Multi-source data fusion visualization method and system based on cloud platform

Abstract

This invention specifically relates to a multi-source data fusion visualization method and system based on a cloud platform, belonging to the field of intelligent inspection and scheduling technology on cloud platforms. It includes: a module for acquiring basic parameters of tasks and equipment; a module for solving the partial differential equation of energy consumption inflation rate; a module for constructing spatiotemporal movement energy consumption game operators; a module for dynamic priority quantification and evaluation calculation; and a module for generating inspection task scheduling sequences. In this invention, an energy consumption inflation rate solution mechanism based on partial differential evolution is constructed. Combined with the category characteristics of abnormal events, the dynamic changing trend of energy consumption in handling is deduced, and the incremental energy consumption effect brought about by the anomaly diffusion process is quantified. Compared with conventional static energy consumption prediction methods, this approach better reflects the real energy consumption needs under dynamic anomaly scenarios.

Description

Technical Field

[0001] This invention relates to the field of intelligent inspection and scheduling technology on cloud platforms, and in particular to a method and system for multi-source data fusion and visualization based on cloud platforms. Background Technology

[0002] Intelligent inspection systems have been gradually applied to facility operation and maintenance and anomaly handling in various scenarios such as industrial plants, power grids, and urban municipal projects.

[0003] The current cloud platform can realize unified access, centralized storage and visualization of multi-source heterogeneous data such as inspection equipment status, task attributes and environmental perception parameters. It provides a data foundation and carrier support for the overall scheduling of inspection tasks and the visualized management and control of the operation and maintenance process, and promotes the digital transformation of inspection operation and maintenance.

[0004] Currently, inspection task scheduling technology typically sets scheduling priorities based on the inherent attributes of the task, and combines the real-time location, remaining energy, and other operating status parameters of the inspection equipment to complete the matching and allocation of tasks and equipment.

[0005] Some optimization schemes incorporate environmental factors such as terrain and obstacle distribution for path planning, and adjust mobile energy consumption and travel speed in conjunction with weather conditions. They have good adaptability in routine inspection tasks and can meet the needs of daily operation and maintenance scenarios.

[0006] In application scenarios where abnormal events evolve dynamically, the energy consumption requirements and urgency of handling inspection tasks will change dynamically as the abnormality spreads. Existing scheduling mechanisms have room for further development in areas such as dynamic extrapolation of energy consumption during abnormality evolution, coupled analysis of energy consumption during equipment movement and the energy consumption effect of abnormalities, and weighted fusion scheduling of multi-dimensional risk factors. Summary of the Invention

[0007] The purpose of this invention is to propose a multi-source data fusion visualization method and system based on a cloud platform in order to solve the above-mentioned problems.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A cloud-based multi-source data fusion and visualization system includes: Task and Equipment Basic Parameter Acquisition Module: Collects the initial occurrence location coordinate vector, initial trigger time parameter, initial baseline energy consumption expectation value, inherent basic urgency parameter, abnormal feature category identifier, current location coordinate vector, effective remaining work quantity, and basic motion energy consumption coefficient; Partial differential equation solution module for energy consumption inflation rate: Substitute the abnormal feature category identifier into the model, and combine the initial baseline energy consumption expectation value to obtain the dynamic energy demand expectation value and the average energy consumption inflation rate coefficient. Spatiotemporal movement energy consumption game operator construction module: Based on the initial location coordinate vector, the current location coordinate vector and the basic motion energy consumption coefficient, the expected movement time parameter and movement energy parameter are calculated. After verifying the functional quantity by the expected value of dynamic energy demand and the effective surplus, the average energy consumption inflation rate coefficient is incorporated to obtain the spatiotemporal movement energy consumption game operator. Dynamic priority quantification evaluation and calculation module: Maps the spatiotemporal movement energy consumption game operator and the inherent basic urgency parameter to the game operator feature value and the basic urgency feature value, calculates the cumulative waiting delay parameter according to the initial trigger time parameter, and obtains the comprehensive dynamic priority score parameter by weighting together with the preset risk multiplier. Inspection task scheduling sequence generation module: The candidate scheduling data element array is obtained by descending the comprehensive dynamic priority scoring parameter. After being filtered by the task allocation status latch table and the equipment occupancy status latch table, the scheduling instruction frame is issued.

[0009] Preferably, in the task and equipment basic parameter acquisition module: The central computer room broadcasts a time synchronization message carrying precise time information. The hardware media access control layer captures the physical arrival time of the message to dynamically compensate and correct the local system time. A timestamp is stamped as the initial trigger time parameter when the inspection task trigger signal is generated. By frequently reading the battery terminal voltage, transient high-current pulse data, internal AC impedance spectrum, and readings from distributed temperature sensors inside the battery compartment, and combining this with a nonlinear discharge curve temperature correction model, the effective remaining work capacity is calculated using ampere-hour integration.

[0010] Preferably, in the partial differential equation solving module for the energy consumption inflation rate, the state diffusion partial differential function is called from the physical normal evolution equation library based on the abnormal feature category identifier, and the instantaneous inflation rate value is calculated according to the following formula: ; in, Indicates task exist The instantaneous inflation rate at any given moment. This represents the energy conversion coefficient per unit volume of spatial scanning. This represents the volumetric parameter representing the anomalous physical field in three-dimensional space. Represents the instantaneous spatial diffusion volume velocity. This represents the penalty coefficient for nonlinear complexity. This represents the virtual time variable in the deduction process. Indicates the initial trigger time parameter. This indicates the time-based inflation index parameter. When an abnormal state is determined to exhibit a chain reaction or an accelerated collapse trend at the micro level, the time inflation index parameter is set to a value greater than one. Based on the discrete time step parameter, the instantaneous inflation rate is discretized and accumulated to obtain the local inflation energy increment micro-element value, and the dynamic demand energy expectation value is recursively calculated. The dynamic energy demand expectation is compared with the maximum permissible inflation energy hard physical threshold parameter. If it exceeds the maximum permissible inflation energy hard physical threshold parameter, the dynamic energy demand expectation is forcibly truncated and anchored to the maximum permissible inflation energy hard physical threshold parameter, and a resource depletion early warning signal is generated. Simultaneously, based on the current real-time state of the system, the system makes significant forward projections in virtual memory to the end of the short-term forecast window in the future, collects all inflation increment micro-elements within the virtual future time window, and calculates the average slope value of the overall evolution trend. The average slope value is then used as the average energy consumption inflation rate coefficient.

[0011] Preferably, the spatiotemporal mobility energy consumption game operator construction module specifically includes: Based on the environmental elevation digital model and the three-dimensional obstacle grid map, the actual walkable obstacle avoidance physical trajectory is explored. The dynamic environmental topology tortuosity coefficient is obtained by dividing the absolute total length of the actual walkable obstacle avoidance physical trajectory by the theoretical three-dimensional Euclidean straight distance. Determine the absolute height difference in three-dimensional space between the target position and the current position. If the target position is rising, multiply it by the nonlinear transmission loss compensation coefficient to obtain the value of the gravitational potential energy work function. If the target position is falling, multiply it by the potential energy conversion and recovery coefficient to obtain the value of the gravitational potential energy work function. When calculating the expected movement time parameter, the system reads on-site meteorological sensor data to generate a real-time environmental resistance loss factor, and uses the real-time environmental resistance loss factor to reduce and correct the expected average movement speed parameter.

[0012] Preferably, the process of obtaining the spatiotemporal movement energy-consuming game operator is as follows: The effective remaining energy consumption is compared with the sum of the total estimated energy expenditure parameter and the dynamically set safe return threshold parameter. If the sum is greater than or equal to the sum, the task physical feasibility indicator variable is assigned a high-level logic flag, and the spatiotemporal movement energy consumption game operator is calculated according to the following formula: ; in, Indicates that it is for the equipment With the task The spatiotemporal movement energy consumption game operator calculated by pairing, Indicates the physical feasibility indicator variable of the mission. This represents the natural exponential function. This represents the inflation penalty weighting factor. This represents the average energy consumption inflation rate coefficient. This indicates the estimated travel time parameter. This represents the moving cost weighting adjustment factor. Indicates the parameter of moving energy. This represents the positive bias mathematical constant buffer amount.

[0013] Preferably, in the dynamic priority quantization evaluation and calculation module, the accumulated waiting delay parameter is obtained by reading the difference between the current system absolute clock time and the initial trigger time parameter through an internal timer, and the result of the nonlinear dynamic penalty function is calculated according to the following formula: ; in, This represents the result of the nonlinear dynamic penalty function. Represents the natural exponent operator. This indicates the time penalty steepness adjustment control parameter. This represents the cumulative waiting time parameter. This represents the timeout tolerance threshold parameter.

[0014] Preferably, the logic for obtaining the comprehensive dynamic priority scoring parameter is as follows: The built-in polygonal electronic fence detection algorithm determines whether a 3D coordinate point falls within a high-risk explosion-proof area posing a danger of flammable, explosive, highly toxic leaks, or high-voltage arc discharge. If it does, a preset amplification factor is extracted and assigned to the risk multiplier. And obtain the eigenvalues ​​of the game operator through normalization. Subsequently, a higher-order weighted linear combination framework and a penalty coupling mechanism are used to fuse features from different dimensions to calculate the comprehensive dynamic priority scoring parameter. ; Comprehensive dynamic priority scoring parameters The operational logic is based on the mechanism of successful dimensionality reduction of multidimensional heterogeneous reference factors, and it is subject to the inherent urgency weight coefficient. Weighting coefficients in spatiotemporal game The mathematical encapsulation and benchmark scale constraints, incorporating basic urgency eigenvalues At the same time, the amplification coefficient is increased through dynamic time penalty. Increase the priority cap for low-priority tasks and utilize risk multipliers. Grant high-risk areas the highest level of intervention privileges, which are above the usual rules.

[0015] Preferably, in the inspection task scheduling sequence generation module, candidate scheduling data elements in the candidate scheduling data element array are popped out from top to bottom. When the flag bit of the corresponding index in the task allocation status latch table and the flag bit of the corresponding index in the device occupancy status latch table are both logical zero, the flag bits of the corresponding index in the task allocation status latch table and the flag bits of the corresponding index in the device occupancy status latch table are all reversed to logical one to resolve concurrent allocation conflicts. If the underlying query results show that any flag has been set to logical one, then the current candidate scheduling data element is discarded without any underlying state change, and the next element in the buffer pool is explored.

[0016] Preferably, the method further includes: The total estimated energy expenditure parameter is forcibly injected into the hardware instruction frame for encapsulation and distribution. If the actual cumulative power consumption exceeds the sum of the total expected energy expenditure parameter plus a fixed proportion of the hardware safety redundancy defense threshold during the movement, the edge computing chip on the device will immediately melt down the current inspection task instruction and unconditionally execute the original route return instruction or the safe emergency landing strategy. After broadcasting the scheduling data packet, if no acknowledgment signal is received within the preset time window specified in the protocol, the link fast retransmission mechanism is activated. If the number of retransmissions is exhausted and no response is detected, the abnormal isolation logic is triggered, the flag in the status latch table is set to the disconnected or lost state, and the latch flag of the corresponding task is reset to the idle and standby state and pushed back to the central waiting pool.

[0017] Multi-source data fusion and visualization methods based on cloud platforms include: Collect the initial occurrence location, initial trigger time, initial baseline energy consumption expectation, inherent basic urgency, anomaly characteristic category, current location, effective remaining work capacity, and basic motion energy consumption coefficient; Substitute the abnormal feature categories into the partial differential evolution model, and combine them with the initial baseline energy consumption expectation to solve for the dynamic energy demand expectation and the average energy consumption inflation rate coefficient. Based on the initial position, current position, and basic motion energy consumption coefficient, the estimated movement time and movement energy are calculated. After verifying the functional quantity by the expected dynamic energy demand and the effective surplus, the average energy consumption inflation rate coefficient is incorporated to obtain the spatiotemporal movement energy consumption game operator. The game operator and the inherent basic urgency are mapped to feature values ​​respectively. The cumulative waiting time is calculated based on the initial trigger time. The combined weighted by the preset risk multiplier is used to obtain the comprehensive dynamic priority scoring parameter. A candidate scheduling array is generated by sorting the scoring parameters in descending order. After double filtering by the task allocation status latch table and the device occupancy status latch table, a scheduling instruction frame is issued.

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention constructs an energy consumption inflation rate solution mechanism based on partial differential evolution, combines the category characteristics of abnormal events to deduce the dynamic change trend of energy consumption in handling, and quantifies the energy consumption increment effect brought about by the abnormal diffusion process. Compared with the conventional static energy consumption prediction method, it can better fit the real energy consumption demand under dynamic abnormal scenarios.

[0019] 2. This invention couples the abnormal energy consumption inflation characteristics with the spatiotemporal energy consumption of equipment to construct a spatiotemporal energy consumption game operator, and combines the remaining energy of the equipment to conduct execution feasibility verification. This can improve the energy adaptability of the task and the equipment, and reduce the risk of task interruption or equipment return due to deviations in energy consumption prediction. Attached Figure Description

[0020] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a system structure diagram of the present invention. Detailed Implementation

[0021] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0022] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0023] Example 1 Its specific implementation method is combined with the appendix Figure 1 Please provide a detailed explanation.

[0024] Appendix Figure 1 The diagram below illustrates the structure of a cloud-based multi-source data fusion visualization method and system provided in this embodiment of the invention. It shows the connection between the task and equipment basic parameter acquisition module and the inspection task scheduling sequence generation module, and marks the main functional interaction flow of each module.

[0025] In this embodiment, it includes: Module 1, Task and Equipment Basic Parameter Acquisition Module: Collects the initial occurrence location coordinate vector, initial trigger time parameter, initial baseline energy consumption expectation value, inherent basic urgency parameter, abnormal feature category identifier, current location coordinate vector, effective remaining work quantity, and basic motion energy consumption coefficient; Inspection tasks are scattered across a vast industrial area. The local hardware clocks inside the alarm nodes and inspection terminal equipment at each edge are inevitably affected by physical factors such as the aging of quartz crystal materials and drastic fluctuations in ambient temperature, resulting in slight clock drift and phase deviation that accumulate over time.

[0026] The system introduces a high-precision network time synchronization protocol (such as the IEEE 1588V2 time protocol) with hardware timestamp capability at the bottom layer of the network communication architecture. The system broadcasts time synchronization messages carrying time information to all online inspection execution devices and fixed sensing aggregation gateways through the master control clock source node in the central computer room according to the set high-frequency packet transmission cycle.

[0027] Each remote receiving node captures the actual physical arrival time of the message at the hardware media access control layer (MAC layer), and calculates the absolute offset of the master and slave clocks and the path difference of the asymmetric network transmission delay by parsing the link delay request and response frames. Then, it performs microsecond-level dynamic compensation and correction on the time register of its local system microcontroller.

[0028] When a sensing node detects an anomaly and generates an inspection task trigger signal, it must add a timestamp that has undergone high-precision synchronization verification to the protocol header of the data packet.

[0029] This timestamp not only records the moment the task was triggered by the physical world, but also serves as the benchmark starting point for all subsequent partial differential equation calculations of "time decay" and "energy delay penalty".

[0030] To address the completely different spatial position description standards that sensors from different manufacturers may use (for example, some indoor relative displacement sensors use relative topological grid coordinates based on building column grids, while outdoor flying devices use the WGS84 geodetic coordinate system and latitude and longitude elevation representation), the system controller incorporates high-order matrix attitude mapping and coordinate system rigid body transformation logic.

[0031] The system backend retrieves the installation and calibration extrinsic parameter matrices of each sensor from the database. Using a homogeneous coordinate transformation matrix that includes rotation and translation vectors, all local relative coordinate data are mapped to a globally defined local northeast-sky (ENU) three-dimensional rectangular coordinate system after attitude corrections for yaw, pitch, and roll angles.

[0032] After matrix multiplication transformation, the occurrence point of each new inspection task, the current parking position of each idle inspection device, and the three-dimensional envelope of known environmental obstacles are all measured and represented by three-dimensional coordinate floating-point vectors under this unified standard coordinate system.

[0033] When an environmental sensing node (such as a high frame rate infrared thermal imaging array, a ppm-level gas concentration detector, a high frequency acoustic emission vibration sensor, etc.) detects that the physical state parameters exceed the safety baseline and reports it, the system receives a packet containing the abnormal raw data stream and extracts various basic parameters that are strongly related to task scheduling through the background packet depth detection engine (DPI).

[0034] For each independently triggered inspection task node registered with the system, the system creates a dedicated data structure in the high-speed memory status database to define and record the following key parameters: The first parameter is the coordinate vector of the initial location of the task. This parameter represents the spatial fixed point location of the task in the form of three-dimensional coordinates, which determines the endpoint coordinate constraint of the spatial movement path planning in the subsequent scheduling algorithm. The second parameter is the initial trigger time parameter of the task, which records the physical point in time when the sampling values ​​of the sensing node first stably exceed the set safety threshold and eliminate transient interference after multiple consecutive sampling values. The third parameter is the initial baseline energy consumption expectation for the mission. The physical engineering significance of this parameter lies in the assumption that the inspection equipment has an idealized "teleportation" capability at the moment the mission is triggered, enabling it to immediately reach the accident site regardless of spatial distance to perform fault detection, partial repair, or high-precision information collection. The absolute basic electrical energy required to complete this series of standard operation actions is obtained directly by querying the standard operation action power consumption characteristic dictionary built into the system background; The fourth parameter is the anomaly characteristic category identifier, used to distinguish the specific physical attribute dimension of the anomaly event. For example, the identifier "Type-A" represents a simple high-temperature heating phenomenon in equipment, the identifier "Type-B" represents the outward diffusion of dangerous flammable gas leakage, and the identifier "Type-C" represents high-frequency irregular vibration anomaly in heavy machinery bearings.

[0035] The drastically different physical properties and their physical evolution trajectory over time are vastly different from the deterioration of energy consumption. Therefore, this feature category identifier is a key guideline for determining which specific form of partial differential equation to call in the next module.

[0036] Parallel telemetry collection and in-depth analysis of the underlying hardware state parameters and kinematic parameters of all available inspection devices are carried out. During standby or operation, the inspection devices send heartbeat maintenance packets and high-density telemetry frames containing hundreds of hardware register states to the scheduling center at a fixed frequency (e.g., once every 50 milliseconds).

[0037] Parse these state data streams to construct dynamic state attribute words for each available device.

[0038] The core parameters obtained from the device side include: The device's current position coordinate vector is used for subsequent calculations of the spatial geometric distance, path tortuosity, and number of navigation grid nodes traversed from the device's current docking point to each candidate high-risk task point; The current effective remaining power level of the device should not be simply calculated based on the rough remaining power percentage (SOC) directly reported by the battery management chip.

[0039] The available energy release capacity of power batteries such as lithium-ion batteries is greatly affected by the current instantaneous discharge rate, the number of cycle aging, and the ambient temperature.

[0040] It is necessary to read the current terminal voltage, transient high current pulse data, internal AC impedance spectrum evaluation parameters, and readings of distributed temperature sensors inside the battery compartment at high frequency. By deeply combining the ampere-hour integration method (coulomb integration) with the nonlinear discharge curve temperature correction model, the "effective remaining work power" that can be called by the motor and sensors under the current physical environment and specific equipment health degradation is calculated. This core energy parameter is calibrated and stored in the standard physical unit "joule". The basic motion energy consumption coefficient of equipment is a standard electrical energy consumption benchmark value for a specific model of equipment under a specific load condition, in a standard laboratory test environment such as flat and windless conditions, to overcome the transmission friction resistance of its own mechanical structure and the basic standby power consumption of the main control electronic components, for each unit geographical distance moved forward.

[0041] The maximum physical speed limit and the maximum instantaneous detection power limit parameters of the equipment are limited by the rated limit speed of the brushless motor and the thermal design power limit of the main control chip. These parameters limit the ability of a specific device to reach the site and perform high-intensity tasks at the objective physical level, thereby preventing the upper-level dispatch center from issuing unreasonable control commands that exceed the limits of the underlying hardware and may cause the motor to burn out.

[0042] After obtaining all the basic parameters of the above tasks and a large number of device status parameters, in order to prevent sudden bit errors in the transmission of industrial wireless data links or errors in the input parameters of command calculation caused by sudden electromagnetic interference on site, it is also necessary to perform necessary digital filtering and consistency verification filtering mechanisms on these parameters.

[0043] Due to strong electromagnetic pulse interference in the industrial environment or the inherent thermal noise of the sensor's analog front end, the reported position coordinate sequence and initial energy consumption expectation may experience irregular numerical jumps within a short period of time.

[0044] A dedicated cache pool is allocated in the main memory for the parameter sequence within the received continuous time window. A smoothing and noise reduction process is performed using a moving average window logical depth combined with a Gaussian probability distribution model. Abnormal spike jumps and dirty data that deviate from the statistical mean by more than three standard deviations (3-Sigma rule) are identified and removed.

[0045] The clean parameters, after consistency checks and noise reduction filtering, will be structured and written into a global dynamic parameter table; This global dynamic parameter table resides in the high-speed volatile memory region of the scheduling system in the form of a large-scale sparse matrix array data structure.

[0046] The row index of the matrix points to each independently generated inspection task entity that is in the state of waiting to be assigned and claimed, while the column index stores the three-dimensional position coordinates, trigger time, initial baseline energy consumption expectation, abnormal feature identifier, and the status update timestamp of the corresponding data frame as defined above.

[0047] For available device parameters registered in the system and that have passed hardware self-test, an independent parallel device resource pool association matrix is ​​constructed, and the attitude position and effective remaining energy of each device are refreshed in real time and at high frequency.

[0048] Module 2, Partial Differential Solution Module for Energy Consumption Inflation Rate: Substitute the abnormal feature category identifier into the model, and combine it with the initial baseline energy consumption expectation value to obtain the dynamic energy demand expectation value and the average energy consumption inflation rate coefficient. In real-world industrial inspection physical evolution scenarios, if an abnormal task is not promptly intervened and properly handled by the inspection equipment, as the delay time continues to lengthen, the local abnormal state often exhibits a strong trend of spreading, worsening, or multi-dimensional spatial diffusion due to the action of objective physical laws.

[0049] For example, if the localized abnormal heating of a large transformer bushing is not investigated and physically cooled in time, the heat energy will continue to be conducted to the surrounding highly sensitive electronic components through heat conduction and infrared thermal radiation. This means that when the inspection robot arrives, it cannot simply stop to take infrared images of a single point. Instead, it needs to turn on the ultra-high power infrared thermal imaging sensor array to cover the entire field of view and perform scanning operations in a huge three-dimensional space. It also needs to stay in a high-temperature, harsh and dangerous environment for a long time and spend many times more computing resources to perform feature comparison and video stream recording. Similarly, in the case of minor leaks of toxic gases in chemical storage tanks and pipelines, as harmful gas molecules diffuse and drift outward under the influence of a micro-wind field in fluid dynamics, the spatial concentration distribution field undergoes drastic and irreversible changes. As a result, the inspection drone must significantly expand its air sampling flight trajectory in three-dimensional space, leading to a significant and non-linear increase in the actual rotor power consumption required to perform this specific task.

[0050] To quantify the worsening of energy consumption inflation over time for different types of high-risk tasks, an independent partial differential equation prediction model for energy consumption inflation is instantiated and allocated in the computing engine for each recorded pending inspection task.

[0051] The input to the prediction model comes from the task triggering time, the initial baseline energy consumption expectation, and the highly instructive anomaly feature category identifiers obtained in Module 1. The final output of the mathematical model is the absolute value of the dynamic energy expectation required for the specific task at any estimated time in the future, and the instantaneous first-order partial derivative of the energy consumption value over time (i.e., the instantaneous inflation rate) that characterizes the rate of deterioration.

[0052] Based on the abnormal feature category identifier extracted from Module 1, a highly matched state diffusion partial differential function is called from the pre-built physical normal evolution equation library at the system's bottom layer.

[0053] Let the system ID of the inspection task currently being evaluated by the computing engine be denoted as . The initial triggering time parameter determined in Module 1 is: The current virtual system time being simulated by the background computing engine (or the estimated future time when the inspection equipment will arrive after a long journey) is... .

[0054] Define task At any given moment of simulation The dynamic energy requirement expectation is The integral equation for energy consumption inflation, which forms the theoretical foundation of the system, is expressed as follows: ; in, This represents the tasks extracted in Module 1. The initial baseline energy consumption expectation; This represents the virtual time elapsed variable in the definite integral derivation process, and its integral domain spans the entire physical time window from the first triggering of the task to the final arrival of the equipment on site. Representative task The core energy-consuming inflation rate partial differential function, its physical meaning is... Within this instantaneous time slice, the energy required for the task to complete the investigation and repair increases instantaneously due to the continuous deterioration of the on-site environment.

[0055] Energy consumption inflation rate function The higher-order construction is of paramount importance in this module, determining the accuracy of the prediction model.

[0056] Based on the diffusion properties of specific anomalies, Expanded into a composite function structure containing a spatial geometry diffusion penalty term and a data analysis complexity amplification penalty term: ; in, Spatial affected range function This term uniformly refers to the volumetric parameter of anomaly physical fields in three-dimensional space (its physical dimension is expressed in cubic meters). Examples include the three-dimensional irregular diffusion envelope volume of harmful gases or the three-dimensional volumetric radiation sweeping space volume.

[0057] This volume parameter is related to time. First-order partial derivatives It represents the instantaneous spatial diffusion volume velocity (its physical dimension is cubic meters per second) of the physical expansion of the three-dimensional affected volume range outward over time.

[0058] The energy conversion coefficient per unit volume of space scanning (its physical dimension is joules per cubic meter).

[0059] The physical engineering significance of this parameter is as follows: As the affected dangerous three-dimensional volume expands, for every cubic meter increase in the volume of the inspection equipment's detection and search in the vast three-dimensional space, the absolute power consumption cost of the environmental sensor matrix (such as lidar rangefinders, ultrasonic phased array probes, etc.) required per unit time increases.

[0060] The nonlinear complexity penalty coefficient is used to characterize the additional computing unit power consumption caused by the long-term persistence of abnormal states, which leads to the aggravation of damage to the internal physical structure of the tested object. This results in the increased order of the operation matrix and the heavier computational load when the edge computing microprocessor on the device performs fault feature extraction and deep data analysis.

[0061] The time inflation index parameter is determined by the specific value of the microscopic physical diffusion mechanism of the anomaly type.

[0062] When the system determines from a lookup table that an abnormal state exhibits a chain reaction or accelerated collapse trend at the microscopic level (such as the rapid heat accumulation period in the early stages of a fire), the following settings are configured. This causes the penalized polynomial to exhibit a steep upward trend; When the anomaly spreads outward at a constant rate, the following settings are made: It exhibits linear growth; when the anomalous evolution is hindered by physical walls and boundaries in space, causing the diffusion rate to gradually and passively slow down, the following settings are made: This causes the polynomial to exhibit a gradual convergence characteristic.

[0063] After clarifying the specific theoretical continuous form of the partial differential equation, due to the objective hardware limitations of the embedded controller built into the scheduling center or the computing resources of the edge computing cluster, it is impossible to continuously solve high-precision continuous symbolic calculus for hundreds or thousands of concurrent burst tasks throughout the plant. The operation and control mechanism is as follows: The system's underlying clock divides the continuous future projection timeline into tiny discrete time step slices of fixed length; The system utilizes numerical integral approximation logic with high numerical stability (such as the classic forward Euler approximation rule or higher-order discrete derivation logic with smaller truncation errors) to perform meticulous discretization and cumulative derivation of the partial differential equation of energy consumption inflation rate.

[0064] At each tiny discrete time node slice of the virtual propulsion, the system substitutes the values ​​of various state characteristics at that instant into the above partial differential evolution function to obtain the value of the local inflation energy increment micro-element generated in the current time slice.

[0065] By algebraically adding the incremental value of the element just obtained, along with the cumulative inflation total energy value calculated from the previous discrete time step, we obtain the absolute value of the expected dynamic energy demand at the latest time step.

[0066] During the continuous high-frequency discrete deduction and solution calculation process, the system establishes an energy boundary condition watchdog blocking mechanism.

[0067] According to the aforementioned nonlinear growth polynomial, when an extremely remote task is not processed by idle equipment for a long time, the theoretically calculated expected value of the required energy will increase infinitely and tend towards infinity, but this is extremely absurd in objective physical reality.

[0068] Any heavy-duty inspection equipment has an absolute physical and chemical limit to the capacity of its high-energy-density battery pack. When the abnormal deterioration of the inspected object reaches a certain level of damage, it will directly trigger the power outage physical protection at the substation level in the plant area.

[0069] For each type of task, a corresponding maximum allowable inflation energy hard physical threshold parameter is set during memory allocation initialization; After each iterative accumulation and recursive calculation is completed, a comparator condition is hard-added at the system hardware level: If the currently calculated dynamic energy expectation exceeds the set maximum allowable inflation energy threshold, the controller will forcibly truncate the current energy requirement value and anchor it to the upper limit of the threshold, preventing the floating-point value from continuing to overflow.

[0070] At the same time, the scheduling system will generate a "resource depletion warning signal" of the highest interruption level for the severed task, reminding human operators to immediately put on protective clothing and intervene manually.

[0071] The computing engine is not limited to calculating the expected dynamic energy demand at the current instant, but also makes forward extrapolations in virtual memory based on the current real time of the system, and calculates the average energy consumption inflation rate coefficient within a short-term forecast window in the future.

[0072] The discrete calculation logic for this coefficient is as follows: The system virtually advances its time step to the end of a future window, collecting all incremental inflationary elements within this virtual future time window, and calculating the average slope of its overall evolutionary trend. This average energy-consuming inflation rate coefficient profoundly and quantitatively quantifies the dramatic deterioration of the task's "becoming more energy-intensive" in the current and near future stages.

[0073] The larger the value of this parameter, the higher the additional electrical energy cost required to execute and extinguish it in the future, on a cold physical level, for every second of delay.

[0074] By utilizing the underlying multi-threaded concurrent architecture, the above partial differential equation solution and numerical approximation iterative derivation process are completed synchronously for all concurrent tasks in the pending allocation state.

[0075] The current dynamic energy demand expectation of each task, the future average energy consumption inflation rate coefficient predicted based on the time window, and the aforementioned physical cutoff warning status flags are integrated and packaged into a large array of inflation characteristic state vectors strongly correlated with the time dimension. This massive array is continuously refreshed and rewritten in the cache at predetermined millisecond intervals as the physical scheduling clock ticks.

[0076] Module 3, Spatiotemporal Movement Energy Consumption Game Operator Construction Module: Based on the initial location coordinate vector, the current location coordinate vector, and the basic motion energy consumption coefficient, the estimated movement time parameter and movement energy parameter are calculated. After verifying the functional quantity by the expected value of dynamic energy demand and the effective surplus, the average energy consumption inflation rate coefficient is incorporated to obtain the spatiotemporal movement energy consumption game operator. The inspection equipment moves from its current random docking point or the location of a recently completed task to the site of the next new high-risk task. This is a continuous motor-driven motion process that must traverse real three-dimensional physical space, overcome environmental resistance, and consume a significant amount of time.

[0077] This module places the "huge electrical cost of the motor running at high speed during physical space movement" and the "energy deterioration penalty cost caused by the continuous expansion of the task itself during the time delay waiting period" in a highly unified, high-order mathematical evaluation system that transcends time and space dimensions, and conducts in-depth game theory measurement and adversarial analysis.

[0078] The system's backend engine calculates a high-dimensional spatiotemporal energy consumption game operator for each unclaimed inspection task and each available inspection device that is currently idle or can be preempted by a higher-level interrupt. This operator, in the form of a single floating-point value, quantifies the "macroeconomic energy recovery benefit" that can be generated by assigning a specific numbered device to handle a specific numbered task.

[0079] Assuming that there are available inspection devices with specific numbers in the current dispatch system's wide area network, the system uses its onboard high-precision dual-frequency satellite positioning receiver and internal microelectromechanical inertial navigation unit (IMU) to perform low-level multi-sensor data fusion and joint calculation to obtain its current instantaneous position vector in the global three-dimensional coordinate system; the system extracts the fixed position vector of the inspection task to be processed in three-dimensional space; The path planning engine in the scheduling backend first calls a high-precision digital model of environmental elevation (DEM) and a fine three-dimensional obstacle grid map. Using heuristic path search algorithms (such as a high-order variant of the A* algorithm), it explores the actual walkable obstacle avoidance physical trajectory in the digital map, starting from the current three-dimensional coordinates of the device, cleverly bypassing all three-dimensional obstacles, and finally safely arriving at the three-dimensional coordinates of the mission site.

[0080] After obtaining this complex trajectory, the system extracts the three-dimensional coordinate vectors of the device and the task in a unified coordinate system, calculates the coordinate differences between the two in the three orthogonal dimensions of the X-axis, Y-axis and Z-axis, then calculates the sum of the squares of these three coordinate differences, and performs a square root operation on the sum of the squares to calculate the theoretical three-dimensional straight-line distance between the two in the case of no obstruction. From this, a key dynamic environment topology tortuosity coefficient is derived.

[0081] This coefficient is always greater than or equal to a single value. The system multiplies the theoretical three-dimensional Euclidean straight-line distance with the environmental topological tortuosity coefficient to obtain the actual planned physical movement distance parameter that closely approximates the real physical constraints.

[0082] After obtaining high-precision actual planned physical movement distance parameters, the system further establishes a device dynamics deduction model based on complex mechanics to calculate the actual physical movement energy parameters required for the specific device to travel to the specific task location and the estimated movement time parameters consumed along the way.

[0083] The energy parameters of equipment movement are not only affected by the rolling friction between the tires or tracks and the ground on a horizontal surface, but also by the work done by the huge gravitational potential energy brought about by the dramatic undulations of the terrain.

[0084] The system-defined computational framework consists of three main parts: The first part is the energy dissipation to overcome the basic friction, which is equal to the actual planned physical movement distance parameter multiplied by the basic motion energy dissipation coefficient of this specific model of equipment; The second part is the fixed startup energy dissipation constant required for the device to cold start from a static dormant state to enter a moving state, overcome huge static friction, and wake up the high-computing-power navigation module. The third part is the key gravitational potential energy as a work quantity term.

[0085] Considering the asymmetrical energy consumption characteristics of the built-in electric motors of mechanical equipment when climbing uphill fully loaded and gliding downhill in the real physical world, the system uses a high-precision segmented physical model for underlying logic expansion and parameter quantization calculation: The system comparator first determines whether the absolute height of the target position in three-dimensional space is higher than the current absolute height of the equipment. If the result is higher (i.e., the equipment needs to perform a gravity-resistant climbing or vertical lifting action), the system calculates the theoretical value of the work done by gravity by multiplying the absolute height difference between the starting and ending points of the equipment in three-dimensional space, the physical mass parameters of the entire inspection equipment of this model pre-registered in the scheduling system, and the standard gravitational acceleration constant.

[0086] As the motor output torque increases dramatically during the climbing process, resulting in huge coil heat loss and additional transmission meshing friction loss of the mechanical gearbox, the system further multiplies the theoretical value of gravity work by a nonlinear transmission loss compensation coefficient greater than one, and finally calculates the absolute value of the gravitational potential energy work function in the ascending state. If the comparator determines that the height of the target position is lower than or equal to the current height of the equipment (i.e., the equipment is in a downhill or horizontal movement state), the system will first calculate the theoretical value of the work done by gravity based on the absolute height difference in three-dimensional space, the physical mass parameters of the equipment itself, and the standard gravitational acceleration.

[0087] Since gravity does positive work, for equipment equipped with an advanced braking energy recovery inverter module, when it goes downhill and converts excess gravitational potential energy into electrical energy to charge the battery pack in reverse, the system multiplies the theoretically based value of the work done by gravity by a potential energy conversion and recovery coefficient between zero and 0.3.

[0088] After calculating the sum of the mobile energy parameters including the above three parts, the system synchronously starts calculation to determine the estimated travel time of the device. Since real-world outdoor industrial environments are often accompanied by severe weather conditions such as strong winds and heavy rain, the equipment cannot maintain its nominal maximum speed indefinitely.

[0089] By reading wind speed or rain / snow sensor data from the on-site weather station, a real-time environmental drag reduction factor between zero and one is dynamically generated. The system uses this drag reduction factor to perform a rigorous multiplicative reduction correction on the expected average moving speed parameter of the equipment under standard undisturbed conditions.

[0090] Divide the previously obtained actual planned physical movement distance parameter by this reduced and corrected actual physical movement speed to obtain the pure travel time.

[0091] To absorb the time delays caused by unexpected short-term congestion or local dynamic obstacle avoidance replanning along the way, the system adds a fixed time buffer margin to the pure travel time, and finally calculates a reliable estimated travel time parameter.

[0092] When the device arrives at the distant mission site after traveling the aforementioned estimated time span, the expected absolute value of the actual energy required for the mission during this long journey, after inflationary deterioration.

[0093] The expected absolute physical time for the equipment to arrive at the site is set as: the current system clock time plus the estimated travel time parameter.

[0094] The system controller calls the energy consumption inflation integral equation model constructed in Module 2, and extends the upper limit variable of the definite integral to the expected arrival time in the future. The total energy value of the inflation at the expected arrival time is obtained by integral calculation.

[0095] Based on the above derivation of all long chains, the system summarizes and calculates: If the dispatch system decides at this moment to assign the equipment to perform the task, the resulting total estimated energy expenditure will be enormous. This total expenditure consists of the energy parameters of the journey and the energy parameters that must be consumed to cope with the highly deteriorating task upon arrival at the site.

[0096] To ensure that dispatch commands are absolutely executable on a cold, physical level, and to resolutely prevent equipment from running out of power midway, crashing, or becoming paralyzed in dangerous areas, The system extracts the current effective remaining functional quantity of the device and sets a dynamic device safety return threshold parameter based on the current ambient temperature (this safety threshold will increase non-linearly and significantly in extremely cold environments to prevent the lithium battery voltage from dropping suddenly and causing power failure).

[0097] The system defines a Boolean task physical feasibility indicator variable in memory: If the current effective remaining energy capacity of the device is consistently greater than or equal to the sum of the total estimated energy expenditure parameter and the safe return threshold parameter, the system determines that the device has sufficient energy reserves to perform the task and assigns a high-level logic flag to the feasibility indicator variable. If the above energy inequality does not hold, it means that assigning the device carries a significant risk of energy depletion leading to death. Therefore, the device will be ruthlessly removed from the current task candidate pairing list and assigned a logic low-level flag.

[0098] After completing the above-mentioned physical boundary filtering, the system formally constructs a high-dimensional spatiotemporal mobility energy consumption game operator for all high-level device and task pairing combinations that meet the feasibility conditions. .

[0099] The game operator was designed to measure a kind of "dynamic energy recovery cost-effectiveness": that is, to what extent the system can curb the avalanche effect caused by energy inflation due to time delay in the target task by investing a certain amount of motor moving energy resources.

[0100] If a task experiences severe inflation and a device is located very close to it in space with minimal energy consumption for movement, then the game operator value of this pairing combination should be non-linearly and drastically amplified to astronomical numbers by mathematical functions.

[0101] Spatiotemporal movement energy consumption game operator The nonlinear calculation formula is constructed as follows: ; in, It is a natural exponential function, which is used to take advantage of its exceptionally steep first derivative to dramatically amplify the nonlinear weights of high-performance combinations and widen the small numerical differences between different pairings. The task solved in Module 2 The average energy consumption inflation rate coefficient within a short future time window; the larger the value, the more severe and unstoppable the deterioration of the task. Product term The core of the molecule that constitutes the exponential part of the game formula has the following engineering and physical implications: if the dispatch center does not immediately assign a specific device that is nearby to handle the task, then during the long period of time that the device is on its way, the task itself will incur an additional and wildly expanding energy inflation penalty in absolute terms. The denominator of the exponential part of the game formula is the main body, representing the real physical tire or rotor movement cost that the system must immediately pay at this moment in order to suppress the above-mentioned huge inflationary penalty. and These are the inflation penalty weighting factor and the moving cost weighting factor, respectively. These two parameters are preset and fine-tuned by senior engineers on-site based on the energy sensitivity of the specific inspection environment. In plant areas equipped with large-capacity redundant power supplies, the adjustment can be increased. To achieve swift and decisive suppression of anomalies; in scenarios where energy is limited and resupply is difficult, such as deep mountains and forests, the efficiency can be significantly increased. To conserve every drop of electricity with extreme frugality; It is a very small positive bias mathematical constant buffer. Its sole purpose is to prevent the underlying microprocessor from crashing due to division overflow when a device happens to be at the exact same absolute geographical coordinates as the newly launched task, resulting in zero energy consumption during movement.

[0102] The system controller initiates a multi-core, multi-threaded concurrent computation mechanism to perform high-frequency cyclic calculations of a fully connected nested matrix for the active task pool and device pool in the current cache.

[0103] After each feasible pairing combination is solved, the calculated spatiotemporal movement energy consumption game operator single-precision floating-point number is used. Write it into a dedicated cache array structure.

[0104] A large-scale game operator feature matrix with a huge dimension is constructed. Each non-zero element in the matrix represents a high-order physical mapping entanglement link that takes into account the risk of future time inflation avalanche and the loss of three-dimensional spatial displacement motor at a specific moment.

[0105] Module 4, Dynamic Priority Quantitative Evaluation and Calculation Module: Maps the spatiotemporal movement energy consumption game operator and the inherent basic urgency parameter to the game operator characteristic value and the basic urgency characteristic value, calculates the cumulative waiting delay parameter according to the initial trigger time parameter, and obtains the comprehensive dynamic priority score parameter by weighting together with the preset risk multiplier.

[0106] This module integrates three heterogeneous features: the inherent static basic urgency level of the task itself, the system-level service penalty index caused by the long queuing time of the task in the buffer pool, and the aforementioned high-precision spatiotemporal energy game operator. It calculates a dimensionless, highly standardized comprehensive dynamic priority score for each candidate execution link of "device-task", thereby successfully reducing the dimensionality of multi-dimensional, heterogeneous, and even logically contradictory reference factors and condensing them into a single scalar numerical sequence, which serves as the basis for the final ordering of scheduling actions.

[0107] The system architecture must directly address and resolve the issues of completely inconsistent physical dimensions and large differences in numerical values ​​between different evaluation index dimensions.

[0108] For example, the game operator calculated in the previous step may be a huge value of tens of thousands or even hundreds of thousands that has been dramatically amplified by an exponential function, while the inherent urgency of the task may only be an integer level of one to five set by the security system.

[0109] If these original heterogeneous values ​​are simply added together in the subsequent comprehensive scoring fusion formula, the massive energy term will ruthlessly devour the tiny urgency term at the algebraic level, causing high-risk, life-threatening alarms to be ignored by the system due to their slightly greater distance, leading to catastrophic human-induced scheduling imbalances.

[0110] The CPU's processing core extracts the set of non-zero game operator values ​​for all valid matching pairs that have passed physical liveness boundary verification and whose flags are high from the game operator matrix in the cache.

[0111] Start a parallel scanning thread to search for and lock the absolute extreme values ​​of the maximum and minimum operators in the current global dataset within a very short clock cycle.

[0112] Instead of using simple arithmetic operations, the system utilizes a linear range mapping mechanism to systematically normalize all spatiotemporal movement energy consumption game operators.

[0113] The specific processing logic is as follows: The system subtracts the previously found minimum operator extreme value from the value of the single game operator currently being evaluated, and obtains a numerator difference value; Simultaneously, the maximum operator extremum is subtracted from the minimum operator extremum to obtain a constant difference value as the denominator range; finally, the system divides the numerator difference value by the denominator range.

[0114] Before this division operation is performed, a conditional branch is introduced at the system hardware level: If the maximum operator extreme value is detected to be equal to the minimum operator extreme value, resulting in the denominator range being zero, the system will directly force the characteristic value of the game operator to be assigned the real number one, thereby avoiding the division by zero overflow crash. If the range span is not zero, continue to perform the above division mapping operation to obtain the game operator feature value that falls within the continuous real number interval from zero to one.

[0115] By using this mapping operation that uses the range as a scale, the system obtains the feature values ​​of all game operators that fall within the continuous real number interval from zero to one.

[0116] After processing the most complex dynamic energy, the system extracts the inherent basic urgency parameters carried by each task in parallel.

[0117] This parameter is an immutable static attribute that is forcibly determined by the system's preset hardware asset importance ledger at the moment the task is reported by the sensing node (for example, if a high-temperature data center server rack that bears the core production computing power shows signs of fire, its basic urgency is naturally and unconditionally higher than the leakage fault of the landscape lights on the periphery of the factory area).

[0118] When various sensing subsystems or third-party security platforms connect to the central dispatch center, their alarm level communication protocol settings may use completely different coding threshold standards. The system must unify the benchmark at the underlying level.

[0119] If a predefined highest possible urgency level for the entire system is set as a globally fixed constant, then the system calculates the normalized base urgency characteristic value as follows: The task's inherent urgency parameter is divided by this globally fixed constant. This characteristic value, after division, also falls steadily within the continuous real number range of zero to one. It is important to note that this parameter is strongly bound to the task's own independent hardware attributes and is absolutely unaffected by any changes in the location or power status of the matched device.

[0120] In actual uninterrupted operation, even if the initial urgency of some peripheral and non-core tasks is low, and the resulting deterioration in energy inflation is relatively mild due to physical barriers, from the macro principle of fairness in global resource allocation and scheduling, no routine task should fall into a state of "starvation deadlock" that is indefinitely shelved.

[0121] The high-frequency timer inside the system obtains the cumulative queuing delay parameter of the task by reading the difference between the current absolute clock time of the system and the initial trigger time of the task.

[0122] To prevent extremely long-tailed isolated tasks from causing infinite divergence in priority scores and leading to complete disruption of the system's sorting function, the system constructs a complex dynamic penalty function based on queuing theory and the principle of nonlinear time decay. .

[0123] The shape of this higher-order mathematical function exhibits a significant "S-shaped" smooth and continuous curve characteristic in the coordinate system: In the initial period after a task is triggered, the time penalty factor increases very little and is almost in a dormant state. When the accumulated waiting time of the task gradually approaches the timeout tolerance threshold parameter set by the system management element, the penalty value rises sharply like a volcanic eruption at the inflection point of the curve. When a timeout has become a given and continues to extend indefinitely, the penalty factor no longer increases uncontrollably, but rather converges smoothly and gently to a fixed upper limit, firmly preventing the entire scheduling system from crashing due to an excessively large value of a single task.

[0124] The expression for the continuous calculation of the partial derivatives of the nonlinear dynamic penalty function is as follows: ; in, For the natural exponent operator; The time penalty steepness adjustment control parameter determines the steepness of the function curve's ascent near the critical point of crossing the time threshold (i.e., the severity of the penalty). The output of this function is naturally constrained within an open interval greater than zero and less than one, possessing excellent smooth and differentiable mathematical properties, and will absolutely not cause computational oscillations or output mutations at the critical point.

[0125] After successfully collecting the three standardized feature data elements that have undergone normalization and nonlinear mapping, the system officially starts calculating the final quantitative priority evaluation score for each feasible "device-task" pair.

[0126] The system defines a single-precision floating-point composite dynamic priority scoring parameter in the memory stack. By utilizing a high-order weighted linear combination framework and a penalty coupling mechanism, features of different dimensions are fused in a high-dimensional and seamless manner.

[0127] The detailed multidimensional structure of the scoring formula is as follows: ; in, This is the inherent urgency weighting coefficient. The higher this value is set by the administrator, the more the scheduling system will inherently tend to unconditionally obey the static hardware alarm level. This is the weighting coefficient for the spatiotemporal game. The higher this value is set, the more the scheduling system pays attention to the dynamic inflation suppression of on-site energy and the meticulous calculation of physical space losses. constraint This is to ensure that the baseline scale of the base combined score does not expand beyond its limits. It is a dynamic time penalty amplification factor, used to forcefully raise the priority ceiling of low-priority tasks by multiplication when they face serious timeout risks, thus breaking the deadlock of low-priority tasks always being at the back of the queue. This is an absolute veto risk multiplier set for specific high-risk environmental areas. The system has a built-in polygonal electronic fence detection algorithm. When the calculation determines that the three-dimensional coordinates of the task unfortunately fall into a high-risk explosion-proof area with the danger of flammable, explosive, highly toxic leaks, or high-voltage arc discharge, Immediately retrieve a preset maximum amplification factor greater than one from read-only memory.

[0128] In a typical secure office area, this multiplier value remains silently at the value of one.

[0129] Through this independent multiplier, the system mathematically grants high-risk area tasks the highest intervention privilege, which overrides all conventional rules.

[0130] Similarly, by utilizing a multi-core concurrent pipeline computing architecture, the corresponding comprehensive score is calculated at high speed, in massive quantities, and without interruption for each feasible legal combination in the matrix.

[0131] For inferior sabotage devices that fail to pass the physical survival boundary red line verification and are paired with tasks, the system resolutely bypasses all complex feature calculations and directly forces a special negative isolation flag at the underlying hardware register level.

[0132] At the end of the current calculation cycle, if the system scanner detects that a task to be executed has a negative score for all available devices in the network during the current cycle, the highest-level hardware interrupt logic of "isolated task offline unreachable exception" will be immediately triggered.

[0133] The system will independently extract all feature vector data packets of the isolated task from the regular dynamic scheduling memory read / write area and transfer them to a dedicated manual intervention privileged flashing alarm queue.

[0134] When the calculation process is fully completed, the system will generate an extremely dense, real-time, high-frequency refreshed two-dimensional priority scoring table in memory, which contains global decision-making criteria.

[0135] The row indexes in this massive numerical table closely correspond to the unique identification codes of the inspection tasks currently pending, the column indexes closely correspond to the unique hardware identification codes of idle or preemptible inspection equipment, and the data stored at the intersections of the tables are the single-precision floating-point priority scores obtained through the aforementioned complex mathematical system at high speed.

[0136] Module 5, Inspection Task Scheduling Sequence Generation Module: The candidate scheduling data element array is obtained by descending the comprehensive dynamic priority scoring parameter. After being filtered by the task allocation status latch table and the equipment occupancy status latch table, the scheduling instruction frame is issued. The main control CPU of the scheduling system initiates the reconstruction and descending mapping process of the one-dimensional scheduling sequence. The scheduling controller scans and traverses the entire two-dimensional matrix table at an extremely high data throughput rate through the direct memory access (DMA) channel.

[0137] The system's underlying data filtering function extracts all valid comprehensive dynamic priority score parameters with values ​​greater than zero one by one. During the extraction process, not only the score itself is extracted, but also its corresponding available device unique index identifier parameter and the task to be processed unique index identifier parameter are encapsulated into an independent and indivisible ternary candidate scheduling data element using a specific software data structure.

[0138] These candidate scheduling data elements, which were originally scattered in a two-dimensional grid space, are uniformly loaded into a linear, continuous dynamic buffer array.

[0139] The system calls use efficient sorting algorithms (such as the optimized QuickSort or HeapSort) that have advantages in time complexity and high stability. Based on the absolute floating-point value of the comprehensive dynamic priority score parameter in the triples, the system performs an irreversible global descending sort on the entire linear dynamic array.

[0140] Once all sorting operations are completed, the data element at the top of the linear array represents the "device-task" matching execution link that is most effective, most urgent, and preferred in addressing the worsening energy consumption and inflation under the current microsecond-level system clock cycle and the global physical situation.

[0141] This linear array, arranged in descending order, forms the basic task queue buffer pool before the system issues physical instructions to the outside world.

[0142] The system enters the multi-dimensional physical resource locking and concurrent conflict resolution logic execution phase. Because in a many-to-many two-dimensional computation matrix, the same critical high-risk inspection task may form different effective pairing scores with multiple different available devices; Similarly, the same well-functioning, fully charged, and dispatchable device may generate different execution scores for multiple different tasks in the vicinity.

[0143] If instructions are blindly issued from top to bottom based solely on scores within the basic queue buffer pool, it will inevitably lead to serious problems: For example, multiple devices that receive instructions may compete for the same narrow passage, causing physical collisions, or multiple devices may be repeatedly assigned by the system to the same single task location, resulting in a serious waste of on-site computing power and energy resources.

[0144] The dispatch center controller has created two completely independent system-level state registry entries in the hardware cache area: The first is the task allocation status latch table, and the second is the equipment occupancy status latch table.

[0145] At the initial dawn phase of each new scheduling calculation cycle, the Boolean flags of all elements in these two registry entries are set to the "idle and ready" state (the logical flag value is assigned to zero) by the underlying initialization and clearing program.

[0146] The system pops candidate ternary scheduling data elements from the basic queue buffer pool one by one from top to bottom, according to the newly generated descending order.

[0147] For each three data element that pops up, the system executor immediately queries the current hardware flag bit of the corresponding device index in the device occupancy status latch table, and the current hardware flag bit of the corresponding task index in the task allocation status latch table.

[0148] If the query results from the underlying hardware show that the flag bits of both remain at logical zero, the system will clearly determine that the high-scoring matching pair is absolutely feasible at the current physical level and has no concurrent allocation conflict.

[0149] The three-dimensional data element is formally upgraded and incorporated into the physical execution queue to be issued. Immediately, using processor-level atomic operations and spinlock mechanisms, the flag bit of the corresponding index in the task allocation status latch table is reversed to logical one (indicating that the task has been explicitly claimed and the system will refuse other devices from taking over the task). At the same time, the flag bit of the corresponding index in the device occupancy status latch table is also reversed to logical one (indicating that the specific device has been formally requisitioned and the system will refuse to send other task assignments to it).

[0150] This atomic state reversal operation, based on the hardware level, ensures that in a high-concurrency computing environment with multiple cores and threads, the state latch table will never experience system data disorder caused by read / write contention.

[0151] If the underlying query results show that any one of the flags has already been set to logical one by another high-priority thread, then it is explicitly stated that: Either the specific task has just been taken over by another device with a higher overall priority score, or the specific device has just been assigned to handle other more critical system tasks.

[0152] When faced with such an irreconcilable resource conflict, the system directly discards the currently popped candidate scheduling data element in memory without making any underlying state changes, and immediately instructs the pointer to continue probing the next element in the linear queue buffer pool.

[0153] Through this serial lookup and elimination mechanism based on the state registry, the system can efficiently parse and generate an absolutely conflict-free scheduling and allocation scheme with a one-to-one physical mapping between devices and tasks without causing multidimensional resource deadlocks.

[0154] For each matching pair that successfully enters the physical execution queue, the system's central controller extracts the specific physical environment attributes of the task from the global dynamic parameter table and uses a dedicated industrial encrypted communication encoding protocol to package it into a standardized downlink scheduling instruction frame.

[0155] The data payload segment of this instruction frame not only contains the key three-dimensional spatial coordinate vector of the mission guiding the equipment to the site and the anomaly feature category identifier used to call the underlying edge analysis algorithm, but also injects the core physical constraint parameters calculated in Module 3: the expected average moving speed parameter and the total expected energy expenditure parameter.

[0156] The engineering purpose of forcing the total projected energy expenditure parameter into the hardware instruction frame is: This establishes an insurmountable physical survival protection barrier for edge devices located far from the dispatch center. When the inspection equipment receives a downlink command and drives the motor to move towards the target area, its internal battery management system (BMS) and underlying motion control components monitor the actual ampere-hours and joules of energy consumed in real time and at high frequency.

[0157] If, during the movement, the equipment encounters unknown falling obstacles not recorded on the map, forcing it to take a significant detour, or encounters severe conditions such as strong headwinds or tracks getting stuck in mud, causing a surge in the actual motor output power, and before the equipment reaches the task coordinates, its actual cumulative power consumption exceeds the total estimated energy expenditure parameter preset in the instruction frame plus a fixed percentage of the hardware safety redundancy threshold (e.g., exceeding the calibrated value by 15%), then the edge computing chip on the device will resolutely, proactively, and without hesitation shut down the currently executing inspection task instruction.

[0158] The equipment will forcibly abandon the original target and unconditionally execute the return-to-base command based on the current remaining power, or execute a safe forced landing or brake lock-up strategy in place when the voltage is extremely critical.

[0159] After the downlink command frame is encapsulated at high speed, the system broadcasts these multi-encrypted scheduling data packets to the target physical devices distributed throughout the field through a high-bandwidth, low-latency industrial wireless communication network (such as an industrial-grade 5G private network with an independent spectrum channel or a frequency-hopping spread spectrum radio frequency communication base station with strong anti-interference capabilities).

[0160] In this crucial air interface communication interaction phase, the system's underlying communication driver enforces a handshake confirmation mechanism with time window constraints.

[0161] Once the RF antenna of the terminal inspection device successfully captures and demodulates its own command frame, and confirms the integrity of the data packet bits through Cyclic Redundancy Check (CRC), its communication module must send a high-priority acknowledgment signal (ACK communication frame) back to the central dispatch center within a very short time window specified by the protocol (e.g., within two hundred milliseconds). Only when the baseband receiver of the dispatch center has received the ACK frame containing the device's unique hardware microcontroller serial number without error will the system officially change the visual status of that specific task from "Assigning" to "Device is en route for processing" in the global data situation dashboard view.

[0162] If the scheduling system fails to receive an ACK frame from a specific terminal device after its internal watchdog timer reaches the timeout threshold, the system will automatically initiate a fast retransmission mechanism for the link a set number of times (e.g., three consecutive times). If no weak response from the terminal is detected after all retransmission attempts have been exhausted, the scheduling system will decisively determine that the specific device has encountered a communication dead zone due to force majeure, strong electromagnetic suppression, or serious physical hardware damage.

[0163] The console triggers high-level exception isolation logic: The missing device is forcibly marked as "disconnected" in the status latch table. At the same time, the latch flag of the specific task originally assigned to the device is reset to "idle and ready". The unattended task is pushed back to the top layer of the central waiting pool so that it can immediately participate in the re-quantification evaluation and resource matching of the next global scheduling cycle, preventing the entire security inspection response chain from being broken due to a single point of failure in device communication.

[0164] Industrial inspection and scheduling is by no means a static observation after a one-time allocation. The complex physical parameters on site evolve with each tick of the clock and are full of unknown changes.

[0165] While a device is on its way to a remote task assignment point, the massive number of multi-source sensing nodes deployed throughout the plant in Module 1 continue to monitor the overall environmental status without interruption.

[0166] If a dangerous sudden change occurs at a low-level task site that has not yet been assigned an inspection device and is waiting in the queue, such as a transformer whose condition changes from slow localized heating to a violent open flame, the optical flame sensing node at the site will immediately report a new serious anomaly characteristic category and an exponentially increasing high temperature value to the center.

[0167] Faced with this sudden high-risk situation, when the scheduling system's central processing unit re-executes the partial differential equation for the energy consumption inflation rate in the next high-frequency computing cycle, the average energy consumption inflation rate coefficient calculated in the model for this combustion task will experience a dramatic nonlinear jump. Due to the combined effect of various normalization and penalty factors, the local data structure in the two-dimensional matrix scoring table generated in the new computing cycle will undergo drastic reconstruction, and the comprehensive dynamic priority scoring parameter corresponding to this sudden high-risk task will experience a huge numerical leap, instantly reaching the top of the sorted array.

[0168] When the system generates a new instruction sequence during the resource allocation phase of Module 5, the high-level interrupt monitor will detect that this sudden high-risk task urgently requires nearby devices performing other routine low-priority tasks to immediately intervene for firefighting or reconnaissance, but the target device's occupancy flag has been locked. At this point, the system determines that the newly generated emergency situation has created a high-intensity resource conflict with the routine sequence being executed on-site, triggering the highest-level "preemptive hardware interrupt logic".

[0169] The system uses a high-priority wireless channel to send a forced instruction overlay data packet containing the highest-privilege mask to the device closest to the fire point that is on its way to a scheduled routine mission.

[0170] The data packet commands the device's underlying navigation components to immediately abandon the original target waypoint, replan the route at the current coordinates, and prioritize handling the current high-intensity emergency mission.

[0171] The previously interrupted and temporarily suspended routine task has been re-entered into the waiting queue by the system state machine. It will continue to accumulate its time-waiting penalty parameters in the background, waiting for the system to assign other idle devices for rescue.

[0172] Example 2 A cloud-based multi-source data fusion and visualization method includes the following components: Collect the initial occurrence location, initial trigger time, initial baseline energy consumption expectation, inherent basic urgency, anomaly characteristic category, current location, effective remaining work capacity, and basic motion energy consumption coefficient; Substitute the abnormal feature categories into the partial differential evolution model, and combine them with the initial baseline energy consumption expectation to solve for the dynamic energy demand expectation and the average energy consumption inflation rate coefficient. Based on the initial position, current position, and basic motion energy consumption coefficient, the estimated movement time and movement energy are calculated. After verifying the functional quantity by the expected dynamic energy demand and the effective surplus, the average energy consumption inflation rate coefficient is incorporated to obtain the spatiotemporal movement energy consumption game operator. The game operator and the inherent basic urgency are mapped to feature values ​​respectively. The cumulative waiting delay is calculated based on the initial trigger time. The combined weighted by the preset risk multiplier is used to obtain the comprehensive dynamic priority scoring parameter.

[0173] A candidate scheduling array is generated by sorting the scoring parameters in descending order. After double filtering by the task allocation status latch table and the device occupancy status latch table, a scheduling instruction frame is issued.

[0174] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A multi-source data fusion visualization system based on a cloud platform, characterized in that, include: Task and Equipment Basic Parameter Acquisition Module: Collects the initial occurrence location coordinate vector, initial trigger time parameter, initial baseline energy consumption expectation value, inherent basic urgency parameter, abnormal feature category identifier, current location coordinate vector, effective remaining work quantity, and basic motion energy consumption coefficient; Partial differential equation solution module for energy consumption inflation rate: Substitute the abnormal feature category identifier into the model, and combine the initial baseline energy consumption expectation value to obtain the dynamic energy demand expectation value and the average energy consumption inflation rate coefficient. Spatiotemporal movement energy consumption game operator construction module: Based on the initial location coordinate vector, the current location coordinate vector and the basic motion energy consumption coefficient, the expected movement time parameter and movement energy parameter are calculated. After verifying the functional quantity by the expected value of dynamic energy demand and the effective surplus, the average energy consumption inflation rate coefficient is incorporated to obtain the spatiotemporal movement energy consumption game operator. Dynamic priority quantification evaluation and calculation module: Maps the spatiotemporal movement energy consumption game operator and the inherent basic urgency parameter to the game operator feature value and the basic urgency feature value, calculates the cumulative waiting delay parameter according to the initial trigger time parameter, and obtains the comprehensive dynamic priority score parameter by weighting together with the preset risk multiplier. Inspection task scheduling sequence generation module: The candidate scheduling data element array is obtained by descending the comprehensive dynamic priority scoring parameter. After being filtered by the task allocation status latch table and the equipment occupancy status latch table, the scheduling instruction frame is issued.

2. The cloud-based multi-source data fusion visualization system according to claim 1, characterized in that, In the task and equipment basic parameter acquisition module: The central computer room broadcasts a time synchronization message carrying precise time information. The hardware media access control layer captures the physical arrival time of the message to dynamically compensate and correct the local system time. A timestamp is stamped as the initial trigger time parameter when the inspection task trigger signal is generated. By frequently reading the battery terminal voltage, transient high-current pulse data, internal AC impedance spectrum, and readings from distributed temperature sensors inside the battery compartment, and combining this with a nonlinear discharge curve temperature correction model, the effective remaining work capacity is calculated using ampere-hour integration.

3. The cloud-based multi-source data fusion visualization system according to claim 1, characterized in that, In the partial differential equation solution module for energy consumption inflation rate, the state diffusion partial differential function is called from the physical normal evolution equation library based on the anomaly characteristic category identifier, and the instantaneous inflation rate value is calculated according to the following formula: ； in, Indicates task exist The instantaneous inflation rate at any given moment. This represents the energy conversion coefficient per unit volume of spatial scanning. This represents the volumetric parameter representing the anomalous physical field in three-dimensional space. Represents the instantaneous spatial diffusion volume velocity. This represents the penalty coefficient for nonlinear complexity. This represents the virtual time variable in the deduction process. Indicates the initial trigger time parameter. This represents the time-based inflation index parameter. When an abnormal state is determined to exhibit a chain reaction or an accelerated collapse trend at the micro level, the time inflation index parameter is set to a value greater than one. Based on the discrete time step parameter, the instantaneous inflation rate is discretized and accumulated to obtain the local inflation energy increment micro-element value, and the dynamic demand energy expectation value is recursively calculated. The dynamic energy demand expectation is compared with the maximum permissible inflation energy hard physical threshold parameter. If it exceeds the maximum permissible inflation energy hard physical threshold parameter, the dynamic energy demand expectation is forcibly truncated and anchored to the maximum permissible inflation energy hard physical threshold parameter, and a resource depletion early warning signal is generated. Simultaneously, based on the current real-time state of the system, the system makes significant forward projections in virtual memory to the end of the short-term forecast window in the future, collects all inflation increment micro-elements within the virtual future time window, and calculates the average slope value of the overall evolution trend. The average slope value is then used as the average energy consumption inflation rate coefficient.

4. The cloud-based multi-source data fusion visualization system according to claim 1, characterized in that, The spatiotemporal movement energy consumption game operator construction module specifically includes: Based on the environmental elevation digital model and the three-dimensional obstacle grid map, the actual walkable obstacle avoidance physical trajectory is explored. The dynamic environmental topology tortuosity coefficient is obtained by dividing the absolute total length of the actual walkable obstacle avoidance physical trajectory by the theoretical three-dimensional Euclidean straight distance. Determine the absolute height difference in three-dimensional space between the target position and the current position. If the target position is rising, multiply it by the nonlinear transmission loss compensation coefficient to obtain the value of the gravitational potential energy work function. If the target position is falling, multiply it by the potential energy conversion and recovery coefficient to obtain the value of the gravitational potential energy work function. When calculating the expected movement time parameter, the system reads on-site meteorological sensor data to generate a real-time environmental resistance loss factor, and uses the real-time environmental resistance loss factor to reduce and correct the expected average movement speed parameter.

5. The cloud-based multi-source data fusion visualization system according to claim 4, characterized in that, The process of obtaining the spatiotemporal movement energy-consuming game operator is as follows: The effective remaining energy consumption is compared with the sum of the total estimated energy expenditure parameter and the dynamically set safe return threshold parameter. If the sum is greater than or equal to the sum, the task physical feasibility indicator variable is assigned a high-level logic flag, and the spatiotemporal movement energy consumption game operator is calculated according to the following formula: ； in, Indicates that it is for the equipment With the task The spatiotemporal movement energy consumption game operator calculated by pairing, Indicates the physical feasibility indicator variable of the mission. This represents the natural exponential function. This represents the inflation penalty weighting factor. This represents the average energy consumption inflation rate coefficient. This indicates the estimated travel time parameter. This represents the moving cost weighting adjustment factor. Indicates the parameter of moving energy. This represents the positive bias mathematical constant buffer amount.

6. The multi-source data fusion visualization system based on a cloud platform according to claim 1, characterized in that, In the dynamic priority quantification evaluation and calculation module, the cumulative waiting delay parameter is obtained by reading the difference between the current system absolute clock time and the initial trigger time parameter through an internal timer, and the result of the nonlinear dynamic penalty function is calculated according to the following formula: ； in, This represents the result of the nonlinear dynamic penalty function. Represents the natural exponent operator. This indicates the time penalty steepness adjustment control parameter. This represents the cumulative waiting time parameter. This represents the timeout tolerance threshold parameter.

7. The cloud-based multi-source data fusion visualization system according to claim 6, characterized in that, The logic for obtaining the comprehensive dynamic priority scoring parameters is as follows: The built-in polygonal electronic fence detection algorithm determines whether a 3D coordinate point falls within a high-risk explosion-proof area posing a danger of flammable, explosive, highly toxic leaks, or high-voltage arc discharge. If it does, a preset amplification factor is extracted and assigned to the risk multiplier. And obtain the eigenvalues ​​of the game operator through normalization. Subsequently, a higher-order weighted linear combination framework and a penalty coupling mechanism are used to fuse features from different dimensions to calculate the comprehensive dynamic priority scoring parameter. ; Comprehensive dynamic priority scoring parameters The operational logic is based on the mechanism of successful dimensionality reduction of multidimensional heterogeneous reference factors, and it is subject to the inherent urgency weight coefficient. Weighting coefficients in spatiotemporal game The mathematical encapsulation and benchmark scale constraints, incorporating basic urgency eigenvalues At the same time, the amplification coefficient is increased through dynamic time penalty. Increase the priority cap for low-priority tasks and utilize risk multipliers. Grant high-risk areas the highest level of intervention privileges, which are above the usual rules.

8. The multi-source data fusion visualization system based on a cloud platform according to claim 1, characterized in that, In the inspection task scheduling sequence generation module, candidate scheduling data elements in the candidate scheduling data element array are popped up from top to bottom. When the underlying query result shows that the flag bits of the corresponding index in the task allocation status latch table and the flag bits of the corresponding index in the device occupancy status latch table are both logical zero, the flag bits of the corresponding index in the task allocation status latch table and the flag bits of the corresponding index in the device occupancy status latch table are all reversed to logical one to resolve concurrent allocation conflicts. If the underlying query results show that any flag has been set to logical one, then the current candidate scheduling data element is discarded without any underlying state change, and the next element in the buffer pool is explored.

9. The cloud-based multi-source data fusion visualization system according to claim 8, characterized in that, Also includes: The total estimated energy expenditure parameter is forcibly injected into the hardware instruction frame for encapsulation and distribution. If the actual cumulative power consumption exceeds the sum of the total expected energy expenditure parameter plus a fixed proportion of the hardware safety redundancy defense threshold during the movement, the edge computing chip on the device will immediately melt down the current inspection task instruction and unconditionally execute the original route return instruction or the safe emergency landing strategy. After broadcasting the scheduling data packet, if no acknowledgment signal is received within the preset time window specified in the protocol, the link fast retransmission mechanism is activated. If the number of retransmissions is exhausted and no response is detected, the abnormal isolation logic is triggered, the flag in the status latch table is set to the disconnected or lost state, and the latch flag of the corresponding task is reset to the idle and standby state and pushed back to the central waiting pool.

10. A cloud-based multi-source data fusion visualization method, and a cloud-based multi-source data fusion visualization system according to any one of claims 1-9, characterized in that, include: Collect the initial occurrence location, initial trigger time, initial baseline energy consumption expectation, inherent basic urgency, anomaly characteristic category, current location, effective remaining work capacity, and basic motion energy consumption coefficient; Substitute the abnormal feature categories into the partial differential evolution model, and combine them with the initial baseline energy consumption expectation to solve for the dynamic energy demand expectation and the average energy consumption inflation rate coefficient. Based on the initial position, current position, and basic motion energy consumption coefficient, the estimated movement time and movement energy are calculated. After verifying the functional quantity by the expected dynamic energy demand and the effective surplus, the average energy consumption inflation rate coefficient is incorporated to obtain the spatiotemporal movement energy consumption game operator. The game operator and the inherent basic urgency are mapped to feature values ​​respectively. The cumulative waiting delay is calculated based on the initial trigger time. The combined weighted by the preset risk multiplier is used to obtain the comprehensive dynamic priority scoring parameter. A candidate scheduling array is generated by sorting the scoring parameters in descending order. After double filtering by the task allocation status latch table and the device occupancy status latch table, a scheduling instruction frame is issued.

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