A hibernation wake-up control method, system, device and medium for a patrol robot

Abstract

The application provides a hibernation wake-up control method, system, device and medium of a patrol robot, relates to the technical field of intelligent robot patrol, and comprises the following steps: combining ontology feature data, running state data and environment parameter data of each patrol object, calculating a first health index; calculating a deviation value of the first health index and an environment baseline parameter; adjusting the first health index according to the deviation value to generate a second health index; adjusting the second health index according to an association index between each patrol object to generate a target health index, and taking a patrol object with a target health index less than a preset index as a target patrol object; obtaining first position information of a plurality of patrol robots and second position information of the target patrol object, and combining the first position information and the second position information to generate a patrol plan of each patrol robot. The application has the technical effect of reasonably configuring patrol resources.

Description

Technical Field

[0001] This application relates to the field of intelligent inspection technology for robots, specifically to a sleep-wake control method, system, device, and medium for an inspection robot. Background Technology

[0002] Photovoltaic power plants typically occupy large areas, have numerous devices, and operate in complex environments. Traditional manual inspection methods are inefficient and cannot achieve 24 / 7 monitoring. As the scale of photovoltaic power plants continues to expand, accurately assessing the health status of equipment and identifying target equipment requiring key inspections has become a pressing technical problem to be solved in the intelligent operation and maintenance of photovoltaic power plants.

[0003] Currently, equipment status is typically assessed and inspection tasks are triggered using alarms based on thresholds for equipment operating parameters. For example, when parameters such as current, voltage, or temperature of a device exceed preset thresholds, the system determines that the device is abnormal and dispatches an inspection robot for inspection. However, this assessment method based on a single equipment operating parameter has limitations. It only focuses on whether the current operating data of the equipment exceeds the standard, while ignoring other important factors affecting the health status of the equipment. This results in an incomplete and inaccurate assessment of the actual health status of the equipment, making it difficult to effectively identify the target equipment that truly needs special attention, and affecting the rational allocation of inspection resources. Summary of the Invention

[0004] This application provides a method, system, device, and medium for controlling the hibernation and wake-up of an inspection robot, which is used to rationally allocate inspection resources.

[0005] In a first aspect, this application provides a sleep-wake control method for inspection robots. The method includes: acquiring the physical characteristic data, operating status data, and environmental parameter data of each inspection object in a photovoltaic power station; calculating a first health index for each inspection object by combining the physical characteristic data, the operating status data, and the environmental parameter data; acquiring the environmental baseline parameters of the photovoltaic power station and calculating the deviation value between the first health index and the environmental baseline parameters; adjusting the first health index of each inspection object according to the deviation value to generate a second health index for each inspection object; acquiring the correlation index between each inspection object and adjusting the second health index according to the correlation index to generate a target health index for each inspection object; designating inspection objects whose target health index is less than a preset index as target inspection objects, wherein the correlation index is used to characterize the electrical connection relationship and fault propagation influence degree between each inspection object; acquiring the first position information of multiple inspection robots and the second position information of the target inspection object, and generating an inspection plan for each inspection robot by combining the first position information and the second position information.

[0006] By adopting the above technical solution, a first health index is calculated by acquiring the physical characteristic data, operating status data, and environmental parameter data of the inspection object. A second health index is generated by adjusting the deviation based on the environmental baseline parameters. Furthermore, a target health index is generated by adjusting the correlation index between the inspection objects. This allows for a comprehensive and accurate assessment of the equipment's health status by considering the equipment's own characteristics, operating status, environmental impact, and electrical relationships between equipment. It effectively identifies the target inspection objects that truly require special attention, avoids overlooking equipment with potential hazards, and rationally generates inspection plans for each inspection robot based on the target health index and location information of the target inspection objects, thus achieving a reasonable allocation of inspection resources.

[0007] Secondly, this application provides a high-concurrency transaction message processing system, the system comprising: a first acquisition module, a combination module, a second acquisition module, a third acquisition module, and a fourth acquisition module; wherein, The first acquisition module is used to acquire the physical characteristic data, operating status data, and environmental parameter data of each inspection object in the photovoltaic power station; the combination module is used to combine the physical characteristic data, operating status data, and environmental parameter data to calculate a first health index for each inspection object; the second acquisition module is used to acquire the environmental baseline parameters of the photovoltaic power station, calculate the deviation value between the first health index and the environmental baseline parameters; adjust the first health index of each inspection object according to the deviation value to generate a second health index for each inspection object; the third acquisition module is used to acquire the correlation index between each inspection object, adjust the second health index according to the correlation index, generate a target health index for each inspection object, and designate inspection objects whose target health index is less than a preset index as target inspection objects, wherein the correlation index is used to characterize the electrical connection relationship and fault propagation impact degree between each inspection object; the fourth acquisition module is used to acquire the first position information of multiple inspection robots and the second position information of the target inspection object, and generate an inspection plan for each inspection robot by combining the first position information and the second position information.

[0008] Thirdly, this application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to make the electronic device execute a computer program such as the sleep-wake control method of any of the above-mentioned inspection robots.

[0009] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned sleep-wake control methods for inspection robots.

[0010] In summary, this application includes at least one of the following beneficial technical effects: By acquiring the physical characteristics, operational status, and environmental parameters of the inspected objects, a first health index is calculated. A second health index is generated by adjusting for deviations based on environmental baseline parameters. Furthermore, a target health index is generated by adjusting the correlation index between the inspected objects. This allows for a comprehensive and accurate assessment of the equipment's health status, taking into account its own characteristics, operational status, environmental impact, and electrical relationships between devices. It effectively identifies the target inspection objects that truly require special attention, avoids overlooking equipment with potential hazards, and rationally generates inspection plans for each inspection robot based on the target health index and location information of the target inspection objects, thus achieving the rational allocation of inspection resources. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating a sleep-wake control method for an inspection robot provided in an embodiment of this application; Figure 2 This is a schematic diagram of a sleep-wake control structure for an inspection robot provided in an embodiment of this application; Figure 3 This is a schematic diagram of the sleep-wake control system for an inspection robot provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0012] Explanation of reference numerals in the attached figures: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0014] In the description of the embodiments in this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0015] Figure 1 This is a flowchart illustrating a sleep-wake control method for an inspection robot provided in an embodiment of this application. Figure 1 As shown, the method includes S101-S105: S101, acquire the physical characteristic data, operating status data and environmental parameter data of each inspection object in the photovoltaic power station.

[0016] In practice, the acquisition of intrinsic characteristic data is primarily achieved through the equipment's built-in sensors and monitoring systems. This intrinsic characteristic data refers to fundamental data reflecting the physical characteristics and technical parameters of the inspected object. Taking photovoltaic modules as an example, the system uses an infrared thermal imager to collect surface temperature distribution data, a current sensor to obtain the conduction status parameters of bypass diodes, and optical inspection equipment to acquire physical characteristic parameters such as surface dirt coverage and glass breakage. For power electronic devices such as inverters, the system collects characteristic data reflecting the equipment's intrinsic quality, such as the thermal resistance parameters of power devices, the equivalent series resistance of filter capacitors, and the thermal conductivity coefficient of the heat dissipation system. This intrinsic characteristic data directly reflects the physical state and potential aging degree of the equipment, providing a basis for calculating the equipment's aging coefficient.

[0017] The acquisition of operational status data is achieved through a distributed data acquisition system. Operational status data refers to the dynamic performance parameters generated by the equipment during actual operation. The system collects electrical parameters such as output power, current, and voltage of each photovoltaic module in real time through power metering devices, monitors and tracks the mechanical operating status of the support system through vibration sensors, and collects power quality parameters such as harmonic content and power factor of the inverter output through a power quality analyzer. Simultaneously, the system also collects historical operational data such as equipment operating time, number of start-stop cycles, and load rate change trends. This operational status data reflects whether the equipment is currently operating normally, providing data support for calculating operational anomaly coefficients.

[0018] Environmental parameter data is acquired through environmental monitoring stations deployed in different areas of the power plant. Environmental parameter data refers to external environmental conditions that affect equipment operation and performance. The meteorological monitoring system collects meteorological data such as solar irradiance, ambient temperature, humidity, wind speed and direction, and rainfall. Air quality monitoring equipment collects air quality parameters such as PM2.5, PM10 concentrations, and corrosive gas concentrations. The soil monitoring system collects geological environmental parameters such as foundation settlement and soil temperature and humidity. In addition, the system also collects power grid environmental parameters such as grid voltage fluctuations and frequency deviations. These environmental parameter data reflect the degree of impact of the external working environment on the equipment's performance and health status, providing a basis for calculating the environmental impact coefficient.

[0019] During data acquisition, the system employs a time synchronization mechanism to ensure time consistency across different data types. A data preprocessing module filters, denoises, and detects outliers in the raw data to guarantee data quality reliability. Different data acquisition strategies are used for different types of inspection targets. For example, the acquisition frequency is set to once per minute for critical main transformers, while for general power distribution equipment, the frequency is set to once per hour. This ensures real-time monitoring of important equipment while avoiding data redundancy and system overload.

[0020] S102, combining the body characteristic data, operation status data and environmental parameter data, calculates the first health index of each inspection object.

[0021] In practical implementation, the system first calculates the first deviation value between each feature parameter in the body feature data and its corresponding standard feature parameter. The standard feature parameter refers to the benchmark value of the technical parameters that the equipment should possess under ideal conditions. Taking photovoltaic modules as an example, when the measured surface temperature distribution of the module is 85 degrees Celsius, while the standard feature parameter is 75 degrees Celsius, the first deviation value is calculated as a positive deviation of 10 degrees Celsius. For the thermal resistance parameter of the inverter's power devices, if the measured value is 0.15 K / W and the standard value is 0.10 K / W, then the first deviation value is 0.05 K / W. The system ensures the comprehensiveness and accuracy of the deviation value calculation by establishing a feature parameter library containing multiple dimensions such as temperature characteristics, electrical characteristics, and mechanical characteristics. Subsequently, the system weights and sums each first deviation value according to a first preset weight to generate the equipment aging coefficient. The first preset weight is a weight coefficient determined based on the importance of different feature parameters to the degree of equipment aging. Temperature-related parameters, because they directly affect equipment lifespan, are usually assigned a high weight value of 0.4, while appearance defect parameters are weighted at 0.2, and electrical parameters at 0.4. The equipment aging factor reflects the degree of aging of the equipment relative to its ideal state; the higher the value, the more severe the aging.

[0022] In terms of operational status data processing, the system calculates the second deviation value between each operational parameter and its corresponding standard operational parameter. The standard operational parameter refers to the expected performance value of the equipment under normal operating conditions. For the output power of photovoltaic modules, when the actual measured value is 450W while the standard operational parameter is 500W, the second deviation value is -50W, indicating that the power output is lower than expected. For the inverter's conversion efficiency, if the actual measured value is 96.5% while the standard value is 98%, the second deviation value is -1.5%. The system establishes a standard value database covering key operational parameters such as power output, efficiency indicators, and power quality, and dynamically adjusts it according to different operating conditions. Next, the system weights and sums each second deviation value according to a second preset weight to generate an operational anomaly coefficient. The setting of the second preset weight reflects the contribution of different operational parameters to the judgment of abnormal equipment states, with the power output parameter weight set to 0.5, the efficiency parameter weight to 0.3, and the power quality parameter weight to 0.2. The larger the operational anomaly coefficient value, the more serious the deviation of the equipment's current operational state from the normal state.

[0023] For processing environmental parameter data, the system calculates the third deviation value of each environmental parameter from its corresponding standard environmental parameter. The standard environmental parameter refers to the baseline value of the environmental parameter under the optimal operating conditions of the equipment. When the actual ambient temperature is 45 degrees Celsius and the standard environmental parameter is 25 degrees Celsius, the third deviation value is 20 degrees Celsius. For solar irradiance, if the measured value is 800 W / m² and the standard value is 1000 W / m², the third deviation value is -200 W / m². The system establishes a standard value system for environmental parameters including temperature, humidity, irradiance, wind speed, and air quality through meteorological data analysis and equipment technical specifications. Subsequently, the system weights and sums each third deviation value according to a third preset weight to generate an environmental impact coefficient. The third preset weight reflects the importance of different environmental factors on equipment performance; the weight for temperature is set to 0.4, for irradiance to 0.3, and for humidity and wind speed to 0.15 each. The environmental impact coefficient quantifies the degree of influence of the external environment on the health status of the equipment.

[0024] After obtaining the equipment aging coefficient, operational anomaly coefficient, and environmental impact coefficient, the system enters the comprehensive calculation stage of the first health index. Based on the numerical range of the equipment aging coefficient, the system classifies the degree of equipment aging into three levels: slight aging, moderate aging, and severe aging, and sets a corresponding initial health index for each aging level. Slight aging corresponds to an initial health index of 90-100, moderate aging to 70-89, and severe aging to 50-69. This grading reflects the basic correspondence between the degree of equipment aging and its health status.

[0025] Next, the system calculates the correlation parameter between the operational anomaly coefficient and the environmental impact coefficient. This parameter uses the Pearson correlation coefficient method to characterize the degree of mutual influence between operational anomalies and environmental factors. The correlation parameter is calculated by dividing the covariance of the two coefficient data sequences by the product of their respective standard deviations. When the correlation parameter is greater than the preset synergy threshold of 0.6, it indicates a significant synergistic relationship between operational anomalies and environmental impact. Based on the relationship between the correlation parameter and the synergy threshold, the system determines the synergy strength level, classifying it into three levels: weak synergy, moderate synergy, and strong synergy, and obtains the corresponding synergy coefficients. The synergy coefficient for the weak synergy level is 1.1, for moderate synergy it is 1.3, and for strong synergy it is 1.5. The synergy coefficient reflects the amplified impact of the superimposed effect of multiple factors on the equipment health status.

[0026] Subsequently, the system corrects the operational anomaly coefficient and environmental impact coefficient based on the synergistic effect coefficient, generating a first correction coefficient and a second correction coefficient. The correction process is achieved by multiplying the original coefficients by the synergistic effect coefficient, reflecting the enhanced impact effect under the synergistic effect of multiple factors. The first correction coefficient equals the operational anomaly coefficient multiplied by the synergistic effect coefficient, and the second correction coefficient equals the environmental impact coefficient multiplied by the synergistic effect coefficient. This correction mechanism can more accurately reflect the cumulative impact effect when multiple adverse factors act simultaneously in the actual operating environment.

[0027] Finally, the system performs a weighted summation of the first and second correction coefficients to generate a comprehensive impact value. In the weighted summation, the weight of the first correction coefficient is set to 0.6, and the weight of the second correction coefficient is set to 0.4, reflecting that the impact of operational anomalies on equipment health is usually more direct and significant than that of environmental factors. The comprehensive impact value determines the overall negative impact of operational status and environmental factors on equipment health. The system adjusts the initial health index based on the comprehensive impact value. Since the initial health index and the comprehensive impact value are negatively correlated, the adjustment formula is the initial health index minus the comprehensive impact value multiplied by the impact coefficient, ultimately generating the first health index for each inspected object.

[0028] Based on the above embodiments, as an optional implementation method, in S102, the calculation of the first health index of each inspection object by combining the ontological feature data, operating status data, and environmental parameter data specifically includes S21-S24: S21, calculate the first deviation value between each feature parameter in the body feature data and the corresponding standard feature parameter; sum the first deviation values ​​according to the first preset weight to generate the equipment aging coefficient of each inspection object.

[0029] The system assesses the physical aging degree of equipment by calculating the first deviation value between each characteristic parameter in the equipment's intrinsic characteristic data and its corresponding standard characteristic parameter. The intrinsic characteristic data is a set of parameters reflecting the equipment's physical state and structural integrity, including physical characteristic indicators such as the equipment's appearance integrity, connection tightness, insulation resistance value, and mechanical vibration amplitude. Standard characteristic parameters are ideal reference values ​​determined based on the equipment's factory specifications and industry standards, representing the characteristic parameter levels that the equipment should possess under optimal physical conditions. The system uses a normalized deviation calculation method, and the formula for calculating the first deviation value is: First Deviation Value = |Measured Characteristic Parameter - Standard Characteristic Parameter| / Standard Characteristic Parameter × 100%. For example, if the standard insulation resistance value of a photovoltaic module is 500MΩ, and the measured value is 420MΩ, then the corresponding first deviation value is |420-500| / 500 × 100% = 16%.

[0030] The system calculates a weighted sum of the first deviation values ​​corresponding to each characteristic parameter according to a first preset weight, generating an equipment aging coefficient. The first preset weight is a weighting coefficient determined based on the importance of each characteristic parameter to the degree of equipment aging, obtained through expert evaluation and historical data analysis. For photovoltaic modules, the weight of insulation resistance is set to 0.3, appearance integrity to 0.25, connection tightness to 0.2, mechanical integrity to 0.15, and other auxiliary parameters to 0.1. The equipment aging coefficient is a comprehensive indicator quantifying the degree of physical aging of equipment; a higher value indicates a more severe degree of aging. Continuing with the previous example, assuming the photovoltaic module's appearance integrity deviation is 12%, connection tightness deviation is 8%, mechanical integrity deviation is 5%, and other parameter deviations are 3%, then the equipment aging coefficient is: 4.8 + 3.0 + 1.6 + 0.75 + 0.3 = 10.45%.

[0031] S22, calculate the second deviation value between each operating parameter in the operating status data and the corresponding standard operating parameter; sum the second deviation values ​​according to the second preset weight to generate the operating anomaly coefficient of each inspection object.

[0032] The system assesses the degree of operational anomaly by calculating the second deviation value of each operating parameter in the operational status data from the corresponding standard operating parameters. Operational status data is a set of parameters reflecting the performance of the equipment during actual operation, including dynamic operating indicators such as power generation, voltage and current, power factor, harmonic content, and temperature distribution. Standard operating parameters are ideal operating parameter values ​​determined based on the equipment's rated operating conditions and optimal operating state, representing the performance level the equipment should achieve under normal operating conditions. The second deviation value is calculated using the same normalization method as the first deviation value to ensure comparability between parameters of different dimensions. For example, if the standard output power of an inverter is 50kW and the measured output power is 45kW, then the second deviation value of the power parameter is |45-50| / 50×100%=10%.

[0033] The system calculates a weighted sum of the second deviation values ​​corresponding to each operating parameter according to a second preset weight, generating an operational anomaly coefficient. The second preset weight is a weighting coefficient determined based on the importance of each operating parameter in indicative of the equipment's operational health status. For the inverter, the weight is set to 0.35 for output power, 0.25 for voltage and current parameters, 0.2 for power factor, 0.15 for temperature, and 0.05 for harmonic content. The operational anomaly coefficient is a comprehensive index quantifying the degree to which equipment deviates from its normal operating state; a higher value indicates a higher degree of operational anomaly. Assuming the inverter's voltage and current deviation is 6%, power factor deviation is 4%, temperature deviation is 8%, and harmonic content deviation is 2%, then the operational anomaly coefficient is: 3.5 + 1.5 + 0.8 + 1.2 + 0.1 = 7.1%.

[0034] S23, calculate the third deviation value of each environmental parameter in the environmental parameter data from the corresponding standard environmental parameter; sum the third deviation values ​​according to the third preset weight to generate the environmental impact coefficient of each inspection object.

[0035] The system assesses the impact of the external environment on equipment by calculating the third deviation value of each environmental parameter in the environmental parameter data from its corresponding standard environmental parameters. The environmental parameter data is a set of parameters reflecting the external environmental conditions of the equipment, including environmental factors such as ambient temperature, relative humidity, wind speed, dust concentration, ultraviolet radiation intensity, and corrosive gas concentration. The standard environmental parameters are ideal environmental parameter values ​​determined based on the equipment's optimal operating environment conditions, representing the environmental state when the equipment receives optimal operating support. The calculation of the third deviation value also uses a normalization method, but for environmental parameters, the impact of bidirectional deviation needs to be considered; that is, excessively high or low environmental parameters may have adverse effects on the equipment.

[0036] The system calculates an environmental impact coefficient by weighting and summing the third deviation values ​​corresponding to each environmental parameter according to a third preset weight. The third preset weight is a weighting coefficient determined based on the importance of each environmental parameter's impact on the equipment's health status. For outdoor photovoltaic equipment, the weight is set as follows: ambient temperature 0.3, relative humidity 0.25, dust concentration 0.2, ultraviolet radiation intensity 0.15, and other environmental factors 0.1. The environmental impact coefficient is a comprehensive indicator that quantifies the negative impact of the external environment on the equipment's health status; a higher value indicates more unfavorable environmental conditions for the equipment's healthy operation.

[0037] S24, combining equipment aging coefficient, operational anomaly coefficient and environmental impact coefficient, calculates the first health index of each inspection object.

[0038] The system combines equipment aging coefficient, operational anomaly coefficient, and environmental impact coefficient to calculate the primary health index for each inspected object using a comprehensive evaluation model. The system employs a multi-factor fusion health index calculation model, converting the coefficients of the three dimensions into factors influencing the health index. The formula for calculating the primary health index is: Primary Health Index = 100 - (Equipment Aging Coefficient × Aging Impact Weight + Operational Anomaly Coefficient × Operational Impact Weight + Environmental Impact Coefficient × Environmental Impact Weight). The aging impact weight, operational impact weight, and environmental impact weight are set to 0.4, 0.4, and 0.2 respectively, reflecting the evaluation logic that the equipment's own condition is more important than external environmental factors.

[0039] Based on the above embodiments, as an optional implementation method, in S24, the calculation of the first health index of each inspection object, combining the equipment aging coefficient, the operational anomaly coefficient, and the environmental impact coefficient, specifically includes S241-S244: S241, Based on the equipment aging coefficient, determine the aging level of each inspection object and obtain the initial health index corresponding to the aging level.

[0040] The system determines the aging level of each inspected object based on the equipment aging coefficient and obtains the initial health index corresponding to the aging level. The aging level is a classification standard based on the equipment aging coefficient, which is divided into four levels: slight aging, moderate aging, severe aging, and extreme aging, according to preset thresholds. Slight aging corresponds to an aging coefficient of 0-10%, moderate aging to 10-25%, severe aging to 25-50%, and extreme aging to over 50%. This classification method is based on statistical analysis of a large amount of equipment aging data and expert experience, and can reflect the typical characteristics and development patterns of equipment aging.

[0041] The system presets a corresponding initial health index benchmark value for each aging level. The initial health index is a health status assessment value that only considers the degree of equipment aging. The initial health index for slight aging is 85-95 points, for moderate aging it is 75-85 points, for severe aging it is 60-75 points, and for extreme aging it is 40-60 points. After determining the level range, the system uses a linear interpolation method to calculate the specific initial health index value. For example, if the equipment aging coefficient of a photovoltaic module is 15%, belonging to the moderate aging level, the initial health index calculated by linear interpolation is 85 - (15-10) / (25-10) × (85-75) = 81.67 points. This aging level-based grading assessment method can better reflect the fundamental impact of equipment aging on health status.

[0042] S242, calculate the correlation parameter between the operational anomaly coefficient and the environmental impact coefficient; determine the synergy intensity level based on the relationship between the correlation parameter and the preset synergy threshold; obtain the corresponding synergy coefficient based on the synergy intensity level; the correlation coefficient is used to characterize the degree of mutual influence between operational anomalies and environmental factors.

[0043] The system quantifies the strength of the synergistic effect between the operational anomaly coefficient and the environmental impact coefficient by calculating the correlation parameter. The correlation parameter is a statistical indicator characterizing the degree of mutual influence between operational anomalies and environmental factors, calculated using the Pearson correlation coefficient method. The system collects paired data of the operational anomaly coefficient and the environmental impact coefficient from historical time series and calculates their linear correlation. The correlation parameter ranges from -1 to 1; a larger absolute value indicates a stronger correlation, with positive values ​​indicating positive correlation and negative values ​​indicating negative correlation. In the health assessment of photovoltaic equipment, the operational anomaly coefficient and the environmental impact coefficient typically show a positive correlation, meaning that the degree of equipment operational anomalies increases when environmental conditions deteriorate.

[0044] The system determines the synergy strength level based on the relationship between the correlation parameter and a preset synergy threshold. The synergy threshold is a critical value used to judge the significance of the synergy effect. The system sets a weak synergy threshold of 0.3, a moderate synergy threshold of 0.6, and a strong synergy threshold of 0.8. When the absolute value of the correlation parameter is less than 0.3, it is judged as having no significant synergy; 0.3-0.6 is weak synergy; 0.6-0.8 is moderate synergy; and greater than 0.8 is strong synergy. Each synergy strength level corresponds to a specific synergy coefficient, which is a moderating factor used to adjust the operational anomaly coefficient and environmental impact coefficient. The synergy coefficient for no significant synergy is 1.0, for weak synergy is 1.1, for moderate synergy is 1.3, and for strong synergy is 1.5. For example, if the correlation parameter between the operational anomaly coefficient and the environmental impact coefficient of a certain equipment is 0.72, it belongs to the moderate synergy level, and the corresponding synergy coefficient is 1.3.

[0045] S243, Based on the synergistic effect coefficient, the operational anomaly coefficient and the environmental impact coefficient are corrected respectively, and the first correction coefficient and the second correction coefficient are generated accordingly.

[0046] The system corrects the operational anomaly coefficient and environmental impact coefficient based on the synergy coefficient, generating a first correction coefficient and a second correction coefficient. The correction process considers the amplifying effect of synergy on the influence of each coefficient. The formula for the first correction coefficient is: First Correction Coefficient = Operational Anomaly Coefficient × Synergy Coefficient × Operational Correction Weight. The formula for the second correction coefficient is: Second Correction Coefficient = Environmental Impact Coefficient × Synergy Coefficient × Environmental Correction Weight. The operational correction weight and environmental correction weight are set to 0.7 and 0.5 respectively, reflecting the dominant role of operational anomalies in the synergy effect. Continuing the previous example, assuming the operational anomaly coefficient is 7.1% and the environmental impact coefficient is 12%, then the first correction coefficient is 7.1% × 1.3 × 0.7 = 6.46%, and the second correction coefficient is 12% × 1.3 × 0.5 = 7.8%.

[0047] S244, the first correction coefficient and the second correction coefficient are weighted and summed to generate a comprehensive impact value; the initial health index is adjusted according to the comprehensive impact value to generate the first health index of each inspection object; the initial health index is negatively correlated with the comprehensive impact value.

[0048] The system generates a comprehensive impact value by weighting and summing the first and second correction coefficients, and then adjusts the initial health index based on this comprehensive impact value. The formula for calculating the comprehensive impact value is: Comprehensive Impact Value = First Correction Coefficient × 0.6 + Second Correction Coefficient × 0.4. The weighting reflects the assessment logic that operational anomalies more directly reflect equipment health status than environmental impacts. The comprehensive impact value quantifies the combined influence of operational and environmental factors; a higher value indicates a more significant negative impact on equipment health.

[0049] The final calculation of the First Health Index uses a negative correlation adjustment model, and the formula is: First Health Index = Initial Health Index - Comprehensive Impact Value × Impact Conversion Coefficient. The Impact Conversion Coefficient is the proportional factor that converts the comprehensive impact value into a deduction value for the health index, typically set between 0.8 and 1.2, adjusted according to the equipment type and importance. Continuing the previous example, the comprehensive impact value is 6.46% × 0.6 + 7.8% × 0.4 = 3.88% + 3.12% = 7%. Assuming the Impact Conversion Coefficient is 1.0, then the First Health Index = 81.67 - 7 × 1.0 = 74.67 points.

[0050] S103: Obtain the environmental baseline parameters of the photovoltaic power station, calculate the deviation value between the first health index and the environmental baseline parameters; adjust the first health index of each inspection object according to the deviation value, and generate the second health index of each inspection object.

[0051] In practical implementation, the system first determines the standard environmental conditions of the photovoltaic power station by establishing an environmental baseline parameter acquisition mechanism. Environmental baseline parameters refer to the statistical benchmark values ​​of environmental conditions in various areas of the photovoltaic power station within a specific time period, comprehensively considering factors such as geographical location, seasonal characteristics, and historical meteorological data. By analyzing environmental monitoring data from the past 12 months, the system uses a moving average algorithm and seasonal adjustment methods to calculate baseline values ​​for key environmental parameters, including benchmark temperature, benchmark humidity, benchmark solar irradiance, and benchmark wind speed. For example, the benchmark temperature for a photovoltaic power station in spring is set at 22 degrees Celsius, the benchmark solar irradiance at 850 W / m², and the benchmark relative humidity at 45%. These environmental baseline parameters are not fixed but dynamically adjusted according to seasonal changes and long-term climate trends to ensure that the baseline parameters accurately reflect the typical environmental conditions of the current period.

[0052] After obtaining the environmental baseline parameters, the system calculates the deviation between the first health index and the environmental baseline parameters. This deviation is not calculated by simple subtraction, but rather through the establishment of an environmental impact model. The system first compares and analyzes the actual environmental parameters of each inspected object's location with the environmental baseline parameters, calculating specific environmental differences such as temperature deviation, humidity deviation, and irradiance deviation. For example, when the actual temperature in the area where a photovoltaic module is located is 35 degrees Celsius, while the baseline temperature is 22 degrees Celsius, the temperature deviation is 13 degrees Celsius. The system uses an environmental impact weight matrix to convert these environmental differences into quantifiable values ​​affecting the equipment's health index. The influence coefficient for temperature deviation is set to -0.5 points / degree Celsius, meaning that for every 1 degree Celsius increase, the apparent health status of the equipment decreases by 0.5 points. However, this decrease may not represent a health problem with the equipment itself, but rather a normal effect caused by environmental conditions.

[0053] Subsequently, the system adjusts the primary health index of each inspected object based on the deviation value. The adjustment process first determines the environmental deviation level of each inspected object based on the numerical range of the deviation value. The system classifies environmental deviations into three levels: slight deviation, moderate deviation, and severe deviation. Slight deviation corresponds to a deviation value in the range of 0-10 points, moderate deviation to 10-20 points, and severe deviation to above 20 points. Each environmental deviation level corresponds to a specific environmental correction factor: 1.05 for slight deviation, 1.15 for moderate deviation, and 1.25 for severe deviation. The design philosophy behind an environmental correction factor greater than 1 is that when equipment is under relatively harsh environmental conditions, its ability to maintain its current health state reflects better environmental adaptability and health reserves, thus warranting positive correction.

[0054] The system multiplies the environmental correction factor with the primary health index to generate an environmentally adjusted health index. This multiplication reflects an environmental compensation mechanism: equipment that maintains good performance in harsh environments receives a moderate boost to its health index, while equipment in favorable environments retains its original assessment result. For example, if an inverter has a primary health index of 75 points, and because it operates in a high-temperature, high-humidity environment (a moderate deviation), with a corresponding environmental correction factor of 1.15, then its environmentally adjusted health index would be 75 × 1.15 = 86.25 points.

[0055] In addition to environmental adjustments, the system also needs to consider the additional risks posed to the equipment by extreme environmental conditions. When the deviation value exceeds a preset deviation threshold, the system calculates an additional attenuation based on the difference between the deviation value and the preset deviation threshold. The preset deviation threshold is typically set at 25 points, representing the extreme tolerance range of environmental conditions. When the deviation value exceeds this threshold, even if the equipment is currently in good condition, the environmental risks it faces have reached a level that may affect the long-term stable operation of the equipment. Therefore, an additional attenuation is needed to reflect this potential risk. The formula for calculating the additional attenuation is (deviation value - preset deviation threshold) × risk coefficient, where the risk coefficient is set to 0.3. For example, when the deviation value of a certain piece of equipment is 30 points, exceeding the preset deviation threshold of 25 points, the additional attenuation is (30-25) × 0.3 = 1.5 points. The system subtracts the additional attenuation from the environmentally adjusted health index to obtain the equipment's second health index as 86.25 - 1.5 = 84.75 points.

[0056] For deviation values ​​that do not exceed the preset deviation threshold, the system directly uses the environmentally adjusted health index as the second health index without additional attenuation processing. This tiered processing mechanism ensures reasonable compensation under normal environmental conditions while avoiding the neglect of risks in extreme environments.

[0057] The entire environmental baseline adjustment process also takes into account the impact of time. The system establishes a time-weighted mechanism, giving more observation time and relatively lenient evaluation criteria to equipment that has just experienced abrupt changes in environmental conditions, while gradually increasing the evaluation criteria for equipment that has been deviating from the environmental baseline for a long time, reflecting the cumulative effect of environmental impact over time.

[0058] Based on the above embodiments, as an optional implementation method, in S103, adjusting the first health index of each inspection object according to the deviation value and generating the second health index of each inspection object specifically includes S31-S34: S31. Determine the environmental deviation level of each inspection object based on the numerical range of the deviation value.

[0059] The system determines the environmental deviation level of each inspected object based on the numerical range of the deviation value. The deviation value represents the degree of difference between the current actual environmental parameters and the standard environmental parameters, and is calculated by weighting multiple environmental factors such as temperature deviation, humidity deviation, wind speed deviation, and dust concentration deviation. The system has established an environmental deviation level classification standard based on statistical analysis, dividing the deviation value into five levels according to the degree of impact on equipment health: normal, slight deviation, moderate deviation, severe deviation, and extreme deviation. The normal level corresponds to a deviation value of 0-5%, indicating that the current environmental conditions are close to ideal; slight deviation corresponds to 5-15%, indicating that the environmental conditions deviate slightly but the impact is limited; moderate deviation corresponds to 15-30%, indicating that the environmental conditions deviate significantly and begin to have an adverse effect; severe deviation corresponds to 30-50%, indicating that the environmental conditions deviate severely and have a significant negative impact on equipment health; and extreme deviation corresponds to more than 50%, indicating that the environmental conditions are extremely harsh and may cause serious damage to the equipment.

[0060] For example, the real-time ambient temperature at the location of a photovoltaic module is 45℃, while the standard ambient temperature is 25℃, a temperature deviation of 80%; the real-time relative humidity is 85%, while the standard relative humidity is 60%, a humidity deviation of 42%; the real-time dust concentration is 0.8 mg / m³, while the standard dust concentration is 0.2 mg / m³, a dust deviation of 300%. Through weighted calculation (temperature weight 0.4, humidity weight 0.3, dust weight 0.3), the comprehensive deviation value is 80%×0.4 + 42%×0.3 + 300%×0.3 = 32% + 12.6% + 90% = 134.6%, which falls into the extreme deviation category. This classification method can accurately identify the intensity of the impact of different degrees of environmental deviation on the health status of equipment.

[0061] S32, obtain the environmental correction factor corresponding to the environmental deviation level.

[0062] The system obtains the environmental correction factor corresponding to the environmental deviation level. The environmental correction factor is a numerical coefficient used to adjust the primary health index to reflect the impact of environmental deviation, determined based on extensive experimental data and statistical analysis. The environmental correction factor for the normal level is 1.0, indicating no correction is needed; for the slight deviation level, it is 0.95, indicating a slight downward adjustment of the health index; for the moderate deviation level, it is 0.88, indicating a moderate downward adjustment; for the severe deviation level, it is 0.75, indicating a significant downward adjustment; and for the extreme deviation level, it is 0.6, indicating a substantial downward adjustment. This decreasing correction factor setting reflects the gradually increasing negative impact of environmental deviation on the health status of the equipment. Continuing with the previous example, the environmental deviation level of this photovoltaic module is extreme deviation, corresponding to an environmental correction factor of 0.6.

[0063] S33, multiply the environmental correction factor with the first health index to generate the environmentally adjusted health index.

[0064] The system multiplies the environmental correction factor with the primary health index to generate an environmentally adjusted health index. This multiplication method allows for proportional adjustment of the health index based on the degree of environmental deviation, ensuring that environmental impacts are reasonably reflected in the health assessment. The environmentally adjusted health index is an assessment value of the equipment's health status after considering the impact of current environmental deviations. The calculation formula is: Environmentally Adjusted Health Index = Primary Health Index × Environmental Correction Factor. Assuming the primary health index of this photovoltaic module is 74.67 points, then the environmentally adjusted health index is 74.67 × 0.6 = 44.8 points. This adjustment reflects the significant negative impact of extreme environmental deviations on the equipment's health status.

[0065] S34. If the deviation value exceeds the preset deviation threshold, calculate the additional attenuation amount based on the difference between the deviation value and the preset deviation threshold; subtract the additional attenuation amount from the health index after environmental adjustment to generate the second health index for each inspection object; if the deviation value does not exceed the preset deviation threshold, use the health index after environmental adjustment as the second health index for each inspection object.

[0066] The system determines whether additional attenuation adjustments are needed based on a preset deviation threshold. This preset deviation threshold, typically set at 50%, is a critical value used to identify extreme environmental conditions. When the deviation exceeds this threshold, it indicates that the environmental conditions have reached a dangerous level that could cause rapid damage to the equipment, requiring an additional attenuation adjustment on top of the existing environmental correction factor adjustment. This additional attenuation is an extra health index deduction value for extreme environmental deviations, calculated considering the degree to which the deviation exceeds the threshold and the equipment's environmental sensitivity.

[0067] The formula for calculating the additional attenuation is: Additional Attenuation = (Deviation Value - Preset Deviation Threshold) × Attenuation Coefficient × Environmental Sensitivity Factor. The attenuation coefficient is typically set between 0.1 and 0.2, while the environmental sensitivity factor is determined based on the equipment type: 1.2 for photovoltaic modules, 0.8 for inverters, and 1.0 for combiner boxes. Continuing with the previous example, the deviation value of this photovoltaic module is 134.6%, exceeding the preset deviation threshold by 50%, with a difference of 84.6%. Assuming an attenuation coefficient of 0.15, the additional attenuation is 84.6% × 0.15 × 1.2 = 15.23 points. The final second health index is 44.8 - 15.23 = 29.57 points.

[0068] If the deviation value does not exceed the preset deviation threshold, the system directly uses the environmentally adjusted health index as the second health index, without calculating additional attenuation. This step-by-step adjustment mechanism ensures the accuracy and rationality of health index correction, avoiding oversensitivity to minor environmental deviations while accurately identifying and quantifying the severe impact of extreme environmental conditions.

[0069] S104. Obtain the correlation index between each inspection object. Based on the correlation index, adjust the second health index and generate the target health index for each inspection object. Select the inspection object whose target health index is less than the preset index as the target inspection object. The correlation index is used to characterize the electrical connection relationship and the degree of fault propagation impact between each inspection object.

[0070] In practice, the system first establishes an equipment association network by acquiring the electrical topology relationships between each inspected object. Electrical topology relationships refer to the electrical network structure relationships formed by the connections of various electrical devices in a photovoltaic power station through cables, switches, transformers, etc. The system establishes a topology relationship database containing all inspected objects by parsing the power station's main electrical wiring diagram and equipment list. For example, photovoltaic modules are connected to a combiner box via DC cables, the combiner box is connected to an inverter via DC cables, the inverter is connected to a box-type transformer via AC cables, and the box-type transformer is connected to the main transformer of the step-up substation via high-voltage cables. The system converts this hierarchical electrical connection relationship into an association relationship matrix, determining the set of associated objects for each inspected object. The set of associated objects refers to the collection of all other devices that have a direct or indirect electrical connection relationship with a specific inspected object.

[0071] After determining the set of associated objects, the system calculates the association strength value between each inspected object and each associated object in the corresponding set. The association strength value is a numerical indicator that quantifies the tightness of electrical connections between devices and the strength of fault propagation. It is determined based on the level and type of electrical connection. The level of electrical connection reflects the electrical path length between two devices; directly connected devices are considered first-level connections, connected through an intermediate device are second-level connections, and so on. The type of electrical connection considers the electrical characteristics of the connection method; series connections have a higher association strength than parallel connections, and main circuit connections have a higher association strength than auxiliary circuit connections. The system uses a hierarchical weighting method to calculate the association strength value: a first-level direct series connection is set to 1.0, a first-level direct parallel connection is set to 0.7, a second-level indirect series connection is set to 0.5, and a second-level indirect parallel connection is set to 0.3. This design ensures that the association strength value of direct electrical connections is greater than that of indirect electrical connections, reflecting the attenuation effect of electrical distance on fault propagation.

[0072] Subsequently, the system normalizes the association strength values ​​corresponding to each associated object, generating an association weight coefficient for each object. Normalization is achieved by dividing the association strength value of each associated object by the sum of the association strength values ​​of all associated objects of that inspected object, ensuring that the sum of all association weight coefficients is 1. For example, a certain inverter's associated object set includes 20 photovoltaic modules, 1 box-type transformer, and 2 adjacent inverters. The association strength value of each photovoltaic module is 0.7, the association strength value of the box-type transformer is 1.0, and the association strength value of each adjacent inverter is 0.3. The total association strength value is 20 × 0.7 + 1.0 + 2 × 0.3 = 15.6. The association weight coefficient for a single photovoltaic module is 0.7 ÷ 15.6 = 0.045, and the association weight coefficient for the box-type transformer is 1.0 ÷ 15.6 = 0.064. The association weight coefficient reflects the relative importance of each associated object to the health status of the target equipment.

[0073] Next, the system obtains the second health index of each associated object in the associated object set, and then multiplies each associated object's second health index by its corresponding association weight coefficient and sums the results to generate a weighted health mean. The weighted health mean is a comprehensive evaluation index of the overall health status of the associated object set; it considers not only the health level of the associated objects but also the influence weight of each associated object on the target device.

[0074] Continuing with the inverter example above, assuming the average second health index of 20 photovoltaic modules is 85 points, the second health index of the box transformer is 78 points, and the second health indices of two adjacent inverters are 82 points and 80 points respectively, then the weighted average health index is: 76.5 + 5.0 + 3.1 + 1.5 = 86.1 points.

[0075] The system calculates the difference between the weighted health mean and the preset health benchmark, and determines the correlation transmission correction based on this difference. The preset health benchmark is an ideal baseline for the health status of the set of related objects, typically set at 80 points, representing a standard of good condition in the related environment. The calculation of the correlation transmission correction follows the risk transmission principle. When the weighted health mean is lower than the preset health benchmark, it indicates that the target device is in a poor related environment, and health problems in other devices may negatively impact it through electrical connections; therefore, the correlation transmission correction is negative. When the weighted health mean is not lower than the preset health benchmark, it indicates that the target device is in a good related environment, and the good condition of surrounding devices provides support; the correlation transmission correction is zero or positive.

[0076] In the specific calculation of the correlation transmission correction, the system determines the risk transmission level based on the numerical range of the difference. When the difference is between -10 and 0, the risk transmission level is mild, with a corresponding transmission impact coefficient of 0.2. When the difference is between -20 and -10, the risk transmission level is moderate, with a transmission impact coefficient of 0.4. When the difference is less than -20, the risk transmission level is high, with a transmission impact coefficient of 0.6. For positive differences, the system determines whether the difference exceeds a preset redundancy threshold of 10. If it does, it indicates that the correlation environment is too good, and a positive correction may be given, with the transmission impact coefficient set to 0.1. If it does not exceed the threshold, the correlation transmission correction is set to zero. Taking the aforementioned inverter as an example, the difference between the weighted health average of 86.1 and the preset health benchmark of 80 is 6.1, which does not exceed the preset redundancy threshold, therefore the correlation transmission correction is 0.

[0077] The system sums the second health index with the associated transmission correction to generate the target health index for each inspected object. The target health index is the final health assessment result that comprehensively considers the equipment's own health status, environmental impact, and the impact of related equipment. Assuming the inverter's second health index is 75 points and the associated transmission correction is 0, the target health index is 75 + 0 = 75 points. For equipment in an adverse associated environment, even if its own health status is relatively good, the target health index will decrease due to the negative associated transmission correction, reflecting the impact of systemic risk on individual equipment.

[0078] Ultimately, the system designates equipment with a target health index lower than a preset index as target inspection targets. The preset index is typically set at 70 points, representing a critical health status that requires close monitoring. Target inspection targets refer to equipment identified as needing priority inspection and maintenance after a comprehensive health assessment. This equipment may have poor inherent health, or it may be in a high-risk associated environment, potentially affected by malfunctions of other equipment, even if its own condition is acceptable.

[0079] Based on the above embodiments, as an optional implementation method, in S104, adjusting the second health index according to the correlation index to generate the target health index for each inspection object specifically includes S41-S45: S41, obtain the electrical topology relationship between each inspection object, and determine the set of associated objects for each inspection object based on the electrical topology relationship.

[0080] The system constructs a device association network by acquiring the electrical topology relationships between each inspected object. The electrical topology relationship is a network diagram describing the electrical connection structure and signal transmission paths of each device in a photovoltaic power station, including key information such as device nodes, connection edges, connection types, and signal flow directions. Based on the power station's electrical schematic diagram and actual wiring information, the system establishes a complete device topology database, recording the upstream and downstream connection relationships, connection methods, and electrical parameter transmission paths for each device. Based on the electrical topology relationships, the system determines the corresponding set of associated objects for each inspected object. The set of associated objects is the collection of all devices that have an electrical connection relationship with a specific inspected object, including directly connected devices and devices indirectly connected through other devices.

[0081] For example, the associated object set of a certain inverter includes 12 photovoltaic module strings directly connected to it, 1 box-type transformer, 3 combiner boxes, and indirectly connected monitoring systems and protection devices. The system determines the associated scope based on the depth of electrical connections, typically considering electrical connections within level 3; connections beyond level 3 are excluded from the associated analysis due to their negligible impact. This method of identifying associated objects based on electrical topology ensures the accuracy and completeness of health conduction analysis.

[0082] S42, calculate the association strength value between each inspection object and each associated object in the corresponding associated object set; the association strength value is determined according to the level of electrical connection and the type of electrical connection, wherein the association strength value of direct electrical connection is greater than the association strength value of indirect electrical connection.

[0083] The system calculates the correlation strength value between each inspected object and each associated object in the corresponding set of associated objects. The correlation strength value is a numerical indicator quantifying the degree of electrical correlation between two devices, primarily determined by the number and type of electrical connection. The number of electrical connection levels refers to the number of intermediate devices required for electrical signal transmission between two devices; fewer levels indicate a stronger correlation. A direct electrical connection has a level of 0, a first-level indirect connection has a level of 1, a second-level indirect connection has a level of 2, and so on. The type of electrical connection refers to the specific method of electrical connection between devices, including different types such as main circuit connections, control circuit connections, and signal circuit connections. Different types of connections have varying degrees of impact on health conduction.

[0084] The system establishes a calculation model for correlation strength values. The basic correlation strength value is determined based on the connection level: 1.0 for direct connection, 0.6 for first-level indirect connection, 0.3 for second-level indirect connection, and 0.1 for third-level indirect connection. Then, adjustments are made based on the connection type: 1.0 for main circuit connections, 0.8 for control circuit connections, 0.5 for signal circuit connections, and 0.7 for protection circuit connections. The final correlation strength value is the product of the basic correlation strength value and the connection type adjustment factor. For example, if a photovoltaic module and inverter are directly connected via a main circuit, the correlation strength value is 1.0 × 1.0 = 1.0; if the module and the box-type transformer are connected via a first-level indirect main circuit, the correlation strength value is 0.6 × 1.0 = 0.6.

[0085] S43, normalize the association strength values ​​corresponding to each associated object to generate the association weight coefficients corresponding to each associated object.

[0086] The system normalizes the association strength values ​​of each associated object to generate association weight coefficients for each object. Normalization ensures that the sum of all association weight coefficients is 1, facilitating subsequent weighted calculations. The association weight coefficient is a standardized coefficient representing the relative importance of each associated object in the health transmission analysis. The formula is: Association Weight Coefficient = Association Strength Value / Sum of Association Strength Values. For example, if an inverter's associated object set contains 15 devices with corresponding association strength values ​​of 1.0, 1.0, 0.6, 0.6, 0.3, etc., and the sum of association strength values ​​is 8.5, then the association weight coefficient for the first associated object is 1.0 / 8.5 = 0.118, the second is 0.118, the third is 0.071, and so on.

[0087] S44, obtain the second health index of each associated object in the associated object set; multiply the second health index of each associated object by the corresponding association weight coefficient and sum them to generate a weighted health mean.

[0088] The system obtains the second health index of each associated object in the associated object set, and multiplies each associated object's second health index by its corresponding association weight coefficient, then sums the results to generate a weighted health mean. The weighted health mean is a comprehensive indicator reflecting the overall health status of the associated equipment of the inspected object, and can reflect the degree of influence of the health status of the associated equipment on the target equipment. The calculation formula is: Weighted Health Mean = Σ(Associated Object Second Health Index × Association Weight Coefficient). Continuing the previous example, assuming the second health indices of each device in the inverter's associated object set are 85, 78, 92, 65, 71, etc., then the weighted health mean is 85×0.118 + 78×0.118 + 92×0.071 + 65×0.071 + 71×0.059 + ... = 76.3 points.

[0089] S45, calculate the difference between the weighted health mean and the preset health benchmark, and determine the correlation transmission correction amount based on the difference; wherein, when the weighted health mean is lower than the preset health benchmark, the correlation transmission correction amount is negative; when the weighted health mean is not lower than the preset health benchmark, the correlation transmission correction amount is zero or positive; sum the second health index and the correlation transmission correction amount to generate the target health index for each inspection object.

[0090] The system determines the correlation correction amount by calculating the difference between the weighted health mean and the preset health benchmark. The preset health benchmark is a reference threshold used to judge whether the overall health status of the associated equipment has a negative impact on the target equipment, and is usually set at 70-75 points. When the weighted health mean is lower than the preset health benchmark, it indicates that the poor health status of the associated equipment will have a negative transmission effect on the target equipment, the correlation correction amount is negative, and the health index of the target equipment needs to be reduced. When the weighted health mean is not lower than the preset health benchmark, it indicates that the health status of the associated equipment is good or has a positive supporting effect on the target equipment, and the correlation correction amount is zero or positive.

[0091] The correlation conduction correction is calculated using a piecewise linear function. When the weighted health mean is lower than the preset health benchmark, the correction is calculated as (weighted health mean - preset health benchmark) × conduction sensitivity coefficient, which is typically set between 0.15 and 0.25. When the weighted health mean is higher than the preset health benchmark, the correction is calculated as min((weighted health mean - preset health benchmark) × 0.1, 5), meaning there is an upper limit to the positive correction. For example, if the preset health benchmark is 72 points and the conduction sensitivity coefficient is 0.2, then the correlation conduction correction for this inverter is (76.3 - 72) × 0.1 = 0.43 points.

[0092] The final target health index is generated by summing the second health index and the associated conduction correction. Assuming the inverter's second health index is 68 points, the target health index is 68 + 0.43 = 68.43 points. This correlation-based correction mechanism accurately reflects the health status of the equipment within the electrical system, avoiding the biases that may arise from isolated assessments.

[0093] Based on the above embodiments, as an optional implementation, in S45, determining the correlation transmission correction amount based on the difference specifically includes S451-S453: S451. Based on the numerical range of the difference, determine the risk transmission level and obtain the transmission impact coefficient corresponding to the risk transmission level.

[0094] The system determines the risk transmission level based on the range of the difference between the weighted health mean and the preset health benchmark, and obtains the corresponding transmission impact coefficient. The risk transmission level is a grading standard used to describe the degree of impact of the health status of related equipment on the target equipment. Based on extensive historical data analysis and expert experience, the system has established a complete grading system covering both negative and positive transmission. For negative transmission, i.e., when the weighted health mean is lower than the preset health benchmark, the system sets four risk transmission levels: slight negative transmission (-10 to -5 points), indicating that the health status of related equipment is slightly lower than expected but the impact is limited; moderate negative transmission (-20 to -10 points), indicating that the health problems of related equipment are beginning to have a significant impact on the target equipment; severe negative transmission (-35 to -20 points), indicating that the health problems of related equipment have a significant negative impact on the target equipment; and extreme negative transmission (below -35 points), indicating that the health status of related equipment is extremely poor and may trigger a chain reaction of failures.

[0095] For positive transmission scenarios, where the weighted average health value exceeds the preset health benchmark, the system sets three risk transmission levels: Slight positive transmission (5-15 points), indicating good health of the associated equipment and providing moderate support; Moderate positive transmission (15-25 points), indicating excellent health of the associated equipment and providing strong support; and Saturated positive transmission (above 25 points), indicating excellent health of the associated equipment but with the positive impact nearing saturation. The transmission impact coefficients corresponding to each risk transmission level are: Slight negative transmission 0.10, Moderate negative transmission 0.18, Severe negative transmission 0.25, Extreme negative transmission 0.35; Slight positive transmission 0.06, Moderate positive transmission 0.10, Saturated positive transmission 0.12. This increasing transmission impact coefficient setting reflects the non-linear characteristics of the impact intensity of different health levels on equipment.

[0096] For example, a box-type transformer has a weighted average health score of 58, a preset health benchmark score of 72, and a difference of -14, which falls under the moderate negative conduction level, with a corresponding conduction influence coefficient of 0.18. Another inverter has a weighted average health score of 89, a preset health benchmark score of 72, and a difference of 17, which falls under the moderate positive conduction level, with a corresponding conduction influence coefficient of 0.10.

[0097] S452, if the weighted health mean is lower than the preset health benchmark, the difference is negative. Multiply the difference by the transmission influence coefficient to generate the correlation transmission correction.

[0098] When the weighted average health value is lower than the preset health benchmark value, the difference will inevitably be negative. The system multiplies the difference by the transmission influence coefficient to generate the associated transmission correction. This negative correction mechanism reflects the transmission impact of the health problems of the associated equipment on the target equipment. A negative correction means that the health index of the target equipment needs to be reduced. The formula for calculating the associated transmission correction is: Associated transmission correction = Difference × Transmission influence coefficient. Since the difference is negative and the transmission influence coefficient is positive, the associated transmission correction is negative. Continuing with the example of the box-type transformer, its associated transmission correction is (-14) × 0.18 = -2.52 points, indicating that due to the poor health of the associated equipment, the second health index of the transformer needs to be reduced by 2.52 points.

[0099] The physical significance of this negative correction lies in the fact that when health problems occur in related equipment electrically connected to the target device, they can adversely affect the target device in various ways. For example, performance degradation of photovoltaic modules can lead to series current mismatch, increasing the operating stress on the inverter; poor contact in the combiner box can generate arcing and heat, affecting the operating environment of adjacent equipment; and monitoring system failures can reduce the observability and controllability of the entire system. Through negative correction, the system can accurately identify these potential risks and reflect them in the health assessment.

[0100] S453, if the weighted health mean is not lower than the preset health benchmark value, then determine whether the difference exceeds the preset redundancy threshold; if the difference exceeds the preset redundancy threshold, then multiply the difference by the transmission influence coefficient to generate the correlation transmission correction amount; if the difference does not exceed the preset redundancy threshold, then set the correlation transmission correction amount to zero.

[0101] When the weighted average health value is not lower than the preset health benchmark, the system needs to further determine whether the difference exceeds a preset redundancy threshold to decide whether to perform a positive correction. The preset redundancy threshold is a critical value used to determine whether the health status of associated devices is sufficiently excellent to generate significant positive support; it is typically set between 5 and 8 points. This threshold is based on the understanding that when the health status of associated devices is only slightly higher than the benchmark, its positive impact is often masked by normal system fluctuations and insufficient to have a substantial positive impact on the target device; only when the health status of associated devices is significantly better than expected can effective health support be provided to the target device.

[0102] If the difference exceeds the preset redundancy threshold, the system multiplies the difference by the conduction influence coefficient to generate a correlation conduction correction. Assuming the preset redundancy threshold is 6 points, and the aforementioned inverter's difference is 17 points, exceeding the redundancy threshold, its correlation conduction correction is 17 × 0.10 = 1.7 points. This means that due to the excellent health of the associated equipment, the inverter's second health index can be increased by 1.7 points. This positive correction reflects the supporting role of high-quality associated equipment for the target equipment. For example, high-performance photovoltaic modules can provide stable current output, reducing the inverter's regulation burden; a reliable monitoring system can promptly detect anomalies and activate protective measures, reducing the risk of equipment failure.

[0103] If the difference does not exceed the preset redundancy threshold, the system sets the correlation propagation correction amount to zero and does not correct the second health index. For example, if the weighted average health score of a certain junction box is 76 points, the preset health benchmark score is 72 points, and the difference is 4 points, which does not exceed the preset redundancy threshold of 6 points, therefore its correlation propagation correction amount is 0, and the second health index remains unchanged. This approach avoids over-correction due to small positive differences, ensuring the rationality and stability of health index correction.

[0104] S105: Obtain the first position information of multiple inspection robots and the second position information of the target inspection object, and combine the first position information and the second position information to generate an inspection plan for each inspection robot.

[0105] In practical implementation, the system first acquires the initial location information of multiple inspection robots and the secondary location information of the target inspection objects through a real-time positioning system. The initial location information refers to the current geographic coordinates of each inspection robot, including latitude and longitude coordinates, elevation information, and spatial parameters such as the robot's orientation angle. The system ensures the accuracy and real-time nature of the location information by fusing multiple positioning technologies, including GPS positioning, inertial navigation, and visual positioning. For example, an infrared thermal imaging inspection robot may currently be located at (X1=1250m, Y1=850m) in the power plant coordinate system, with an orientation angle of 45 degrees northeast, a battery level of 75%, and a remaining runtime of approximately 4 hours. The secondary location information refers to the fixed geographic coordinates of each target inspection object within the power plant. This location information is obtained through the power plant's digital map and equipment ledger system. The system has established a spatial database containing the precise coordinates of all equipment, with each target inspection object having a corresponding coordinate identifier and equipment type label.

[0106] After obtaining location information, the system combines the first and second location information to calculate the spatial distance between each inspection robot and each target inspection object. The spatial distance calculation considers not only straight-line distance but also the actual travel conditions within the power plant, such as the road network, terrain obstacles, and restricted areas. The system employs an improved A* path search algorithm, based on the power plant's digital topographic map and road network map, to calculate the actual shortest path distance and estimated travel time for each robot to reach each target location. For example, the aforementioned infrared thermal imaging inspection robot has a straight-line distance of 800 meters to a certain inverter target inspection object, but considering the need to bypass the photovoltaic array and avoid wetland areas, the actual travel distance is 1200 meters, and the estimated travel time is 15 minutes. The system establishes a distance matrix and a time matrix for each inspection robot to reach all target inspection objects, providing basic data for subsequent task allocation.

[0107] Next, the system determines the inspection priority based on the target health index of each target inspection object. The inspection priority is a task execution order level based on the urgency of the equipment's health status. The target health index is inversely proportional to the inspection priority; that is, equipment with a lower health index has a higher inspection priority. The system divides the target health index into four priority ranges: urgent, high, medium, and low. Equipment with a target health index below 50 is designated as urgent priority, 50-60 as high priority, 60-70 as medium priority, and equipment with an index above 70 but still identified as a target inspection object is designated as low priority. Urgent priority equipment must be inspected within 2 hours, high priority equipment within 4 hours, medium priority equipment within 8 hours, and low priority equipment can be scheduled for inspection within 24 hours. This priority setting ensures that critical potential faults can be detected and addressed promptly.

[0108] After determining the inspection priority, the system calculates the task matching degree of each inspection robot for each target inspection object by combining spatial distance and inspection priority. Task matching degree is a comprehensive evaluation index that considers distance cost, time urgency, and robot capability adaptability. The system uses a multi-objective optimization algorithm to weight and fuse distance factors, priority factors, and capability matching factors. The distance factor is expressed as the reciprocal of distance; the closer the distance, the higher the matching degree. The priority factor converts priority levels into numerical weights: urgent priority has a weight of 4.0, high priority 3.0, medium priority 2.0, and low priority 1.0. The capability matching factor considers the degree of matching between the robot's functional characteristics and the inspection task requirements; infrared thermal imaging robots have a high matching degree for electrical equipment inspection, and visible light inspection robots have a high matching degree for mechanical equipment inspection. The formula for calculating task matching degree is: Matching degree = (1 / distance) × priority weight × capability matching coefficient × α, where α is a comprehensive adjustment factor, usually set to 100 for easy numerical processing.

[0109] Based on the task matching degree calculation results, the system uses task allocation algorithms such as the Hungarian algorithm to assign each target inspection object to the corresponding inspection robot, generating a target inspection object list for each inspection robot. During the task allocation process, the system ensures that each target inspection object is assigned to the robot with the highest task matching degree, while ensuring that the workload of each robot is relatively balanced. The target inspection object list is a list of equipment that each inspection robot needs to complete inspection tasks, including detailed information such as equipment number, location coordinates, priority, and estimated inspection time. For example, the target inspection object list assigned to an infrared thermal imaging inspection robot includes 8 inverters, 12 combiner boxes, and 3 box-type transformers, totaling 23 inspection tasks.

[0110] Finally, for each inspection robot's target inspection object list, the system plans the inspection path sequence based on the inspection priority and second location information of each target inspection object. Path planning employs an improved traveling salesman problem algorithm to find the inspection sequence with the shortest total path distance while satisfying priority constraints. The system first sorts the target inspection objects according to priority, ensuring that urgent and high-priority equipment is inspected first. Then, among equipment of equal priority, it optimizes the inspection path sequence using a combination of nearest neighbor and genetic algorithms. Path planning also considers the robot's endurance and charging station locations. When the estimated inspection path exceeds the single-charge endurance, the system inserts a return-to-charging node into the path to ensure the continuity of task execution.

[0111] By comprehensively considering dynamic factors such as robot battery status, weather conditions, and equipment accessibility, the system generates a complete inspection plan for each inspection robot. The inspection plan includes detailed time arrangements, path planning, task priorities, and estimated completion times. For example, the inspection plan for the aforementioned infrared thermal imaging inspection robot shows that it will first inspect three emergency priority inverters from 8:00 to 8:30, then inspect five high priority combiner boxes from 8:30 to 10:00, return to the charging station to replenish power from 10:00 to 10:30, and complete the inspection of the remaining medium and low priority equipment from 10:30 to 12:00.

[0112] Based on the above embodiments, as an optional implementation, in S105, generating the inspection plan for each inspection robot by combining the first position information and the second position information specifically includes S51-S54: S51, combining the first position information and the second position information, calculate the spatial distance between each inspection robot and each target inspection object.

[0113] The system combines the first and second location information to calculate the spatial distance between each inspection robot and each target inspection object. The first location information refers to the real-time position coordinates of each inspection robot, obtained through multi-sensor fusion technology including GPS positioning system, inertial navigation system, and LiDAR, encompassing three-dimensional spatial information such as latitude and longitude coordinates, altitude, and orientation angle. The second location information refers to the fixed installation position coordinates of each target inspection object. This information was determined and entered into the system database during the power plant construction phase, including the precise geographical coordinates, installation height, and orientation angle of each device.

[0114] The spatial distance calculation employs an improved Euclidean distance algorithm, taking into account the constraints of the actual terrain and travel paths of the photovoltaic power station. The system first calculates the straight-line distance between each inspection robot and each target inspection object using the formula: Straight-line distance = √[(x2-x1)²+(y2-y1)²+(z2-z1)²], where (x1, y1, z1) are the robot's position coordinates, and (x2, y2, z2) are the target object's position coordinates. Then, based on the power station's internal road network and terrain obstacle distribution, the actual traversable distance is calculated using a path planning algorithm. The system maintains a complete power station road network map, including paths of different levels such as main roads, equipment access roads, and maintenance access roads, and marks path attributes such as slope, turning radius, and travel width.

[0115] S52, determine the inspection priority of each target inspection object based on the target health index of each target inspection object; wherein, the target health index is inversely proportional to the inspection priority.

[0116] The system determines the corresponding inspection priority based on the target health index of each target inspection object. The target health index and inspection priority are inversely proportional. Inspection priority is a numerical indicator representing the urgency and importance of equipment inspection, used to guide the priority allocation and scheduling decisions of inspection resources. This inverse relationship is based on the following logic: a lower target health index indicates a worse equipment health condition and higher potential risks, thus requiring a higher inspection priority to ensure timely detection and handling of problems; conversely, a higher target health index indicates a better equipment operating condition, allowing for a more appropriate reduction in inspection priority to conserve resources.

[0117] The system establishes a mapping function from the target health index to the inspection priority: Inspection Priority = 100 - Target Health Index × Adjustment Factor, where the adjustment factor is typically set to 0.85-0.95 to control the distribution range and sensitivity of the priority. To avoid excessive concentration of priorities, the system also sets priority range divisions: Priority 80-100 is the emergency level, corresponding to devices with a target health index of 0-25, requiring immediate inspection; Priority 60-79 is the high level, corresponding to devices with a target health index of 26-50, requiring inspection within the same day; Priority 40-59 is the medium level, corresponding to devices with a target health index of 51-75, which can be inspected within 2-3 days; Priority 20-39 is the low level, corresponding to devices with a target health index of 76-100, which can be inspected within one week.

[0118] S53, combining spatial distance and inspection priority, calculate the task matching degree of each inspection robot to each target inspection object; based on the task matching degree, assign each target inspection object to the corresponding inspection robot, and generate a target inspection object list for each inspection robot.

[0119] The system calculates the task matching degree of each inspection robot to each target inspection object by combining spatial distance and inspection priority, and then allocates tasks based on the matching degree. The task matching degree is a comprehensive evaluation index that takes into account the urgency of inspection and the execution cost, and is used to determine which robot is most suitable to perform the inspection task for a specific target. The higher the matching degree, the greater the overall benefit of the robot in performing the task.

[0120] The formula for calculating task matching degree is: Task matching degree = α × (Inspection priority / 100) + (1 - α) × (1 - Spatial distance / Maximum distance), where α is a weighting coefficient, usually set to 0.6-0.7, indicating that the importance of inspection priority is relatively higher than that of spatial distance. (1 - Spatial distance / Maximum distance) in the formula is a normalization process for the distance factor, ensuring that the closer the distance, the higher the matching degree. Maximum distance refers to the maximum spatial distance from all robots to all target objects within the current scheduling cycle, used for normalization.

[0121] For example, if the distance from robot A to inverter 1 is 500 meters, inverter 1's inspection priority is 85, its maximum distance is 2000 meters, and its weighting coefficient α = 0.65, then the task matching degree = 0.65 × (85 / 100) + 0.35 × (1 - 500 / 2000) = 0.55 + 0.26 = 0.81. The system calculates the task matching degree of each robot to all target objects, forming a matching degree matrix.

[0122] Based on the task matching degree matrix, the system employs an improved Hungarian algorithm for task allocation to ensure the achievement of the global optimization objective. The core idea of ​​the algorithm is to find the allocation scheme that maximizes the total matching degree, while considering robot workload balancing and inspection time window constraints. The system also includes a dynamic adjustment mechanism: when a robot's task list becomes too heavy, some low-priority tasks are transferred to robots with lighter workloads. Finally, a target inspection object list is generated for each inspection robot, clearly defining the specific equipment list that each robot is responsible for inspecting.

[0123] S54: For each inspection robot, based on the list of target inspection objects, the inspection path sequence is planned according to the inspection priority and second location information of each target inspection object, and an inspection plan for each inspection robot is generated.

[0124] The system plans the inspection path sequence for each inspection robot based on its target inspection object list, the inspection priority of each target inspection object, and its second location information, generating the final inspection plan. Optimizing the inspection path sequence is a combinatorial optimization problem with multiple objectives, requiring minimizing the total inspection time and movement cost while satisfying priority constraints.

[0125] The system employs an improved Traveling Salesman Problem (TSP) algorithm to solve for the optimal inspection path, combining priority constraints and time window constraints for multi-objective optimization. The algorithm first sorts target objects according to inspection priority, ensuring that high-priority devices are inspected first. Then, while satisfying the priority constraints, it optimizes the path order using a genetic algorithm or simulated annealing algorithm to minimize the total travel distance and inspection time. The system also considers practical constraints such as the robot's battery life, inspection time window, and weather conditions to ensure the feasibility of the inspection plan.

[0126] For example, robot B is assigned 8 target objects with priorities of 85, 78, 72, 65, 58, 45, 32, and 28. The system first sorts them by priority, and then, while ensuring that the first 5 high-priority objects are inspected first, determines the specific inspection order through a path optimization algorithm. The final generated inspection plan includes complete information such as the inspection order, estimated arrival time, inspection content, and contingency plan for handling anomalies.

[0127] Figure 2 This is a schematic diagram of a sleep / wake-up control structure for an inspection robot provided in an embodiment of this application, as shown below. Figure 2As shown, firstly, multi-dimensional data collection is performed to obtain the ontological characteristics, operating status, and environmental parameters of each inspection object to calculate the first health index. Then, the environmental baseline correction stage is entered, where environmental baseline parameters are introduced to calculate the deviation value, and the first health index is adjusted accordingly to generate the second health index. Next, correlation analysis and screening are performed, using correlation indices characterizing the impact of electrical connections and fault propagation to further adjust the second health index, generating a target health index and screening out target inspection objects whose indices are lower than preset values. Finally, in the location fusion and plan generation stage, the real-time location information of multiple inspection robots and the location information of the target inspection objects are combined to comprehensively plan and generate the final inspection plan, achieving closed-loop control from data perception to intelligent decision-making.

[0128] Based on the above method, this application also discloses a sleep-wake control system for an inspection robot, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of the sleep-wake control system for an inspection robot according to an embodiment of this application. The system includes: a first acquisition module, a combination module, a second acquisition module, a third acquisition module, and a fourth acquisition module; wherein, The first acquisition module is used to acquire the physical characteristic data, operating status data, and environmental parameter data of each inspection object in the photovoltaic power station. The combination module is used to combine the physical characteristic data, operating status data, and environmental parameter data to calculate the first health index of each inspection object. The second acquisition module is used to acquire the environmental baseline parameters of the photovoltaic power station and calculate the deviation value between the first health index and the environmental baseline parameters. Based on the deviation value, the first health index of each inspection object is adjusted to generate the second health index of each inspection object. The third acquisition module is used to acquire the correlation index between each inspection object. Based on the correlation index, the second health index is adjusted to generate the target health index of each inspection object. Inspection objects with a target health index less than a preset index are designated as target inspection objects. The correlation index is used to characterize the electrical connection relationship and the degree of fault propagation impact between each inspection object. The fourth acquisition module is used to acquire the first position information of multiple inspection robots and the second position information of the target inspection object. Based on the first position information and the second position information, the inspection plan of each inspection robot is generated.

[0129] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0130] Please see Figure 4This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0131] The communication bus 1002 is used to realize the connection and communication between these components.

[0132] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0133] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0134] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1001 and may be implemented as a separate chip.

[0135] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 4 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a sleep-wake control method for an inspection robot.

[0136] exist Figure 4 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call an application program stored in the memory 1005 for a sleep-wake control method for an inspection robot. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0137] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more of the methods described in the above embodiments.

[0138] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0139] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0143] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 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 application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0144] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for controlling the sleep and wake-up of an inspection robot, characterized in that, The method includes: Acquire the physical characteristics, operational status, and environmental parameters of each inspection object in the photovoltaic power station; By combining the ontological feature data, the operational status data, and the environmental parameter data, a first health index is calculated for each of the inspected objects; Obtain the environmental baseline parameters of the photovoltaic power station, calculate the deviation value between the first health index and the environmental baseline parameters; adjust the first health index of each of the inspection objects according to the deviation value, and generate the second health index of each of the inspection objects; Obtain the correlation index between each of the inspection objects, adjust the second health index according to the correlation index, generate the target health index for each of the inspection objects, and take the inspection objects whose target health index is less than the preset index as the target inspection objects. The correlation index is used to characterize the electrical connection relationship and the degree of fault propagation influence between each of the inspection objects. The first location information of multiple inspection robots and the second location information of the target inspection object are obtained. The inspection plan of each inspection robot is generated by combining the first location information and the second location information.

2. The sleep-wake control method for the inspection robot according to claim 1, characterized in that, The step of combining the ontological feature data, the operational status data, and the environmental parameter data to calculate the first health index of each of the inspected objects includes: Calculate the first deviation value between each feature parameter in the body feature data and the corresponding standard feature parameter; sum the first deviation values ​​according to the first preset weight to generate the equipment aging coefficient of each of the inspection objects; Calculate the second deviation value between each operating parameter in the operating status data and the corresponding standard operating parameter; sum the second deviation values ​​according to the second preset weight to generate the operating anomaly coefficient of each of the inspection objects; Calculate the third deviation value between each environmental parameter in the environmental parameter data and the corresponding standard environmental parameter; sum the third deviation values ​​according to the third preset weight to generate the environmental impact coefficient of each of the inspection objects; By combining the equipment aging coefficient, the operational anomaly coefficient, and the environmental impact coefficient, the first health index of each of the inspected objects is calculated.

3. The sleep-wake control method for the inspection robot according to claim 2, characterized in that, The calculation of the first health index for each of the inspected objects, combining the equipment aging coefficient, the operational anomaly coefficient, and the environmental impact coefficient, includes: Based on the equipment aging coefficient, determine the aging level of each of the inspection objects, and obtain the initial health index corresponding to the aging level; Calculate the correlation parameter between the operational anomaly coefficient and the environmental impact coefficient; determine the synergy intensity level based on the relationship between the correlation parameter and a preset synergy threshold; obtain the corresponding synergy coefficient based on the synergy intensity level; the correlation coefficient is used to characterize the degree of mutual influence between operational anomalies and environmental factors. Based on the synergistic effect coefficient, the operational anomaly coefficient and the environmental impact coefficient are corrected respectively, and a first correction coefficient and a second correction coefficient are generated accordingly. The first correction coefficient and the second correction coefficient are weighted and summed to generate a comprehensive impact value; the initial health index is adjusted according to the comprehensive impact value to generate a first health index for each of the inspection objects; wherein the initial health index is negatively correlated with the comprehensive impact value.

4. The sleep-wake control method for the inspection robot according to claim 1, characterized in that, The step of adjusting the first health index of each of the inspected objects based on the deviation value and generating a second health index for each of the inspected objects includes: Based on the numerical range of the deviation values, the environmental deviation level of each of the inspected objects is determined; Obtain the environmental correction factor corresponding to the environmental deviation level; The environmental correction factor is multiplied by the first health index to generate an environmentally adjusted health index. If the deviation value exceeds the preset deviation threshold, an additional attenuation is calculated based on the difference between the deviation value and the preset deviation threshold; the additional attenuation is subtracted from the environmentally adjusted health index to generate a second health index for each of the inspected objects; if the deviation value does not exceed the preset deviation threshold, the environmentally adjusted health index is used as the second health index for each of the inspected objects.

5. The sleep-wake control method for an inspection robot according to claim 1, characterized in that, The step of adjusting the second health index based on the correlation index to generate a target health index for each of the inspection objects includes: Obtain the electrical topology relationship between each of the inspected objects, and determine the set of associated objects for each of the inspected objects based on the electrical topology relationship; Calculate the association strength value between each of the inspection objects and each associated object in the corresponding set of associated objects; the association strength value is determined according to the level and type of electrical connection, wherein the association strength value of a direct electrical connection is greater than the association strength value of an indirect electrical connection. The association strength value corresponding to each of the associated objects is normalized to generate the association weight coefficient corresponding to each of the associated objects. Obtain the second health index of each of the associated objects in the set of associated objects; multiply the second health index of each of the associated objects by the corresponding association weight coefficient and sum them to generate a weighted health mean; Calculate the difference between the weighted health mean and the preset health benchmark value, and determine the correlation transmission correction amount based on the difference; wherein, when the weighted health mean is lower than the preset health benchmark value, the correlation transmission correction amount is negative; when the weighted health mean is not lower than the preset health benchmark value, the correlation transmission correction amount is zero or positive. The second health index is summed with the associated transmission correction amount to generate the target health index for each of the inspection objects.

6. The sleep-wake control method for the inspection robot according to claim 5, characterized in that, The step of determining the correlation transmission correction amount based on the difference includes: Based on the numerical range of the difference, the risk transmission level is determined, and the transmission impact coefficient corresponding to the risk transmission level is obtained; If the weighted health mean is lower than the preset health benchmark value, the difference is negative. The difference is multiplied by the transmission influence coefficient to generate the correlation transmission correction amount. If the weighted health mean is not lower than the preset health benchmark value, then it is determined whether the difference exceeds the preset redundancy threshold; if the difference exceeds the preset redundancy threshold, then the difference is multiplied by the transmission influence coefficient to generate the correlation transmission correction amount; if the difference does not exceed the preset redundancy threshold, then the correlation transmission correction amount is set to zero.

7. The sleep-wake control method for an inspection robot according to claim 1, characterized in that, The step of combining the first location information and the second location information to generate an inspection plan for each of the inspection robots includes: By combining the first location information and the second location information, the spatial distance between each inspection robot and each target inspection object is calculated. Based on the target health index of each target inspection object, the inspection priority of each target inspection object is determined; wherein, the target health index is inversely proportional to the inspection priority. Combining the spatial distance and the inspection priority, calculate the task matching degree of each inspection robot to each target inspection object; according to the task matching degree, assign each target inspection object to the corresponding inspection robot to generate a target inspection object list for each inspection robot. For each inspection robot, based on the target inspection object list, the inspection path sequence is planned according to the inspection priority and second location information of each target inspection object, and an inspection plan for each inspection robot is generated.

8. A sleep / wake-up control system for an inspection robot, characterized in that, The system includes: a first acquisition module, a combination module, a second acquisition module, a third acquisition module, and a fourth acquisition module; wherein, The first acquisition module is used to acquire the physical characteristic data, operating status data and environmental parameter data of each inspection object in the photovoltaic power station; The combining module is used to combine the ontological feature data, the operational status data, and the environmental parameter data to calculate the first health index of each of the inspection objects. The second acquisition module is used to acquire the environmental baseline parameters of the photovoltaic power station, calculate the deviation value between the first health index and the environmental baseline parameters, adjust the first health index of each of the inspection objects according to the deviation value, and generate the second health index of each of the inspection objects. The third acquisition module is used to acquire the correlation index between each of the inspection objects, adjust the second health index according to the correlation index, generate the target health index of each of the inspection objects, and take the inspection objects whose target health index is less than the preset index as the target inspection objects. The correlation index is used to characterize the electrical connection relationship and the degree of fault transmission influence between each of the inspection objects. The fourth acquisition module is used to acquire the first location information of multiple inspection robots and the second location information of the target inspection object, and to generate an inspection plan for each inspection robot by combining the first location information and the second location information.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1-7.

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