Photovoltaic power station global unattended integrated intelligent management and control method and system

Abstract

This invention provides a method and system for integrated intelligent management and control of unmanned photovoltaic power plants, comprising: synchronously collecting and integrating multi-source heterogeneous data from various systems across the entire photovoltaic power plant to construct a unified data stream covering five dimensions: equipment, environment, security, fire protection, and network; uniformly parsing and identifying events within the unified data stream, and generating collaborative control commands based on equipment operating constraints, environmental safety boundaries, security response rules, fire protection linkage conditions, and network security strategies; and driving corresponding execution systems to perform cross-system linkage and closed-loop actions according to the collaborative control commands. This invention enables collaborative control of various systems, thereby improving operation and maintenance efficiency and reducing safety risks.

Description

Technical Field

[0001] This invention relates to the field of integrated intelligent control technology, and in particular to an integrated intelligent control method and system for unmanned operation of a photovoltaic power station. Background Technology

[0002] Currently, large-scale ground-mounted photovoltaic power plants are generally developing towards unmanned operation and minimally staffed maintenance. These plants typically deploy multiple independent systems, including those for substation rail-mounted robot inspection, video surveillance, intelligent security, fire suppression systems, environmental monitoring, photovoltaic area smoke and fire detection, drone inspections, and network security protection. However, these systems usually operate independently, with incompatible data formats and communication protocols, making information sharing and collaborative operation difficult. This leads to low maintenance efficiency and high safety risks. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide an integrated intelligent management and control method and system for unmanned operation of photovoltaic power plants, which can realize the coordinated control of various systems, thereby improving operation and maintenance efficiency and reducing safety risks.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Firstly, this invention provides an integrated intelligent control method for unmanned operation of a photovoltaic power station, comprising: synchronously collecting and integrating multi-source heterogeneous data from various systems across the entire photovoltaic power station to construct a unified data stream covering five dimensions: equipment, environment, security, fire protection, and network; uniformly parsing and identifying events in the unified data stream, and generating collaborative control instructions based on equipment operation constraints, environmental safety boundaries, security response rules, fire protection linkage conditions, and network security strategies; and driving the corresponding execution systems to perform cross-system linkage and closed-loop actions according to the collaborative control instructions.

[0005] Optionally, unified parsing and event identification are performed on the unified data stream, including: unified parsing and normalization of the unified data stream, and real-time identification and limit judgment of equipment operating status, environmental parameters, security events, fire protection conditions and network security status based on the processed data; among which, limit judgment includes: identification of abnormal meter readings, identification of physical status of disconnectors / circuit switches / pressure plates, judgment of excessive infrared temperature on equipment surface, detection of illegal intrusion by personnel or vehicles, identification of smoke and fire characteristics in photovoltaic areas, judgment of excessive environmental temperature and humidity / water immersion, and identification of abnormal network connection or unauthorized access behavior.

[0006] Optionally, based on equipment operating constraints, environmental safety boundaries, security response rules, fire linkage conditions, and network security strategies, collaborative control instructions are generated, including: when any device status exceeds the limit, automatically triggering the inspection frequency adjustment rule, dynamically increasing the inspection frequency of the corresponding device and related areas according to the level of exceeding the limit, and generating a special inspection task instruction; after receiving a security intrusion event signal, triggering the video linkage and audible and visual alarm rules, and generating video and audible and visual alarm instructions; when the smoke and fire detection system outputs a fire confirmation signal, triggering the zone power outage and gas extinguishing activation rules, and generating fire linkage instructions for each preset zone; when environmental parameters exceed the set threshold, triggering the dynamic environmental parameter limit adjustment rule, and generating control instructions for the corresponding device; when a network security event is detected, triggering the security enhancement rule, and generating an emergency response instruction.

[0007] Optionally, the corresponding execution systems are driven to perform cross-system linkage and closed-loop actions according to collaborative control commands, including: driving the rail-mounted robot, video monitoring unit, intelligent security device, fire-fighting actuator, dynamic environment adjustment equipment, UAV platform, and network security equipment to perform cross-system linkage and closed-loop actions according to collaborative control commands; among which, the cross-system linkage and closed-loop actions include: target tracking coordination between the rail-mounted robot and the fixed camera, automatic retrieval of multi-angle video and synchronization of sound and light warnings after security intrusion is triggered, electrical isolation and fire-fighting medium spraying coordination after smoke and fire warning is triggered, combined start and stop of environmental adjustment equipment after dynamic environment over-limit is triggered, UAV automatically planning and executing refined inspection routes based on defect location and type, network security system automatically executing encrypted channel establishment according to policy, and dynamic revocation of user permissions and full recording of operation behavior.

[0008] Optionally, it also includes: generating multi-level alarm information based on the severity of the event, and automatically constructing five types of structured operation reports based on the alarm information, namely inspection, security, fire protection, environmental protection and network security; and uploading all multi-source heterogeneous data, event identification results, collaborative control commands, alarm information and structured operation reports to the remote centralized control center.

[0009] Optionally, it also includes: during the collaborative control process, real-time monitoring of the equipment status, environmental parameters, security events, and network status of each system; when the equipment status, environmental parameters, security events, or network status change, real-time updating of the monitoring results, regenerating collaborative control instructions, and synchronously adjusting the execution actions of each execution system.

[0010] Optionally, the multi-source heterogeneous data may include at least: inspection images and positioning trajectories of the rail-mounted robot, video streams and AI recognition results from fixed / pan-tilt cameras, infrared thermal imaging temperature measurement data, outputs from temperature, humidity and water immersion sensors, SF6 gas concentration, chromatographic analysis values ​​of main transformer oil, smoke / temperature alarm signals, electronic fence trigger signals, access control and vehicle passage records, smoke and fire recognition results, drone aerial images and defect annotations, and access logs, encryption status and permission change records generated by network security devices.

[0011] Secondly, this invention provides an integrated intelligent control system for unmanned operation across the entire photovoltaic power station, comprising: a data acquisition module for synchronously acquiring and integrating multi-source heterogeneous data from various systems across the entire photovoltaic power station, constructing a unified data stream covering five dimensions: equipment, environment, security, fire protection, and network; a collaborative control command generation module for uniformly parsing and identifying events in the unified data stream, and generating collaborative control commands based on equipment operating constraints, environmental safety boundaries, security response rules, fire protection linkage conditions, and network security strategies; and a multi-device collaborative execution module for driving the corresponding execution systems to perform cross-system linkage and closed-loop actions according to the collaborative control commands.

[0012] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the steps of the method provided in any of the first aspects above.

[0013] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the steps of the method provided in any of the first aspects above.

[0014] This invention brings the following beneficial effects: The above-mentioned integrated intelligent control method and system for unmanned operation of photovoltaic power plants provided by this invention firstly collects and integrates multi-source heterogeneous data from various systems across the entire photovoltaic power plant, constructing a unified data stream covering five dimensions: equipment, environment, security, fire protection, and network. Then, it performs unified parsing and event identification on the unified data stream, and generates collaborative control commands based on equipment operation constraints, environmental safety boundaries, security response rules, fire protection linkage conditions, and network security strategies. Finally, it drives the corresponding execution systems to perform cross-system linkage and closed-loop actions according to the collaborative control commands. In this method, by simultaneously collecting and integrating multi-source heterogeneous data from various systems across the entire photovoltaic power plant, data silos between systems are broken down, enabling comprehensive perception of the status quo across the five dimensions of equipment, environment, security, fire protection, and network. By integrating equipment operation constraints, environmental safety boundaries, security response rules, fire protection linkage conditions, and network security strategies to generate collaborative control commands, it avoids action conflicts or response gaps caused by independent decision-making by a single system, achieving unmanned integrated intelligent control of autonomous perception, collaborative decision-making, and closed-loop execution, improving the operation and maintenance efficiency of the power plant, and reducing safety risks.

[0015] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an integrated intelligent control method for unmanned operation of a photovoltaic power station, provided as an embodiment of the present invention; Figure 2 This is a schematic diagram of the overall architecture and device deployment provided in an embodiment of the present invention; Figure 3 A flowchart of an integrated intelligent control method for unattended operation across the entire domain, provided by an embodiment of the present invention; Figure 4 This is a schematic diagram of a multi-system collaborative control logic provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an integrated intelligent control system for unmanned operation of a photovoltaic power station, provided in an embodiment of the present invention. Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Currently, power stations generally deploy multiple independent systems, including those for booster station rail-mounted robot inspection, video surveillance, intelligent security, fire suppression gas systems, environmental monitoring, photovoltaic area smoke and fire detection, drone inspection, and network security protection. However, these systems typically operate independently, with incompatible data formats and communication protocols, making it difficult to achieve information sharing and collaborative operation. This leads to problems such as low maintenance efficiency and high safety risks.

[0021] Based on this, the present invention provides a photovoltaic power station unmanned integrated intelligent management and control method and system, which can realize the coordinated control of various systems, thereby improving operation and maintenance efficiency and reducing safety risks.

[0022] To facilitate understanding of this embodiment, a detailed description of the integrated intelligent management and control method for unmanned operation of a photovoltaic power station, as disclosed in this embodiment of the invention, will be provided first. This method can be executed by electronic devices, such as smartphones, computers, and tablets. See also... Figure 1 The flowchart shown illustrates an integrated intelligent control method for unmanned operation of a photovoltaic power station, which mainly includes the following steps S101 to S103: Step S101: Synchronously collect and integrate multi-source heterogeneous data from all systems across the entire photovoltaic power station to construct a unified data flow covering five dimensions: equipment, environment, security, fire protection, and network.

[0023] In one implementation, edge acquisition nodes deployed in photovoltaic fields, substations, power collection lines, and transmission lines are used to collect data in parallel using different communication protocols, including but not limited to: images and positioning trajectories of rail-mounted robots, video streams and AI recognition results from fixed / pan-tilt-zoom cameras, infrared thermal imaging temperature measurement data, outputs from temperature, humidity and water immersion sensors, SF6 gas concentration, chromatographic analysis values ​​of main transformer oil, smoke / temperature alarm signals, electronic fence trigger signals, access control and vehicle passage records, smoke and fire recognition results, drone aerial images and defect annotations, and access logs, encryption status and permission change records generated by network security equipment.

[0024] Furthermore, by utilizing timestamp synchronization mechanisms and spatial coordinate mapping, the collected multi-source heterogeneous data is spatiotemporally aligned, and feature-level fusion is performed based on a pre-defined data fusion model to form a unified data stream covering five dimensions: equipment, environment, security, fire protection, and network. Specifically, a unified format timestamp tag is attached to all raw data packets, including the absolute time of acquisition and the sequence number of the acquisition period. For video stream data, a dual calibration mechanism of PTS (Presentation Timestamp) and DTS (Decoding Timestamp) is used to temporally bind each frame of image with electrical parameters and environmental data at the same time.

[0025] A three-dimensional spatial coordinate system for the entire photovoltaic power station is pre-established. The three-dimensional coordinates and orientation angles of all fixed equipment (combiner boxes, inverters, transformer substations, cameras, temperature and humidity sensors, etc.) are calibrated. For mobile terminals (rail-mounted robots, drones), their spatial pose is acquired in real time through their onboard positioning modules or UWB indoor positioning systems. Based on this, a ternary mapping table of "spatial coordinates - device ID - data source type" is established to associate and bind non-spatial data (such as electrical quantities and network traffic) with spatial locations. For infrared thermal imaging data, its pixel coordinates are mapped to the three-dimensional world coordinate system through the camera intrinsic parameter matrix and perspective projection model, enabling precise location of temperature anomalies to specific equipment components.

[0026] Building upon the aforementioned spatiotemporal alignment, a strategy based on multimodal feature extraction and fusion is adopted. Specifically, temporal statistical features (mean, variance, rate of change, peak value) and frequency domain features (harmonic components, spectral energy distribution) are extracted from electrical quantity data; temperature distribution gradient features, smoke texture features, and moving target contour features are extracted from video and infrared images using a lightweight neural network; and trend change features are extracted from environmental data. Subsequently, all extracted feature vectors are organized according to a unified time window and spatial grid, and input into a pre-defined multimodal feature fusion model. This model uses an attention mechanism to adaptively weight features across various dimensions, dynamically adjusting the fusion weight coefficients between equipment parameters, visual features, and environmental variables based on the current operating conditions, ultimately outputting a structured unified data stream.

[0027] In this embodiment of the invention, the unified data stream can be organized in a hierarchical structure, which includes, from bottom to top: a raw data layer for storing unprocessed raw sampled data from each system; a feature extraction layer for extracting equipment vibration features, temperature change rate, image edge features, and network packet sequence features from the raw data; a situational awareness layer for assessing equipment health, classifying environmental risk levels, calibrating security situation, and quantifying network threats based on the features; and a decision support layer for outputting collaborative control suggestions after comprehensive analysis.

[0028] Step S102: Perform unified parsing and event identification on the unified data stream, and generate collaborative control instructions based on equipment operation constraints, environmental security boundaries, security response rules, fire linkage conditions, and network security policies.

[0029] In one implementation, a combination of a lightweight edge inference engine and a rule engine can be used to perform time-series behavior modeling and abnormal pattern matching on the data pool, thereby achieving accurate mapping from raw signals to semantic events, such as distinguishing between normal device degradation, operational disturbances, false alarms and real threats.

[0030] Furthermore, the event identification results are input into a multi-dimensional constraint decision engine. This engine concurrently calls four types of constraint rule bases: equipment operation constraints ensure that control commands do not exceed the rated power of electromechanical equipment, temperature limits, and fatigue life; environmental safety boundaries strictly control temperature and humidity, harmful gas concentrations, and structural vibration thresholds; security response rules dynamically match expulsion, blocking, or alarm strategies based on intrusion level, personnel density, and regional importance; fire linkage conditions trigger fire extinguishing, smoke extraction, and broadcast guidance based on fire spread models and evacuation route status; and network security strategies isolate controlled network segments, block abnormal traffic, and audit operation tracing. Finally, the engine uses priority arbitration and conflict resolution algorithms to synthesize the optimal output under the above multi-dimensional constraints into a collaborative control command. This command is issued to the execution layer in a digital twin readable format, along with the expected effect and rollback plan, ensuring that the entire system maintains a safe, stable, and efficient operating state throughout the dynamic game.

[0031] Step S103: Drive the corresponding execution system to perform cross-system linkage and closed-loop actions according to the coordinated control instructions.

[0032] In one implementation, after the command is issued, the unified format control semantics are first decomposed into native protocol messages recognizable by each subsystem by a command translator, and then synchronously distributed to the equipment control unit, environmental control device, security actuator, fire extinguishing equipment, and network security gateway through redundant communication links. Upon receiving the command, each execution terminal automatically loads preset action parameter packages and safety interlock conditions, and triggers physical actions such as start / stop, adjustment, opening / closing, release, or blocking sequentially or in parallel according to a multi-dimensional mapping relationship of time coordination, spatial coordination, and functional coordination.

[0033] The above-mentioned integrated intelligent control method for unmanned photovoltaic power plants provided in this invention breaks down data silos between systems by synchronously collecting and integrating multi-source heterogeneous data from various systems across the entire photovoltaic power plant. This enables comprehensive perception of the status quo across five dimensions: equipment, environment, security, fire protection, and network. By integrating equipment operation constraints, environmental safety boundaries, security response rules, fire protection linkage conditions, and network security strategies, collaborative control commands are generated. This avoids action conflicts or response gaps caused by independent decision-making by a single system, achieving unmanned integrated intelligent control with autonomous perception, collaborative decision-making, and closed-loop execution. This improves the operation and maintenance efficiency of the power plant and reduces safety risks.

[0034] In one implementation, for the aforementioned step S102, i.e., when performing unified parsing and event identification on the unified data stream, the following methods may be adopted, including but not limited to: performing unified parsing and normalization processing on the unified data stream, and performing real-time identification and limit judgment on equipment operating status, environmental parameters, security events, fire protection conditions, and network security status based on the processed data; wherein, limit judgment includes: meter reading abnormality identification, knife switch / circuit breaker / pressure plate physical status identification, equipment surface infrared temperature exceeding limit judgment, personnel or vehicle illegal intrusion detection, photovoltaic area smoke and fire feature identification, environmental temperature and humidity / water immersion exceeding limit judgment, and network connection abnormality or unauthorized access behavior identification.

[0035] In practical implementation, the meter reading anomaly identification includes: image acquisition or data reading of analog or digital meters at the photovoltaic area combiner box, inverter, and step-up transformer; multi-parameter correlation verification of the read values ​​with the corresponding power generation, light intensity, and ambient temperature; when the read value deviates from the expected value range obtained by fitting the power generation, light intensity, and ambient temperature by more than a preset deviation threshold, it is determined as a meter reading anomaly, and a fine-grained identification result including the location of the abnormal meter, the type of anomaly, and the degree of deviation is output.

[0036] Physical status identification of disconnectors / circuit breakers / lockers includes: (1) Obtain the real-time status signals of the disconnectors, air switches and protection pressure plates deployed in each electrical compartment of the photovoltaic power station; wherein, the real-time status signals include the switching signals output by the auxiliary contacts and / or the image data of the equipment body.

[0037] (2) When the acquired signal is a switch signal, the signal is checked for consistency with the current operating condition of the corresponding device. If the switch signal indicates a switch state that does not match the expected state calculated based on current / voltage, it is determined to be a physical state abnormality, and the identification result containing the abnormal device ID, state conflict type and conflict duration is output.

[0038] (3) When the acquired data is the image data of the device body, a semantic segmentation model based on deep learning is used to perform pixel-level segmentation and state classification of the moving / static contacts of the knife switch, the operating handle of the circuit breaker and the engagement / disengagement position of the pressure plate in the image, and output the engagement / disengagement status and state confidence of each device; wherein, the output layer of the semantic segmentation model sets independent classification branches for the knife switch, circuit breaker and pressure plate respectively, so as to adapt to the different state representation forms of the three types of devices.

[0039] (4) When the results of the two identification methods are consistent, the physical state is confirmed and written into the unified data stream; when the results of the two identification methods are inconsistent, a differential alarm is triggered and the rail-mounted robot is dispatched to the target equipment location for close-range verification.

[0040] The photovoltaic area smoke and fire feature recognition includes: joint discrimination based on visible light images and infrared thermal imaging data in a unified data stream, using a multispectral fusion smoke and fire detection algorithm; the multispectral fusion smoke and fire detection algorithm includes: detecting smoke texture and flame color features in the visible light channel, and detecting the morphology and temperature change features of high-temperature areas in the infrared channel; a fire confirmation signal is output only when the corresponding features are detected in both the visible light channel and the infrared channel and the spatial overlap is greater than a preset overlap threshold.

[0041] The determination of excessive infrared temperature on the equipment surface includes: periodic or triggered temperature measurement of key equipment nodes using a fixedly installed infrared thermal imaging camera and an infrared temperature measurement module mounted on a rail-mounted robot; obtaining a temperature distribution map of the equipment surface; dynamically adjusting the temperature over-limit judgment threshold according to the equipment type and current load rate; and providing early warning based on the temperature change rate trend; among which, key equipment nodes include: cable joints, busbar connections, power module heat sinks, and circuit breaker contacts.

[0042] Unauthorized intrusion detection of personnel or vehicles includes: (1) Video image sequences are collected in real time by fixed cameras deployed at the perimeter and inside the photovoltaic power station and mobile cameras mounted on the rail-mounted robot. The video image sequences are then input into a multi-target detection network based on an improved YOLO architecture for joint detection and tracking of personnel and vehicles. The multi-target detection network includes a multi-scale feature extraction backbone network and an independent target classification branch. The personnel classification branch is used to distinguish personnel, helmet wearing status, and work clothes identification. The vehicle classification branch is used to distinguish vehicle type, body color, and license plate information. At the same time, the spatiotemporal continuity features of the detection box are used as the basis for target tracking, forming structured target information that includes target ID, target category, motion trajectory, and continuous tracking duration.

[0043] (2) After detecting a target person, perform dual permission verification based on the electronic fence area preset by the power station and the whitelist of personnel permissions for the current time period: first determine whether the target person's location falls into the fence restricted area, and then determine whether the person's identity has been registered in the whitelist of the corresponding area for the current time period; when the location falls into the restricted area and the identity is not in the whitelist, it is determined to be an illegal intrusion event, and the event type label is output as "illegal intrusion-person".

[0044] (3) After detecting the vehicle target, the behavior analysis is performed based on the vehicle passage and restricted area map preset by the power station, combined with the real-time movement trajectory and speed vector of the vehicle: when the vehicle trajectory deviates from the preset passage or enters the restricted area, and the dwell time exceeds the preset behavior tolerance time threshold, it is determined to be an illegal intrusion or violation of regulations event, and the event type label is "illegal intrusion-vehicle" and structured event information containing the predicted path of the vehicle trajectory is output.

[0045] Environmental temperature and humidity / water immersion exceeding limits judgment includes: (1) Multiple temperature and humidity sensors and water immersion sensors deployed in various areas of the photovoltaic power station continuously collect environmental temperature and humidity data and water immersion status signals at a preset sampling period. Among them, the temperature and humidity sensors include spatial distribution sensors, micro-environment sensors in equipment cabinets and air conditioning return air vent sensors, and the water immersion sensors include electrode-type detection lines and float level switches. The two types of sensors form redundant detection.

[0046] (2) Adjust the temperature and humidity limit judgment threshold dynamically according to the current operating conditions of the equipment and environmental conditions: When the CPU load rate or data recording load of the equipment in the monitored area exceeds the preset heavy load threshold, the high temperature warning threshold is automatically lowered; when the outdoor ambient temperature and solar radiation intensity exceed the preset combined threshold, the high temperature warning threshold is simultaneously lowered; the dynamic adjustment is calculated based on the equipment thermal tolerance characteristic curve and the heat conduction model of the external enclosure structure.

[0047] (3) Calculate the change rate of temperature and humidity data for multiple time windows, including short-term change rate and long-term trend change rate. When the change rate exceeds the preset change rate warning threshold, add a trend warning label to the over-limit judgment result. The preset change rate warning threshold is dynamically adjusted according to seasonal patterns and weather forecast information. The humidity rise change rate threshold is narrowed in rainy weather mode to improve the sensitivity to sudden increase in humidity.

[0048] (4) The water immersion limit judgment adopts a graded judgment system: Level 1 warning is when the electrode-type detection line detects a water immersion signal and the duration exceeds the anti-false shaking time threshold (e.g., 3 seconds); Level 2 alarm is when the Level 1 warning continues and the float level switch detects that the water level exceeds the first height threshold (e.g., 15cm); Level 3 emergency is when the water level exceeds the second height threshold (e.g., 40cm). The second height threshold corresponds to the bottom elevation of the cable laying layer. Different voltage level cables correspond to different second height thresholds.

[0049] (5) When temperature and humidity exceeding limits and water immersion signals coexist in the same spatial area or occur sequentially in time, a coupling correlation analysis is triggered. Specifically, the temporal correlation between historical temperature and humidity and water immersion data of the area is retrieved, and the time delay coefficient and contribution factor between temperature and humidity changes and water immersion events are calculated. When the contribution factor exceeds the preset coupling threshold, water immersion is determined to be one of the causes of abnormal temperature and humidity. The generated collaborative control instructions will prioritize the treatment actions for the source of water immersion, and then include the adjustment actions for the temperature and humidity environment.

[0050] Identification of network connectivity anomalies includes the detection of at least one of the following anomalies: (1) Communication interruption anomaly: When the preset heartbeat monitoring data packet does not return a response from the target device within multiple consecutive heartbeat cycles, it is determined that the network connection of the device is interrupted, and the scope of the interruption is automatically determined according to the device's position and role in the network topology.

[0051] (2) Abnormal communication delay: By calculating the round-trip delay and jitter value of communication data packets between devices, when the round-trip delay exceeds the preset delay upper limit threshold or the jitter value exceeds the preset jitter upper limit threshold, the network connection quality is determined to be abnormally degraded.

[0052] (3) Abnormal traffic flow: The real-time traffic characteristics are compared with the historical behavior baseline of the corresponding time period. When the deviation of the traffic from the baseline exceeds the preset deviation threshold, it is judged as an abnormal traffic flow. The deviation is measured by Mahalanobis distance or relative entropy to comprehensively consider the joint deviation of multiple feature dimensions.

[0053] Unauthorized access is identified using a triple verification mechanism: The first layer of verification is MAC address access verification: the source MAC address of the requesting access device is compared with a pre-established whitelist MAC address table. If the source MAC address is not in the whitelist, it is directly determined as an unauthorized access behavior.

[0054] The second layer of verification is the IP / MAC binding relationship verification: For devices that pass the first layer of verification, the binding relationship between their IP address and MAC address is further verified to see if it is consistent with the preset binding table. When it is detected that the IP address is occupied by a non-bound MAC device, it is determined to be an unauthorized access behavior of IP spoofing.

[0055] The third layer of verification is behavioral fingerprint verification: For devices that pass the first two layers of verification but whose source MAC address has appeared on other access ports within a preset time window, or whose source MAC address has switched access more frequently than a preset frequency threshold within a preset time period, their behavioral fingerprint features are extracted, including communication protocol stack fingerprint, TCP window size, initial TTL value, and application layer protocol features. These features are then compared with the compliant fingerprints of this type of device in the baseline fingerprint database. When the similarity is lower than a preset similarity threshold, it is determined to be an unauthorized access behavior of MAC cloning.

[0056] In one implementation, for the aforementioned step S102, i.e., when generating collaborative control instructions based on equipment operating constraints, environmental safety boundaries, security response rules, fire linkage conditions, and network security policies, the following methods may be adopted, including but not limited to: (1) When any device status is detected to be out of limit, the inspection frequency adjustment rule is automatically triggered, and the inspection frequency of the corresponding device and related area is dynamically increased according to the level of exceeding the limit, and a special inspection task instruction is generated.

[0057] In practice, the monitoring platform continuously acquires operating parameters such as voltage, current, temperature, and vibration through sensors and data acquisition terminals deployed on various devices at fixed polling intervals. When a parameter exceeds a preset threshold, the monitoring platform encapsulates the exceedance event and its severity into a standard alarm message and sends it to the task scheduling engine. The task scheduling engine maintains a dynamic inspection strategy table, defining the mapping relationships between different device types, exceedance levels, inspection frequencies, and inspection items. Upon receiving a message, the engine matches the device identifier and exceedance level against the table to generate a new inspection task template, including task priority, execution cycle, inspection item list, and assigned inspection personnel roles. Subsequently, the engine calls the task dispatch interface to write the task into the maintenance work order system and pushes notifications to relevant personnel's mobile terminals via message middleware. Simultaneously, the engine marks the device's inspection status as a priority until a message indicating that the parameter has returned to normal is received, at which point the inspection frequency is gradually rolled back to the baseline value.

[0058] (2) Upon receiving a security intrusion event signal, trigger the video linkage and sound and light alarm rules, and generate video and sound and light alarm commands.

[0059] In practice, when a perimeter detector (such as a vibrating fiber optic, infrared beam, or microwave radar) detects an intrusion signal, it uploads the alarm signal to the security data acquisition gateway via a switch signal or RS485 bus. The gateway converts the signal into a standard event format and pushes it to the platform's event processing center. The event processing center retrieves the associated camera group and audible / visual alarm number from a pre-set linkage mapping table based on the alarm point number. It then sends a pre-set position call command to the video matrix controller, a relay closing command to the alarm host to activate the audible / visual equipment, and a light shifting command to the intelligent lighting control module. These control commands are issued in parallel to ensure that video switching, alarm activation, and supplemental lighting are completed synchronously. Simultaneously, the platform records the alarm event, video stream address, and action sequence in the database and forwards the monitoring footage to the duty room's large screen and mobile client via a real-time streaming media protocol for security personnel to confirm and verify.

[0060] (3) When the smoke and fire detection system outputs a fire confirmation signal, it triggers the zone power outage and gas extinguishing activation rules and generates fire linkage instructions for each preset zone.

[0061] In practical implementation, the fire alarm control panel pre-defines the input-output mapping relationships for each fire compartment through programming: the input side connects to the alarm output signals of smoke detectors, heat detectors, and image-type fire detectors, while the output side connects to the compartment's power circuit breaker, fire damper actuator, and gas extinguishing control panel. When any detector outputs a fire alarm confirmation signal (including dual-detection composite confirmation logic), the main control unit of the fire alarm control panel drives the output module step by step according to the delay parameters in the preset linkage timing register: first, it outputs a trip signal to the power circuit breaker of that compartment to cut off power and lighting; then, it sends a closing command to the fire damper actuator of that compartment, causing the damper actuator to reset to the sealed position; finally, after the delay timer expires, it sends a start pulse to the gas extinguishing control panel, which then opens the corresponding selector valve and cylinder valve to release the extinguishing agent. The entire control process is implemented using hard-wiring and programmable logic, without relying on upper-level software systems, ensuring reliability. Simultaneously, the status feedback interface of the control panel uploads the completion signal of each action to the fire graphic display terminal and the building equipment management system.

[0062] (4) When environmental parameters exceed the set threshold, trigger the dynamic environmental parameter over-limit adjustment rule and generate the corresponding control command for the device.

[0063] In practical implementation, the environmental monitoring host has multiple built-in setpoint registers to store the upper and lower limits and hysteresis ranges of parameters such as temperature, humidity, differential pressure, harmful gas concentration, and liquid level. The host collects sensor data distributed in various areas of the computer room at second-level intervals and compares it with the setpoints in the registers. When a parameter exceeds the limit, the host enters adjustment mode, and its internal state machine jumps to the corresponding adjustment subroutine according to the type of limit exceedance—for example, a cooling subroutine is executed when the temperature is too high, a humidification subroutine is executed when the humidity is too low, and a drainage subroutine is executed when the water level exceeds the limit. During the operation of the adjustment subroutine, the host adopts a segmented approximation control strategy to gradually adjust the output: sending start / stop and set temperature adjustment signals to the precision air conditioning controller through dry contacts or analog output channels, sending frequency command signals to the variable frequency drive of the variable frequency exhaust fan, and sending start / stop signals to the contactor of the water pump. After each adjustment, the host resamples the current parameter. If it is still outside the limit, the host continues to increase or adjust the adjustment range until the parameter falls back into the hysteresis range. Then the host will control the output to return to the maintenance value or standby state, completing the entire closed-loop adjustment process.

[0064] (5) When a network security incident is detected, the security enhancement rules are triggered and an emergency response command is generated.

[0065] In practice, the security management platform continuously receives alarm event streams from firewalls, intrusion detection systems, bastion hosts, and endpoint security software. When the event aggregation and analysis module determines that an event or combination of events (such as multiple failed login attempts, abnormal traffic surges, or access during off-peak hours) meets the security enhancement trigger conditions, the platform initiates a multi-level execution process according to a predefined emergency response script. First, the platform calls the management interface of the identity authentication system to temporarily lock the account involved and issues a forced re-authentication command to the authentication system, redirecting all online sessions to the secondary verification page. Second, the platform sends a policy update command to the encryption gateway, requiring all sessions pointing to the involved terminal or network segment to use an encrypted transmission protocol and closing the corresponding plaintext service port. Third, the platform issues an audit policy change request to the configuration center of the log audit system, expanding the audit scope from routine operations to all sensitive operations, adjusting the log archiving interval to the minimum, and registering additional real-time alarm rules in the audit engine. This causes log entries matching high-risk operation characteristics (such as deleting files, modifying permissions, or exporting data) to trigger independent alarm channels, directly pushing them to the security supervisor's terminal. All of the above execution steps have a confirmation receipt mechanism. The platform will only continue to the next step after receiving a confirmation receipt for the successful execution of each step. If any step fails to execute, it will be switched to a manual intervention process to ensure the complete implementation of the security strategy.

[0066] In one implementation, for the aforementioned step S103, i.e., when driving the corresponding execution system to perform cross-system linkage and closed-loop actions according to the collaborative control instructions, the following methods may be adopted, but are not limited to: driving the rail-mounted robot, video monitoring unit, intelligent security device, fire-fighting execution mechanism, dynamic environment adjustment equipment, UAV platform, and network security equipment to perform cross-system linkage and closed-loop actions according to the collaborative control instructions; wherein, the cross-system linkage and closed-loop actions include: target tracking coordination between the rail-mounted robot and the fixed camera, automatic retrieval of multi-angle video and synchronization of sound and light warnings after security intrusion is triggered, electrical isolation and fire-fighting medium spraying coordination after smoke and fire warning is triggered, combined start and stop of environmental adjustment equipment after dynamic environment over-limit is triggered, UAV automatically planning and executing refined inspection routes according to defect location and type, network security system automatically executing encrypted channel establishment according to policy, and dynamic revocation of user permissions and full recording of operation behavior.

[0067] In practical implementation, the target tracking collaboration between the rail-mounted robot and the fixed camera includes: when the fixed camera detects a moving target, it sends the target's position information and motion vector to the rail-mounted robot control system; based on the position information and motion vector, the rail-mounted robot autonomously plans its movement path and adjusts the focal length and angle of its onboard gimbal camera to achieve relay tracking with the fixed camera; when the target moves out of the fixed camera's field of view, the rail-mounted robot automatically takes over the main tracking role and guides other nearby fixed cameras or drones into a collaborative tracking state.

[0068] The UAV automatically plans and executes a refined inspection route based on the location and type of defects. Specifically, the UAV receives the defect location coordinates and defect type labels from the collaborative control command; then, based on the 3D point cloud map of the photovoltaic power station and combined with the optimal shooting angle and distance requirements corresponding to the defect type label, it automatically generates a refined inspection route that includes waypoints, heading, flight speed, and gimbal pitch angle; and during the execution of the refined inspection route, it detects wind speed and direction in real time, dynamically corrects flight attitude and route deviation, and ensures that the image acquisition quality meets the defect recognition requirements.

[0069] In one implementation, the method further includes: generating multi-level alarm information based on the severity of the event, and automatically constructing five types of structured operation reports based on the alarm information, namely, inspection, security, fire protection, environmental protection and network security; and uploading all multi-source heterogeneous data, event identification results, collaborative control commands, alarm information and structured operation reports to the remote centralized control center.

[0070] In practice, alarms of three levels—critical, severe, and general—are generated based on the severity of the incident, and five types of structured operational reports are automatically constructed, including inspection, security, fire protection, environmental monitoring, and network security. The report content includes at least: statistics on the completion rate and timeliness of inspection tasks, closed-loop records of security incident types and handling, status and action logs of fire protection devices, trend and control response records of environmental monitoring parameters, and network security incident types, handling measures, and audit and traceability information.

[0071] Furthermore, multi-source heterogeneous data, event identification results, collaborative control commands, alarm information, and structured operation reports are all uploaded to the remote centralized control center. Specifically, a hierarchical compression and priority queue scheduling strategy can be used for data upload. Alarm information and collaborative control commands with real-time requirements exceeding a first preset threshold are uploaded in real time via a high-speed priority channel. Non-real-time multi-source heterogeneous data, identification results, and structured operation reports are compressed and uploaded in batches using off-peak bandwidth. During the upload process, a breakpoint resume and data verification mechanism are used to ensure that the data received by the remote centralized control center is complete and consistent with the local data.

[0072] In addition, the remote control center supports video viewing, equipment control, policy adjustment, and log auditing initiated by the control terminal. Specifically, the interactive module of the remote control center can remotely view video streams for any time period, issue inspection commands for designated paths to the rail-mounted robot, remotely start and stop air conditioning or ventilation equipment, remotely perform electronic fence arming / disarming operations, view the real-time pressure and valve status of fire-fighting devices, and view and export network security audit logs within a specified period.

[0073] In one embodiment, the method further includes: during the collaborative control process, real-time monitoring of the equipment status, environmental parameters, security events, and network status of each system; when the equipment status, environmental parameters, security events, or network status change, real-time updating of the monitoring results, regenerating collaborative control instructions, and synchronously adjusting the execution actions of each execution system.

[0074] In practical implementation, a sliding time window method can be used to continuously monitor the device status, environmental parameters, security events, and network situation. A situation comparison is triggered every time a monitoring cycle is updated. When the comparison result shows that the change in any dimension parameter exceeds the preset re-trigger threshold, the replanning process is triggered: the previously unexecuted collaborative control instructions are retrieved, the collaborative control instructions are recalculated based on the updated monitoring results, and the updated collaborative control instructions are issued to each execution system according to the new execution sequence.

[0075] For ease of understanding, this embodiment of the invention also provides a schematic diagram of the overall architecture and device deployment, see below. Figure 2 As shown, the photovoltaic power station's fully unmanned integrated intelligent management and control platform is connected to the monitoring platform. The intelligent management and control platform includes: a robot, a camera intelligent inspection module, a drone inspection module, a security module, an environmental protection module, a fire protection module, a photovoltaic area smoke and fire recognition module, and a unified network security module.

[0076] See Figure 3 The flowchart shown here illustrates a fully unattended integrated intelligent control method, which mainly includes: 1. Unified collection of heterogeneous data from multiple sources across the entire domain.

[0077] The power plant is equipped with rail-mounted robots, various cameras, temperature and humidity / water immersion / air conditioning / ventilation sensors, fire-fighting heptafluoropropane units, electronic fences, access control and vehicle management systems, high-altitude smoke and fire detection terminals, drones, and network security equipment such as vertical encryption / firewalls / switches. All equipment is uniformly connected to the station's intelligent management and control platform.

[0078] 2. Data parsing, normalization, state recognition, and threshold determination.

[0079] Specifically, the platform uniformly receives robot inspection data, camera recognition data, environmental data, fire protection data, security data, photovoltaic area smoke and fire data, drone inspection data, and network security data to complete status identification and limit judgment.

[0080] Traditional multi-point threshold judgment ignores the spatiotemporal coupling relationship between states, easily generating isolated false limit-crossing alarms. In this embodiment of the invention, a causal inversion monitoring method based on spatiotemporal feature folding and dynamic baseline alignment can be used, specifically including the following steps: (1) Time alignment and event imprint extraction of multi-source heterogeneous data. Specifically, the data streams of equipment, environment, security, fire protection and network security are sliced ​​according to a uniform time granularity. The sudden change segment, stationary segment and oscillation segment in the signal waveform of each slice are extracted to generate imprint triplet for each type of event.

[0081] (2) Spatiotemporal features are folded into a state hypergraph. Specifically, spatial location is used as a node and time is used as an edge to map different imprints across domains to the same hypergraph. Each hyperedge represents all domain state combinations within a time window.

[0082] (3) Dynamic baseline self-learning. Specifically, for each hyperedge, the normal combination pattern under the same operating mode in the most recent period is continuously learned in a rolling manner to form a flexible baseline that drifts slowly over time, rather than a fixed upper and lower limit.

[0083] (4) Causal inversion deviation judgment. Specifically, compare the current superedge with the flexible baseline: if the deviation exceeds the tolerance, do not directly report the limit violation, but trace back to which domain the feature mutation occurred first, and the mutation propagation path.

[0084] (5) Collaborative Limit Exceedance Judgment. Specifically, a true limit exceedance event is determined only when there is a propagation chain from cause to effect and the deviation of all related domains on the chain exceeds the historical fluctuation range of that domain, and a unified status code and root cause location are output.

[0085] 3. Generate collaborative control strategies.

[0086] Specifically, if infrared temperature readings exceed limits, the system automatically increases the frequency of inspections and pushes the notification to the centralized control system; if personnel intrusion occurs, it automatically triggers video and audible / visual alarms and records personnel and vehicle information; if smoke or fire is detected in the photovoltaic area, it automatically issues warnings, cuts off power to the affected areas, and triggers fire sprinklers; if water immersion, excessive temperature, or excessive humidity occurs, it automatically starts the air conditioning, ventilation, and water pumps; if third-party access occurs, it automatically encrypts the connection, automatically reclaims temporary permissions upon expiration, and generates logs.

[0087] In this embodiment of the invention, when generating a collaborative control strategy, the initial rule trigger can be used as the starting point of the causal graph. The chain of preconditions that might cause the trigger is reasoned backward, and a feedforward mechanism is used to generate compensatory actions that block the propagation of adverse causal relationships, rather than simply responding to the trigger itself. This includes: (1) Construct a multi-domain historical event causal graph. Based on the sequence and causal relationship of events in five domains in the offline learning history, a directed graph is constructed. Nodes represent events (such as equipment temperature exceeding the threshold, unauthorized access control, smoke alarm, air conditioner failure, network port abnormality), and edges represent causal relationships and their average propagation time.

[0088] (2) Receive the initial rule trigger signal and locate the causal graph node. When the first rule is triggered in any domain, find the corresponding event node in the causal graph as the currently active node.

[0089] (3) Reverse reasoning of possible preceding causal chains. Starting from the active node, search backward along the causal graph for all possible preceding paths that can reach this node. Each path represents a sequence of events that may have caused the current event but have not yet occurred. For example, if the current event is an overheating device, reverse reasoning can lead to the possibility that a network attack caused the fan to stop.

[0090] (4) Generate a set of feedforward compensation actions. For each preceding causal chain obtained through reverse reasoning, generate a compensation action at the earliest possible node to interrupt the causal chain. Compensation actions include: temporarily raising the threshold of a certain device, activating backup equipment in advance, isolating suspicious network ports, and pre-activating fire protection pre-action status, etc. Compensation actions do not directly respond to the current trigger, but act on possible cause paths.

[0091] (5) Evaluate the synergistic effect between the compensation action and the original rule. Combine the main action required by the original rule with all feedforward compensation actions and simulate the causal graph propagation after execution. If the compensation action blocks the original adverse propagation path and does not generate a new dangerous path, then retain the compensation action; otherwise, adjust the compensation intensity or delete it.

[0092] (6) Generate an integrated execution plan that includes feedforward compensation and main response. The output plan is divided into two stages: the first stage immediately executes all feedforward compensation actions to interrupt the causal chain in advance; the second stage executes the main response actions required by the original rules. The action sequences of the two stages are merged and output to each execution system in time.

[0093] (7) Dynamically correct the cause-effect graph during execution. New triggering events are continuously collected during execution. If the actual propagation path is found to be inconsistent with the cause-effect graph, the edge weights and propagation time of the cause-effect graph are updated online for the next reverse reasoning.

[0094] 4. Multi-device collaborative execution (inspection / firefighting / security / environmental protection / drones / cybersecurity).

[0095] See Figure 4The diagram illustrates a multi-system collaborative control logic, which specifically includes: automatic robot inspection according to a strategy; camera-linked tracking; automatic sprinkler system for fire protection; automatic adjustment of air conditioning / fans; automatic take-off and inspection by drones; and automatic execution of network security strategies.

[0096] 5. Tiered alarms and automatic report generation.

[0097] Specifically, tiered alarms will be pushed to the station and centralized control platform in real time, reports will be automatically generated and uploaded, and centralized remote control will be supported.

[0098] 6. All data is uploaded to the central control center and remotely controlled.

[0099] Specifically, all data is uploaded to the centralized control platform, which supports remote viewing of video, control of robots, adjustment of air conditioning, arming and disarming, viewing of fire status, and auditing of network security logs, achieving unmanned operation at the station and centralized management.

[0100] 7. Dynamic updates and continuous iterations.

[0101] Specifically, the system updates judgment results and control strategies in real time when the environment, equipment, events, or network status change, ensuring stable unattended operation.

[0102] The method provided in this invention can realize integrated management and control of the entire photovoltaic power station, including inspection, security, fire protection, environmental monitoring, photovoltaic area, network security, and centralized control and transmission, eliminating system silos; automatic equipment collaboration, automatic alarm closure, and automatic event handling, truly achieving unmanned operation; unified access to the centralized control center, meeting the technical specifications for power grid security and unmanned operation; reducing reliance on manual labor, improving operation and maintenance efficiency, reducing safety risks, and possessing strong engineering feasibility.

[0103] In addition to the integrated intelligent management and control method for unmanned operation of photovoltaic power plants provided in the foregoing embodiments, this invention also provides an integrated intelligent management and control system for unmanned operation of photovoltaic power plants. (See [link to relevant documentation]). Figure 5 The diagram shown illustrates the structure of an integrated intelligent control system for unmanned operation of a photovoltaic power station, indicating that the system mainly includes: The data acquisition module 501 is used to synchronously collect and integrate multi-source heterogeneous data from various systems across the entire photovoltaic power station, and construct a unified data stream covering five dimensions: equipment, environment, security, fire protection, and network.

[0104] The collaborative control instruction generation module 502 is used to perform unified parsing and event identification on the unified data stream, and generate collaborative control instructions based on equipment operation constraints, environmental safety boundaries, security response rules, fire linkage conditions and network security policies.

[0105] The multi-device collaborative execution module 503 is used to drive the corresponding execution system to perform cross-system linkage and closed-loop actions according to the collaborative control instructions.

[0106] The photovoltaic power station unmanned integrated intelligent control system provided in this embodiment of the invention breaks down data silos between systems by synchronously collecting and integrating multi-source heterogeneous data from various systems across the entire photovoltaic power station. This enables comprehensive perception of the status quo across five dimensions: equipment, environment, security, fire protection, and network. By integrating equipment operation constraints, environmental safety boundaries, security response rules, fire protection linkage conditions, and network security strategies, collaborative control commands are generated. This avoids action conflicts or response gaps caused by independent decision-making by a single system, achieving unmanned integrated intelligent control with autonomous perception, collaborative decision-making, and closed-loop execution. This improves the operation and maintenance efficiency of the power station and reduces safety risks.

[0107] It should be noted that the system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned method embodiment. The specific numerical values ​​provided in the implementation of this invention are merely exemplary and are not intended to limit the scope of the invention.

[0108] This invention also provides an electronic device, specifically, the electronic device includes a processor and a storage device; the storage device stores a computer program, and the computer program, when run by the processor, executes the method described in any of the above embodiments.

[0109] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 100 includes: a processor 60, a memory 61, a bus 62, and a communication interface 63. The processor 60, the communication interface 63, and the memory 61 are connected through the bus 62. The processor 60 is used to execute executable modules, such as computer programs, stored in the memory 61.

[0110] The memory 61 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 63 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0111] Bus 62 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0112] The memory 61 is used to store programs. After receiving an execution instruction, the processor 60 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0113] Processor 60 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 60 or by instructions in software form. Processor 60 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 61. Processor 60 reads the information in memory 61 and, in conjunction with its hardware, completes the steps of the above method.

[0114] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0115] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0116] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for integrated intelligent control of a photovoltaic power station with unmanned operation across the entire area, characterized in that, include: Simultaneously collect and integrate multi-source heterogeneous data from all systems across the entire photovoltaic power station, and construct a unified data flow covering five dimensions: equipment, environment, security, fire protection, and network. The unified data stream is parsed and events are identified in a unified manner, and collaborative control instructions are generated based on equipment operation constraints, environmental safety boundaries, security response rules, fire linkage conditions and network security policies. The corresponding execution system is driven to perform cross-system linkage and closed-loop actions according to the aforementioned collaborative control instructions.

2. The method according to claim 1, characterized in that, Unified parsing and event identification of the unified data stream include: The unified data stream is subjected to unified parsing and normalization processing, and based on the processed data, real-time identification and limit judgment are performed on equipment operating status, environmental parameters, security events, fire protection conditions, and network security status. Among them, limit judgment includes: identification of abnormal meter readings, identification of physical status of disconnectors / circuit breakers / pressure plates, judgment of excessive infrared temperature on equipment surface, detection of illegal intrusion by personnel or vehicles, identification of smoke and fire characteristics in photovoltaic areas, judgment of excessive environmental temperature and humidity / water immersion, and identification of abnormal network connection or unauthorized access behavior.

3. The method according to claim 1, characterized in that, Based on equipment operating constraints, environmental safety boundaries, security response rules, fire alarm linkage conditions, and network security policies, generate collaborative control instructions, including: When any device is detected to be out of bounds, the inspection frequency adjustment rule is automatically triggered. The inspection frequency of the corresponding device and related area is dynamically increased according to the level of the out-of-bounds violation, and a special inspection task instruction is generated. Upon receiving a security intrusion event signal, the video linkage and audible and visual alarm rules are triggered, and video and audible and visual alarm commands are generated. When the smoke and fire detection system outputs a fire confirmation signal, it triggers the zone power outage and gas extinguishing activation rules and generates fire linkage instructions for each preset zone. When environmental parameters exceed the set threshold, the dynamic environmental parameter over-limit adjustment rule is triggered, and control commands for the corresponding equipment are generated. When a cybersecurity incident is detected, security enhancement rules are triggered and emergency response instructions are generated.

4. The method according to claim 1, characterized in that, Drive the corresponding execution system to perform cross-system linkage and closed-loop actions according to the aforementioned coordinated control instructions, including: Drive the rail-mounted robot, video surveillance unit, intelligent security device, fire-fighting actuator, dynamic environment adjustment equipment, drone platform and network security equipment to perform cross-system linkage and closed-loop actions according to the aforementioned collaborative control instructions; The cross-system linkage and closed-loop actions include: target tracking coordination between the rail-mounted robot and the fixed camera; automatic retrieval of multi-angle video and synchronization of sound and light warnings after security intrusion is triggered; electrical isolation and fire-fighting medium spraying coordination after smoke and fire warning is triggered; combined start-up and shutdown of environmental control equipment after dynamic environment over-limit is triggered; automatic planning and execution of refined inspection routes by UAVs based on defect location and type; automatic establishment of encrypted channels by network security system based on policy; and dynamic revocation of user permissions and full recording of operation behavior.

5. The method according to claim 1, characterized in that, Also includes: Based on the severity of the event, multi-level alarm information is generated, and based on the alarm information, five types of structured operation reports are automatically constructed: inspection, security, fire protection, environmental protection, and network security. The multi-source heterogeneous data, event identification results, collaborative control commands, alarm information, and structured operation reports are all uploaded to the remote centralized control center.

6. The method according to claim 1, characterized in that, Also includes: During the collaborative control process, the equipment status, environmental parameters, security events, and network status of each system are monitored in real time. When the device status, environmental parameters, security events, or network situation change, the monitoring results are updated in real time, collaborative control instructions are regenerated, and the execution actions of each execution system are adjusted synchronously.

7. The method according to claim 1, characterized in that, The multi-source heterogeneous data includes at least: inspection images and positioning trajectories of the rail-mounted robot, video streams and AI recognition results from fixed / pan-tilt cameras, infrared thermal imaging temperature measurement data, outputs from temperature, humidity and water immersion sensors, SF6 gas concentration, chromatographic analysis values ​​of main transformer oil, smoke / temperature alarm signals, electronic fence trigger signals, access control and vehicle passage records, smoke and fire recognition results, drone aerial images and defect annotations, and access logs, encryption status and permission change records generated by network security equipment.

8. A fully unmanned integrated intelligent control system for photovoltaic power plants, characterized in that: include: The data acquisition module is used to synchronously collect and integrate multi-source heterogeneous data from various systems across the entire photovoltaic power station, and construct a unified data stream covering five dimensions: equipment, environment, security, fire protection, and network. The collaborative control instruction generation module is used to perform unified parsing and event identification on the unified data stream, and generate collaborative control instructions based on equipment operation constraints, environmental safety boundaries, security response rules, fire linkage conditions and network security policies. The multi-device collaborative execution module is used to drive the corresponding execution system to perform cross-system linkage and closed-loop actions according to the collaborative control instructions.

9. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the steps of the method described in any one of claims 1 to 7.

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