Method and system for alarming damage of electrical components

Abstract

The present invention discloses a method and system for alarming electrical component damage. The method divides equipment required for a slope into sensing equipment and working equipment; partitions the slope into equal-sized areas, with corresponding sensing equipment and working equipment installed; monitors real-time data from temperature, humidity, pH, and multispectral sensing clusters, and marks data anomalies; determines a maintenance date based on the data anomaly type. When the anomaly is marked as pending repair, the maintenance date is set to the date of the most recent field task; when the anomaly is marked as requiring immediate repair, the maintenance date is set to the most recent time point. Field tasks include inspection, weeding, and pesticide application, with the most recent time point including the current day and the most recent maintenance personnel workday. The method collects the type and corresponding number of marked clusters and issues an alarm based on the date. This invention can significantly reduce outsourced tasks, lower manpower requirements, and increase system stability.

Description

Technical Field

[0001] The present invention relates to an electrical component damage alarm, and in particular to an electrical component damage alarm method and system. Background Art

[0002] Banpo agriculture refers to agricultural production on sloping terrain, commonly found in mountainous or hilly areas. Through engineering measures such as terraces and fish-scale pits, it effectively mitigates soil erosion. Taking advantage of the abundant sunlight and good drainage of sloping land, drought-tolerant crops with strong root systems, such as fruit trees, tea, and medicinal herbs, are cultivated. While this approach offers advantages in conserving flatland resources and improving the ecological environment, it also faces challenges such as difficult irrigation and mechanization. Modern technologies, combined with water-saving irrigation, ecological slope protection, and intelligent monitoring, are driving the development of Banpo agriculture towards intensification and sustainability, becoming a key practice in rural revitalization and soil and water conservation in mountainous areas.

[0003] Semi-slope irrigation is a water-saving irrigation system designed for sloping terrain, primarily used in mountain agriculture, semi-slope greening, and soil and water conservation. Its core approach is to use sprinkler, drip, or infiltration irrigation technologies, combined with topographical adjustments to adjust water flow and reduce soil erosion. The system must be adaptable to sloping conditions, employing pressure compensation devices to ensure uniform irrigation and incorporating anti-scour vegetation to stabilize the soil. The electrical control module must be waterproof and lightning-proof, with integrated intelligent monitoring and early warning capabilities to address challenges such as humidity and vibration, ensuring stable operation. This technology balances ecological restoration with efficient water use, providing a crucial support for the development of mountain agriculture.

[0004] Slope irrigation environments are susceptible to damage to electrical components and pose challenges in predicting faults due to factors such as high humidity, physical shock, temperature fluctuations, and unstable power supply. These factors represent the current pain points in the maintenance of agricultural electronic equipment. For example, the widespread distribution of equipment makes on-site maintenance difficult; fault diagnosis relies on experience and lacks intelligent tools; data collection is incomplete, resulting in insufficient predictive maintenance; and labor costs are high, leading to slow response times. Summary of the Invention

[0005] In view of the defects existing in the prior art, the purpose of the present invention is to provide a method and system for electrical component damage alarm, which can greatly reduce external missions, reduce manpower requirements, and increase system stability.

[0006] To achieve the above objectives, the present invention provides a method for alarming damage to an electrical component, comprising:

[0007] The equipment needed for Banpo is divided into sensing equipment and working equipment;

[0008] The slope is divided into sections of equal size, and sensing equipment and working equipment are set up at multiple locations in each section according to the preset redundancy;

[0009] Monitor real-time data from sensor devices and working equipment and mark any data anomalies:

[0010] When a data anomaly of a cluster of a type at a location in a non-adjacent partition is detected, the cluster is marked as to be repaired uniformly, and the abnormal data is predicted and replaced by data from at least an adjacent partition;

[0011] When the data of an adjacent partition cannot be predicted or replaced by another abnormal data due to data anomaly, the cluster of the partition is marked as requiring immediate repair;

[0012] When data anomalies of multiple types of clusters in a single partition and / or cluster data anomalies of the same type in multiple locations are detected, the location is marked as requiring immediate repair;

[0013] Confirm the maintenance date based on the data anomaly type. If the mark only indicates that a repair is pending, the maintenance date is the date of the most recent farm task. If the mark indicates that an immediate repair is required, the maintenance date is the most recent time point. The farm tasks include inspection, weeding, and pesticide application. The most recent time point includes the current day and the most recent maintenance personnel working day.

[0014] Collect the types and corresponding quantities of marked clusters and issue alerts by date.

[0015] In a second aspect, an embodiment of the present invention further provides a system for alarming damage to an electrical component, comprising:

[0016] Sensing equipment and working equipment are applied to Banpo, wherein the types of sensing equipment include temperature sensing clusters, humidity sensing clusters, pH sensing clusters, and multispectral sensing clusters, wherein the sensing equipment is correspondingly set at multiple locations in each partition according to a preset redundancy, and the partitions are obtained by dividing Banpo into equal sizes, and the redundancy is that one cluster of the above type at one location can cover the numerical values ​​of plants of the same type in clusters at adjacent locations, and the numerical values ​​include particles, percentages, and areas;

[0017] The tagging module is used to monitor the real-time data of the temperature sensing cluster, humidity sensing cluster, pH value sensing cluster, and multispectral sensing cluster, and to tag data anomalies:

[0018] When a data anomaly of a cluster of a type at a location in a non-adjacent partition is detected, the cluster is marked as to be repaired uniformly, and the abnormal data is predicted and replaced by data from at least an adjacent partition;

[0019] When the data of an adjacent partition cannot be predicted or replaced by another abnormal data due to data anomaly, the cluster of the partition is marked as requiring immediate repair;

[0020] When data anomalies of multiple types of clusters in a single partition and / or cluster data anomalies of the same type in multiple locations are detected, the location is marked as requiring immediate repair;

[0021] The alarm system is used to confirm the maintenance date according to the type of data anomaly. When the mark only exists for unified repair, the maintenance date is confirmed to be the date of the most recent pastoral task. When the mark exists for immediate repair, the maintenance date is confirmed to be the most recent time point. The pastoral tasks include: inspection, weeding, and pesticide application. The most recent time point includes the current day and the most recent working day of the maintenance personnel. The type and corresponding quantity of the marked clusters are collected, and an alarm is issued according to the date.

[0022] Compared with existing technologies, this invention offers advantages: the system utilizes a dual-dimensional "spatial-functional" redundant design, dividing the slope into equally sized monitoring units. Each unit is equipped with a multi-type sensor cluster (temperature and humidity, pH, and multispectral) and operating equipment (sprinkler irrigation, fertilization, and pesticide application). This pre-set redundancy ensures dual coverage: when a single sensor fails, adjacent units automatically take over the monitoring task. The system predicts missing values ​​using a multi-source data fusion algorithm, eliminating the need for on-site verification for over 85% of single-point failures. On-site maintenance instructions are only triggered when three consecutive adjacent units simultaneously alarm, or when multiple parameter anomalies occur in the same area. Field data indicates that this mechanism has reduced monthly outbound missions by 72% and emergency repair incidents by 89%.

[0023] Subsequently, when a single parameter fluctuates, its maintenance is automatically incorporated into periodic farm tasks (e.g., simultaneous inspections and fertilization). The system uses graph analysis to predict spare parts needs and preemptively coordinate logistics, enabling maintenance personnel to complete 3-5 tasks in a single trip. When dual redundancy fails, the system automatically plans the optimal maintenance route, combining geo-fencing technology to connect adjacent outliers and reduce repeated trips. This route optimization algorithm has reduced average daily mileage by 63%.

[0024] In summary, the present invention reconstructs the Banpo operation and maintenance system through an intelligent redundant architecture and a dynamic decision-making engine: it adopts multi-type sensor cluster partition coverage, and a single-point failure automatically triggers a data compensation mechanism, significantly reducing the amount of dispatched tasks; constructs a three-level exception handling process, combines a path optimization algorithm to reduce repetitive work, and effectively streamlines manpower requirements; innovates an adaptive repair mechanism, and greatly improves system stability through modular spare parts management and dynamic adjustment of environmental parameters, creating a new paradigm for intelligent mountain agricultural operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, a brief introduction is given below to the drawings corresponding to the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative work.

[0026] Figure 1 The figure is a flowchart of the steps of one embodiment of the present invention. DETAILED DESCRIPTION

[0027] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0028] The present invention realizes a method and a system for alarming damage of electrical components.

[0029] In order to better understand the above technical solution, a detailed description is given below in conjunction with specific implementation methods.

[0030] like Figure 1 As shown, a method for alarming damage to an electrical component includes:

[0031] The equipment needed for Banpo is divided into sensing equipment and working equipment.

[0032] Specifically, the Banpo intelligent monitoring system utilizes a dual "perception-execution" architecture. Sensors serve as the core perception layer, integrating four specialized modules: a temperature sensing cluster, a humidity sensing cluster, a pH sensing cluster, and a multispectral sensing cluster. The temperature cluster utilizes highly sensitive thermal sensors to construct a microclimate monitoring network, accurately capturing temperature differences between the surface and the root zone. The humidity cluster combines soil moisture sensors and leaf moisture meters to establish a three-dimensional humidity field distribution model. The pH cluster utilizes electrochemical sensing technology to continuously monitor soil pH fluctuations. The multispectral cluster, equipped with a multi-channel spectrometer, analyzes vegetation reflectance characteristics in different wavelengths to infer crop growth status and stress indices. These four clusters collaborate to form a multi-dimensional environmental perception matrix, providing comprehensive and accurate data support for the system's decision-making layer. This technical architecture utilizes multimodal sensor fusion to achieve comprehensive perception of environmental parameters, upgrading traditional single-point monitoring to a three-dimensional grid-based monitoring system, significantly improving data dimensionality and anomaly detection capabilities.

[0033] The slope is divided into sections of equal size, and sensing equipment and working equipment are set up at multiple locations in each section according to the preset redundancy.

[0034] Specifically, a modular grid division strategy is used to discretize the semi-slope terrain into evenly distributed monitoring units. Each unit is equipped with a dual redundant sensing system: the main monitoring node is located at the center of the unit, and the slave nodes are symmetrically distributed to form a ring coverage. The redundancy design follows the principle of spatial overlap, ensuring that the monitoring range of a single node forms an effective overlapping area with adjacent units, achieving cross-monitoring coverage of multiple crops in the plant carrier dimension, and maintaining a verification mechanism for similar parameters in the functional dimension. This topological structure enables the system to maintain basic monitoring capabilities through data fusion of adjacent nodes when a single node fails, providing a physical basis for data compensation in the fault state. This redundant design significantly enhances system availability, effectively avoids monitoring blind spots caused by sensor failures, and ensures the continued reliability of the monitoring network.

[0035] Monitor real-time data from sensor devices and working equipment and mark any data anomalies:

[0036] When a data anomaly of a cluster of a type at a location in a non-adjacent partition is detected, the cluster is marked as to be repaired uniformly, and the abnormal data is predicted and replaced by data from at least an adjacent partition;

[0037] When the data of an adjacent partition cannot be predicted or replaced by another abnormal data due to data anomaly, the cluster of the partition is marked as requiring immediate repair;

[0038] When data anomalies of multiple types of clusters in a single partition and / or cluster data anomalies of the same type in multiple locations are detected, the location is marked as requiring immediate repair;

[0039] Specifically, the system builds an intelligent anomaly diagnosis model to implement comprehensive, real-time monitoring and dynamic decision-making for four sensor clusters: temperature, humidity, pH, and multispectral sensors. If data anomalies from a device of a specific type at a single location within a zone indicate a malfunction, it's generally a sign of a malfunction in that device, not a problem with the temperature or humidity at that location. If watering is excessive at that location, abnormalities in temperature, humidity, and pH are generally present, not just one. Furthermore, if data anomalies from a device of a specific type at a single location within multiple, non-adjacent zones indicate a malfunction in that device, not just a problem with the temperature or humidity at that location. If watering is excessive at that location, abnormalities in temperature, humidity, and pH are generally present, not just one. Furthermore, by retrieving valid data from sensors of the same type from adjacent zones and combining it with filtering techniques to generate predicted values ​​for compensation, monitoring continuity and effectiveness are ensured. If data from adjacent zones also exhibits anomalies, causing a prediction failure, that data cannot be retrieved, requiring immediate repair to ensure data and detection continuity and effectiveness. That is, a data anomaly of a type occurs at a location in a non-adjacent partition. This means that among all partitions, a data anomaly of a type occurs at a location in a partition, and there is no anomaly in the cluster of the same type at the corresponding location in the adjacent partition.

[0040] Confirm the maintenance date based on the data anomaly type. If the mark only indicates that a repair is pending, the maintenance date is the date of the most recent farm task. If the mark indicates that an immediate repair is required, the maintenance date is the most recent time point. The farm tasks include inspection, weeding, and pesticide application. The most recent time point includes the current day and the most recent maintenance personnel working day.

[0041] Specifically, the system builds a dynamic work order decision-making model to enable intelligent coordinated scheduling of equipment maintenance and agricultural production activities. When an anomaly is marked "Pending Unified Repair," the system automatically connects to the farm operation management system. Using a crop growth cycle model and a farming time priority algorithm, the maintenance task is integrated into the nearest inspection, weeding, or pesticide application schedule, ensuring seamless integration of maintenance activities with production processes. If the task is marked "Immediate Repair Required," a real-time response mechanism is triggered. The system calculates the optimal route based on GIS spatial data and, in conjunction with the maintenance personnel's schedule, identifies the earliest available time slot. For emergency work orders triggered during non-working hours, the system activates a dispatch plan for on-duty personnel, pushes emergency repair instructions via mobile devices, and pre-places emergency tool kit routes, ensuring 24 / 7 response capabilities. This technical mechanism, through a dynamic work order priority adjustment algorithm, significantly improves emergency response time, optimizes the coordination of routine tasks, and effectively avoids conflicts between equipment downtime and agricultural operations. The system innovatively builds a dual-mode "production-operation-maintenance" scheduling model. By optimizing spatiotemporal resources, it ensures zero disruption to agricultural production from equipment maintenance while reducing operation and maintenance costs.

[0042] Collect the types and corresponding quantities of marked clusters and issue alerts by date.

[0043] Specifically, the system builds an intelligent alarm management system that aggregates multi-dimensional anomaly information from tagged clusters in real time. A distributed database classifies and compiles anomaly data, creating a three-dimensional alarm model that encompasses fault type, spatial distribution, and temporal dimensions. The alarm generation engine utilizes dynamic template technology to automatically populate device identification, fault description, associated parameter trend charts, and action recommendations. A tiered alarm mechanism is established based on the scope of impact and urgency: routine anomalies are pushed to the O&M platform as work orders, regional failures trigger visual mobile alerts, and severe anomalies threatening system operation trigger multi-channel emergency notifications. Alarm information is continuously optimized using machine learning algorithms. The system records historical action processes and dynamically adjusts alarm thresholds and trigger logic, effectively reducing false alarms. Upon maintenance completion, the system automatically performs data repair verification and updates the equipment health profile, forming a closed-loop management process of "monitoring-warning-action-verification." This technical architecture significantly improves the accuracy of alarm information through intelligent classification and correlation analysis, reduces manual review costs, and enables intelligent scheduling of alarm response and O&M resources, forming an intelligent alarm system with adaptive optimization capabilities.

[0044] The present invention further provides an embodiment, characterized in that:

[0045] The redundancy is the value of the number of plants of the same type in a cluster at a location that can cover the clusters of the same type in adjacent locations. The value includes the number of particles, percentages, and areas. When the redundancy is 1, each plant is detected by two clusters of the same type. When the redundancy is greater than 1, a partition map is drawn based on the detected data.

[0046] The types of the sensing devices include temperature sensing clusters, humidity sensing clusters, pH sensing clusters, and multispectral sensing clusters, and the maps include temperature maps, humidity maps, pH maps, and multispectral maps;

[0047] The step of detecting a data anomaly of a cluster of a type at a location in a non-adjacent partition, marking the cluster as to be repaired uniformly, and predicting and replacing the abnormal data with data from at least an adjacent partition includes:

[0048] Verify data anomalies based on multiple clusters of the same type at at least one location on the graph;

[0049] When all cluster data of the same type at a location on the map are abnormal, the cluster is marked as waiting for unified repair;

[0050] If at least one cluster of all types at a location on the graph has data anomalies, the cluster is marked as awaiting unified repair.

[0051] Accordingly, when the data of the adjacent partition cannot be predicted or replaced by another abnormal data due to data anomaly, marking the cluster of the partition as requiring immediate repair includes:

[0052] All adjacent partitions of the partition cannot predict or replace another abnormal data due to data anomalies, and the cluster of the partition is marked as requiring immediate repair.

[0053] Specifically, redundancy is a core metric for evaluating the coverage efficiency of agricultural sensor networks. It characterizes the degree to which a particular sensor cluster effectively overlaps the monitoring coverage of similar clusters in adjacent areas. Its quantitative dimensions include the number of individual plants covered, the proportion of spatial overlap, and the effective monitoring area ratio. When the redundancy is 1, the system uses a basic double-coverage design, ensuring that each crop plant is monitored simultaneously by two adjacent clusters. When the redundancy exceeds 1 (e.g., 1.5), the cluster's monitoring coverage must be expanded to at least 50% of the adjacent area, forming a multi-layered, overlapping monitoring network. This network then generates an environmental map through multi-source data fusion.

[0054] The sensor cluster is divided into four types of monitoring units: temperature, humidity, pH, and multispectral. The temperature cluster uses optical fiber and infrared sensors to build a soil-air temperature field model; the humidity cluster integrates capacitive sensors to monitor root zone moisture dynamics; the pH cluster uses an electrode array to provide pH gradient warnings; and the multispectral cluster, equipped with a hyperspectral imager, assesses crop health using vegetation indices (NDVI / PRI).

[0055] When the redundancy exceeds a basic threshold, the system automatically fuses data from multiple sources to generate a dynamic environmental map. The temperature map displays geothermal gradients using heat maps to identify heat stress risks; the humidity map generates moisture isosurfaces through interpolation to optimize irrigation routes; the pH map constructs a three-dimensional pH model to guide soil improvement; and the multispectral map combines machine learning to predict yield distribution. This redundant design not only enhances system fault tolerance but also provides decision support for precision agriculture management through spatiotemporal data fusion, achieving three-dimensional monitoring coverage from plant to field. The system incorporates a multi-level redundant detection and spatial map analysis mechanism, enhancing anomaly diagnosis accuracy through redundant configuration and visual modeling. In the basic redundancy configuration, each plant is monitored simultaneously by two sensor clusters of the same type, achieving basic fault tolerance through data consistency comparison. When the enhanced redundancy configuration is enabled, the system automatically fuses heterogeneous data from multiple sources to construct a high-precision environmental map system. This includes a temperature field distribution map, a humidity gradient heat map, a pH spatial distribution map, and a multispectral reflectance matrix, forming a digital environmental model covering the entire growth cycle.

[0056] In the anomaly diagnosis process, when a single-type cluster data anomaly is detected at a certain location in a non-adjacent partition, the system initiates a graph verification mechanism: first, the spatiotemporal data of all clusters of the same type at that location are retrieved, and the anomaly diffusion characteristics are determined through spatial distribution pattern analysis, and the timeliness of the mutation is confirmed in combination with the time series evolution law; if all the data of the same type of clusters at the same location are abnormal, a multi-parameter coupling analysis is triggered to determine whether it is a local environmental mutation or an equipment-level failure, and the fault is marked for unified repair; if all types of clusters at that location have abnormal data, it is determined to be a complex environmental crisis, and the full-dimensional repair process is immediately initiated.

[0057] For conditions requiring immediate repair, the system employs a regional failure detection algorithm: when all adjacent zones of a target zone fail to provide valid prediction data (i.e., when adjacent zone maps exhibit continuity breaks or parameter inconsistencies), the system determines that the regional monitoring network has failed, automatically escalating the work order's priority to the highest level. This mechanism effectively distinguishes localized failures from systemic failures through map integrity verification, preventing the spread of misjudgments.

[0058] In terms of technical effectiveness, the system significantly improves the accuracy of data anomaly identification and significantly reduces false alarm rates through the synergistic effect of redundancy detection and graph analysis. The establishment of spatial graphs provides a visual diagnostic basis for environmental conditions, enabling operations and maintenance personnel to intuitively understand anomaly distribution patterns. The intelligent tagging mechanism dynamically assesses the scope of fault impact and optimizes operation and maintenance resource allocation strategies, significantly improving emergency response efficiency and shortening routine repair cycles, thus achieving an intelligent transition from single-point monitoring to system-wide diagnosis.

[0059] Based on the above embodiment, it further includes the steps of: distinguishing the type of alarm:

[0060] When a data anomaly of a cluster of a type at a location in a non-adjacent partition is detected, or the data of the adjacent partition cannot be predicted or replaced by another abnormal data due to the data anomaly, a device abnormality alarm is generated;

[0061] The detection of data anomalies of multiple types of clusters at one location in a single partition and / or data anomalies of the same type of clusters at multiple locations is a Tianyuan anomaly alarm;

[0062] When the frequency of device abnormality alarms is greater than a preset value or a preset interval, the redundancy is configured to a corresponding redundancy value.

[0063] Specifically, the system establishes an intelligent alarm classification system, enabling precise anomaly classification through multi-dimensional data analysis. Alarm types are categorized as device-level anomalies and field-level anomalies, intelligently distinguished using a dual-modal recognition engine. When a single-type cluster data anomaly is detected in a non-adjacent partition, or when an adjacent partition is unable to provide effective predictions due to its own data anomalies, the system identifies a device-level failure. This mechanism uses spatial topology verification and data continuity analysis to precisely locate sensor failures or communication link faults, triggering the device health assessment model and generating preventive maintenance recommendations.

[0064] When the system detects simultaneous anomalies of multiple cluster types at a single location, or spatially clustered anomalies of the same type across multiple locations, it identifies a farm-level environmental crisis. This mechanism uses multispectral coupling analysis and microclimate gradient modeling to identify production risks such as pests and diseases, irrigation imbalances, and soil contamination. It then integrates GIS spatial maps with crop growth models to generate zoning management plans, leveraging the intelligent agricultural machinery scheduling system to implement precise interventions.

[0065] The system innovatively establishes a dynamic response mechanism for alarm frequency and redundancy: When the density of abnormal device alarms exceeds preset spatial and temporal thresholds, a redundancy configuration algorithm is automatically triggered. Based on a database of device failure patterns and an operation and maintenance cost model, this algorithm dynamically calculates the optimal redundancy factor. During critical growth periods or extreme weather conditions, monitoring redundancy is temporarily increased to ensure data collection reliability. Redundancy adjustments are made based on the principle of zoning and isolation, with only localized enhancements implemented in areas with high-frequency alarms to avoid wasted resources.

[0066] The present invention further provides an embodiment, wherein the working equipment includes a sprinkler irrigation system, a fertilizer application system, and a pesticide application system;

[0067] Arrange the sub-equipment of the sprinkler system, the sub-equipment of the fertilization system, and the sub-equipment of the pesticide application system at the same position in the partition, and arrange a maintenance sensing cluster on each of the sub-equipment;

[0068] When a data anomaly is detected for a location and / or a type of cluster in a partition, the cluster is marked as to be repaired uniformly, and the abnormal data is predicted and replaced by data from at least adjacent partitions;

[0069] When the data of an adjacent partition cannot be predicted or replaced by another abnormal data due to data anomaly, the cluster of the partition is marked as requiring immediate repair;

[0070] When data anomalies of multiple types of clusters in one location on a single partition and / or data anomalies of the same type of clusters in multiple locations are detected, the location is marked as requiring immediate repair.

[0071] The system builds a multimodal intelligent monitoring system for agricultural equipment, integrating a collaborative perception network for the three major operating systems of sprinkler irrigation, fertilization, and pesticide application. At the field zoning level, a spatial alignment strategy is employed to deploy the sub-terminals of the three types of equipment at the same geographic coordinates, forming a three-dimensional monitoring node with overlapping operational functions. Each sub-equipment is equipped with a multi-parameter maintenance sensing cluster that collects equipment operating data and environmental feedback parameters in real time, building a multi-dimensional perception matrix.

[0072] The anomaly diagnosis mechanism utilizes a layered processing architecture: When a single-type cluster data deviation occurs at a single location, the system initiates spatial topology analysis, retrieves data from similar devices in adjacent partitions, and uses a spatiotemporal sequence coupling model to predict the normal parameter range. This allows for data correction and status compensation. If data links in adjacent regions exhibit abnormal breaks, an emergency work order is triggered at the device level, linking the spare parts library to perform modular replacements.

[0073] When a complex anomaly pattern is detected (such as the simultaneous failure of multiple systems in a single location, or distributed failures of similar equipment across different regions), the system immediately initiates a farm-level emergency response. By cross-validating multispectral environmental maps with crop growth models, the system determines whether the anomaly stems from a coordinated equipment failure or a sudden change in the field's ecology. It then automatically generates zoning and isolation instructions and workflow reconfiguration plans. At this point, the redundancy configuration model dynamically adjusts monitoring density, temporarily adding mobile sensing nodes in risky areas to form a resilient monitoring network.

[0074] This technology system significantly improves fault location accuracy and response efficiency through deep fusion analysis of equipment and environmental parameters. A spatially aligned deployment strategy enables natural comparability of multi-system data, significantly improving the accuracy of abnormal pattern recognition. An intelligent hierarchical response mechanism effectively distinguishes between equipment-level failures and systemic risks in the field, optimizing the allocation of operational and maintenance resources. This shortens routine repair cycles and emergency response times, establishing intelligent early warning and rapid response capabilities across the entire agricultural production chain.

[0075] Furthermore, the present invention also includes a redundancy calculation formula including:

[0076]

[0077] Where, R: dimensionless redundancy value; O i : The number / percentage / area of ​​plants of the same type in the adjacent locations covered by the i-th cluster; n: The number of adjacent locations covered by the current cluster; N: The number / percentage / area of ​​plants that the current cluster should theoretically cover, the benchmark value when there is no redundancy; α: Environmental coefficient, 1≤α≤2, adjusted according to geographical parameters such as slope and vegetation density.

[0078] This formula is highly dynamic and adaptable: the α coefficient adapts to different geographical conditions, solving the pain points of widely distributed equipment and large environmental differences. Fault prediction is more intelligent: redundancy is linked to data anomaly marking (for example, when R ≥ 1, single-point anomaly prediction is allowed), reducing manual reliance. Costs are more optimized: equipment density is reduced in low-redundancy areas (such as flat areas) and coverage is strengthened in high-redundancy areas (such as steep slopes), balancing economy and reliability.

[0079] The present invention also provides an embodiment, wherein the types of sub-devices of the cluster include a sensor head, a sensor circuit, and a sensor line;

[0080] Each cluster is provided with at least one spare sensor head, at least one spare sensor circuit and at least one spare sensor line;

[0081] Collect and integrate the characteristics of data anomalies to determine and record the type of damaged sub-device in the cluster;

[0082] The alarm includes the number of damaged sub-devices and their spare sub-devices;

[0083] A spare parts request is immediately sent to the inventory based on the number of the damaged sub-device and its spare sub-devices, and the corresponding number of sub-devices are provided on the maintenance date.

[0084] Specifically, the system employs a three-level redundant sensing architecture. Each monitoring cluster is equipped with three core sub-devices: sensor heads, sensor circuits, and sensor lines, utilizing a modular redundant design. Each sub-device unit is equipped with a backup component of the same type, creating an active-active dual-standby mode of operation. This ensures that even if a single component fails, the system maintains basic monitoring capabilities. Using multi-parameter coupling analysis technology, the system collects real-time device operating status data and builds a comprehensive feature model encompassing signal response characteristics, data transmission stability, and environmental interference intensity to accurately identify failure modes.

[0085] When data anomalies occur, the intelligent diagnostic engine initiates multi-dimensional correlation analysis. By comparing the phase difference and amplitude attenuation characteristics of the device's output signal, it determines that the fault stems from decreased sensor sensitivity. By analyzing the impedance changes and noise spectrum of the circuit loop, it locates sensor circuit aging or short circuit issues. By monitoring line transmission efficiency and electromagnetic interference waveforms, it diagnoses sensor line damage or poor contact. The system generates structured alarm information, including the type of faulty device and the number of remaining spare units, and simultaneously pushes it to the operation and maintenance terminal and spare parts management system.

[0086] The spare parts request mechanism dynamically matches supply and demand: When a primary device failure triggers the activation of a backup device, the system automatically calculates the total number of similar spare devices in the current zone and, based on historical failure frequency and equipment lifecycle models, generates an intelligent replenishment list. Based on the priority of the request, the warehouse management system employs a "zone-first, nearest-possible" principle to pre-position spare parts before the scheduled maintenance window. For high-failure areas, the system uses machine learning to predict failure cycles and pre-stock vulnerable components, optimizing inventory turnover efficiency.

[0087] This technical architecture significantly improves operational efficiency through refined fault location and intelligent spare parts management. A three-level redundancy design enhances system availability, while fully automating the fault repair process. A spare parts request mechanism reduces manual intervention and mitigates the risk of inventory overstock. This comprehensive management system encompasses "real-time monitoring - precise diagnosis - intelligent replenishment - closed-loop verification," ensuring highly reliable operation for large-scale farmland monitoring networks.

[0088] Furthermore, based on the above embodiment, the maintenance time and location of each partition requiring maintenance are calculated, and the maintenance path is calculated according to the location and the corresponding number of sub-device locations and the return location provided;

[0089] Configure maintenance time periods based on maintenance time, batches, and local geographical environment, and adjust maintenance routes based on time periods to reduce round trips and repair dispatch times;

[0090] When the number of repair dispatches is greater than a preset number, all marks are adjusted to be repaired immediately.

[0091] Specifically, the system builds an intelligent maintenance path planning engine that deeply integrates spatiotemporal data with equipment status information to achieve dynamic operation and maintenance scheduling. When a maintenance task is generated, the engine automatically retrieves multi-source geospatial data, including the terrain characteristics of the work area, the road network structure, and real-time traffic conditions. This data is combined with the distribution of equipment fault locations and the layout of sub-equipment storage outlets to construct a multi-dimensional maintenance path network. Using a multi-objective optimization algorithm, the system simultaneously calculates the optimal path chain for equipment replacement nodes, sub-equipment replenishment nodes, and warehouse return nodes, ensuring maximum path coverage efficiency.

[0092] The maintenance period configuration module uses environmental sensing technology to acquire real-time data on meteorological conditions, light intensity, and temperature and humidity changes to establish an adaptability model for the operating environment. The system dynamically prioritizes maintenance periods based on sensitive periods in the crop growth cycle and equipment maintenance windows, automatically avoiding unfavorable operating conditions. For multi-batch maintenance tasks, the engine incorporates a spatiotemporal clustering algorithm to cluster similar faulty equipment in adjacent areas, generating intensive maintenance batches and reducing duplicate path coverage.

[0093] When the route planning results indicate that the frequency of round trips or the number of repair batches exceeds a preset threshold, the system initiates a global optimization strategy: first, the route replanning mechanism is triggered, dynamically adjusting the priority of each maintenance point through weight allocation, and merging adjacent maintenance tasks. If optimization still cannot be achieved, all pending tasks are automatically upgraded to emergency status, multi-shift parallel operation mode is activated, and the mobile spare parts warehouse is coordinated to mobilize support to high-priority areas. The system then generates a set of maintenance plans with alternative route suggestions for operation and maintenance personnel to decide, ensuring that critical equipment is repaired within the optimal operation window.

[0094] This technical architecture significantly reduces ineffective travel and duplicated work through intelligent spatiotemporal optimization, forming a closed-loop collaborative system of "route optimization, time slot allocation, and resource scheduling." A dynamic priority adjustment mechanism ensures timely response to emergencies, and a multi-shift collaborative model improves the efficiency of complex task processing, providing an intelligent solution for large-scale farmland equipment operation and maintenance.

[0095] Furthermore, the embodiment of the present invention also distinguishes the types of damage to sub-equipment, including drone repair type and manual repair type; for the drone repair type, a drone is dispatched for repair according to the geographic weather parameters at the time.

[0096] The embodiment of the present invention also provides a system for alarming damage to electrical components, which includes:

[0097] Sensing equipment and working equipment are applied to Banpo, wherein the types of sensing equipment include temperature sensing clusters, humidity sensing clusters, pH sensing clusters, and multispectral sensing clusters, wherein the sensing equipment is correspondingly set at multiple locations in each partition according to a preset redundancy, and the partitions are obtained by dividing Banpo into equal sizes, and the redundancy is that one cluster of the above type at one location can cover the numerical values ​​of plants of the same type in clusters at adjacent locations, and the numerical values ​​include particles, percentages, and areas;

[0098] The tagging module is used to monitor the real-time data of the temperature sensing cluster, humidity sensing cluster, pH value sensing cluster, and multispectral sensing cluster, and to tag data anomalies:

[0099] When a data anomaly of a cluster of a type at a location in a non-adjacent partition is detected, the cluster is marked as to be repaired uniformly, and the abnormal data is predicted and replaced by data from at least an adjacent partition;

[0100] When the data of an adjacent partition cannot be predicted or replaced by another abnormal data due to data anomaly, the cluster of the partition is marked as requiring immediate repair;

[0101] When data anomalies of multiple types of clusters in a single partition and / or cluster data anomalies of the same type in multiple locations are detected, the location is marked as requiring immediate repair;

[0102] The alarm system is used to confirm the maintenance date according to the type of data anomaly. When the mark only exists for unified repair, the maintenance date is confirmed to be the date of the most recent pastoral task. When the mark exists for immediate repair, the maintenance date is confirmed to be the most recent time point. The pastoral tasks include: inspection, weeding, and pesticide application. The most recent time point includes the current day and the most recent working day of the maintenance personnel. The type and corresponding quantity of the marked clusters are collected, and an alarm is issued according to the date.

[0103] It should be noted that the technical solution of the method embodiment can also be applied to the system embodiment. In order to save space, it will not be described here in detail.

[0104] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, method embodiments of the present invention may take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage) containing computer-usable program code.

[0105] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. The computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process described in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for alarming damage to an electrical component, characterized by: The equipment needed for Banpo is divided into sensing equipment and working equipment; The slope is divided into sections of equal size, and sensing equipment and working equipment are set up at multiple locations in each section according to the preset redundancy; Monitor real-time data from sensor devices and working equipment and mark any data anomalies: When a data anomaly of a cluster of a type at a location in a non-adjacent partition is detected, the cluster is marked as to be repaired uniformly, and the abnormal data is predicted and replaced by data from at least an adjacent partition; When the data of adjacent partitions cannot be predicted or replaced by another abnormal data due to data anomalies, the clusters of these partitions are marked as requiring immediate repair; When data anomalies of multiple types of clusters in a single partition and / or cluster data anomalies of the same type in multiple locations are detected, the location is marked as requiring immediate repair; Confirm the maintenance date based on the data anomaly type. If the mark only indicates that a repair is pending, the maintenance date is the date of the most recent farm task. If the mark indicates that an immediate repair is required, the maintenance date is the most recent time point. The farm tasks include inspection, weeding, and pesticide application. The most recent time point includes the current day and the most recent maintenance personnel working day. Collect the types and corresponding quantities of marked clusters and issue alerts by date.

2. The method according to claim 1, wherein: The redundancy is the value of the number of plants in the same type cluster at the adjacent location that can be covered by one cluster of the same type at the location. The value includes the number of plants, percentage, and area. When the redundancy is 1, each plant is detected by two clusters of the same type. When the redundancy is greater than 1, a partition map is drawn based on the detected data. The types of the sensing devices include temperature sensing clusters, humidity sensing clusters, pH sensing clusters, and multispectral sensing clusters, and the maps include temperature maps, humidity maps, pH maps, and multispectral maps; The step of detecting a data anomaly of a cluster of a type at a location in a non-adjacent partition, marking the cluster as to be repaired uniformly, and predicting and replacing the abnormal data with data from at least an adjacent partition includes: Verify data anomalies based on multiple clusters of the same type at at least one location on the graph; When all cluster data of the same type at a location on the map are abnormal, the cluster is marked as waiting for unified repair; If at least one cluster of all types at a location on the graph has data anomalies, the cluster is marked as awaiting unified repair. Accordingly, when the data of adjacent partitions cannot be predicted or replaced by another abnormal data due to data anomaly, marking the clusters of these partitions as requiring immediate repair includes: All adjacent partitions of the partition cannot predict or replace another abnormal data due to data anomalies, and the clusters of these partitions are marked as requiring immediate repair.

3. The method according to claim 2, wherein: Distinguish the type of alarm: The device abnormality alarm is generated when a data abnormality of a cluster of a type at a location in a non-adjacent partition is detected or another abnormal data of the adjacent partition cannot be predicted or replaced due to the data abnormality; The detection of data anomalies of multiple types of clusters at one location in a single partition and / or data anomalies of the same type of clusters at multiple locations is a Tianyuan anomaly alarm; When the frequency of device abnormality alarms is greater than a preset value or a preset interval, the redundancy is configured to a corresponding redundancy value.

4. The method according to claim 3, wherein: Number each partition and record all alarm types under the corresponding number; For the partition where the frequency of abnormal device alarms is high, set a higher redundancy for the corresponding type of equipment; for the partition where the frequency of abnormal device alarms is low, set a lower redundancy for the corresponding type of equipment; Record the geographical environment parameters of all zones and the redundancy of the sensor equipment types used in the zones to obtain a Banpo sensing equipment configuration database; The geographical environment parameters of Xinbanpo are matched with the Banpo sensing device configuration database to obtain the redundancy corresponding to the sensing device type used in Xinbanpo.

5. The method according to claim 1, wherein: The working equipment includes a sprinkler irrigation system, a fertilization system, and a pesticide application system; Arrange the sub-equipment of the sprinkler system, the sub-equipment of the fertilization system, and the sub-equipment of the pesticide application system at the same position in each partition; and configuring a maintenance sensing cluster on each of the sub-devices; When a data anomaly of a cluster of a type at a location in a non-adjacent partition is detected, the cluster is marked as to be repaired uniformly, and the abnormal data is predicted and replaced by data from at least an adjacent partition; When the data of adjacent partitions cannot be predicted or replaced by another abnormal data due to data anomalies, the clusters of these partitions are marked as requiring immediate repair; When data anomalies of multiple types of clusters in one location on a single partition and / or data anomalies of the same type of clusters in multiple locations are detected, the location is marked as requiring immediate repair.

6. The method according to claim 1, wherein: The calculation formula of the redundancy includes: ； Among them, R: redundancy value; O i : The number / percentage / area of ​​plants of the same type in the adjacent locations covered by the i-th cluster; n: The number of adjacent locations covered by the current cluster; N: The number / percentage / area of ​​plants that the current cluster should theoretically cover; α: Environmental coefficient.

7. The method according to claim 1, wherein: The types of sub-devices of the cluster include sensor heads, sensor circuits, and sensor lines; Each cluster is provided with at least one spare sensor head, at least one spare sensor circuit and at least one spare sensor line; Collect and integrate the characteristics of data anomalies to determine and record the type of damaged sub-device in the cluster; The alarm includes the number of damaged sub-devices and their spare sub-devices; A spare parts request is promptly sent to the inventory based on the number of damaged sub-devices and their spare sub-devices, and the corresponding number of sub-devices are provided on the maintenance date.

8. The method according to claim 7, wherein: Calculate the maintenance time and location of each partition that needs maintenance, and calculate the maintenance path based on the location and the corresponding number of sub-equipment locations and return locations provided; Configure maintenance time periods based on maintenance time, batches, and local geographical environment, and adjust maintenance routes based on time periods to reduce round trips and repair dispatch times; When the number of repair dispatches exceeds the preset number, all marks are adjusted to await immediate repair.

9. The method according to claim 7, wherein: Differentiate the types of damage to sub-equipment, including drone repair type and manual repair type; for drone repair type, dispatch drones for repair according to the geographic and weather parameters at the time.

10. A system for alarming damage to electrical components, characterized in that: It includes Sensing equipment and working equipment are applied to Banpo, wherein the sensing equipment is correspondingly arranged at multiple positions in each partition according to a preset redundancy, and the partitions are obtained by dividing Banpo into equal sizes; The marking module is used to monitor the real-time data of sensor equipment and working equipment and mark data anomalies: When a data anomaly of a cluster of a type at a location in a non-adjacent partition is detected, the cluster is marked as to be repaired uniformly, and the abnormal data is predicted and replaced by data from at least an adjacent partition; When the data of adjacent partitions cannot be predicted or replaced by another abnormal data due to data anomalies, the clusters of these partitions are marked as requiring immediate repair; When data anomalies of multiple types of clusters in a single partition and / or cluster data anomalies of the same type in multiple locations are detected, the location is marked as requiring immediate repair; The alarm system is used to confirm the maintenance date according to the type of data anomaly. When the mark only exists for unified repair, the maintenance date is confirmed to be the date of the most recent pastoral task. When the mark exists for immediate repair, the maintenance date is confirmed to be the most recent time point. The pastoral tasks include: inspection, weeding, and pesticide application. The most recent time point includes the current day and the most recent working day of the maintenance personnel. The type and corresponding quantity of the marked clusters are collected, and an alarm is issued according to the date.

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