56 results about "Cellular automaton" patented technology

The invention discloses an intelligent monitoring and early warning system and method for agricultural non-point source pollution, and relates to the technical field of environmental monitoring, accurate prediction of water pollutant concentration is realized through multi-source heterogeneous data acquisition and fusion, a dynamic attention mechanism and a PINN-Transformer coupling model, the system extracts data spatial and temporal characteristics by using an optimized Transformer model, and the method is applied to the intelligent monitoring and early warning of agricultural non-point source pollution. A water pollution diffusion physical constraint is embedded, it is ensured that a prediction result conforms to an actual hydrodynamic law, and based on high-precision spatial-temporal distribution data, an intelligent algorithm is adopted to track a pollution diffusion path and rapidly lock a pollution source; meanwhile, the cellular automaton model simulates pollution risk dynamic diffusion and assists regional risk assessment, the system also combines a block chain technology to carry out credible evidence storage on key monitoring data, and real-time data processing and early warning pushing are realized through a cloud edge collaborative architecture. And an efficient and reliable technical solution is provided for agricultural water environment management and pollution prevention and control.

The invention discloses a nuclear radiation index early warning system based on multi-algorithm fusion, and the system comprises an environment data collection module which is used for collecting gamma-ray dose rate data, wind speed data, wind direction data and geographic position information, and constructing a nuclear radiation environment data set; the dose rate prediction module is used for predicting a future change trend of gamma ray dose rate data based on a sparse recurrent neural network model; the anomaly detection module is used for identifying an abnormal mutation point and positioning the position of a high-risk sensor; the diffusion region modeling module is used for calculating a nuclear radiation diffusion influence region; the grid risk evolution module is used for simulating time sequence evolution of a regular grid risk state based on the multi-state cellular automaton; and the fusion judgment module is used for fusing output results and generating a final early warning grade and a spatial risk distribution result. According to the invention, accurate prediction of the nuclear radiation risk and dynamic space early warning are realized, and the early warning accuracy and response efficiency are significantly improved.

The invention relates to the technical field of power system scheduling, and discloses an intra-day look-ahead scheduling rapid solving method considering large-scale new energy cluster power generation volatility. Comprising the following steps of S1, new energy cluster space-time fluctuation scene generation based on a neuron cellular automaton, S2, power grid dynamic security domain definition and simplification based on a physical information neural network, S3, scheduling rapid optimization solution based on model prediction path integration, and S4, scheduling scheme dynamic elasticity and stability evaluation based on a Kupman operator theory. The new energy cluster space-time fluctuation scene generation method based on the neuron cell automaton can effectively generate a space-time scene reflecting large-scale new energy cluster power generation volatility, supports uncertainty analysis, has the advantages of being high in calculation efficiency and scene authenticity, and is suitable for large-scale new energy cluster power generation. The problem that scene generation is inaccurate due to the fact that a traditional statistical model ignores space-time coupling is solved.

The invention provides a data model dual-drive-based water pollution prediction method and device, and relates to the technical field of water pollution monitoring, and the method comprises the steps: obtaining multi-source information reflecting an incidence relation between a pollution source position and water quality abnormity; training a preset random forest regression model by using the multi-source information and a particle swarm optimization algorithm to obtain a target random forest regression model, inputting real-time data of which the type is the same as that of the multi-source information into the target random forest regression model, and determining pollution source position information; constructing a one-dimensional pollutant diffusion model based on the spatial-temporal characteristic information, and expanding the one-dimensional pollutant diffusion model to a two-dimensional cellular model in combination with a cellular automaton simulation method; and inputting pollution source position information and environmental influence information into the two-dimensional cellular model to generate a water pollution diffusion prediction result. Through comprehensive application of the algorithm and the model, the accuracy and the real-time performance of water pollution prediction are improved.

The invention discloses a wind power plant voltage and frequency cooperative tuning control method, and relates to the technical field of tuning control, and the method comprises the steps: obtaining the real-time operation data of a wind power plant, carrying out the preprocessing of the real-time operation data of the wind power plant, carrying out the voltage fluctuation and frequency disturbance prediction through a cellular automaton model in combination with the pre-obtained power grid data, and carrying out the voltage fluctuation and frequency disturbance prediction. Obtaining a prediction state matrix; carrying out multi-objective optimization solution in combination with a pre-constructed multi-objective optimization problem and constraint conditions, and obtaining an optimal droop coefficient vector in combination with a preset rule; and the optimal droop coefficient vector is used as a control reference, and voltage and frequency cooperative tuning is carried out in combination with grid-connected point voltage and frequency obtained in real time and communication network topology data. According to the method, prediction is carried out through the cellular automaton model, the rapid and cooperative regulation capability of the voltage and the frequency of the wind power plant is improved, and therefore the urgent demand of a power system for rapid, accurate and cooperative control under the access of high-proportion new energy is better met.

Systems, computer program products, and methods are described herein for dynamically determining procedures for electronic communications using cellular automaton processing. The present invention is configured to identify at least one enquiry message; determine, based on the an enquiry message(s), a case type(s) associated with the enquiry message(s); trigger an intelligent classification component, wherein the intelligent classification component is configured to determine a procedure(s) for the enquiry message(s); trigger a cellular automaton engine which comprises an array of a plurality of cells in a grid-based structure, and wherein the cellular automaton engine is configured to select and perform at least one procedure for the enquiry message(s); determine, by the cellular automaton engine, a best procedure based on the selection and performance of the procedure(s) for the enquiry message(s); and generate a response(s) for the enquiry message(s) based on the determined best procedure by the cellular automaton engine.

The invention discloses a user credit risk assessment method, system, equipment and medium based on multi-industry federated learning in order to improve data security of federated learning, and belongs to the technical field of information. The method comprises the following steps: extracting operator data and bank financial data, and performing encryption preprocessing through a Hash mechanism; performing federated learning on the operator data and the bank financial data after encryption preprocessing, accessing a PSI module as a plug-in to an FATE core, converting RSA of an FATE algorithm into an elliptic curve, and performing secret sharing through a hexagonal grid cellular automaton to obtain a trained federated model; and evaluating the credit risk of the user by using the trained federal model. According to the method, the requirement of hiding a keyword of a queried object or a client ID is met, the hidden query service provides a matched query result, calculation can be carried out without going out data of both parties, and the method follows the data authentication and access process of multi-party security calculation and the overall business process. Data barriers among different industries are broken through, and deep data cooperation of multiple industries is realized.

The invention relates to the technical field of corrugated pipe detection, and discloses corrugated pipe fatigue life detection equipment and a corrugated pipe fatigue life detection method, and the corrugated pipe fatigue life detection method comprises the following steps: building an acoustic-electric topological coupling detection unit through an acoustic metamaterial electromagnetic impedance topological coupling model; constructing a sensor network with topology protection boundary characteristics; obtaining a topological characteristic data set of the state of the corrugated pipe through a topological protection boundary sensing equation; generating a spatial distribution mapping graph of corrugated pipe damage; inputting the topological characteristic data set and the spatial distribution mapping graph into a topological characteristic spectrum pattern recognition algorithm, and outputting the type and severity of the fatigue damage of the corrugated pipe; according to the invention, a series of technical problems in corrugated pipe fatigue damage detection are innovatively solved through the deep coupling acoustic metamaterial and electromagnetic impedance technology and introduction of a physical cellular automaton theory and a topology protection mechanism.

The invention discloses a region planning search and rescue method and system based on multi-agent cooperation, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: obtaining the deployment position of an unmanned aerial vehicle and the initial position of a to-be-searched and rescued target; acquiring height field information from the deployment position to the initial position; performing point-to-point path planning from the deployment position to a preset search and rescue starting position by using a cellular automaton algorithm, and obtaining an unmanned aerial vehicle moving path from the deployment position to the preset search and rescue starting position; dividing the plurality of unmanned aerial vehicles into a leader and a follower, and controlling the leader and the follower to arrive at a preset search and rescue initial position according to the moving path of the unmanned aerial vehicles; based on the predetermined and divided search and rescue areas, performing search path distribution of a plurality of unmanned aerial vehicles by using an improved genetic algorithm; and respectively controlling each unmanned aerial vehicle to search along the corresponding search path. The search time of each unmanned aerial vehicle can be fully considered, the target to be searched and rescued can be quickly positioned, the search and rescue efficiency is remarkably improved, and the labor cost is reduced.

The invention discloses a hyper-converged Internet of Things gateway, and relates to the technical field of Internet of Things. Comprising a network state preloading unit, a cellular automaton control unit and a protocol stack mapping unit, the network state preloading unit is used for carrying out multi-dimensional state acquisition on a heterogeneous access network connected with the hyper-converged Internet of Things gateway and generating a network state preloading set; the cellular automaton control unit is used for creating a cellular automaton node for each piece of network state baseline data based on the network state preloading set, and converging to obtain an evolution result; and the protocol stack mapping unit is used for mapping the to-be-transmitted data packet into a target protocol stack format according to the evolution result, adding a security label, and outputting the to-be-transmitted data packet to a corresponding external network interface. The method has the beneficial effects of improving the network adaptability, the abnormity identification precision, the protocol selection flexibility and the data transmission security, and is suitable for complex, dynamic and high-concurrency application scenes of the Internet of Things.

The invention relates to the technical field of electric power Internet of Things security, in particular to an electric power Internet of Things penetration test method based on dynamic attack surface analysis, which comprises the following steps: analyzing the close degree and attack accessibility between equipment, and determining the state transition relationship of the attacked information domain equipment; utilizing a cellular automaton model to calculate the probability that each device in the information domain is affected; calculating the influence degree of the information domain in the specific attack scene; monitoring a physical domain state of the electric power Internet of Things system, and determining a state transition relationship when a physical domain node is attacked; calculating the probability that each physical domain node is affected by using a cellular automaton model, and calculating the influence degree of the physical domain in a specific attack scene; the traditional attack surface calculation method only considers the attack cost and the vulnerability availability, and the attack surface calculation method comprehensively considers the three parameters of the attack cost, the vulnerability availability and the overall influence degree of the attack on the system to calculate the attack surface, and is more in line with the goal of finding the security risk through the penetration test of the information physical system of the electric power Internet of Things.

The invention discloses an image encryption method based on trans-ring Josephs scrambling and a two-dimensional cellular automaton. Firstly, a trans-ring Josephs scrambling algorithm is provided, and elements removed each time depend on inter-ring step length and in-ring step length at the same time. Then, a two-dimensional asynchronous cellular automaton diffusion mechanism is designed, and six different two-neighbor structures are defined; and the image is encrypted safely and efficiently through a scrambling diffusion framework.

The application relates to the technical field of power system dispatching, and discloses an intra-day forward-looking dispatching fast solving method considering large-scale new energy cluster power generation fluctuation, which comprises the following steps: S1: new energy cluster space-time fluctuation scene generation based on neuron cellular automata, S2: power grid dynamic security domain definition and simplification based on physical information neural network, S3: dispatching fast optimization solving based on model prediction path integral, and S4: dispatching scheme dynamic elasticity and stability evaluation based on the theory of Koopman operator. The new energy cluster space-time fluctuation scene generation method based on neuron cellular automata can effectively generate a space-time scene reflecting the large-scale new energy cluster power generation fluctuation, support uncertainty analysis, has the advantages of high calculation efficiency and scene authenticity, and solves the problem that the traditional statistical model ignores space-time coupling, thereby causing inaccurate scene generation.

A particle track discovery method based on graph neural network cellular automaton, which utilizes a high-energy physics detector to record a plurality of particle hit data generated in a particle collision event, including the position, direction and momentum of the particle at its hit point; using a plurality of particle hit data generated in multiple particle collision events to supervise the training of a graph neural network, and equivalently calculating a generated graph neural cellular automaton; adding the graph neural cellular automaton conversion rule into a conversion rule library; selecting a plurality of graph neural cellular automaton conversion rules from the conversion rule library, equivalently calculating a corresponding plurality of graph neural networks; applying the plurality of graph neural networks in step three to a new particle collision experiment, inputting a plurality of groups of measurement values of the same particle obtained in the new particle collision experiment into the plurality of graph neural networks, obtaining a plurality of different predicted tracks, and discovering the real track of the particle from the different predicted tracks according to the known physical properties of the particle.

The invention discloses a frame structure design method for material and thickness collaborative optimization under multiple working conditions, and the method comprises the steps: constructing a frame structure subregion cellular automaton model, and employing an internal and external loop iteration mechanism to achieve the optimal distribution solution of the structure material and quality under multiple working conditions. The outer cycle sets target cost and target quality for the inner cycle to provide optimization guidance and construct a combined target field variable function, and the inner cycle drives dynamic updating of cellular thickness and materials under multiple working conditions based on a material and thickness collaborative updating strategy and guides cellular field variables to approach target field variables, so that the structural performance is remarkably improved. In order to break through the algorithm efficiency bottleneck, the global search capability and convergence speed of the algorithm are effectively enhanced through the strategy of parallel point adding and search area expanding, and the method is particularly suitable for optimization design of a frame structure with a large number of materials and thickness design variables under the constraint of multiple working conditions. And an efficient and reliable technical path is provided for lightweight design and performance optimization of the frame structure.

The invention discloses a wetland nitrogen cycle optimization method and system based on machine learning, and belongs to the technical field of ecological engineering.The method comprises the steps that according to a comprehensive environment data set, a transverse lateral infiltration path and a vertical priority flow path are recognized, spatio-temporal evolution simulation is conducted through a cellular automaton model, and a nitrogen migration path is constructed; analyzing the interaction between the water flow driving flux and the underground water level on the path resistance, and determining the broadening degree of the transverse lateral seepage path; if the widening degree exceeds a preset threshold value, integrating the water body turbulence intensity data and the flow velocity shear effect value, and obtaining a bubble escape rate and a surface sediment disturbance frequency; calculating the nitrogen saturation and the spatial distribution of the sediment particle suspension amount, and obtaining the total re-release flux through flux overlay analysis; and constructing a nitrogen loss flux model in combination with the fluctuation rule, simulating the change of the nitrogen loss proportion, and determining the net nitrogen loss flux. According to the invention, fine monitoring of the dynamic change of wetland nitrogen can be realized.

The method may comprise determining a geometry of a stockpile based on the haulage data, engineering drawings and material characteristics; predicting, by the one or more processors using an algorithm implementing a cellular automaton, material movements within the stockpile, based on the geometry of the stockpile, physical parameters of the material movements and emergent rules for the material movements; predicting material movements exiting the stockpile, based on the material movements within the stockpile; predicting, using a concentrator transit model, the material movements across a comminution process in a concentrator, based on material movements exiting the stockpile; and optimizing mining operations based on the knowledge of the material movements through the comminution process. A determination of the material movements may include a determination of the material characteristics of the material.

The invention provides a fire hazard wind field and spread dynamic prediction method and system based on radar echo evolution characteristics. The method comprises the following steps: step 1, carrying out ground clutter suppression, radial high-frequency oscillation filtering, speed deblurring and time-space consistency check on reflectivity Z, radial speed V, spectral width W and dual polarization quantity of a plurality of weather radars; 2, under a three-dimensional variational 3DVAR framework, taking a numerical mode background field as prior, and performing inversion to obtain a boundary layer three-dimensional wind field; 3, establishing an anisotropic cellular automaton (CA) and live wire particle swarm coupling model under the constraints of a digital elevation model (DEM), combustible material types, water content and meteorological elements; 4, performing real-time correction and short-term and temporary extrapolation on the fire point and live wire states; and step 5, result output and early warning. By applying the technical scheme, the minute-level short-term and temporary prediction capability can be improved, and the safety of key assets such as power transmission channels is ensured.

The invention relates to a method for determining thermal properties of a printed circuit board by means of a thermal cellular automaton model, comprising the following steps: modeling the printed circuit board in the thermal cellular automaton model in such a way that the printed circuit board is divided into individual cells (1, 2, 3), each cell (1, 2, 3) being implemented as a cellular automaton; defining a plurality of thermal states for each cell (1, 2, 3) such that each cell (1, 2, 3) is in one of said states in each time step; defining a rule based on the Fourier thermal conduction law, and according to the rule, determining the state of each cell (1, 2, 3) in each time step; and ascertaining the state of all cells (1, 2, 3) in parallel step by step in order to ascertain the thermal properties of the circuit board in real time. The invention also relates to a control device, a computer program product and a computer readable storage medium.

The application relates to the technical field of robot autonomous navigation, in particular to a cellular genetic path planning method based on multi-population cooperation adaptability, which comprises that one cell corresponds to one grid in a grid map; the number of sub-populations is set according to a path planning task; a plurality of initial path individuals of the sub-populations are generated through cell evolution; in each sub-population, a path individual with the maximum gene coding fitness is extracted, and the rest path individuals are randomly paired, the cell state matrix is adjusted based on a crossover and mutation rule, and iterative evolution is carried out; the individual with the maximum fitness and the individual obtained through evolution are used as sub-population representative individuals; the cooperation fitness is calculated according to the sub-population representative individuals of each sub-population; the population is updated according to the cooperation fitness ranking; an optimal solution is output; the application considers the population cooperation relationship as an optimization condition, and evolution of a cellular automaton is utilized, so that the best path in a multi-task complex scene can be quickly generated.

This invention discloses a nuclear radiation indicator early warning system based on multi-algorithm fusion. The system includes an environmental data acquisition module for collecting gamma-ray dose rate data, wind speed data, wind direction data, and geographic location information to construct a nuclear radiation environmental dataset; a dose rate prediction module for predicting future trends in gamma-ray dose rate data based on a sparse recurrent neural network model; an anomaly detection module for identifying abnormal mutation points and locating high-risk sensors; a diffusion region modeling module for calculating the impact area of ​​nuclear radiation diffusion; a grid risk evolution module for simulating the temporal evolution of regular grid risk states based on a multi-state cellular automaton; and a fusion judgment module for fusing output results to generate a final warning level and spatial risk distribution. This invention enables precise prediction of nuclear radiation risks and dynamic spatial early warning, significantly improving warning accuracy and response efficiency.

The application relates to the technical field of computer simulation, in particular to a two-dimensional surface calculation method of a cellular automaton, which comprises the following steps: based on terrain data acquisition and recording, terrain data of multiple regions are analyzed, elevation and slope are recorded, terrain data is processed by using a cellular automaton, a terrain model is constructed, and regional terrain information is obtained. In the application, by means of terrain data acquisition and terrain model construction, the quality of basic data of the model is improved, accurate input data is provided for simulation, key positions are identified, the size and distribution of cells are adjusted, the simulation resolution of key regions is optimized, the local precision of simulation is improved, the consumption of computing resources is reduced, parameters of rainfall simulation are adjusted according to actual meteorological data, the reliability of prediction is enhanced, soil erosion and surface runoff are simulated, the erosion effect and anti-erosion capacity of multiple regions are evaluated, and the prediction accuracy and practicability of the two-dimensional surface calculation are improved.

The invention discloses a coal mine fire extinguishing intelligent command method, a coal mine fire extinguishing intelligent command system, coal mine fire extinguishing intelligent command equipment and a computer readable storage medium. The method comprises the following steps: acquiring underground multi-source heterogeneous data, performing space-time alignment and fusion processing, and constructing a data basis for reflecting a fire scene state in real time; mapping the fusion data to a pre-constructed digital twinborn model, and generating a three-dimensional visual expression of the fire scene; a cellular automaton model is utilized to dynamically predict a fire spreading trend, and an optimal fire extinguishing resource allocation scheme is generated through a scheduling optimization algorithm by taking response time and a resource utilization rate as optimization targets in combination with a current fire and a resource distribution state; and finally, the scheduling scheme is visually presented in the digital twinning scene, and a scheduling instruction is issued to a related execution unit. According to the invention, fire behavior prediction and resource scheduling are realized, and systematic technical support is provided for coal mine underground fire emergency disposal.

The invention discloses a DNA sequence direct encryption method and system based on automaton cryptography, and relates to the technical field of DNA storage security. By designing a DS-mealy machine and a DS-cellular automaton, direct encryption of a DNA sequence is achieved, and the problems that in an existing DNA storage encryption method, compatibility with a coding model is poor, safety is insufficient, performance is low, and expandability is weak are solved. According to the method, sequence-level encryption is realized through base diffusion and rotation operation, so that base distribution is more uniform and random, and the security of DNA storage data is improved; meanwhile, the space resource consumption of encryption and decryption is reduced, the processing speed is increased, and the method is suitable for various DNA storage scenes and particularly suitable for storage of sensitive information such as medical data with high requirements for safety and efficiency.

The invention relates to an image encryption method. The method comprises the steps of obtaining three groups of encryption keys based on a Hash method and superposition of preset real number key parameters; dividing the plaintext image into P11, P12 and P13; enabling the five-dimensional dual-memristor chaotic system to generate a sequence through the first group of encryption keys, and carrying out the global scrambling of P11, P12 and P13, thereby obtaining P21, P22 and P23; performing cat mapping scrambling on the P21, the P22 and the P23 to obtain P31, P32 and P33; performing block DNA operation on the P31, the P32 and the P33 through the second group of encryption key generation sequences; performing block cell automaton operation on the block DNA operation result to obtain P41, P42 and P43; performing block affine transformation scrambling on the P41, the P42 and the P43 to obtain P51, P52 and P53; and carrying out finite field diffusion on P51, P52 and P53 through the third group of encryption key generation sequences, and then merging layers to obtain a ciphertext image. According to the method, the differential attack resistance of the system can be improved, the key space of the system is improved, and the security of image data is improved.

The application discloses a corpus selection method and device, electronic equipment and a readable storage medium. The method comprises the following steps: generating a to-be-processed grid according to a plurality of segmented words obtained by segmenting a to-be-processed corpus and a preset rule; determining a first space state grid matched with the to-be-processed grid from a changing space state grid of a meta-cellular automaton in a change process of the space state grid according to the preset rule; assigning a corresponding related segmented word to each cell grid with a target value in the first space state grid; determining a to-be-corrected corpus corresponding to each target grid according to the related segmented word assigned to the cell grid with the target value in each target grid intercepted from the first space state grid; and selecting a target corpus corresponding to the to-be-processed corpus from the to-be-corrected corpora. According to the method, the purpose of quickly constructing a corpus is achieved.

The application discloses a development zone overall space structure evolution prediction and land scale calculation method considering industrial agglomeration effect, and belongs to the technical field of city planning and land management. The application firstly constructs a space economic model containing enterprise microsite selection behavior, industrial chain correlation degree and factor flow; secondly, the attraction relationship between the present industrial agglomeration zone and the city function node is identified by using multi-source space-time big data (POI, mobile signaling, night light); then, the improved cellular automaton model is introduced, the industrial agglomeration externality is quantified as a driving factor in the conversion rule, and the dynamic evolution path of the development zone space structure under different policy scenarios is simulated; finally, based on the evolution result, the reasonable land scale of each functional plate is calibrated and calculated in reverse by combining the input-output table and the employment density threshold method. The application solves the problem of land supply and industrial demand dislocation in the traditional static planning, and improves the scientificity and forwardness of land scale calculation.

The invention provides an ultrasonic magnetic field assisted low-speed wire cut electrical discharge machining microstructure simulation method, belongs to the technical field of wire cut electrical discharge machining, and aims to introduce ultrasonic waves and a magnetic field into existing low-speed wire cut electrical discharge machining, establish a thermomechanical coupling model, and combine finite element analysis and a cellular automaton model to simulate the microstructure of the low-speed wire cut electrical discharge machining. Accurate simulation is performed on distribution of a temperature field, a stress field and a strain field in the machining process, and meanwhile, dynamic evolution of crystal grains in a material cutting surface metamorphic layer is simulated through a cellular automaton, so that a visual microstructure evolution simulation result is obtained. According to the method, surface microstructures of materials such as shape memory alloy are remarkably improved by introducing ultrasound and a magnetic field, the grain size is reduced, and the uniformity of grains is improved; ultrasonic, magnetic field and thermomechanical coupling are combined, the influence of different factors on the microstructure of the material can be comprehensively considered, the limitation of a single factor in the prior art is broken through, and therefore more efficient machining optimization is achieved under the complex technological condition.

The application discloses a medical image morphing registration method and system, and belongs to the technical field of medical image processing. The application aims to solve the problems of insufficient precision, topological structure damage and limited cross-scale modeling capability of existing registration methods when processing large-scale non-rigid deformation. The proposed method combines the semantic perception ability of the diffusion model and the local evolution characteristics of the neural cellular automaton. By constructing a multi-scale image pyramid, a conditional diffusion model is introduced at each scale to extract potential semantic scores, guiding the NCA module to iteratively model local regions and generate a deformation field that maintains topological consistency. Finally, through the layer-by-layer progressive method between scales, a high-precision non-rigid registration result is obtained.

An artificial intelligence system can be implemented to identify relationships through the propagation of ripple patterns through a grid. In such a system, the grid may comprise cells which operate as cellular automata. Relationships may be identified based on collisions of signals detected by the cells in the grid, and, when a relationship is identified, it may be used to create high speed connections between cells.