central control system receives the key information in real time, and dynamically adjusts vehicle path planning in real time through a Q-learning algorithm. According to the method, through learning and optimization of the Q-learning algorithm, the central control system can find the shortest path, the transportation time and distance are reduced, and the transportation efficiency is improved; the path planning is dynamically adjusted through the central control system, so that the congestion and conflicts of track routes are avoided, and the smoothness of the transportation process is ensured; by using the Q-learning algorithm, the method can adapt to a dynamically changing environment, and normal operation of the system can be ensured even if an accident occurs.

The invention discloses a multi-laser 3D printing path planning method and system based on collaborative optimization, and relates to the technical field of multi-laser 3D printing path planning and optimizing.The method comprises the steps that a 3D model to be printed is subjected to equal-area division; adjusting a real-time path based on a collision avoidance rule load balance; and finishing self-adaption and summarizing the data. According to the multi-laser 3D printing path planning method based on collaborative optimization, through the step of equal area division, the overall working efficiency of the system is improved, through the step of refining hierarchical division, the system can more accurately process a complex geometric structure, the printing safety and smoothness are improved, and through real-time load balance and path adjustment, the multi-laser 3D printing path planning method based on collaborative optimization can be used for realizing multi-laser 3D printing. And delay caused by too large task load of some laser heads is avoided. Through monitoring and automatic adjustment, the system achieves optimization of a self-adaptive working path, the overall printing efficiency is improved, and better effects are achieved in the aspects of fluency, safety and printing efficiency.

The invention relates to the field of robot collaborative optimization, in particular to an industrial robot collaborative optimization system. Comprising the following steps: creating a task distribution matrix, optimizing the task distribution matrix by adopting a multi-objective optimization strategy, setting constraint conditions, and introducing a self-adaptive feedback adjustment mechanism to obtain an optimized task distribution matrix; and performing path optimization based on the optimized task allocation matrix. The problems that a fixed or simple scheduling algorithm is adopted for existing task allocation, and overload of part of industrial robots is likely to be caused; a dynamic feedback adjustment mechanism for resource consumption and state of the industrial robot is lacked, and optimization of task allocation cannot be performed according to real-time load and resource state of the industrial robot; the technical problems that path planning is prone to falling into a local optimal solution, and an industrial robot may frequently select a fixed path when executing a task, resulting in path resource competition and overall efficiency reduction are solved.

The invention discloses a chip built-in self-test memory grouping method based on a BPPC algorithm, and belongs to the technical field of calculation, reckoning or counting. The method comprises the following steps: extracting memory file information, preprocessing, and storing in a memory information structure array; through a K-means + + algorithm, performing preliminary clustering on the memories according to the attributes of the memories; constructing a constraint relationship between the memories through distance constraint, and representing the constraint relationship by using a conflict matrix; a compatible memory group is decomposed into memory groups without a distance relation by using a vertex coloring problem model, the memory groups without a constraint relation are subjected to one-dimensional boxing by using an FFD method, a grouping condition is optimized by using a particle swarm algorithm, and the grouping condition is assigned to a corresponding controller. A BPPC algorithm and an MBIST test method are combined, and an optimal solution with the minimum number of controllers is sought under the limitation of the memory distance and the maximum power consumption. According to the method, a multi-stage optimization method and a multi-target method are adopted, so that the grouping result is more accurate.

The invention provides a shared memory-based data interaction method for an embedded virtualization platform, which comprises the following steps of: dividing a shared memory area, including a management information area and a shared data area, initializing the management information area, including setting a magic number, configuring a mutual exclusion access flag and recording use information of the shared data area; acquiring a physical initial address of the shared memory, verifying the availability of the management information area, and ensuring that the management information area is correctly initialized; a proper data transmission mode is selected according to the data size, small data volume can be directly transmitted through an inter-core communication register, and large data volume is transmitted through a shared memory; the Szymanski algorithm is used to realize mutual exclusion access control, and the consistency of data is ensured; after data interaction is finished, if shared memory resources which do not need to be used any more need to be released, use information in the management information area is updated; and other related operation areas or virtual machines are notified according to needs, and state changes of data interaction are notified to the operation areas or the virtual machines.

The invention discloses an IP address allocation conflict detection method based on multi-protocol ARP data acquisition, and the method comprises the steps: obtaining ARP table information from a core device in a network through a plurality of protocols when a DHCP service is started; the ARP table records the corresponding relation between the MAC addresses of the network equipment and the IP addresses allocated to the network equipment, and the MAC addresses and the IP addresses are summarized to form an MAC-IP mapping table which is stored in the system. When the terminal equipment requests to distribute the IP address to the DHCP server, checking whether the MAC address of the equipment is in the MAC-IP mapping table, and if so, directly distributing the IP address; and if not, allocating a new IP address from the IP pool. Before the IP address is allocated, the corresponding relation between the existing MAC address and the requested IP address in the network is compared, whether a conflict possibly occurs or not is judged, ARP table information is regularly pulled from network equipment, a local MAC-IP mapping table is updated, it is ensured that the MAC-IP mapping relation in the network is always kept newest, and the IP address allocation efficiency is improved. And the IP address conflict caused by equipment change or lease synchronization delay is effectively avoided.

The invention provides a voice emotion recognition model training method and system, and belongs to the field of model training. The problem of low model training efficiency is solved; the method specifically comprises the following steps: S1, obtaining an MFCC coefficient of each piece of voice information; s2, acquiring an LPCC coefficient of each piece of voice information; performing supervised training and unsupervised training on the original model; obtaining a to-be-determined model and to-be-determined output until output results of the supervised training and the unsupervised training of the original model are the same; s3, comparing the performance of the to-be-determined model and the performance of the hot model; if the performance of the to-be-determined model is weaker than that of the hot model, weak items of the to-be-determined model are analyzed, and a lifting report is obtained; otherwise, not processing; s4, summarizing the promotion report, and updating the voice information; according to the invention, through defining an extraction method of voice information features, the model can accurately capture text features and voiceprint features of different emotion voices in an iteration process, and the training efficiency is improved.

The invention discloses an energy-saving box-type heating furnace, and relates to the technical field of heating furnaces, the energy-saving box-type heating furnace comprises a heating furnace body, one side of the heating furnace body is provided with an energy-saving mechanism, the energy-saving mechanism comprises a heat preservation unit, the heat preservation unit is arranged on one side of the heating furnace body, and the heat preservation unit is used for preventing a large amount of temperature in the heating furnace body from leaking; the energy-saving mechanism further comprises a door opening unit, the door opening unit is used in cooperation with the heat preservation unit, the door opening unit is used for sealing the heating furnace body, a feeding mechanism is arranged at the top of the heat preservation unit, the feeding mechanism is used in cooperation with the heat preservation unit, and the feeding mechanism is used for conveying materials. According to the energy-saving box-type heating furnace, the heat preservation unit is arranged, high temperature in the heating furnace is prevented from leaking outwards, the stable temperature environment in the heating furnace can be maintained, the heating efficiency is improved, energy waste caused by temperature leakage is avoided, and the production efficiency and the energy utilization rate are improved.

The invention provides a communication method of multi-application communication based on a serial interface and a storage medium, the communication method is applied to a protocol stack of multi-application communication, a hardware layer of the protocol stack uses an asynchronous full-duplex serial interface, and the communication method comprises the following steps: establishing a point-to-point transmission channel between applications through the asynchronous full-duplex serial interface; a sending end sets sending numbers for data packets, the data packets are packaged into data frames through a data link layer of a protocol stack, and the data frames are sequentially sent through a transmission layer of the protocol stack according to priorities of point-to-point transmission channels of different applications in all the applications; and the receiving end receives the data frame and verifies the transmission integrity of the data packet according to the sending number. The technical problems that the design of an existing communication method in a multi-chip ECU board is complex, and communication of multiple applications in two chips through one interface cannot be achieved are solved.

The invention discloses a flow-based local message classification method and device, and relates to the field of message classification, and the method comprises the steps: carrying out the parallel comparison of a keyword of a to-be-classified message with a keyword temporarily stored in a shift register, obtaining a comparison result, and generating an index matching result through an arbiter according to the comparison result; according to the locality principle of a message flow, historically queried table item information is cached into a dynamic cache table, an arbiter arbitrates a comparison result of a shift register, calculates an offset address of the dynamic cache table and obtains a matching result, and whether an original rule table needs to be queried and whether a dynamic cache table memory needs to be updated are determined according to a search result. Under the condition that the matching is unsuccessful, the dynamic cache table memory can guide the same message to directly use the offset address to obtain the table item information in the next search without searching from an original rule table, so that the hit rate is increased, the matching efficiency is improved, and the storage resources are saved.

The invention belongs to the technical field of dual-computer hot standby, and discloses a dual-computer hot standby system and a dual-computer switching method. The dual-machine hot standby system comprises a machine A and a machine B which are hot standby for each other, and an internal switch; one of the A machine and the B machine is a host used for performing data communication with an external system, and the other one is a slave used for backing up data of the host; the machine A and the machine B are connected with an external system through respective external communication interfaces; and the machine A and the machine B are connected with respective reflective memory cards through optical fibers to serve as main synchronization channels, and are connected with an internal switch through the Ethernet to serve as a standby synchronization channel through respective internal communication network ports, so that data synchronization and backup between the machine A and the machine B are realized. The invention further provides a corresponding switching method. According to the invention, the reliability and stability of long-time uninterrupted operation of the dual-computer hot standby system can be improved, the switching recovery time is shortened, and the requirements of different network environments and application scenes can be met.

The invention discloses a dynamic obstacle avoidance method and system for a substation inspection robot. The position of the robot is positioned based on laser point cloud data, and static and dynamic obstacle features are extracted at the same time. An environment association graph is constructed through perception information in the advancing process, and feature information of each node serves as an attribute of the environment association graph; calculating the distance, the relative speed and the relative angle between the edge weights of the nodes according to the nodes; on the basis of the environment association graph, aggregating all-graph obstacle node information to generate real-time feature representation for robot nodes, and correcting a calculation mode of attention weight according to edge weight between the nodes; and finally outputting a real-time feature vector of the robot node. And defining a reinforcement learning framework and executing path planning. According to the invention, through combination of GAT and dynamic planning, efficient obstacle avoidance and path planning of the robot in a substation environment are realized, and the decision and execution capability of the robot in a dynamic complex scene can be improved.

The invention provides a data acquisition component optimization method based on a distributed storage all-in-one machine, a medium and equipment. A distributed lock and version control combined method is adopted. Specifically, the distributed lock mechanism can ensure that only one node performs write operation on certain data at the same moment, so that data conflict and write competition are avoided. In the version control, a version number is added to each piece of data, so that the system can detect and solve conflicts occurring in the data synchronization process. When one node prepares to write data, whether the version number of the data is consistent with the latest version or not is checked firstly, if not, the data of the latest version needs to be acquired again, and the data is written after being merged and changed. Therefore, the data consistency is ensured, and the stability and reliability of the system are improved. Through the method, the problem of data consistency in the optimization process of the data acquisition component can be effectively solved, so that the overall performance of the distributed storage all-in-one machine is improved.

The invention discloses a building equipment configuration expansion method and system based on the Internet of Things, equipment and a medium, and relates to the technical field of the Internet of Things, and the method comprises the steps: obtaining the basic information of a building space; according to the basic information of the building space, determining and distributing an Internet of Things model of each device; determining a corresponding control parameter for each equipment instance, and associating the control parameter with an Internet of Things model of the equipment; and generating a control instruction according to the Internet of Things model of the equipment and the associated control parameters, and executing space linkage control. According to the building equipment configuration expansion method based on the Internet of Things, unified configuration of equipment functions is realized by standardizing the Internet of Things model and the control parameters, the compatibility problem is reduced, and the management flexibility and expandability are improved. Meanwhile, through accurate parameter matching and verification, manual intervention is reduced, and the automation degree and the equipment execution accuracy are improved.