Horizontal warehouse operation control terminal

Abstract

The invention discloses a horizontal warehouse operation control terminal, and relates to the field of horizontal warehouse operation control, the horizontal warehouse operation control terminal comprises a networking communication module, an emergency decision module, a grain condition regulation and control module, an intelligent scheduling module and a collaborative protection module, dynamic networking communication analysis is carried out by collecting distribution data of various protocol communication devices in a granary, and total communication energy consumption is obtained; equipment fault occurrence time data is collected for fault emergency decision analysis, cooperative response time delay is obtained, grain condition self-adaptive regulation and control analysis is carried out by obtaining grain temperature impurity airflow data and communication total energy consumption of a grain pile, and a minimum energy consumption curve is obtained. Operation chain intelligent scheduling is performed by acquiring position state task attributes and a minimum energy consumption curve of equipment nodes, an equipment start-stop sequence and a disturbance response index are obtained, safety regulation and control are performed based on collaborative response time delay and the equipment start-stop sequence, an equipment control instruction frame is obtained, and closed-loop risk suppression of ventilation enhancement, load reduction operation and sudden stop grading is realized.

Description

Technical Field

[0001] This invention relates to the field of flat warehouse operation control, specifically a flat warehouse operation control terminal. Background Technology

[0002] Existing control technologies for flat warehouse operations generally suffer from drawbacks such as low efficiency of multi-source heterogeneous equipment collaboration, delayed fault response, and insufficient energy consumption optimization. Network communication relies on static bandwidth allocation and does not consider protocol anomalies and spatial distance attenuation, leading to easy congestion of real-time data streams. Furthermore, the energy consumption modeling for multi-protocol conversion is inaccurate, failing to accurately quantify the impact of entropy weight amplification, resulting in consistently high total communication energy consumption. Fault emergency decision-making often relies on fixed threshold alarms, lacking predictive capabilities based on the failure rate of equipment throughout its entire lifecycle. The collaborative shutdown sequence does not integrate command propagation delay and mobile link stability, resulting in poor cascading fault suppression.

[0003] In the grain condition control process, existing methods simplify the thermodynamic model to isolated temperature control, failing to consider the dynamic coupling effect between the impurity concentration field and the oxygen respiration field. Ventilation strategies are not linked to global energy consumption, and fan power adjustment is not linked to communication energy consumption constraints, resulting in energy consumption curves that deviate significantly from actual operating conditions. Intelligent scheduling often adopts simple priority rules, and conflict detection lacks the spatiotemporal correlation of hypergraph tensors with equipment tasks. Furthermore, start-up and shutdown do not consider safety sensitivity coefficients and location risk coefficients, leading to lax enforcement of reverse order constraints and delayed decisions on equipment load reduction and emergency stop. Safety commands are not considered in relation to risk factors such as equipment status, sudden changes in grain temperature, and impurity gradients, making it impossible to accurately and dynamically map control commands. This hinders the achievement of closed-loop risk suppression through enhanced ventilation, load reduction operation, and graded emergency stop, severely impacting grain storage safety and operational continuity.

[0004] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0005] To address the technical problems raised in the background section, this invention is proposed. Specifically, an embodiment of this invention provides a control terminal for flat warehouse operations.

[0006] The objective of this invention can be achieved through the following technical solution: a flat warehouse operation control terminal, comprising a network communication module, an emergency decision-making module, a grain condition regulation module, an intelligent scheduling module, and a collaborative protection module. The network communication module is used to collect the distribution data of various protocol communication devices in the grain warehouse and perform dynamic network communication analysis to obtain the total communication energy consumption.

[0007] The emergency decision-making module is used to collect equipment failure time data for emergency decision analysis and obtain collaborative response delay.

[0008] The grain condition control module obtains grain temperature, impurity airflow data and total communication energy consumption of the grain pile, performs adaptive grain condition control analysis, and obtains the minimum energy consumption curve.

[0009] The intelligent scheduling module performs intelligent scheduling of the work chain by acquiring the location status, task attributes, and minimum energy consumption curve of the equipment nodes, and obtains the equipment start-up and shutdown sequence and disturbance response indicators.

[0010] The collaborative protection module performs security control based on collaborative response delay and device start-stop sequence to obtain device control command frames.

[0011] Furthermore, the total communication energy consumption analysis steps are as follows:

[0012] The distribution data of various protocol communication devices in the grain warehouse is collected. Based on the distribution data of various protocol communication devices, a protocol heterogeneous entropy weight model is constructed to perform protocol difference quantification and distance attenuation compensation, and the entropy weight vector is obtained.

[0013] A bandwidth allocation matrix is ​​obtained by jointly optimizing bandwidth and latency based on entropy weight vector and real-time data volume.

[0014] A multi-protocol conversion energy consumption model is established using the bandwidth allocation matrix and entropy weight vector to obtain the total communication energy consumption.

[0015] Furthermore, the collaborative response delay analysis steps are as follows:

[0016] The command propagation speed is calculated by multiplying the mobile link stability of the device with the speed of light. The command propagation speed and the third-order fault propagation tensor are then used for spatiotemporal planning of the downtime sequence to obtain the downtime sequence.

[0017] Based on the mobile link stability and downtime series of the devices, an emergency stop collaborative control equation is constructed, and spatiotemporal collaborative optimization of multiple devices is performed to obtain the collaborative response delay.

[0018] Furthermore, the real-time failure probability analysis steps for the device are as follows:

[0019] Collect all failure time data of each device within three years, input these failure time data into the Weibull distribution model for curve fitting, use the maximum likelihood estimation method to solve for the best matching parameter value, and obtain the shape parameter and characteristic lifetime parameter by taking the logarithm and partial derivative and the Newton-Raphson method iteratively solving. Based on the shape parameter and characteristic lifetime parameter, substitute them into the Weibull distribution model to obtain the time-varying failure rate of the k-th device.

[0020] The production impact value, maintenance cost value, and replacement difficulty value of each piece of equipment are obtained, and multiplied by the corresponding weighting factor coefficient to obtain the importance of each piece of equipment;

[0021] Based on the entropy weight vector and the real-time communication data volume of the devices, the traffic weighted entropy weight of each device is calculated. The traffic weighted entropy weight of each device is multiplied by the importance of each device to obtain the traffic entropy weight of each device.

[0022] A dynamic fault tree model is established based on the time-varying failure rate of the k-th device and the flow-weighted entropy weight of each device, and the real-time failure probability of the device is output.

[0023] Further, the mobile link stability analysis steps for the device are as follows:

[0024] The equipment's operating status, load rate, and temperature are obtained to construct the equipment state vector. The fault coupling strength between equipment is calculated by using the second-order mixed partial derivative of the equipment state vector with respect to the real-time fault probability. A spatial attenuation factor is introduced to obtain the third-order fault propagation tensor.

[0025] The stability of the mobile link of the device is obtained by evaluating the dynamic stability of the spatial signal through the acquisition of the device's moving speed and signal strength gradient.

[0026] Furthermore, the minimum energy consumption curve analysis steps are as follows:

[0027] Based on the updated temperature field, the airflow field is forcibly set at the vent, the wall surface is subjected to no-slip condition, the Navier-Stokes equation is solved in a coupled manner, the wall surface is adopted as a zero-flux boundary when updating the impurity field, the particulate transport equation is solved, the updated impurity field is obtained, and the four-dimensional field of grain temperature mass flow is output.

[0028] Based on the total energy consumption of communication and the four-dimensional field of grain temperature, mass flow, and flow, a game optimization analysis was conducted using a game model of energy consumption and ventilation efficiency to obtain the optimal fan power.

[0029] Based on the optimal fan power and the four-dimensional field of grain temperature, mass flow, and flow, a dynamic programming equation for gas regulation is constructed, and the optimal gas velocity sequence is obtained through the Hamilton-Jacobi-Bellman equation.

[0030] Global energy consumption dynamic optimization is performed on gas flow rate, fan power, and total communication energy consumption to obtain the minimum energy consumption curve.

[0031] Furthermore, the updated temperature field analysis steps are as follows:

[0032] A thermodynamic coupled partial differential equation for the grain pile was established. The three-dimensional space of the grain silo was divided into a computational grid. Each grid node was associated with variables. The initial temperature distribution, initial impurity distribution, initial airflow velocity field and initial oxygen concentration of the grain pile were measured through the sensor network of the grain silo. Boundary constraints were set. When updating the oxygen field, the exchange boundary was applied on the surface of the grain pile. The grain pile respiration model was solved, and the oxygen change rate was calculated by the finite difference method.

[0033] Substituting the oxygen change rate into the coupled partial differential equation of grain pile thermodynamics, the spatial derivative is discretized, and the discretized terms are combined to form a linear equation system. The system of equations is solved by iterative method to obtain a new temperature distribution and the updated temperature field.

[0034] Furthermore, the analysis steps for the device start-up / shutdown sequence and disturbance response indicators are as follows:

[0035] Priority is obtained by multiplying the operation type coefficient, location risk coefficient, and timeliness urgency by the corresponding scaling factor. The delivery volume is multiplied by the priority to obtain the task utility value. Based on the task utility value, conflict area identification, and minimum energy consumption curve, a spatiotemporal safety efficiency game model is established, and location constraints are applied. Following the reverse start-stop rule, the equipment start-stop sequence is output.

[0036] A joint equation for fault propagation communication is established based on the device start-up and shutdown sequence, the device's mobile link stability, and the third-order fault propagation tensor. The disturbance response index is output. If the disturbance response index is greater than the set threshold, the device will automatically switch to local reverse shutdown mode when communication is interrupted, and the device start-up and shutdown sequence will be recalculated.

[0037] Further, the conflict area identification analysis steps:

[0038] Extract all device nodes, each device has location attributes, state attributes and task capability attributes, construct a hyperedge set, formalize the hypergraph structure, calculate the adjacency tensor, define the tensor elements, and obtain the hypergraph adjacency tensor;

[0039] The hypergraph adjacency tensor is extended into a spatiotemporal hypergraph adjacency tensor through spatiotemporal state injection;

[0040] Spatiotemporal conflict manifold detection is performed on the adjacency tensor of the spatiotemporal hypergraph to obtain conflict region identifiers.

[0041] Further, the device control command frame analysis steps:

[0042] Based on the real-time failure probability and collaborative response delay of the equipment, a coupled analysis of equipment status and grain condition is performed to obtain the real-time risk value.

[0043] Map risk actions to real-time risk values ​​to obtain safety control instructions;

[0044] Device control command frames are generated by using safety control instructions and start / stop sequence states.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] 1. This invention collects distribution data of various protocol communication devices within a grain warehouse to obtain dynamic network communication analysis and total communication energy consumption. It also collects equipment failure time data for fault emergency decision analysis to obtain collaborative response latency. Furthermore, it acquires grain temperature, impurity, and airflow data from the grain pile, along with total communication energy consumption, and performs adaptive grain condition control analysis to obtain a minimum energy consumption curve. By acquiring the location, status, task attributes, and minimum energy consumption curve of equipment nodes, it performs intelligent scheduling of the work chain, obtaining equipment start-up / shutdown sequences and disturbance response indicators. The network communication relies on static bandwidth allocation, considering protocol anomalies and spatial distance attenuation, reducing real-time data stream congestion. The multi-protocol conversion energy consumption model is accurate, precisely quantifying the impact of entropy weight amplification, and maintaining low total communication energy consumption. Fault emergency decision-making is based on the predictive capability of equipment failure rates throughout its entire lifecycle, integrating command propagation latency and mobile link stability into the collaborative shutdown sequence, resulting in good cascading fault suppression.

[0047] 2. This invention achieves safety regulation based on collaborative response delay and equipment start-stop sequence, resulting in equipment control command frames. In the grain condition regulation process, it considers the dynamic coupling effect of impurity concentration field and oxygen respiration field, the correlation between ventilation strategy and global energy consumption, and the correlation between fan power adjustment and communication energy consumption constraints, ensuring that the energy consumption curve conforms to actual working conditions. Intelligent scheduling and conflict detection consider the spatiotemporal correlation of hypergraph tensor to equipment tasks. At the same time, start-stop considers safety sensitivity coefficient and location risk coefficient, and the reverse order constraint is strictly enforced, ensuring timely decisions on equipment load reduction and emergency stop. Safety commands are considered in conjunction with risk factors such as equipment status, sudden changes in grain temperature, and impurity gradient, enabling accurate dynamic mapping of control commands. This achieves closed-loop risk suppression through enhanced ventilation, load reduction operation, and graded emergency stop, ensuring grain storage safety and operational continuity. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The following drawings are not drawn to scale according to the actual size, but are intended to show the main idea of ​​the present invention.

[0049] Figure 1 This is a system block diagram of the present invention;

[0050] Figure 2 This is a structural diagram of the product housing of the present invention;

[0051] Figure 3 This is a flowchart of the emergency decision-making module method of the present invention. Detailed Implementation

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are also within the scope of protection of the present invention.

[0053] like Figure 1 As shown, a flat warehouse operation control terminal includes a network communication module, an emergency decision-making module, a grain condition regulation module, an intelligent scheduling module, and a collaborative protection module.

[0054] The network communication module is used to collect the distribution data of various protocol communication devices in the grain warehouse, perform dynamic network communication analysis, and obtain the total communication energy consumption.

[0055] The emergency decision-making module is used to collect equipment failure time data for emergency decision analysis and obtain collaborative response delay.

[0056] The grain condition control module obtains grain temperature, impurity airflow data and total communication energy consumption of the grain pile, performs adaptive grain condition control analysis, and obtains the minimum energy consumption curve.

[0057] The intelligent scheduling module performs intelligent scheduling of the work chain by acquiring the location status, task attributes, and minimum energy consumption curve of the equipment nodes, and obtains the equipment start-up and shutdown sequence and disturbance response indicators.

[0058] The collaborative protection module performs security control based on collaborative response delay and device start-stop sequence to obtain device control command frames.

[0059] Specifically, the analysis of the network communication module is as follows:

[0060] Distribution data of various protocol communication devices within the grain warehouse is collected. Based on this data, a heterogeneous entropy weight model is constructed to quantify protocol differences and compensate for distance attenuation, resulting in an entropy weight vector. Specifically, the communication devices include mobile operation equipment groups (grain receiving equipment, grain distribution equipment, and grain extraction equipment), fixed processing equipment groups (cleaners, conveyors, and intelligent ventilation systems), environmental sensing equipment groups (grain condition monitoring and control sensors, gas concentration sensors, and displacement sensors), and terminal devices (PLC controllers, LoRa modules, and touch screens). The heterogeneous entropy weight model includes:

[0061] Where n represents the total number of communication protocol categories, and i represents the protocol category index. This indicates the number of devices corresponding to the i-th type of protocol. This represents the total number of devices across all protocol categories (i.e., the total number of devices in the system). This represents the average distance between the i-th type of device and the communication terminal. H represents the distance attenuation coefficient, H represents the protocol heterogeneous entropy weight of the entire system, and the output entropy weight vector is... ;

[0062] Bandwidth and latency are jointly optimized based on entropy weight vector and real-time data volume to obtain bandwidth allocation matrix. Specifically, the bandwidth and latency are jointly optimized as follows:

[0063] ,in Represents the entropy weight vector One component represents the normalized composite weight of the i-th type of communication protocol after considering its device quantity distribution and spatial layout. This represents the total data volume of all devices using the i-th protocol, such as the total data generated per second by all LoRa devices. The total bandwidth allocated to all devices sharing the i-th type of protocol is a decision variable, and its optimal value is... , This indicates the total bandwidth available to the terminal. This represents the entropy weighting influence factor. This indicates a constraint: the sum of the bandwidth allocated to all protocol types does not exceed the total bandwidth. The value represents the maximum ratio of entropy weight to bandwidth. To avoid allocating too little bandwidth to certain high-weight protocols and causing bottlenecks, min represents minimization. Using the KKT conditional analytical solution, we obtain... ,in Represents the Lagrange multipliers. The indicator function is defined if and only if the i-th type of protocol is used to... The value is 1 if it is the largest class, and 0 otherwise. This represents the bandwidth allocation amount for the i-th type of protocol, resulting in the bandwidth allocation matrix. ;

[0064] A multi-protocol conversion energy consumption model is established using the bandwidth allocation matrix and entropy weight vector to obtain the total communication energy consumption. The multi-protocol conversion energy consumption model includes:

[0065] ,in Indicates the total energy consumption of communication. This represents the energy consumption coefficient for the i-th type of protocol conversion, and represents the basic energy consumption per unit bandwidth and per unit time of communication, which depends on the protocol conversion type. It indicates the communication time, a system-defined time window (such as the time of a job cycle). This represents the protocol entropy weight energy consumption amplification factor (dimensionless), used to amplify the impact of entropy weight on energy consumption.

[0066] like Figure 3As shown, the specific analysis of the emergency decision-making module is as follows:

[0067] Data on the occurrence time of all failures of each device over three years was collected. This failure time data was then input into a Weibull distribution model for curve fitting. The maximum likelihood estimation method was used to solve for the optimal matching parameter value—the shape parameter, which characterizes the trend of failure rate over time (less than 1 indicates a decrease in initial failures, and greater than 1 indicates an increase in aging failures). The characteristic lifetime parameter corresponds to the time point when 63.2% of the devices failed. The Weibull distribution model includes: ,in This represents the time-varying power efficiency of the k-th device. Indicates characteristic lifetime parameters, Let t represent the shape parameter and t represent time. The likelihood function includes... ,in Represents the likelihood function. The symbol represents a product, where i represents the sample index and n represents the total number of samples. Let represent the time of the i-th failure, and e represent the natural constant. By taking the logarithm and partial derivative and iteratively solving using the Newton-Raphson method, the shape parameter and characteristic lifetime parameter are obtained. Based on the shape parameter and characteristic lifetime parameter, the time-varying failure rate of the k-th device is obtained by substituting them into the Weibull distribution model.

[0068] The production impact value, maintenance cost value, and replacement difficulty value of each piece of equipment are obtained, and multiplied by the corresponding weight factor coefficient to obtain the importance of each piece of equipment. The production impact value of a specific piece of equipment is equal to the average hourly output loss caused by equipment failure divided by the total system capacity, the maintenance cost value is equal to the annual average maintenance cost divided by the original value of the equipment, and the replacement difficulty value is equal to 1 / (number of spare equipment + 1).

[0069] Based on the entropy weight vector and the real-time communication data volume of the devices, the traffic-weighted entropy weight of each device is calculated. Specifically, the real-time data traffic distribution of the devices on each communication protocol is monitored, and the entropy weight values ​​of the corresponding protocols are weighted by the proportion of the data volume of each protocol to the total data volume of the devices. Finally, the comprehensive entropy weight of the device is obtained. For example, if a device transmits 70% of the data through the WiFi-6E protocol (corresponding to an entropy weight of 0.28) and transmits 30% of the data through the LoRa protocol (entropy weight of 0.15), then its traffic-weighted entropy weight is 0.7×0.28 + 0.3×0.15=0.241. This value integrates the protocol complexity and the actual communication characteristics of the devices, and directly reflects the dynamic weight of the devices in the communication system.

[0070] A dynamic fault tree model is established based on the time-varying failure rate of the k-th device and the flow-weighted entropy weight of each device, outputting the real-time failure probability of the device. The dynamic fault tree model includes: ,in Indicates the real-time failure probability. Let τ represent the traffic-weighted entropy weight of the k-th device, and τ represent the integral time variable;

[0071] The system acquires the operating status, load rate, and temperature of the equipment to construct an equipment state vector. It then calculates the fault coupling strength between equipment using the second-order mixed partial derivative of the equipment state vector with respect to the real-time fault probability. Finally, it introduces a spatial attenuation factor to obtain a third-order fault propagation tensor. The specific third-order fault propagation tensor... ,in Indicates the real-time failure probability. The device state vector is equal to [operating state, load rate, temperature]. The operating state is 0 = shutdown and 1 = operation. The i and j dimensions represent the device pair (such as grain unloader and conveyor), and the k dimension represents the failure mode (mechanical / electrical / control). This represents the device's UWB coordinates. UWB coordinates are three-dimensional spatial positioning data of the device based on Ultra-Wideband technology. Indicates the spatial attenuation coefficient. Indicates Euclidean distance;

[0072] By acquiring the device's moving speed and signal strength gradient, the dynamic stability of the spatial signal is assessed, thus obtaining the device's mobile link stability. Where v represents the device moving speed, which is collected by the device encoder or UWB positioning system; VP represents the signal strength gradient, which is calculated differentially by dual WiFi probes; r represents the switching threshold; h represents the gain coefficient of the Sigmoid function (dimensionless); and Δt represents the time window (in seconds, the time required for the device to pass through its own dimensions, such as the device length divided by the speed).

[0073] The command propagation speed is calculated by multiplying the device's mobile link stability with the speed of light. Then, the command propagation speed and the third-order fault propagation tensor are used for spatiotemporal planning of the downtime sequence to obtain the downtime time series. The specific spatiotemporal planning of the downtime sequence is as follows:

[0074] ,in Indicates the time of the shutdown command for device i. The instruction propagation speed is represented by 'st', and the constraint 'st' is determined by 's'. A mixed-integer programming problem is used to solve this problem, yielding the downtime time series. The shutdown time of each device, where m represents the total number of devices that need to be coordinated to shut down in the current fault event;

[0075] Based on the mobile link stability and downtime time series of the devices, an emergency stop collaborative control equation is constructed. Multi-device spatiotemporal collaborative optimization is performed to obtain the collaborative response delay. The specific emergency stop collaborative control equation is as follows:

[0076] ,in This represents the downtime time series, where n represents the index symbol of the downtime equipment, n=1,2,…,m. Represents the downtime series The real-time coordinates of the corresponding device in three-dimensional space. This indicates the grain temperature at the location of the equipment. Indicates the location of the emergency stop button. Let represent the Heaviside step function, which equals 1 when greater than or equal to 0, and 0 otherwise. Let c represent the speed of light. This indicates the delay in coordinated response.

[0077] Specifically, the analysis of the grain situation control module is as follows:

[0078] Establish the thermodynamic coupled partial differential equations of the grain pile , where T represents the temperature field (the variable to be solved). Indicates the thermal conductivity of grain. This represents the airflow velocity vector (containing three spatial components). This represents the impurity concentration field (specifically, dust concentration). This represents the coefficient indicating the effect of impurities on temperature. Represents the oxygen concentration field. The heat production coefficient of grain respiration is represented by t, which represents the time variable. Denotes divergence, This represents the vector dot product operator, which divides the three-dimensional space of the grain silo into a computational grid, with each grid node associated with variables (T, Z, ...). , The initial temperature distribution, initial impurity distribution (specifically dust concentration), initial airflow velocity field, and initial oxygen concentration of the grain pile are measured using a sensor network in the grain silo. All of these are three-dimensional coordinate measurements. Boundary constraints are set: grain silo wall temperature (forced to be constant at 15℃), vent velocity (fixed velocity vector setting at the inlet mesh node, e.g., (0,0,2.5) m / s), and oxygen exchange coefficient on the grain pile surface (calculated from the grain pile porosity, e.g., 0.15 m / s). These are physical parameters describing the ability of oxygen to exchange with the external environment on the grain pile surface. The boundary conditions directly affect the evolution of the oxygen concentration field. When updating the oxygen field, an exchange boundary is applied to the grain pile surface. Where D is the oxygen diffusion coefficient. The exchange coefficient is 0.15 m / s. Representing the ambient oxygen concentration, solve the grain pile respiration model. ,in Let Ea be the respiration rate constant, R be the activation energy, and T be the gas constant. The oxygen change rate is calculated by the finite difference method and is equal to the concentration change in the adjacent time step divided by the time step.

[0079] The oxygen change rate is substituted into the coupled thermodynamic partial differential equations of the grain pile, and the spatial derivatives are discretized. The discretized terms are combined to form a system of linear equations. An iterative method is used to solve this system of equations to obtain a new temperature distribution and the updated temperature field. The discretization process includes: heat diffusion terms. Central difference scheme , Indicates the summation sign over adjacent grid points. This represents the grid step size in the x-direction. This represents the temperature value of the point adjacent to the current grid. This is the temperature value at the current calculation grid point, and the convection term. Use the upwind differential scheme (e.g., x-direction) Similarly, in the y and z directions. This represents the temperature value at grid point (i,j,k), and the impurity term. Substitute the current dust concentration value of the grid node into the respiration term. Substituting the rate of change of oxygen, the linear equation system... , where [A] is the coefficient matrix, {b} is the right-hand side term, and {T} is the temperature vector (a column vector composed of the temperature values ​​of all grid points);

[0080] Based on the updated temperature field, the airflow field is forcibly set at the vent. =(0,0,2.5)m / s, no-slip condition applied to the wall surface. =0, coupled solution of the Navier-Stokes equations Where ρ is air density, p is pressure, μ is dynamic viscosity, and g is gravitational acceleration. Here is the coefficient of thermal expansion, T0 is the reference temperature, and the buoyancy term is... The updated temperature field was used, and the wall adopted a zero-flux boundary when updating the impurity field. Let n represent the boundary normal, and solve the particulate transport equation. ,in The impurity diffusion coefficient is used to obtain the updated impurity field, and the output is a four-dimensional field of grain temperature, mass flow, and distribution. ;

[0081] Based on the total energy consumption of communication and the four-dimensional field of grain temperature, mass flow, and energy consumption, a game-theoretic optimization analysis was conducted using a ventilation efficiency energy consumption game model to obtain the optimal fan power. The ventilation efficiency energy consumption game model includes:

[0082] ,in dV represents the temperature gradient norm of the four-dimensional field of grain temperature-mass flow, and dV represents the volume element (integrated over the entire grain silo). The penalty coefficient represents the concentration of impurities (the more impurities, the lower the ventilation efficiency), and E represents the real-time energy consumption of the ventilation system. It is an energy consumption allocation coefficient, with a constraint on the rate of change of ventilation power: ,in The wind turbine power is represented by a variable, and the optimal wind turbine power is obtained by optimizing the solution using sequential quadratic programming. ;

[0083] Based on the optimal fan power and the four-dimensional field of grain temperature, mass flow, and velocity, a controlled atmosphere dynamic programming equation is constructed. The optimal gas velocity sequence is obtained through the Hamiltonian-Jacobi-Bellman equation. The specific controlled atmosphere dynamic programming equation is as follows:

[0084] ,in The norm representing the deviation between the temperature and the target temperature in the four-dimensional field of grain temperature-mass flow. The norm representing the deviation between the oxygen field and the target concentration in the four-dimensional field of grain temperature-mass flow. It is the optimal gas flow rate sequence (adjustment target). This represents the weighting coefficient for oxygen concentration regulation. ,and ,in This indicates the aerodynamic coefficient of the fan. Indicates the dissipation coefficient of the wind turbine foundation. The current wind turbine power is represented by the Hamilton-Jacobi-Bellman equation, which is used to solve for the optimal gas velocity sequence.

[0085] Global dynamic energy consumption optimization is performed on gas flow rate, fan power, and total communication energy consumption to obtain the minimum energy consumption curve. Specifically, the minimum energy consumption curve... ,in These represent the energy consumption coefficients of the fan power, the gas flow rate, and the communication energy consumption, respectively.

[0086] Specifically, the analysis of the intelligent scheduling module is as follows:

[0087] Extract all device nodes Includes all device nodes (such as conveyors, sensors, fans), where n represents the total number of devices, and each device Has location attribute (Spatial coordinates), state attributes (Runtime / Failure) and Task Capability Attributes (Functional description: temperature monitoring, wind speed adjustment), constructing a hyperedge set. , where e represents a single hyperedge (a task unit). It is to perform tasks A subset of equipment (such as "grain conveying" and "temperature and humidity monitoring") It is a task unit in a task chain, with m hyperedges, where × represents the Cartesian product (representing the combination relationship between equipment and tasks), and is a formalized hypergraph structure. Calculate the adjacency tensor A third-order tensor, where dimension 1 corresponds to the device index i (range 1≤i≤n), dimension 2 corresponds to the hyperedge index k (range 1≤k≤m), and dimension 3 corresponds to the relation type r (0: position association, 1: state dependency, 2: capability matching). Tensor elements are defined as follows: when r=0... ,in Indicates spatial proximity. Indicates the attenuation coefficient. Representing the equipment Location and Task Position; when r=1 This reflects state compatibility, among which Indicates device status. This indicates the task status requirement; when r=2, ,in The capability matching indicator function is used to obtain the hypergraph adjacency tensor. ;

[0088] hypergraph adjacency tensor Extended to a spatiotemporal hypergraph adjacency tensor through spatiotemporal state injection. Specifically, spatiotemporal state injection refers to the injection of state data into the device's state time series. and changes in task requirements Inject time information, at device location and mission location Injecting spatial information and updating the extended spatiotemporal hypergraph adjacency tensor , where t represents the time dimension and L represents the space dimension;

[0089] Spatiotemporal conflict manifold detection is performed on the adjacency tensor of the spatiotemporal hypergraph to obtain conflict region identifiers. The spatiotemporal conflict manifold detection is as follows: ,in It is the collision detection threshold. Denotes the Frobenius norm. Represents spatial location coordinates, It represents the spatiotemporal domain (the integral region of spatial location and time dimension). Indicates conflict zone markers;

[0090] A spatiotemporal safety efficiency game model is established based on conflict zone identification and minimum energy consumption curves, with location constraints and following a reverse start-stop rule to output the device start-stop sequence. The spatiotemporal safety efficiency game model includes:

[0091] ,in This represents the energy consumption deviation tolerance coefficient. This represents the planned energy consumption of device i. Represents the normalized game value. This represents the task utility value of device i, where M is the total number of devices participating in the scheduling. The task utility value equals the transport volume multiplied by the priority. The priority is obtained by multiplying the operation type coefficient, location risk coefficient, and timeliness urgency by the corresponding scaling factor. Specifically, the operation type coefficient is: inbound / outbound: 0.9, closing: 0.7, ventilation: 0.6, inspection: 0.5. The location risk coefficient is equal to... The urgency level is equal to the total planned time divided by the remaining work time. This represents the distance sensitivity attenuation coefficient. This represents the planned stop time and planned start time of device i. Indicates the security sensitivity coefficient. This represents the spatial coordinates of device i. The normalized game value is compared with the set comparison intervals Xg1, Xg2, and Xg3. If the normalized game value falls within interval Xg1, it corresponds to rated power operation and a value of 0. If the normalized game value falls within interval Xg2, it corresponds to reduced load operation and a value of 1. If the normalized game value falls within interval Xg3, it corresponds to emergency shutdown and a value of 2. Position constraints refer to... The reverse start / stop rule means that for any downstream device j (relative to device i), the following must be satisfied: This yields the device start / stop sequence [device ID, tstart, tstop, status], where status refers to rated power operation, reduced load operation, or emergency stop.

[0092] Based on the device start-stop sequence, the device's mobile link stability, and the third-order fault propagation tensor, a joint equation for fault propagation and communication is established, outputting a disturbance response index. The joint equation for fault propagation and communication includes:

[0093] ,in This indicates the disturbance response index. Let F represent the state deviation sequence, the sequence of differences between the planned state and the actual state, and let F represent the third-order fault propagation tensor. This represents the total bandwidth, the sum of the communication bandwidth of all devices. This indicates the amount of data that device i needs to transmit. Represents tensor matrix product. This represents the Frobenius norm. If the disturbance response index is greater than the set threshold, the system will automatically switch to local reverse shutdown mode and recalculate the device start-up and shutdown sequence when communication is interrupted.

[0094] Specifically, the analysis of the collaborative protection module is as follows:

[0095] Based on the real-time failure probability and collaborative response delay of the equipment, a coupled analysis of equipment status and grain condition is performed to obtain the real-time risk value.

[0096] , where represents the real-time risk value, This represents the real-time failure probability of device k. This indicates the grain temperature at the location of device k. Indicates the safe grain temperature threshold. Indicates the impurity concentration gradient. Let represent the Heaviside step function, which takes the value 1 when the grain temperature is >50 degrees Celsius, and 0 otherwise. λ represents the time delay risk weighting coefficient. Indicates the maximum tolerable delay;

[0097] Risk actions are mapped to real-time risk values ​​to obtain safety control instructions. K1, K2 and K3 are respectively the ventilation enhancement threshold, load reduction operation threshold and emergency stop threshold. Ventilation enhancement will automatically start the fan. Emergency stop will automatically switch to local reverse sequence shutdown mode and recalculate the equipment start-up and shutdown sequence when communication is interrupted. Load reduction operation will reduce the conveyor speed and adjust the equipment start-up and shutdown sequence.

[0098] Equipment control command frames are generated through safety control commands (enhanced ventilation / reduced load operation / emergency stop) and start / stop sequence states (rated power operation, reduced load operation, or emergency stop). Specifically, command units are constructed based on the equipment ID (2-byte unique code), command type (1-byte opcode), and parameters (4-byte control value). Rated power operation (0x60) corresponds to the rated power value (IEEE754), enhanced ventilation command (0x61) corresponds to the optimal fan power (IEEE754 floating-point code), reduced load operation command (0x62) corresponds to the load percentage integer value, and emergency stop command (0x63) corresponds to the 32-bit stop sequence mask. N equipment command units are concatenated into a complete Modbus TCP frame through byte-level splicing operations and sent to the equipment slave station via the PLC's 502 port to achieve centralized control of various devices.

[0099] The network communication module collects multi-protocol device distribution data, constructs a protocol heterogeneous entropy weight model to calculate the entropy weight vector, and combines real-time data volume with bandwidth and latency joint optimization to generate a bandwidth allocation matrix. Then, a multi-protocol conversion energy consumption model is used to output the total communication energy consumption. Simultaneously, the emergency decision module uses a Weibull distribution model to fit device failure time data to obtain time-varying failure rates, combines traffic-weighted entropy weights to construct a dynamic fault tree model to output real-time failure probabilities, and integrates device state vectors and spatial attenuation factors to form a third-order fault propagation tensor. Based on mobile link stability, the command propagation speed is calculated, and the propagation is then determined by stopping... The machine sequence spatiotemporal planning and emergency stop coordinated control equations generate coordinated response delays; the grain condition control module establishes coupled partial differential equations of grain pile thermodynamics, combines initial grain temperature, impurities, airflow and oxygen field data, it iteratively solves and updates the temperature field through the finite difference method, coupled the Navier-Stokes equations to update the airflow field and impurity field, outputs a four-dimensional field of grain temperature, mass flow and so on, and then uses a ventilation efficiency energy consumption game model to optimize the fan power, combines the gas control dynamic programming equations to solve the optimal gas velocity sequence, and finally globally optimizes the gas velocity, fan power and total communication energy consumption to generate the minimum energy consumption curve.

[0100] Device node attributes are extracted to construct a hypergraph adjacency tensor, which is then expanded into a spatiotemporal hypergraph adjacency tensor through spatiotemporal state injection. Conflict regions are identified through spatiotemporal conflict manifold detection. Based on this region and the minimum energy consumption curve, a spatiotemporal safety efficiency game model is established to calculate the task utility value and normalized game value. Combined with position constraints and reverse start-stop rules, the device start-stop sequence (including rated / reduced load / emergency stop states) is output. Furthermore, the start-stop sequence, mobile link stability, and third-order fault propagation tensor are fused through the fault propagation communication joint equation to generate disturbance response indicators. The collaborative protection module performs coupled analysis of device status and conditions based on real-time fault probability and collaborative response delay, outputs real-time risk values, and maps them to safety control instructions (enhanced ventilation / reduced load operation / emergency stop). Finally, the start-stop sequence states are combined to generate device control instruction frames containing device IDs, operation codes, and control values. These frames are then byte-level concatenated to form Modbus TCP frames for execution. This terminal optimizes communication energy consumption through protocol heterogeneous entropy weighting, provides early warning of potential risks through dynamic fault tree, precisely regulates the environment through grain pile thermodynamic model, ensures the efficiency of the work chain through spatiotemporal security game, and suppresses joint disturbances through fault propagation tensor, thereby achieving centralized control and closed-loop risk management of various equipment, which greatly improves the safety of grain storage and the continuity of operations.

[0101] As another embodiment, the control terminal's casing adopts a double-layered, waterproof, wall-mounted structure. The operation panel and control buttons feature an embedded waterproof and dustproof design, ensuring stable operation in the outdoor environment of the grain depot for extended periods. Additionally, a double-layered door lock ensures the control terminal's security and prevents vandalism. The terminal casing is as follows: Figure 2As shown, 1: outer protective panel of the machine cover, 2: cabinet door lock, 3: rainproof top cover, 4: inner panel, 5: inner cabinet door lock, 6: heat dissipation vent.

[0102] The foregoing description is illustrative of the invention and should not be construed as limiting it. Although several exemplary embodiments of the invention have been described, those skilled in the art will readily understand that many modifications can be made to the exemplary embodiments without departing from the novel teachings and advantages of the invention. Therefore, all such modifications are intended to be included within the scope of the invention as defined in the claims. It should be understood that the foregoing description is illustrative of the invention and should not be construed as limiting it to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The invention is defined by the claims and their equivalents.

Claims

1. A control terminal for flat warehouse operations, comprising a network communication module, an emergency decision-making module, a grain condition control module, an intelligent scheduling module, and a collaborative protection module, characterized in that, The network communication module is used to collect the distribution data of various protocol communication devices in the grain warehouse and perform dynamic network communication analysis to obtain the total communication energy consumption. The emergency decision-making module is used to collect equipment failure time data for emergency decision analysis and obtain collaborative response delay. The grain condition control module obtains grain temperature, impurity airflow data and total communication energy consumption of the grain pile, performs adaptive grain condition control analysis, and obtains the minimum energy consumption curve. The intelligent scheduling module performs intelligent scheduling of the work chain by acquiring the location status, task attributes, and minimum energy consumption curve of the equipment nodes, and obtains the equipment start-up and shutdown sequence and disturbance response indicators. The collaborative protection module performs security control based on collaborative response delay and device start-stop sequence to obtain device control command frames.

2. The control terminal for flat warehouse operations according to claim 1, characterized in that, The steps for analyzing total communication energy consumption are as follows: The distribution data of various protocol communication devices in the grain warehouse is collected. Based on the distribution data of various protocol communication devices, a protocol heterogeneous entropy weight model is constructed to perform protocol difference quantification and distance attenuation compensation, and the entropy weight vector is obtained. A bandwidth allocation matrix is ​​obtained by jointly optimizing bandwidth and latency based on entropy weight vector and real-time data volume. A multi-protocol conversion energy consumption model is established using the bandwidth allocation matrix and entropy weight vector to obtain the total communication energy consumption.

3. The control terminal for flat warehouse operations according to claim 1, characterized in that, The collaborative response delay analysis steps are as follows: The command propagation speed is calculated by multiplying the mobile link stability of the device with the speed of light. The command propagation speed and the third-order fault propagation tensor are then used for spatiotemporal planning of the downtime sequence to obtain the downtime sequence. Based on the mobile link stability and downtime series of the devices, an emergency stop collaborative control equation is constructed, and spatiotemporal collaborative optimization of multiple devices is performed to obtain the collaborative response delay.

4. The flat warehouse operation control terminal according to claim 3, characterized in that, The real-time failure probability analysis steps of the device are as follows: Collect all failure time data of each device within three years, input these failure time data into the Weibull distribution model for curve fitting, use the maximum likelihood estimation method to solve for the best matching parameter value, and obtain the shape parameter and characteristic lifetime parameter by taking the logarithm and partial derivative and the Newton-Raphson method iteratively solving. Based on the shape parameter and characteristic lifetime parameter, substitute them into the Weibull distribution model to obtain the time-varying failure rate of the k-th device. The production impact value, maintenance cost value, and replacement difficulty value of each piece of equipment are obtained, and multiplied by the corresponding weighting factor coefficient to obtain the importance of each piece of equipment; Based on the entropy weight vector and the real-time communication data volume of the devices, the traffic weighted entropy weight of each device is calculated. The traffic weighted entropy weight of each device is multiplied by the importance of each device to obtain the traffic entropy weight of each device. A dynamic fault tree model is established based on the time-varying failure rate of the k-th device and the flow entropy weights of each device, and the real-time failure probability of the device is output.

5. A control terminal for flat warehouse operations according to claim 3, characterized in that, The mobile link stability analysis steps for the device are as follows: The equipment's operating status, load rate, and temperature are obtained to construct the equipment state vector. The fault coupling strength between equipment is calculated by using the second-order mixed partial derivative of the equipment state vector with respect to the real-time fault probability. A spatial attenuation factor is introduced to obtain the third-order fault propagation tensor. The stability of the mobile link of the device is obtained by evaluating the dynamic stability of the spatial signal through the acquisition of the device's moving speed and signal strength gradient.

6. The control terminal for flat warehouse operations according to claim 1, characterized in that, The minimum energy consumption curve analysis steps are as follows: Based on the updated temperature field, the airflow field is forcibly set at the vent, the wall surface is subjected to no-slip condition, the Navier-Stokes equation is solved in a coupled manner, the wall surface is adopted as a zero-flux boundary when updating the impurity field, the particulate transport equation is solved, the updated impurity field is obtained, and the four-dimensional field of grain temperature mass flow is output. Based on the total energy consumption of communication and the four-dimensional field of grain temperature, mass flow, and flow, a game optimization analysis was conducted using a game model of energy consumption and ventilation efficiency to obtain the optimal fan power. Based on the optimal fan power and the four-dimensional field of grain temperature, mass flow, and flow, a dynamic programming equation for gas regulation is constructed, and the optimal gas velocity sequence is obtained through the Hamilton-Jacobi-Bellman equation. Global energy consumption dynamic optimization is performed on gas flow rate, fan power, and total communication energy consumption to obtain the minimum energy consumption curve.

7. A control terminal for flat warehouse operations according to claim 6, characterized in that, The updated temperature field analysis steps are as follows: A thermodynamic coupled partial differential equation for the grain pile was established. The three-dimensional space of the grain silo was divided into a computational grid. Each grid node was associated with variables. The initial temperature distribution, initial impurity distribution, initial airflow velocity field and initial oxygen concentration of the grain pile were measured through the sensor network of the grain silo. Boundary constraints were set. When updating the oxygen field, the exchange boundary was applied on the surface of the grain pile. The grain pile respiration model was solved, and the oxygen change rate was calculated by the finite difference method. Substituting the oxygen change rate into the coupled partial differential equation of grain pile thermodynamics, the spatial derivative is discretized, and the discretized terms are combined to form a linear equation system. The system of equations is solved by iterative method to obtain a new temperature distribution and the updated temperature field.

8. The flat warehouse operation control terminal according to claim 1, characterized in that, The analysis steps for the equipment start-up / shutdown sequence and disturbance response indicators are as follows: Priority is obtained by multiplying the operation type coefficient, location risk coefficient, and timeliness urgency by the corresponding scaling factor. The delivery volume is multiplied by the priority to obtain the task utility value. Based on the task utility value, conflict area identification, and minimum energy consumption curve, a spatiotemporal safety efficiency game model is established, and location constraints are applied. Following the reverse start-stop rule, the equipment start-stop sequence is output. A joint equation for fault propagation communication is established based on the device start-up and shutdown sequence, the device's mobile link stability, and the third-order fault propagation tensor. The disturbance response index is output. If the disturbance response index is greater than the set threshold, the device will automatically switch to local reverse shutdown mode when communication is interrupted, and the device start-up and shutdown sequence will be recalculated.

9. A control terminal for flat warehouse operations according to claim 8, characterized in that, The conflict zone identification analysis steps are as follows: Extract all device nodes, each device has location attributes, state attributes and task capability attributes, construct a hyperedge set, formalize the hypergraph structure, calculate the adjacency tensor, define the tensor elements, and obtain the hypergraph adjacency tensor; The hypergraph adjacency tensor is extended into a spatiotemporal hypergraph adjacency tensor through spatiotemporal state injection; Spatiotemporal conflict manifold detection is performed on the adjacency tensor of the spatiotemporal hypergraph to obtain conflict region identifiers.

10. A control terminal for flat warehouse operations according to claim 1, characterized in that, The device control command frame analysis steps are as follows: Based on the real-time failure probability and collaborative response delay of the equipment, a coupled analysis of equipment status and grain condition is performed to obtain the real-time risk value. Map risk actions to real-time risk values ​​to obtain safety control instructions; Device control command frames are generated by using safety control instructions and start / stop sequence states.

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