Photovoltaic construction resource dynamic optimization configuration management platform

Abstract

The invention relates to the technical field of photovoltaic construction management, and discloses a photovoltaic construction resource dynamic optimization configuration management platform. The platform comprises a processor, a resource monitoring module, a dynamic configuration optimization module, a configuration efficiency monitoring module and a resource early warning module. The resource monitoring module tracks the construction process of a plurality of photovoltaic construction sites, the process is divided into material preparation, assembly installation and grid-connected debugging stages, each stage is propelled according to a preset standard, and abnormal signals are generated and transmitted to related modules. The resource early warning module receives the abnormal signal and then carries out visual prompt and activates an audible and visual alarm; and the dynamic configuration optimization module adaptively adjusts the resource allocation scheme of the corresponding construction site. And configuring an efficiency monitoring module to monitor the construction from the regulation and control starting point until the construction is recovered to normal, and judging whether resource regulation and control are qualified or not and counting by comparing the actual regulation and control duration with the preset regulation and control time limit. According to the platform, dynamic management and optimal configuration of photovoltaic construction resources are realized, and the construction process is helped to be efficiently propelled.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic construction management, and in particular to a photovoltaic construction resource dynamic optimization configuration management platform. Background Art

[0002] Amid the rapid growth of photovoltaic energy, the scale of photovoltaic power station construction continues to expand, and construction scenarios are becoming increasingly complex. The diverse resources involved are becoming increasingly complex, and their deployment is becoming significantly more difficult. Currently, resource management during photovoltaic construction relies heavily on traditional manual planning and empirical judgment, making it difficult to adapt to the dynamic demands of the construction process. During the material preparation phase, the transportation and scheduling of photovoltaic panels and supporting electrical equipment lacked real-time monitoring, leading to frequent material supply delays and excessive stockpiling. Control of the storage environment also relied heavily on manual inspections, making fluctuations in parameters like temperature, humidity, and ventilation difficult to detect in a timely manner. This could potentially damage material properties and impact subsequent construction quality. During the component installation phase, progress varied widely across workstations, and the allocation of equipment and personnel lacked a flexible mechanism for adjustment. This led to an imbalance where some workstations were idle while others were in short supply, slowing the overall pace of construction. During the grid-connection commissioning phase, a critical step in construction, system performance testing often faces issues such as insufficient equipment compatibility and a shortage of skilled personnel due to improper early resource allocation, leading to extended commissioning cycles. Furthermore, when anomalies arise during various stages of construction, traditional management models lack a rapid response mechanism. The transmission paths for abnormal signals are ambiguous, and resource adjustment plans are delayed and lack specificity, making it difficult to restore normal construction order in a short period of time. Furthermore, the lack of quantitative standards for evaluating the effectiveness of resource regulation makes it impossible to accurately judge the effectiveness of regulatory measures, leading to an inefficient cycle in resource management and hindering the overall efficiency and quality stability of photovoltaic construction. Summary of the Invention

[0003] The purpose of the present invention is to provide a photovoltaic construction resource dynamic optimization configuration management platform to solve the problems raised in the above background technology.

[0004] To achieve the above objectives, the present invention provides a photovoltaic construction resource dynamic optimization configuration management platform, the platform comprising: a processor, a resource monitoring module, a dynamic configuration optimization module, a configuration efficiency monitoring module and a resource early warning module; The resource monitoring module continuously tracks the construction progress of multiple photovoltaic construction sites. The construction process is divided into a material preparation stage, a component installation stage, and a grid-connected commissioning stage. In the material preparation stage, photovoltaic panels and supporting electrical equipment are transported to a designated construction site warehouse and the storage environment is monitored. When the storage environment parameters meet the preset preparation standards, the component installation stage begins. Mechanical fixation and electrical connection of the photovoltaic array are completed at designated workstations. After the continuous construction reaches the preset working hours, the grid-connected commissioning stage begins to perform system performance testing. The resource monitoring module generates material preparation abnormality signals, component installation abnormality signals, or grid connection debugging abnormality signals through analysis, and transmits the abnormality signals to the dynamic configuration optimization module and the resource early warning module via the processor; the resource early warning module provides a visual prompt for the received abnormality signals and activates an audible and visual alarm; and the dynamic configuration optimization module performs adaptive adjustments to the resource allocation plan for the corresponding construction site upon receiving the abnormality signals; The configuration efficiency monitoring module collects the moment when the dynamic configuration optimization module responds to the abnormal signal as the control starting point, and continues monitoring until the construction process returns to normal to determine the actual control time. When the actual control time is lower than the preset control time limit, the resource control is judged to be qualified and the qualified count is accumulated. When the actual control time exceeds the preset control time limit, the resource control is judged to be unqualified and the unqualified count is accumulated.

[0005] Preferably, the operation logic of the resource monitoring module includes: During the material preparation phase, the temperature fluctuation value, humidity deviation value, and material turnover rate of the construction site warehouse are obtained in real time. The difference between the temperature fluctuation value and the standard preparation temperature is marked as the temperature change anomaly. The humidity deviation anomaly and turnover rate anomaly are simultaneously calculated. If the temperature change anomaly, humidity deviation anomaly, or turnover rate anomaly exceeds the corresponding threshold, a material preparation anomaly signal is generated. During the component installation phase, the deviation between the number of construction personnel configured and the standard number is monitored and marked as the manpower deviation value. The equipment failure rate and the engineering vehicle scheduling delay rate are collected, and the difference between the equipment failure rate and the baseline failure rate is marked as the equipment abnormality. The scheduling delay abnormality is also calculated simultaneously. If the manpower deviation value, equipment abnormality, or scheduling delay abnormality exceeds the corresponding threshold, a component installation abnormality signal is generated. During the grid-connected debugging phase, the grid access delay duration, the inverter output fluctuation value, and the detection equipment calibration error are obtained, and the difference between the grid access delay duration and the planned duration is marked as the grid-connected delay degree. The output fluctuation abnormality degree and the calibration error abnormality degree are calculated simultaneously; if the grid-connected delay degree, the output fluctuation abnormality degree, or the calibration error abnormality degree exceeds the corresponding threshold, a grid-connected debugging abnormality signal is generated.

[0006] Preferably, the processor is connected to a construction fluency assessment module, which performs the following operations: counting the frequency of occurrence of various abnormal signals within the target construction period and marking them as abnormal frequency indicators, while obtaining the qualified count and unqualified count recorded by the configuration efficiency monitoring module, and marking the quotient of the unqualified count and the qualified count as the control failure coefficient; processing the abnormal frequency indicator and the control failure coefficient through a weighted fusion algorithm to generate a construction fluency evaluation value, and when the construction fluency evaluation value exceeds a preset fluency threshold, generating a construction abnormality warning signal and transmitting it to the resource early warning module.

[0007] Preferably, the processor is connected to a cross-site resource analysis module, which performs the following operations: setting a monitoring period, summarizing the resource scheduling records and abnormal response records of all photovoltaic sites within the monitoring period, and generating a global warning signal or a global normal signal through multi-dimensional data analysis; the global warning signal is transmitted to the resource warning module via the processor and triggers a cross-site resource allocation instruction.

[0008] Preferably, the processing flow of the cross-construction site resource analysis module includes: calculating the periodic efficiency index through the construction efficiency evaluation algorithm, and at the same time collecting the number of construction abnormality warning signals generated by the construction fluency evaluation module, and marking the ratio of the number of signals to the total number of construction projects in the period as the proportion of abnormal projects; inputting the periodic efficiency index and the proportion of abnormal projects into the resource aggregation analysis model to generate a global risk value; activating the global warning signal when the global risk value exceeds the preset risk critical value.

[0009] Preferably, the construction efficiency evaluation algorithm performs the following steps: determining whether the construction of each construction site meets the resource utilization standard, marking the ratio of the number of non-compliant construction sites to the total number of construction sites as an inefficient construction coefficient, extracting the deviation between the actual resource consumption and the planned resource consumption of each non-compliant construction site and marking it as resource dissipation, and performing mean calculation on the resource dissipation of all construction sites to obtain a periodic dissipation index; and inputting the inefficient construction coefficient and the periodic dissipation index into the efficiency calculation model to generate the periodic efficiency index.

[0010] Preferably, the judgment logic of the resource utilization compliance standard is: obtain the planned human resource input and the actual human resource input of the target construction site, and mark the percentage of the actual input to the planned input as the human resource matching rate; when the human resource matching rate is lower than the preset matching threshold, the construction site is judged to be a substandard construction site.

[0011] Preferably, the server is communicatively connected to the equipment efficiency diagnosis module. When the cross-site resource analysis module generates a global normal signal, the server forwards the global normal signal to the equipment efficiency diagnosis module. After receiving the signal, the equipment efficiency diagnosis module performs an operating status analysis on the construction machinery used in all photovoltaic construction sites within the specified construction period, generates an equipment efficiency unqualified signal based on the analysis results, and transmits it to the resource early warning module via the server.

[0012] Preferably, the equipment performance diagnosis module performs the following steps: deploying a number of sensor nodes at key components of the construction machinery, collecting real-time working parameters of each node during construction, calculating the mean of the parameters of all nodes to obtain a baseline performance value, marking the absolute deviation between the real-time parameters of each node and the baseline performance value as a node abnormality; and marking a node as a defective node if the node abnormality exceeds a preset abnormality threshold; The total operating time of the construction machinery during the monitoring period is accumulated, the number of times each sensor node is marked as a defective node is counted and marked as the node abnormality frequency, and the ratio of the node abnormality frequency to the total operating time is marked as the node failure ratio; the nodes are divided into high-risk defective nodes, medium-risk defective nodes or low-risk defective nodes through the classification judgment rules; if there are high-risk defective nodes, an equipment performance failure signal is generated; if there are no high-risk defective nodes, the ratio of the number of medium-risk defective nodes to the number of low-risk defective nodes is marked as the equipment risk value, and when the equipment risk value exceeds the preset risk threshold, an equipment performance failure signal is generated.

[0013] Preferably, the grading judgment rule is: if the node failure ratio exceeds the preset failure ratio upper limit, it is judged as a high-risk defective node; if the node failure ratio is lower than the preset failure ratio lower limit, it is judged as a low-risk defective node; if the node failure ratio is within the preset failure ratio range, it is judged as a medium-risk defective node.

[0014] Compared with the prior art, the present invention has the following beneficial effects: Through the coordinated operation of multiple modules, refined resource management is achieved throughout the entire photovoltaic construction process. The resource monitoring module continuously tracks the three phases of the construction process, incorporating key aspects of each stage into the monitoring scope, allowing for timely capture of various issues during material preparation, component installation, and grid-connected commissioning. During the material preparation phase, real-time monitoring of the storage environment prevents environmentally-induced material problems from impacting subsequent construction. During the component installation phase, continuous monitoring of workstation construction can promptly identify irregularities in the construction rhythm. System performance monitoring and tracking during the grid-connected commissioning phase helps to identify potential issues early on. When anomalies occur at any stage, the resource monitoring module generates anomaly signals that are simultaneously transmitted to the dynamic configuration optimization module and the resource early warning module, forming a dual mechanism for response. The resource early warning module's visual prompts and audible and visual alarms quickly alert relevant personnel to anomalies, facilitating timely intervention and resolution. Upon receiving anomaly signals, the dynamic configuration optimization module adaptively adjusts the resource allocation plan for the corresponding construction site. These adjustments, based on real-time anomalies, better align with actual construction needs and effectively alleviate resource allocation imbalances. The configuration efficiency monitoring module quantitatively evaluates resource control effectiveness by recording the control start point and actual control duration. The accumulated pass and fail counts provide a visual basis for optimizing resource management strategies. This closed-loop monitoring and evaluation mechanism clearly demonstrates the effectiveness of resource control measures, contributing to the continuous improvement of resource allocation models. This ensures more efficient resource utilization during photovoltaic construction, reduces resource waste and idleness, and streamlines the transition between construction phases, improving the consistency and stability of the construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a working principle diagram of the photovoltaic construction resource dynamic optimization configuration management platform of the present invention; Figure 2 Flowchart for abnormal signal generation of resource monitoring module; Figure 3 Flowchart for global signal generation across the site resource analysis module; Figure 4 Flowchart for risk value calculation for cross-site resource analysis module; Figure 5 Flowchart triggered by the equipment performance diagnosis module. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] See also Figure 1 The present invention provides a photovoltaic construction resource dynamic optimization configuration management platform, which includes: a processor, a resource monitoring module, a dynamic configuration optimization module, a configuration efficiency monitoring module, and a resource early warning module. The specific implementation is as follows: The resource monitoring module continuously tracks the construction progress of multiple photovoltaic construction sites, which is divided into the material preparation phase, the module installation phase, and the grid-connected commissioning phase. During the material preparation phase, the photovoltaic panels and supporting electrical equipment are transported to a designated on-site warehouse, where the storage environment is monitored. Once the storage environment parameters meet the preset preparation standards, the module installation phase begins. Mechanical mounting and electrical connections are completed at designated workstations. After the preset construction hours have been met, the grid-connected commissioning phase begins, where system performance testing is performed.

[0018] The resource monitoring module generates signals indicating abnormal material preparation, component installation, or grid connection and commissioning. These signals are then transmitted via a processor to the dynamic configuration optimization module and the resource early warning module. The resource early warning module displays these signals visually and activates audible and visual alarms. Upon receiving these signals, the dynamic configuration optimization module adaptively adjusts the resource allocation plan for the corresponding construction site.

[0019] The configuration efficiency monitoring module collects the moment when the dynamic configuration optimization module responds to an abnormal signal as the control starting point, and continuously monitors until the construction process returns to normal to determine the actual control duration. If the actual control duration is less than the preset control time limit, the resource control is considered qualified and the qualified count is accumulated; if the actual control duration exceeds the preset control time limit, the resource control is considered unqualified and the unqualified count is accumulated.

[0020] Example 1: See Figure 2 The operating logic of the resource monitoring module sets targeted monitoring dimensions and abnormal judgment mechanisms according to different stages of construction, so as to achieve precise control of the entire photovoltaic construction process.

[0021] During the material preparation phase, the resource monitoring module uses temperature and humidity sensors, material identification equipment, and a turnover recording system deployed in the construction site warehouse to collect three core parameters in real time: temperature fluctuation, humidity deviation, and material turnover rate. Temperature fluctuation is the difference in warehouse temperature over two consecutive hours. The standard preparation temperature is pre-set based on the storage requirements of the photovoltaic panels and supporting electrical equipment. Temperature fluctuation anomaly is the absolute difference between the temperature fluctuation and the standard preparation temperature. Humidity deviation is the deviation between the actual measured humidity and the standard humidity, and humidity deviation anomaly is the percentage of this deviation to the standard humidity. Material turnover rate is the total number of photovoltaic panels and supporting electrical equipment entering and leaving the warehouse per unit time. Turnover rate anomaly is the difference between the actual turnover rate and the planned turnover rate as a percentage of the planned turnover rate. If the temperature fluctuation anomaly exceeds the set range, the humidity deviation anomaly reaches a certain percentage, or the turnover rate anomaly exceeds a specified percentage, the resource monitoring module immediately generates a material preparation anomaly signal.

[0022] During the component installation phase, the resource monitoring module focuses on construction manpower allocation, equipment operating status, and construction vehicle dispatch efficiency. Construction manpower allocation is determined through a facial recognition attendance system or workstation clock-in records. The standard allocation is determined based on the construction area, component installation density, and the preset man-machine ratio. The manpower deviation is the difference between the actual allocation and the standard allocation. The equipment failure rate is the ratio of the number of failures of construction machinery (such as bracket installation equipment and cable laying equipment) to the total number of operations per unit time. The baseline failure rate is determined based on the equipment model, age, and historical operating data. The equipment anomaly is the difference between the equipment failure rate and the baseline failure rate. The construction vehicle dispatch delay rate is the ratio of the delay between the actual and scheduled arrival times of a construction vehicle to the scheduled travel time. The dispatch delay anomaly is the difference between this delay rate and the preset baseline delay rate. When the absolute value of the manpower deviation reaches a certain number of people, or the equipment anomaly exceeds a specific ratio, or the dispatch delay anomaly reaches a set ratio, the resource monitoring module generates a component installation anomaly signal.

[0023] During the grid-connection commissioning phase, the resource monitoring module focuses on grid access efficiency, equipment output stability, and detection accuracy. The grid access delay is calculated from the actual access time and the planned access time reported by the grid dispatching system. The grid-connection delay is the ratio of this delay to the planned access time. The inverter output fluctuation value is the maximum fluctuation of the inverter's actual output power per unit time. The output fluctuation anomaly is the percentage of this fluctuation value to the rated output power. The calibration error of the detection equipment is the deviation between the measured value of the detection instrument (such as a multimeter, power analyzer, etc.) and the standard calibration component. The calibration error anomaly is the percentage of this error to the instrument's range. When the grid-connection delay exceeds the set ratio, the output fluctuation anomaly reaches a specific value, or the calibration error anomaly exceeds the specified range, the resource monitoring module generates a grid-connection commissioning anomaly signal.

[0024] The abnormal signals generated by the resource monitoring module at each stage are sent to the processor in real time through the data transmission protocol, and are synchronously forwarded by the processor to the dynamic configuration optimization module and the resource early warning module. The dynamic configuration optimization module initiates the corresponding resource adjustment strategy based on the type and severity of the abnormal signal. For example, when temperature abnormalities occur during the material preparation stage, the parameter adjustment instructions of the warehouse air-conditioning system are triggered; when manpower deviations occur during the component installation stage, the cross-team personnel deployment process is initiated; when output fluctuations occur during the grid-connected debugging stage, the operating parameters of the inverter are adjusted. After receiving the abnormal signal, the resource early warning module uses the display screen of the monitoring center to visualize the abnormality type, occurrence time, and involved construction sites, and activates the sound and light alarm devices installed in the construction site duty room and the monitoring center to prompt relevant personnel to deal with it in a timely manner.

[0025] Throughout the monitoring process, the resource monitoring module maintains a high frequency of data collection and analysis. Collection intervals are set based on the importance of the construction phase, with shorter intervals during the material preparation and grid-connection commissioning phases and longer intervals during the component installation phase. This ensures monitoring accuracy while avoiding excessive system load caused by data redundancy. The abnormality thresholds for each stage can be dynamically adjusted based on factors such as the site's climate, the construction team's technical level, and the age of the equipment. Adjustment instructions are issued by the processor based on historical operating data and input from management personnel.

[0026] Example 2: See Figure 3 The construction fluency evaluation module connected to the processor and the cross-site resource analysis module work together to form a multi-dimensional control mechanism for the overall and local photovoltaic construction.

[0027] The construction fluency assessment module operates in units of time based on the target construction cycle, and the cycle length can be flexibly set according to the scale of the construction project. During the cycle, the module continuously counts the frequency of occurrence of various abnormal signals, including material preparation abnormal signals, component installation abnormal signals, and grid connection and commissioning abnormal signals, and combines the total number of these signals into an abnormal frequency index. At the same time, the module extracts the qualified count and unqualified count from the configuration efficiency monitoring module, calculates the quotient of the unqualified count and the qualified count, and defines this quotient as the control failure coefficient. Subsequently, the module uses a weighted fusion algorithm to process the abnormal frequency index and the control failure coefficient. The algorithm assigns fixed weights to the two parameters and generates a construction fluency evaluation value through numerical calculations. When the construction fluency evaluation value exceeds the preset fluency threshold, the module generates a construction abnormality warning signal and transmits the signal to the resource warning module through the processor. After receiving the signal, the resource warning module marks the corresponding construction project with a striking color on the monitoring interface and updates the abnormal status list.

[0028] The operation of the cross-site resource analysis module begins with the setting of a monitoring cycle. The cycle can be selected to be consistent with the target construction cycle of the construction fluency assessment module, or it can be set independently according to management needs. During the monitoring cycle, the module summarizes the resource scheduling records of all photovoltaic sites through the data interface, including specific information such as manpower deployment, equipment allocation, and material transportation. At the same time, it collects the response records of each site to abnormal signals, covering the time of abnormal occurrence, treatment measures, recovery status, etc. The module performs multi-dimensional data analysis on these records, and the analysis dimensions include the timeliness of resource scheduling, the coordination of abnormal response, and the balance of resource flow across sites. Based on the analysis results, the module generates a global early warning signal or a global normal signal.

[0029] When a global warning signal is generated, it is transmitted via the processor to the resource warning module, triggering cross-site resource redundancy instructions. These redundancy instructions are tailored to resource redundancies and shortages. For example, if one site experiences a shortage of manpower during component installation, while another site has a surplus of manpower, the instructions specify the appropriate number of construction workers from the latter to the former, along with a designated time window and transportation arrangements. If a site experiences frequent equipment failures and runs out of spare equipment, the instructions allocate the same type of equipment from a site with sufficient reserves, while also coordinating transport vehicles and loading and unloading personnel.

[0030] When the cross-site resource analysis module generates a global normal signal, it does not trigger resource allocation instructions. Instead, it synchronizes the signal to the system backend, serving as reference data for subsequent resource planning. The module also regularly archives resource scheduling and exception response data within the monitoring cycle to form a historical database. This database, organized chronologically, contains the global signal type, resource allocation details, and exception handling results for each cycle, facilitating subsequent tracing and analysis.

[0031] The Construction Fluency Assessment Module and the Cross-Site Resource Analysis Module maintain data interoperability. The number of construction anomaly warning signals generated by the Construction Fluency Assessment Module serves as one of the input parameters of the Cross-Site Resource Analysis Module, influencing the generation of global warning signals or global normal signals. Furthermore, resource scheduling records from the Cross-Site Resource Analysis Module are fed back to the Construction Fluency Assessment Module to refine the calculation of the next cycle's anomaly frequency indicator and control failure coefficient, creating a closed-loop management system.

[0032] During data processing, both modules utilize an encrypted transmission protocol to ensure the security of resource and construction data, preventing leakage or tampering. The module's operational status is monitored in real time by the processor. In the event of data transmission interruptions or computational errors, the processor generates a module fault notification and sends it to the system maintenance terminal, prompting maintenance personnel to conduct an investigation. Furthermore, the module supports manual intervention, allowing managers to adjust monitoring cycle parameters and analysis dimension weightings based on actual conditions to meet the management needs of different construction phases.

[0033] Example 3: See Figure 4 The processing flow of the cross-site resource analysis module revolves around the calculation of cycle performance indicators and the proportion of abnormal projects. The global risk value is generated through the resource aggregation analysis model, and then it is determined whether to activate the global warning signal.

[0034] After the cross-site resource analysis module is activated, the cycle efficiency index is first calculated using the construction efficiency evaluation algorithm. The algorithm must first determine whether the construction at each site meets the resource utilization standard. This determination involves analyzing the matching degree between resource input and output at each site. The ratio of the number of sites that fail to meet the standard to the total number of sites is labeled as the inefficiency coefficient. For example, if there are 30 photovoltaic sites in a monitoring cycle and 8 of them fail to meet the resource utilization standard, the inefficiency coefficient is the ratio of 8 to 30. For each site that fails to meet the standard, the deviation between actual resource consumption and planned resource consumption is extracted. This deviation is the resource dissipation coefficient, which covers the consumption deviation of various resources such as manpower, equipment, and materials. The resource dissipation coefficients of all sites are arithmetic averaged to obtain the cycle dissipation index. The resource dissipation coefficient of each site is included in the averaging operation with equal weight, regardless of site size or resource input.

[0035] After obtaining the inefficient construction coefficient and cycle dissipation index, they are input into the performance calculation model to generate the cycle performance index. The performance calculation model uses a linear combination method, multiplying the inefficient construction coefficient and the cycle dissipation index by their corresponding coefficients and then adding them together. The coefficients are set based on the focus of resource management. For example, if the overall efficiency of resource utilization is more important, the coefficient of the cycle dissipation index can be appropriately increased.

[0036] While calculating cycle performance indicators, the Cross-Site Resource Analysis Module collects the number of construction anomaly warning signals generated by the Construction Fluency Assessment Module. The ratio of this number to the total number of construction projects during the monitoring period is labeled the abnormal project percentage. For example, if there are 25 construction projects during the monitoring period and 6 of them generate construction anomaly warning signals, the abnormal project percentage is the ratio of 6 to 25.

[0037] The cycle performance index and the proportion of abnormal projects are input into the resource aggregation analysis model, which generates a global risk value through a preset algorithm. In the algorithm, the cycle performance index and the proportion of abnormal projects are assigned different weights. The weights are determined based on the degree of impact of each on the global construction risk based on historical data. For example, if historical data shows that the proportion of abnormal projects has a greater impact on the global risk, a higher weight can be assigned. The global risk value is calculated as follows:

[0038] in, represents the global risk value, Represents the cycle performance index, Represents the proportion of abnormal items, and Represent the weight coefficients of the period performance index and the proportion of abnormal items, respectively, and .

[0039] After the resource aggregation analysis model generates a global risk value, it compares it to a preset risk threshold. This threshold is determined based on industry standards, corporate management objectives, and historical construction risk data. For example, the maximum risk value within a 90% confidence interval based on three years of construction risk records is used as a reference threshold. If the global risk value exceeds the preset risk threshold, the cross-site resource analysis module activates a global warning signal. If the global risk value is lower than or equal to the preset risk threshold, the module determines that the current global construction status is stable and does not generate a global warning signal.

[0040] After a global warning signal is generated, it's transmitted via the processor to the resource warning module, triggering the creation and execution of cross-site resource redeployment instructions. These instructions include information such as resource type, redeployment quantity, starting and ending sites, transportation routes, and completion deadlines. For example, five experienced construction workers might be redeployed from a site with high resource utilization and no out-of-date projects to a site with a high proportion of out-of-date projects, while also allocating two backup inverters to mitigate potential equipment failures.

[0041] Throughout the entire processing process, the Cross-Site Resource Analysis Module continuously receives real-time data from each construction site, dynamically updating cycle performance indicators, the proportion of abnormal projects, and the global risk value. Update frequency is determined by the construction progress; for example, during peak construction periods, updates may be made every four hours, while during slower periods, updates may be made every 12 hours. If the updated value causes the global risk value to cross a preset risk threshold, the module immediately adjusts the generated signal type and triggers the corresponding subsequent actions.

[0042] The module also archives the results of each monitoring cycle, including the calculation process of the cycle's performance indicators, the specific composition of the abnormal item ratio, the change curve of the global risk value, and the generated signal type. This archived data can be retrieved through the query interface and used to review and optimize resource management strategies, as well as provide reference for parameter setting in subsequent monitoring cycles.

[0043] Example 4: See Figure 5 The determination of resource utilization standards and the operation of the equipment efficiency diagnosis module form a linkage mechanism, covering the entire process of resource investment rationality assessment and equipment status monitoring.

[0044] The logic for determining whether resource utilization meets the target standard revolves around human resource input. For each target construction site, the system pre-enters the planned human resource input, which is determined based on the construction area, the number of modules to be installed, the planned construction period, and per capita work efficiency. Actual human resource input is collected in real time through the site attendance system, including the number of people on duty each day, actual working hours, and the staffing of each type of work. The ratio of actual to planned human resource input is converted to a percentage to obtain the human resource matching rate. For example, if a construction site plans to deploy 30 construction workers and 27 actually report on a given workday, the human resource matching rate for that day is 90%. If the human resource matching rate falls below the preset matching threshold, the site is deemed to have failed to meet the standard. The preset matching threshold is dynamically adjusted based on the construction stage. The thresholds can be appropriately lowered during the material preparation and grid connection and commissioning stages. However, the threshold is set higher during the module installation stage, which requires more manpower.

[0045] The server and the Equipment Performance Diagnostic Module establish a communication connection via a wired network. Data transmission utilizes a timed synchronization mechanism to ensure timely information updates. When the Cross-Site Resource Analysis Module generates a global normal signal, the server forwards this signal to the Equipment Performance Diagnostic Module via a pre-set data interface. Upon receiving this signal, the Equipment Performance Diagnostic Module initiates an operational status analysis of all construction machinery used at all PV sites during the designated construction period. This construction machinery includes various types of construction and testing equipment, including support erectors, PV panel lifters, cable laying equipment, and grid-connection testers.

[0046] The Equipment Effectiveness Diagnostic Module analyzes equipment operating hours, fault records, maintenance visits, and fluctuations in key parameters within a specified construction period. The module accesses the equipment management systems at each construction site, extracts this data, and categorizes and aggregates it. For example, for a support installation machine, this module collects data such as total operating hours, cumulative downtime due to faults, and component replacement records within the specified construction period. For grid-connected test equipment, this module collects data such as calibration records, measurement error ranges, and battery life changes during each use.

[0047] During the analysis process, the module compares various data sets against the equipment's factory-set technical parameters and the average operating performance of similar equipment. For example, the module compares the actual operating speed of a photovoltaic panel lift at a construction site with the factory-set standard speed to calculate the deviation range; or compares the failure rate of cable-laying equipment with the average failure rate of similar equipment under the same operating conditions to determine if any anomalies exist. Through this multi-dimensional comparison, the module identifies equipment whose operating status deviates from the normal range and generates a signal indicating that the equipment has failed performance testing.

[0048] Equipment failure signals contain information such as the equipment number, construction site, abnormal parameter type, and specific values. These information is transmitted via the server to the resource warning module. Upon receiving the signal, the module records the relevant information in the equipment failure log and marks it in the equipment management interface of the monitoring terminal. These markings are color-coded: mild failures are yellow, moderate failures are orange, and severe failures are red, making it easy for managers to quickly identify key failures.

[0049] The following is a summary table of the operating status analysis data of some construction machinery within a specified construction period:

[0050] The equipment performance diagnostic module uses this data and pre-set rules to determine whether to generate a failure signal. For example, if the deviation of a key parameter of a grid-connected test instrument exceeds ±10%, or if a rack installation machine experiences more than five failures and shutdowns within a specified period, the module will be considered unqualified and a corresponding signal will be generated.

[0051] After completing the analysis, the Equipment Effectiveness Diagnosis module compiles the operational status assessment results for all equipment into a report. This report includes a list of qualified and unqualified equipment, along with details of abnormal parameters. This report is then fed back to the equipment management department via a server, serving as a reference for equipment repair, replacement, and redeployment. The module also archives all data generated during the analysis process, with the archiving period coinciding with the designated construction period, to facilitate subsequent tracing and comparison with historical data.

[0052] Example 5: The equipment performance diagnosis module achieves accurate assessment of the equipment operating status through real-time monitoring and multi-dimensional analysis of key components of engineering machinery.

[0053] The operation of the equipment performance diagnostic module begins with the deployment of sensor nodes. Several sensor nodes are installed on key components of engineering machinery, including engines, hydraulic pumps, transmission gears, braking systems, control systems, and other core components that directly affect the equipment's operating performance. The type of sensor node is determined based on the monitoring parameters, such as temperature sensors for collecting component operating temperatures, pressure sensors for monitoring hydraulic system pressure, vibration sensors for recording component vibration frequencies, and current sensors for measuring motor operating currents. The sensor nodes establish a connection with the equipment performance diagnostic module via wireless transmission. The transmission frequency is set based on the importance of the component. The sensor data transmission interval for core components is relatively short, while the transmission interval for non-core components can be appropriately extended.

[0054] During construction, each sensor node continuously collects real-time operating parameters and sends this data to the equipment performance diagnostic module in real time. After receiving the data, the module first calculates the mean of all node parameters to obtain a baseline performance value. This baseline performance value covers the parameters of all sensor nodes within the same time period. For example, if five temperature sensors record values ​​of 80°C, 82°C, 79°C, 81°C, and 83°C within a given minute, the baseline performance value for that time period is 81°C. The module then calculates the absolute deviation between each node's real-time parameter and the baseline performance value and labels this deviation as the node's abnormality. For example, if a temperature sensor's real-time parameter is 85°C and its baseline performance value is 81°C, the node's abnormality is 4°C. If the node's abnormality exceeds a preset threshold, the module marks the node as defective. This threshold is set based on the sensor type and the characteristics of the monitored parameter. For example, the abnormality threshold for a temperature sensor might be ±5°C, or ±0.5 mm / s for a vibration sensor.

[0055] The equipment performance diagnosis module accumulates the total operating time of construction machinery during the monitoring cycle. Total operating time is the cumulative operating time from the time the equipment is started to the time it is stopped, excluding downtime for rest or maintenance. The module also counts the number of times each sensor node is marked as a defective node and labels this number as the node anomaly frequency. The node failure ratio is the ratio of the node anomaly frequency to the total operating time. For example, if a sensor node is marked as a defective node six times during the monitoring cycle, and the equipment has a total operating time of 300 hours, the node failure ratio is 6 times / 300 hours.

[0056] The module classifies nodes into different levels using hierarchical determination rules. If a node's failure rate exceeds a preset upper limit, it is classified as a high-risk defective node; if it falls below a preset lower limit, it is classified as a low-risk defective node; and if the failure rate falls between the preset upper and lower limits, it is classified as a medium-risk defective node. The preset upper and lower limits are determined based on the equipment's safe operation requirements and historical fault data. For example, for engine-related sensor nodes, the upper limit is set lower to identify potential risks earlier.

[0057] If a high-risk defective node exists, the device performance diagnosis module directly generates a device performance failure signal. If no high-risk defective nodes exist, the module calculates the ratio of the number of medium-risk defective nodes to the number of low-risk defective nodes and marks this ratio as the device risk value. When the device risk value exceeds the preset risk threshold, the module generates a device performance failure signal. This signal contains information such as the device number, defective node location, risk level, and abnormal parameters. It is transmitted via the server to the resource warning module, which updates the device status list based on the signal content and triggers the corresponding notification mechanism.

[0058] During operation, the equipment performance diagnostic module stores all monitoring data and analysis results in real time. This data includes each sensor node's original parameters, baseline performance values, node anomaly levels, defective node markings, node failure rates, and the resulting signal type. This data is archived chronologically to form an equipment operation profile, facilitating subsequent inquiries into the equipment's historical status and performance trends.

[0059] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0060] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A photovoltaic construction resource dynamic optimization configuration management platform, characterized by: Includes processor, resource monitoring module, dynamic configuration optimization module, configuration efficiency monitoring module and resource early warning module; The resource monitoring module continuously tracks the construction progress of multiple photovoltaic construction sites. The construction process is divided into a material preparation stage, a component installation stage, and a grid-connected commissioning stage. In the material preparation stage, photovoltaic panels and supporting electrical equipment are transported to a designated construction site warehouse and the storage environment is monitored. When the storage environment parameters meet the preset preparation standards, the component installation stage begins. Mechanical fixation and electrical connection of the photovoltaic array are completed at designated workstations. After the continuous construction reaches the preset working hours, the grid-connected commissioning stage begins to perform system performance testing. The resource monitoring module generates a material preparation abnormality signal, a component installation abnormality signal or a grid connection debugging abnormality signal through analysis, and transmits the abnormality signal to the dynamic configuration optimization module and the resource early warning module via the processor; The resource early warning module provides a visual prompt for the received abnormal signal and activates an audible and visual alarm. The dynamic configuration optimization module performs adaptive adjustments to the resource allocation plan for the corresponding construction site when the abnormal signal is received. The configuration efficiency monitoring module collects the moment when the dynamic configuration optimization module responds to the abnormal signal as the control starting point, and continues monitoring until the construction process returns to normal to determine the actual control time. When the actual control time is lower than the preset control time limit, the resource control is judged to be qualified and the qualified count is accumulated. When the actual control time exceeds the preset control time limit, the resource control is judged to be unqualified and the unqualified count is accumulated.

2. The photovoltaic construction resource dynamic optimization configuration management platform according to claim 1 is characterized in that: The operation logic of the resource monitoring module includes: During the material preparation phase, the temperature fluctuation value, humidity deviation value, and material turnover rate of the construction site warehouse are obtained in real time. The difference between the temperature fluctuation value and the standard preparation temperature is marked as the temperature change anomaly. The humidity deviation anomaly and turnover rate anomaly are simultaneously calculated. If the temperature change anomaly, humidity deviation anomaly, or turnover rate anomaly exceeds the corresponding threshold, a material preparation anomaly signal is generated. During the component installation phase, the deviation between the number of construction personnel configured and the standard number is monitored and marked as the manpower deviation value. The equipment failure rate and the engineering vehicle scheduling delay rate are collected, and the difference between the equipment failure rate and the baseline failure rate is marked as the equipment abnormality. The scheduling delay abnormality is also calculated simultaneously. If the manpower deviation value, equipment abnormality, or scheduling delay abnormality exceeds the corresponding threshold, a component installation abnormality signal is generated. During the grid connection debugging phase, the grid connection delay duration, inverter output fluctuation value, and detection equipment calibration error are obtained. The difference between the grid connection delay duration and the planned duration is marked as the grid connection delay degree, and the output fluctuation abnormality degree and calibration error abnormality degree are simultaneously calculated. If the grid connection delay, output fluctuation abnormality or calibration error abnormality exceeds a corresponding threshold, a grid connection debugging abnormality signal is generated.

3. The photovoltaic construction resource dynamic optimization configuration management platform according to claim 1 is characterized in that: The processor is connected to a construction fluency assessment module, which performs the following operations: counting the frequency of occurrence of various abnormal signals within the target construction period and marking them as abnormal frequency indicators, while obtaining the qualified count and unqualified count recorded by the configuration efficiency monitoring module, and marking the quotient of the unqualified count and the qualified count as the control failure coefficient; processing the abnormal frequency indicator and the control failure coefficient through a weighted fusion algorithm to generate a construction fluency evaluation value, and when the construction fluency evaluation value exceeds a preset fluency threshold, generating a construction abnormality warning signal and transmitting it to the resource early warning module.

4. The photovoltaic construction resource dynamic optimization configuration management platform according to claim 3 is characterized in that: The processor is connected to a cross-site resource analysis module, which performs the following operations: setting a monitoring period, summarizing the resource scheduling records and abnormal response records of all photovoltaic sites within the monitoring period, and generating a global warning signal or a global normal signal through multi-dimensional data analysis; the global warning signal is transmitted to the resource warning module via the processor and triggers a cross-site resource allocation instruction.

5. The photovoltaic construction resource dynamic optimization configuration management platform according to claim 4 is characterized in that: The processing flow of the cross-site resource analysis module includes: calculating a periodic efficiency index using a construction efficiency evaluation algorithm, collecting the number of construction abnormality warning signals generated by the construction fluency evaluation module, marking the ratio of the number of signals to the total number of construction projects in the period as the abnormal project ratio; inputting the periodic efficiency index and the abnormal project ratio into a resource aggregation analysis model to generate a global risk value; The global warning signal is activated when the global risk value exceeds a preset risk threshold.

6. The photovoltaic construction resource dynamic optimization configuration management platform according to claim 5, characterized in that: The construction efficiency evaluation algorithm performs the following steps: determining whether the construction of each construction site meets the resource utilization standard, marking the ratio of the number of non-compliant construction sites to the total number of construction sites as an inefficient construction coefficient, extracting the deviation between the actual resource consumption and the planned resource consumption of each non-compliant construction site and marking it as resource dissipation, and calculating the mean of the resource dissipation of all construction sites to obtain a periodic dissipation index; and inputting the inefficient construction coefficient and the periodic dissipation index into an efficiency calculation model to generate the periodic efficiency index.

7. The photovoltaic construction resource dynamic optimization configuration management platform according to claim 6, characterized in that: The judgment logic of the resource utilization compliance standard is as follows: obtain the planned human resource input and the actual human resource input of the target construction site, and mark the percentage of the actual input to the planned input as the human resource matching rate; when the human resource matching rate is lower than the preset matching threshold, the construction site is judged to be a non-compliant construction site.

8. The photovoltaic construction resource dynamic optimization configuration management platform according to claim 4, characterized in that: The server is communicatively connected to the equipment efficiency diagnosis module. When the cross-site resource analysis module generates a global normal signal, the server forwards the global normal signal to the equipment efficiency diagnosis module. After receiving the signal, the equipment efficiency diagnosis module analyzes the operating status of the construction machinery used in all photovoltaic construction sites within the specified construction period, generates an equipment efficiency unqualified signal based on the analysis results, and transmits it to the resource early warning module via the server.

9. The photovoltaic construction resource dynamic optimization configuration management platform according to claim 8, characterized in that: The equipment performance diagnosis module executes: deploying a number of sensor nodes on key components of the construction machinery, collecting real-time working parameters of each node during construction, calculating the mean of the parameters of all nodes to obtain a baseline performance value, and marking the absolute deviation between the real-time parameters of each node and the baseline performance value as the node abnormality; If the node abnormality exceeds the preset abnormality threshold, it will be marked as a defective node; The total operating time of the construction machinery during the monitoring period is accumulated, and the number of times each sensor node is marked as a defective node is counted and marked as the node abnormality frequency. The ratio of the node abnormality frequency to the total operating time is marked as the node failure ratio. The nodes are divided into high-risk defective nodes, medium-risk defective nodes, or low-risk defective nodes through classification judgment rules. If there are high-risk defect nodes, a device performance failure signal is generated; If there are no high-risk defective nodes, the ratio of the number of medium-risk defective nodes to the number of low-risk defective nodes is marked as the equipment risk value. When the equipment risk value exceeds the preset risk threshold, an equipment performance failure signal is generated.

10. The photovoltaic construction resource dynamic optimization configuration management platform according to claim 9, characterized in that: The classification judgment rule is: if the node failure rate exceeds the preset failure rate upper limit, it is judged as a high-risk defect node; if the node failure rate is lower than the preset failure rate lower limit, it is judged as a low-risk defect node; If the node failure rate is within the preset failure rate range, it is determined to be a medium-risk defective node.

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