Electrical component damage alarm method and system

Abstract

The invention discloses an electrical component damage alarm method and system. Equipment needing to be used on a half slope is divided into sensing equipment and working equipment; the half slope is partitioned according to the equal size, and sensing equipment and working equipment are correspondingly arranged; monitoring real-time data of the temperature sensing cluster, the humidity sensing cluster, the acid-base value sensing cluster and the multispectral sensing cluster, and marking according to data abnormity; according to the data exception type, determining a maintenance date, determining that the maintenance date is the nearest field task date when only there is a mark to be subjected to unified repair, and determining that the maintenance date is the nearest time point when there is a mark to be subjected to immediate repair, the field task comprises routing inspection, weeding and pesticide application, and the nearest time point comprises the current day and the nearest maintainer workday; and collecting the type and the corresponding number of the marked clusters, and giving an alarm according to the date. According to the invention, dispatched tasks can be greatly reduced, the manpower demand is reduced, and the system stability is improved.

Description

Technical Field

[0001] The 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 an agricultural production model carried out on sloping terrain, which is common in mountainous or hilly areas. Through the construction of terraces, fish-scale pits and other engineering measures, soil erosion can be effectively slowed down, and drought-resistant crops with root-fixing soil (such as fruit trees, tea, medicinal herbs, etc.) can be planted by taking advantage of the slope's abundant sunlight and good drainage. Its advantages are saving flat land resources and improving the ecological environment, but it faces challenges such as inconvenient irrigation and difficult mechanization. Modern technology combined with water-saving irrigation, ecological slope protection and intelligent monitoring has promoted the development of Banpo agriculture in an intensive and sustainable direction, becoming an important practice for rural revitalization and soil and water conservation in mountainous areas.

[0003] Banpo irrigation is a water-saving irrigation system designed for slope terrain. It is mainly used in mountain agriculture, banpo greening and soil and water conservation. Its core is to adjust water flow distribution in combination with terrain through sprinkler irrigation, drip irrigation or infiltration irrigation technology to reduce soil erosion. The system needs to adapt to the slope environment, use pressure compensation devices to ensure uniform irrigation, and be equipped with anti-scouring vegetation to stabilize the soil. The electrical control module needs to have waterproof and lightning protection design, integrated intelligent monitoring and early warning functions to cope with challenges such as humidity and vibration, and ensure stable operation. This technology takes into account both ecological restoration and efficient water use, and is an important support for the development of mountain agriculture.

[0004] Due to factors such as high humidity, physical shock, temperature fluctuations and unstable power supply in the slope irrigation environment, electrical components are easily damaged and fault warning is difficult. That is, the pain points of current agricultural electronic equipment maintenance. For example, the equipment is widely distributed and on-site maintenance is difficult; fault diagnosis relies on experience and lacks intelligent tools; data collection is not comprehensive and predictive maintenance is insufficient; labor costs are high and response speed is slow, etc. 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 electrical component damage alarm, which comprises: 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 correspondingly set at multiple locations in each section according to the preset redundancy; Monitor real-time data from sensor devices and working equipment, and mark data anomalies: When a data anomaly of a type of cluster at a location of 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 of at least an adjacent partition; 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; When data anomalies of multiple types of clusters in one location of 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; According to the data anomaly type, the maintenance date is confirmed. 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 maintenance personnel working day. Collect the types and corresponding quantities of marked clusters and issue alerts by date.

[0007] In a second aspect, an embodiment of the present invention further provides a system for alarming damage of an electrical component, comprising: Sensing equipment and working equipment are applied to Banpo, wherein the types of sensing equipment include temperature sensing cluster, humidity sensing cluster, pH value sensing cluster, and multi-spectral sensing cluster, wherein the sensing equipment is correspondingly arranged at multiple positions in each partition according to a preset redundancy, and the partition is obtained by dividing Banpo according to equal sizes, and the redundancy is that a cluster of the type at one of the positions can cover the numerical values ​​of plants of the same type of cluster at adjacent positions, and the numerical values ​​include particles, percentages, and areas; The marking module is used to monitor the real-time data of the temperature sensing cluster, humidity sensing cluster, pH value sensing cluster, and multi-spectral sensing cluster, and mark the data according to abnormalities: When a data anomaly of a type of cluster at a location of 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 of at least an adjacent partition; 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; When data anomalies of multiple types of clusters in one location of 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; The alarm system is used to confirm the maintenance date according to the data anomaly type. 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 and needs to be repaired immediately, 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 types and corresponding quantities of the marked clusters are collected, and alarms are issued according to the date.

[0008] Compared with the prior art, the advantages of the present invention are: the system adopts a "space-function" dual-dimensional redundant design, divides the slope into equal-sized monitoring units, and each unit is equipped with a multi-type sensor cluster (temperature and humidity / pH / multi-spectrum) and working equipment (sprinkler irrigation / fertilization / pesticide application). Double coverage is achieved through preset redundancy: when a single sensor is abnormal, the adjacent unit equipment automatically takes over the monitoring task, and the system predicts missing values ​​based on a multi-source data fusion algorithm, so that more than 85% of single-point failures do not require manual on-site confirmation. Only when three consecutive adjacent units alarm at the same time or when multiple parameters are abnormal in the same area, the on-site maintenance instruction is triggered. Measured data show that this mechanism reduces the monthly outbound task volume by 72% and emergency repair incidents by 89%.

[0009] Subsequently, when a single parameter fluctuates, its maintenance is automatically included in the periodic farm tasks (such as simultaneous processing with inspection and fertilization). The system predicts spare parts demand through map analysis and completes logistics allocation in advance, so that maintenance personnel can complete 3-5 tasks in a single trip; when dual redundancy fails, the system automatically plans the optimal maintenance path, combines geographic fence technology to connect adjacent abnormal points in series, and reduces repeated round trips. Through the path optimization algorithm, the average daily mileage is reduced by 63%.

[0010] In summary, the present invention reconstructs the Banpo operation and maintenance system through an intelligent redundant architecture and a dynamic decision-making engine: adopts multi-type sensor cluster partition coverage, and a single-point failure automatically triggers a data compensation mechanism, which significantly reduces 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

[0011] 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 creative work.

[0012] Figure 1 The figure is a flowchart of one embodiment of the present invention. DETAILED DESCRIPTION

[0013] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0014] The present invention realizes a method and a system for alarming damage of electrical components.

[0015] In order to better understand the above technical solution, a detailed description is given below in conjunction with specific implementation methods.

[0016] like Figure 1 As shown, a method for alarming damage to an electrical component comprises: The equipment needed for Banpo can be divided into sensing equipment and working equipment.

[0017] Specifically, in the Banpo intelligent monitoring system, the equipment system adopts a "perception-execution" binary architecture, in which the sensor equipment serves as the core perception layer, integrating four professional modules: temperature sensing cluster, humidity sensing cluster, pH sensing cluster, and multispectral sensing cluster. The temperature cluster uses high-sensitivity thermal elements to build a microclimate monitoring network to accurately capture the temperature difference between the surface and the root layer; the humidity cluster combines soil moisture sensors and leaf humidity meters to establish a three-dimensional humidity field distribution model; the pH cluster uses electrochemical sensing technology to continuously monitor soil pH fluctuations; the multispectral cluster is equipped with a multi-channel spectrometer to analyze the reflection characteristics of vegetation in different bands to invert crop growth status and stress index. The four types of clusters work together to form a multi-dimensional environmental perception matrix to provide comprehensive and accurate data support for the system decision-making layer. This technical architecture realizes full-factor perception of environmental parameters through multimodal sensor fusion, upgrades traditional single-point monitoring to three-dimensional grid monitoring, and significantly improves data dimensions and abnormality recognition capabilities.

[0018] The slope is divided into sections of equal size, and sensing equipment and working equipment are correspondingly set at multiple locations in each section according to a preset redundancy.

[0019] Specifically, a modular grid division strategy is used to discretize the semi-slope terrain into evenly distributed monitoring units, and each unit is equipped with a dual redundant sensor 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 to ensure that the monitoring range of a single node forms an effective overlapping area with the adjacent units, and realizes cross-monitoring coverage of multiple crops in the plant carrier dimension. In the functional dimension, the verification mechanism of similar parameters is maintained. 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 continuous reliability of the monitoring network.

[0020] Monitor real-time data from sensor devices and working equipment, and mark data anomalies: When a data anomaly of a type of cluster at a location of 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 of at least an adjacent partition; 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; When data anomalies of multiple types of clusters in one location of 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; Specifically, the system builds an intelligent abnormality diagnosis model to implement full-dimensional real-time monitoring and dynamic decision-making for four types of sensor clusters: temperature, humidity, pH value, and multi-spectrum. If the data of a type of device at only one location in a partition is abnormal, it is generally because the device is faulty, rather than the temperature and humidity corresponding to the location. If the location is watered too much, the temperature, humidity, and pH value will generally have abnormal data, not just one data abnormality. Furthermore, if multiple partitions are not adjacent and the data of a type of device at only one location is abnormal, it is generally because the device is faulty, rather than the temperature and humidity corresponding to the location. If the location is watered too much, it is generally because the temperature, humidity, and pH value will have abnormal data, not just one data abnormality. At the same time, by retrieving the valid data of the same type of sensors in adjacent partitions, combined with filtering technology to generate predicted values ​​for compensation, the continuity and effectiveness of monitoring are ensured. If the data of adjacent partitions is also abnormal and causes the prediction to fail, the data cannot be obtained, so it needs to be repaired immediately to ensure the continuity and effectiveness of data and detection. That is, a data anomaly of a type at a position in a non-adjacent partition refers to a data anomaly of a type at a position in a partition in all partitions, and a cluster of the same type at a corresponding position in an adjacent partition has no anomaly.

[0021] According to the data anomaly type, the maintenance date is confirmed. 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 maintenance personnel working day. Specifically, the system builds a dynamic work order decision model to achieve intelligent coordinated scheduling of equipment maintenance and agricultural production activities. When the abnormality is marked as "to be repaired uniformly", the system automatically connects to the farm operation management system, and integrates the maintenance task into the nearest inspection, weeding or pesticide application operation plan through the crop growth cycle model and the agricultural time priority algorithm to ensure seamless connection between operation and maintenance activities and production processes; if it is marked as "need to be repaired immediately", the real-time response mechanism is triggered. The system calculates the optimal path based on GIS spatial data and locks the earliest accessible time period in combination with the maintenance personnel's schedule. For emergency work orders triggered during non-working hours, the system starts the on-duty personnel scheduling plan, pushes emergency repair instructions through the mobile terminal, and presets the emergency tool kit path to form an all-weather response capability. This technical mechanism significantly improves the response time of emergency tasks, optimizes the collaborative efficiency of routine tasks, and effectively avoids conflicts between equipment downtime and agricultural operations through the dynamic adjustment algorithm of work order priorities. The system innovatively builds a "production-operation and maintenance" dual-mode scheduling model, and achieves zero interference of equipment maintenance on agricultural production through spatiotemporal resource coupling optimization, while reducing operation and maintenance costs.

[0022] Collect the types and corresponding quantities of marked clusters and issue alerts by date.

[0023] Specifically, the system builds an intelligent alarm management system to gather multi-dimensional abnormal information of marked clusters in real time. The abnormal data is classified and counted through a distributed database to establish a three-dimensional alarm model including fault type, spatial distribution, and time dimension. The alarm generation engine uses dynamic template technology to automatically fill in equipment identification, fault description, related parameter trend chart and disposal suggestions, and set a hierarchical alarm mechanism based on the scope of impact and urgency: conventional abnormalities are pushed to the operation and maintenance platform in the form of work orders, regional faults trigger visual reminders on the mobile terminal, and serious abnormalities that endanger system operation initiate multi-channel emergency notifications. Alarm information is continuously optimized through machine learning algorithms, the system records historical disposal processes, dynamically adjusts alarm thresholds and trigger logic, and effectively reduces false alarm rates. After the maintenance is completed, the system automatically performs data repair verification, updates the equipment health file, and forms a closed-loop management process of "monitoring-early warning-disposal-verification". Through intelligent classification and association analysis, this technical architecture significantly improves the accuracy of alarm information, reduces the cost of manual review, realizes intelligent scheduling of alarm response and operation and maintenance resources, and forms an intelligent alarm system with adaptive optimization capabilities.

[0024] The present invention also provides an embodiment, characterized in that: The redundancy is the value of a cluster of the type at one location that can cover the plants of the same type of cluster at an adjacent location, and the value includes 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. The types of the sensing devices include temperature sensing clusters, humidity sensing clusters, pH sensing clusters, and multi-spectral sensing clusters, and the maps include temperature maps, humidity maps, pH maps, and multi-spectral maps; When a data anomaly of a type of cluster at a location of a non-adjacent partition is detected, marking the cluster as to be repaired uniformly, and predicting and replacing the abnormal data by data of 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 graph are abnormal, the cluster is marked as waiting for unified repair; If at least one cluster data anomaly exists in all types at a position on the graph, the cluster is marked as to be repaired uniformly; Accordingly, when the data of the adjacent partition cannot predict or replace another abnormal data due to data anomaly, marking the cluster of the partition as requiring immediate repair includes: All adjacent partitions to 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.

[0025] Specifically, redundancy is a core indicator for evaluating the coverage efficiency of agricultural sensor networks, which characterizes the degree of effective overlap of a certain type of sensor cluster with the monitoring range of similar clusters in adjacent areas. Its quantitative dimensions include the number of individual plant coverage, spatial overlap ratio, and effective monitoring area ratio: when the redundancy is 1, the system adopts a basic double coverage design to ensure that each crop is monitored by two adjacent clusters at the same time; when the redundancy exceeds 1 (such as 1.5), the cluster monitoring range needs to be expanded to more than 50% of the adjacent area, forming a multi-layer overlapping monitoring network, and generating an environmental map through multi-source data fusion.

[0026] The sensor equipment cluster is divided into four types of monitoring units: temperature, humidity, pH value, and multi-spectrum. 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 the moisture dynamics of the root layer; the pH value cluster uses an electrode array to achieve pH value gradient warning; the multi-spectral cluster is equipped with a hyperspectral imager to evaluate the health of crops through vegetation index (NDVI / PRI).

[0027] When the redundancy exceeds the basic threshold, the system automatically fuses multi-source data to generate a dynamic environmental map. The temperature map displays the geothermal gradient in a heat map to identify the risk of heat stress; the humidity map generates a water isosurface through interpolation to optimize the irrigation path; the pH map constructs a pH three-dimensional model to guide soil improvement; and the multispectral map combines machine learning to predict yield distribution. This redundant design not only enhances the system's fault tolerance, but also provides decision support for precision agricultural management through spatiotemporal data fusion, achieving three-dimensional monitoring coverage from plants to fields. The system builds a multi-level redundant detection and spatial map analysis mechanism, and improves the accuracy of abnormal diagnosis through redundant configuration and visual modeling. When the basic redundant configuration is adopted, each plant is monitored synchronously by two sensor clusters of the same type, and basic fault tolerance is achieved through data consistency comparison; when the enhanced redundant configuration is enabled, the system automatically fuses multi-source heterogeneous data to build a high-precision environmental map system, including temperature field distribution maps, humidity gradient heat maps, pH spatial distribution maps, and multispectral reflectance matrices, forming a digital environmental model covering the entire growth cycle.

[0028] 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 starts the graph verification mechanism: first, the spatiotemporal data of all clusters of the same type at the location are retrieved, and the anomaly diffusion characteristics are determined through spatial distribution pattern analysis, and the mutation timeliness 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 it is marked as waiting for unified repair; if all types of clusters at the location have abnormal data, it is determined to be a complex environmental crisis, and the full-dimensional repair process is immediately initiated.

[0029] For the judgment conditions that require immediate repair, the system uses a regional failure detection algorithm: when all adjacent partitions of the target partition cannot provide valid prediction data (that is, the adjacent regional maps have continuity breaks or parameter contradictions), it is judged as a regional monitoring network failure and the work order priority is automatically upgraded to the highest level. This mechanism effectively distinguishes local failures from systemic collapses through map integrity verification to avoid the spread of misjudgments.

[0030] In terms of technical effects, the system significantly improves the accuracy of data anomaly identification and greatly reduces the false alarm rate through the synergy of redundant detection and graph analysis. The establishment of spatial graphs provides a visual diagnosis basis for environmental status, allowing operation and maintenance personnel to intuitively grasp the abnormal distribution pattern. The intelligent marking mechanism dynamically evaluates the scope of fault impact, optimizes the operation and maintenance resource allocation strategy, significantly improves emergency response efficiency, shortens the conventional repair cycle, and forms an intelligent transition from single-point monitoring to system diagnosis.

[0031] Based on the above embodiment, it also includes the steps of: distinguishing the type of alarm: When a data anomaly of a type of cluster at a location of a non-adjacent partition is detected or another abnormal data of the adjacent partition cannot be predicted or replaced due to data anomaly, an equipment abnormality alarm is issued; 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 field anomaly alarm; When the frequency of abnormal alarms of the equipment is greater than a preset value or a preset interval, the redundancy is configured to be a corresponding redundancy value.

[0032] Specifically, the system builds an intelligent alarm classification system to achieve accurate anomaly classification through multi-dimensional data analysis. Alarm types are divided into two categories: device-level anomalies and field-level anomalies, and a dual-modal recognition engine is used for intelligent distinction: when a single-type cluster data anomaly is detected at a certain location in a non-adjacent partition, or an adjacent partition cannot provide an effective prediction due to its own data anomaly, the system determines it as a device-level failure. This mechanism accurately locates sensor failure or communication link failure through spatial topology verification and data continuity analysis, triggers the equipment health assessment model and generates preventive maintenance recommendations.

[0033] When a synchronous anomaly of multiple types of clusters at a single location is detected, or when a spatial agglomeration anomaly of the same type of clusters at multiple locations is detected, the system determines it as a pastoral environmental crisis. This mechanism uses multi-spectral coupling analysis and microclimate gradient modeling to identify production risks such as pests and diseases, irrigation imbalance or soil pollution, integrates GIS spatial maps and crop growth models to generate zoning control plans, and links the intelligent agricultural machinery scheduling system to implement precise intervention.

[0034] The system innovatively establishes a dynamic response mechanism for alarm frequency and redundancy: when the density of abnormal alarms of equipment exceeds the preset time and space thresholds, the redundancy configuration algorithm is automatically triggered. The algorithm dynamically calculates the optimal redundancy coefficient based on the equipment failure mode library and operation and maintenance cost model, temporarily improves monitoring redundancy during critical growth cycles or extreme climate conditions, and ensures data collection reliability. Redundancy adjustment follows the principle of partition isolation, and only implements local enhancement in high-frequency alarm areas to avoid resource waste.

[0035] The present invention also provides an embodiment, wherein the working equipment includes a sprinkler irrigation system, a fertilizer application 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 of the partition, and arrange the maintenance sensing cluster on each of the sub-equipment; When a data anomaly is detected for a location and / or a type of cluster of a partition, the cluster is marked as to be repaired uniformly, and the abnormal data is predicted and replaced by data of at least adjacent partitions; 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; When data anomalies of multiple types of clusters in one location of 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.

[0036] The system builds a multi-modal intelligent monitoring system for agricultural equipment, integrating the collaborative perception network of the three major operating systems of sprinkler irrigation, fertilization, and pesticide application. At the field zoning planning level, a spatial alignment strategy is used to deploy the sub-terminals of the three types of equipment at the same geographic coordinates to form a three-dimensional monitoring node with superimposed operating functions. Each sub-equipment is equipped with a multi-parameter maintenance sensing cluster to collect equipment operation data and environmental feedback parameters in real time to build a multi-dimensional perception matrix.

[0037] The abnormal diagnosis mechanism adopts a hierarchical processing architecture: when a single-type cluster data at a single location deviates, the system starts spatial topology analysis, retrieves the data of the same type of equipment in adjacent partitions, predicts the normal parameter range through the spatiotemporal sequence coupling model, and implements data correction and status compensation. If the data links in adjacent areas are abnormally broken, an equipment-level emergency work order is triggered, and the spare parts library is linked to perform modular replacement.

[0038] When a complex abnormal pattern is detected (such as the simultaneous failure of a multi-system cluster in a single location, or distributed failures of similar equipment in different areas), the system immediately initiates a field-level emergency response. Through cross-validation of multispectral environmental maps and crop growth models, it is determined whether the root cause of the abnormality is a linkage failure of equipment or a sudden change in the field ecology, and partition isolation instructions and work process reconstruction plans are automatically generated. At this time, the redundancy configuration model will dynamically adjust the monitoring density, temporarily add mobile sensing nodes in risk areas, and form a flexible monitoring network.

[0039] This technical system significantly improves fault location accuracy and response efficiency through deep fusion analysis of equipment and environmental parameters. The spatial alignment deployment strategy makes multi-system data naturally comparable, and the accuracy of abnormal pattern recognition is significantly improved. The intelligent hierarchical response mechanism effectively distinguishes equipment-level failures from field systemic risks, optimizes the allocation of operation and maintenance resources, shortens the routine repair cycle, and compresses the emergency response time, forming an intelligent early warning and rapid disposal capability covering the entire chain of agricultural production.

[0040] Furthermore, the present invention also includes a redundancy calculation formula including: Where, R: dimensionless redundancy value; i: The number / percentage / area of ​​plants in the same type of clusters at adjacent locations covered by the ith 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 geographic parameters such as slope and vegetation density.

[0041] The formula has high dynamic adaptability: the α coefficient is used to adapt to different geographical conditions, solving the pain points of wide equipment distribution and large environmental differences. Fault prediction is smarter: redundancy is linked to data anomaly marking (such as allowing single-point anomaly prediction when R≥1), reducing manual dependence. Cost is more optimized: reduce equipment density in low-redundancy areas (such as flat areas), strengthen coverage in high-redundancy areas (such as steep slopes), and balance economy and reliability. 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; Each cluster is correspondingly 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 sub-device that is damaged in the cluster; The alarm includes the number of damaged sub-devices and their spare sub-devices; According to the number of the damaged sub-device and its spare sub-devices, a spare parts request is immediately sent to the inventory, and the corresponding number of sub-devices are provided on the maintenance date.

[0042] Specifically, the system builds a three-level redundant sensing architecture. Each monitoring cluster is equipped with three types of core sub-devices: sensor head, sensor circuit, and sensor line, and adopts a modular redundant design. Each sub-device unit is equipped with the same type of spare components to form a "active-active" operation mode to ensure that the system can still maintain basic monitoring capabilities when a single component fails. Through multi-parameter coupling analysis technology, the system collects equipment operation status data in real time, establishes a comprehensive feature model including signal response characteristics, data transmission stability, and environmental interference intensity, and accurately identifies failure modes.

[0043] When data anomalies occur, the intelligent diagnosis engine starts multi-dimensional correlation analysis: by comparing the phase difference and amplitude attenuation characteristics of the device output signal, it is determined that the fault is caused by the decrease in sensitivity of the sensor head; by analyzing the impedance change and noise spectrum of the circuit loop, the sensor circuit aging or short circuit problem is located; by monitoring the line transmission efficiency and electromagnetic interference waveform, the sensor line is damaged or poor contact is diagnosed. The system generates structured alarm information including the type of faulty equipment and the remaining number of spare equipment, and pushes it to the operation and maintenance terminal and spare parts management system simultaneously.

[0044] The spare parts request mechanism achieves dynamic supply and demand matching: When the failure of the main equipment triggers the activation of the spare equipment, the system automatically calculates the total amount of spare equipment of the same type in the current partition, and generates an intelligent replenishment list based on the historical failure frequency and equipment life model. The warehouse management system adopts the principle of "partition priority and nearby allocation" according to the request priority to complete the spare parts pre-positioning before the preset maintenance window period. For high-frequency failure areas, the system predicts the failure cycle through machine learning, reserves vulnerable components in advance, and optimizes inventory turnover efficiency.

[0045] This technical architecture significantly improves operation and maintenance efficiency through refined fault location and intelligent spare parts management. The three-level redundancy design enhances system availability, and the fault repair process is fully automated. The spare parts request mechanism reduces manual intervention and reduces the risk of inventory backlog, forming a full-process management system of "real-time monitoring-accurate diagnosis-intelligent replenishment-closed-loop verification", providing high-reliability operation guarantee for large-scale farmland monitoring networks.

[0046] Further, 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-equipment locations and the return location provided; Configure maintenance time periods according to maintenance time, batches, and local geographical environment, and adjust maintenance routes according to time periods to reduce the number of round trips and repair dispatches; When the number of repair dispatches is greater than a preset number, all marks are adjusted to be repaired immediately.

[0047] Specifically, the system builds an intelligent maintenance path planning engine, deeply integrating spatiotemporal data with equipment status information to achieve dynamic operation and maintenance scheduling. When generating maintenance tasks, the engine automatically retrieves multi-source geospatial data, including terrain features of the work area, road network structure, and real-time traffic status, and combines the distribution of equipment fault locations with the layout of sub-equipment storage outlets to build a multi-dimensional maintenance path network. Through a multi-objective optimization algorithm, the optimal path chain of equipment replacement nodes, sub-equipment replenishment nodes, and warehouse return nodes is simultaneously calculated to ensure maximum path coverage efficiency.

[0048] The maintenance period configuration module uses environmental perception technology to obtain real-time data on meteorological conditions, light intensity, and temperature and humidity changes, and establish an adaptability model for the operating environment. The system dynamically prioritizes maintenance periods based on the sensitive period of the crop growth cycle and the equipment maintenance window period, and automatically avoids unfavorable operating conditions. For cross-batch maintenance tasks, the engine introduces a spatiotemporal clustering algorithm to aggregate similar faulty equipment in adjacent areas in time and space, generate intensive maintenance batches, and reduce repeated path coverage.

[0049] When the path planning results show that the round-trip frequency or the repair batch exceeds the preset threshold, the system starts the global optimization strategy: first, the path re-planning mechanism is triggered, the priority of each maintenance point is adjusted through dynamic weight allocation, and adjacent maintenance tasks are merged; if optimization is still not possible, 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 provide mobile support to high-priority areas. At this time, the system generates a maintenance plan set containing alternative path suggestions for operation and maintenance personnel to make decisions to ensure that key equipment is repaired within the optimal operation window.

[0050] This technical architecture significantly reduces invalid trips and repeated dispatches through intelligent spatiotemporal optimization, forming a closed-loop collaborative system of "path optimization-time period configuration-resource scheduling". The dynamic priority adjustment mechanism ensures that emergency failures are responded to in a timely manner, and the multi-shift collaborative mode improves the efficiency of complex task processing, providing intelligent solutions for large-scale farmland equipment operation and maintenance.

[0051] 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.

[0052] The embodiment of the present invention also provides a system for alarming damage of electrical components, which comprises Sensing equipment and working equipment are applied to Banpo, wherein the types of sensing equipment include temperature sensing cluster, humidity sensing cluster, pH value sensing cluster, and multi-spectral sensing cluster, wherein the sensing equipment is correspondingly arranged at multiple positions in each partition according to a preset redundancy, and the partition is obtained by dividing Banpo according to equal sizes, and the redundancy is that a cluster of the type at one of the positions can cover the numerical values ​​of plants of the same type of cluster at adjacent positions, and the numerical values ​​include particles, percentages, and areas; The marking module is used to monitor the real-time data of the temperature sensing cluster, humidity sensing cluster, pH value sensing cluster, and multi-spectral sensing cluster, and mark the data according to abnormalities: When a data anomaly of a type of cluster at a location of 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 of at least an adjacent partition; 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; When data anomalies of multiple types of clusters in one location of 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; The alarm system is used to confirm the maintenance date according to the data anomaly type. 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 and needs to be repaired immediately, 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 types and corresponding quantities of the marked clusters are collected, and alarms are issued according to the date.

[0053] It should be noted that the technical solution of the method embodiment can also be applied to the system embodiment, and in order to save space, it will not be repeated here.

[0054] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the method embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 disk storage and optical storage, etc.) containing computer-usable program codes.

[0055] 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. 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.

[0056] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if the modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include the modifications and variations.

Claims

1. A method for alarming damage to an electrical component, characterized in that: 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 correspondingly set at multiple locations in each section according to the preset redundancy; Monitor real-time data from sensor devices and working equipment and mark them based on data anomalies: When a data anomaly of a type of cluster at a location of 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 of at least an adjacent partition; 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; When data anomalies of multiple types of clusters in one location of 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; According to the data anomaly type, the maintenance date is confirmed. 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 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, characterized in that: The redundancy is the value of a cluster of the type at one location that can cover the plants of the same type of cluster at an adjacent location, and the value includes 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. The types of the sensing devices include temperature sensing clusters, humidity sensing clusters, pH sensing clusters, and multi-spectral sensing clusters, and the maps include temperature maps, humidity maps, pH maps, and multi-spectral maps; The step of detecting a data anomaly of a type of cluster at a location of a non-adjacent partition, marking the cluster as to be repaired uniformly, and predicting and replacing the abnormal data by data of at least an adjacent partition comprises: 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 graph are abnormal, the cluster is marked as waiting for unified repair; If at least one cluster data anomaly exists in all types at a location on the graph, the cluster is marked as to be repaired uniformly; 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: All adjacent partitions to 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.

3. The method according to claim 2, characterized in that: Differentiate the types of alarms: The device abnormality alarm is detected when a data abnormality of a type of cluster at a location of a non-adjacent partition is detected or another abnormal data of the adjacent partition cannot be predicted or replaced due to 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 field anomaly alarm; When the frequency of abnormal alarms of the equipment is greater than a preset value or a preset interval, the redundancy is configured to be a corresponding redundancy value.

4. The method according to claim 3, characterized in that: Number each partition and record all alarm types under the corresponding number; A higher redundancy is provided for the corresponding type of equipment in the partition with a high frequency of abnormal equipment alarms, and a lower redundancy is provided for the corresponding type of equipment in the partition with a low frequency of abnormal equipment alarms; Record the geographical environment parameters of all sub-areas and the redundancy corresponding to the type of sensor equipment used in the sub-areas to obtain a Banpo sensing equipment configuration database; The geographical environment parameters of Xinbanpo are matched in 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, characterized in that: 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 of the partition, and arrange the maintenance sensing cluster on each of the sub-equipment; When a data anomaly of a type of cluster at a location of 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 of at least an adjacent partition; 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; When data anomalies of multiple types of clusters in one location of 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, characterized in that: The calculation formula of the redundancy includes: ； Where, R: dimensionless redundancy value; i : The number / percentage / area of ​​plants in the same type of clusters at adjacent locations covered by the ith 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 geographic parameters such as slope and vegetation density.

7. The method according to claim 1, characterized in that: The types of sub-devices of the cluster include sensor heads, sensor circuits, and sensor lines; Each cluster is correspondingly 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 sub-device that is damaged in the cluster; The alarm includes the number of damaged sub-devices and their spare sub-devices; According to the number of the damaged sub-device and its spare sub-devices, a spare parts request is immediately sent to the inventory, and the corresponding number of sub-devices are provided on the maintenance date.

8. The method according to claim 7, characterized in that: 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 according to maintenance time, batches, and local geographical environment, and adjust maintenance routes according to time periods to reduce the number of round trips and repair dispatches; When the number of repair dispatches exceeds the preset number, all marks are adjusted to be repaired immediately.

9. The method according to claim 7, characterized in that: Differentiate the types of damage to sub-equipment, including drone repair type and manual repair type; for drone repair type, dispatch a drone for repair according to the geographic 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 them according to data anomalies: When a data anomaly of a type of cluster at a location of 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 of at least an adjacent partition; 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; When data anomalies of multiple types of clusters in one location of 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; The alarm system is used to confirm the maintenance date according to the data anomaly type. 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 and needs to be repaired immediately, 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 types and corresponding quantities of the marked clusters are collected, and alarms are issued according to the date.

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