A dynamic optimization and allocation management platform for photovoltaic construction resources

Abstract

This invention relates to the field of photovoltaic construction management technology and discloses a dynamic optimization and allocation management platform for photovoltaic construction resources. The platform includes 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 progress of multiple photovoltaic construction sites, which is divided into material preparation, component installation, and grid connection commissioning stages. Each stage proceeds according to preset standards and generates abnormal signals that are transmitted to the relevant modules. Upon receiving an abnormal signal, the resource early warning module provides a visual prompt and activates an audible and visual alarm. The dynamic configuration optimization module adaptively adjusts the resource allocation scheme for the corresponding construction site. The configuration efficiency monitoring module monitors from the start of regulation until construction returns to normal. By comparing the actual regulation time with the preset regulation time limit, it determines whether the resource regulation is qualified and counts the results. This platform realizes the dynamic management and optimized allocation of photovoltaic construction resources, facilitating the efficient advancement of the construction process.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic construction management technology, specifically a dynamic optimization and allocation management platform for photovoltaic construction resources. Background Technology

[0002] Against the backdrop of rapid development in photovoltaic energy, the construction scale of photovoltaic power plants is constantly expanding, construction scenarios are becoming increasingly complex, and the types of resources involved are numerous and their allocation difficulty is significantly increasing. Currently, during photovoltaic construction, resource management relies heavily on traditional manual planning and experience-based judgment, which is difficult to adapt to the dynamically changing needs during the construction process.

[0003] During the material preparation phase, the transportation and scheduling of photovoltaic panels and supporting electrical equipment lack real-time monitoring, often resulting in material supply delays or excessive stockpiling. Control of the storage environment also relies heavily on manual inspections, making it difficult to detect fluctuations in parameters such as temperature, humidity, and ventilation in a timely manner. This can lead to damage to material performance and affect the quality of subsequent construction. During the module installation phase, the construction progress at different workstations varies, and the allocation of equipment and personnel lacks a flexible adjustment mechanism. This can easily lead to an imbalance where some workstations have idle resources while others are short of resources, slowing down the overall construction pace.

[0004] As a critical phase of construction, the grid connection and commissioning stage often suffers from problems such as insufficient equipment compatibility and a shortage of technical personnel due to improper initial resource allocation, leading to extended commissioning cycles. Furthermore, when anomalies occur at any stage of construction, traditional management models lack rapid response mechanisms, the transmission paths of anomaly signals are unclear, and the development of resource adjustment plans is delayed and lacks specificity, making it difficult to restore normal construction order in a short period. Simultaneously, the evaluation of resource regulation effectiveness lacks quantitative standards, making it impossible to accurately determine the effectiveness of regulation measures, thus trapping resource management in an inefficient cycle and hindering the overall efficiency and quality stability of photovoltaic construction. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic optimization and management platform for photovoltaic construction resources to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a dynamic optimization and management platform for photovoltaic construction resources, 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;

[0007] The resource monitoring module continuously tracks the construction progress of multiple photovoltaic construction sites. The construction process is divided into the material preparation stage, the component installation stage, and the grid connection and commissioning stage. In the material preparation stage, the photovoltaic panels and supporting electrical equipment are transported to the designated construction site warehouse and the storage environment is monitored. When the storage environment parameters reach the preset preparation standards, the component installation stage begins. The mechanical fixing and electrical connection of the photovoltaic array are completed at the designated work station. After the construction continues for the preset working hours, the grid connection and commissioning stage begins to perform system performance testing.

[0008] The resource monitoring module analyzes and generates abnormal signals such as material preparation abnormal signals, component installation abnormal signals, or grid connection debugging abnormal signals, and transmits these abnormal signals to the dynamic configuration optimization module and the resource early warning module via the processor. The resource early warning module provides visual prompts and activates audible and visual alarms upon receiving the abnormal signals, and the dynamic configuration optimization module adaptively adjusts the resource allocation plan for the corresponding construction site upon receiving an abnormal signal.

[0009] The configuration efficiency monitoring module collects the moment when the dynamic configuration optimization module responds to the abnormal signal as the starting point of the control, and continuously monitors until the construction process returns to normal to determine the actual control duration. When the actual control duration is lower than the preset control time limit, the resource control is deemed qualified and the qualified count is accumulated. When the actual control duration exceeds the preset control time limit, the resource control is deemed unqualified and the unqualified count is accumulated.

[0010] Preferably, the operating logic of the resource monitoring module includes:

[0011] During the material preparation stage, the temperature fluctuation value, humidity deviation value and material turnover rate of the construction site warehouse are acquired in real time. The difference between the temperature fluctuation value and the standard preparation temperature is marked as the temperature change anomaly degree. The humidity deviation anomaly degree and the turnover rate anomaly degree are calculated simultaneously. If the temperature change anomaly degree, humidity deviation anomaly degree or turnover rate anomaly degree exceeds the corresponding threshold, a material preparation anomaly signal is generated.

[0012] During the component installation phase, the deviation between the number of construction personnel and the standard configuration number is monitored and marked as the manpower deviation value. The equipment failure rate and the engineering vehicle dispatch delay rate are collected. The difference between the equipment failure rate and the baseline failure rate is marked as the equipment anomaly degree. The dispatch delay anomaly degree is calculated simultaneously. If the manpower deviation value, equipment anomaly degree, or dispatch delay anomaly degree exceeds the corresponding threshold, a component installation anomaly signal is generated.

[0013] During the grid connection commissioning phase, the grid connection delay time, inverter output fluctuation value, and detection equipment calibration error are obtained. The difference between the grid connection delay time and the planned time is marked as the grid connection delay degree. The output fluctuation anomaly degree and calibration error anomaly degree are calculated simultaneously. If the grid connection delay degree, output fluctuation anomaly degree, or calibration error anomaly degree exceeds the corresponding threshold, a grid connection commissioning anomaly signal is generated.

[0014] Preferably, the processor is connected to a construction smoothness assessment module, which performs the following actions: statistically analyzing the frequency of occurrence of various abnormal signals within the target construction period and marking them as abnormal frequency indicators; simultaneously acquiring the qualified and unqualified counts recorded by the configuration performance monitoring module, and marking the quotient of the unqualified counts and qualified counts as the control failure coefficient; processing the abnormal frequency indicators and control failure coefficients through a weighted fusion algorithm to generate a construction smoothness evaluation value; and generating a construction abnormality warning signal and transmitting it to the resource early warning module when the construction smoothness evaluation value exceeds a preset smoothness threshold.

[0015] Preferably, the processor is connected to a cross-site resource analysis module, which performs the following actions: setting a monitoring period, summarizing resource scheduling records and abnormal response records of all photovoltaic construction sites within the monitoring period, and generating a global early warning signal or a global normal signal through multi-dimensional data analysis; the global early warning signal is transmitted to the resource early warning module via the processor and triggers a cross-site resource allocation command.

[0016] Preferably, the processing flow of the cross-site resource analysis module includes: calculating the periodic performance index through the construction performance evaluation algorithm, and simultaneously collecting the number of construction anomaly warning signals generated by the construction smoothness evaluation module, marking the ratio of the number of signals to the total number of construction projects within the period as the percentage of abnormal projects; inputting the periodic performance index and the percentage of abnormal projects into the resource aggregation analysis model to generate a global risk value; and activating the global early warning signal when the global risk value exceeds a preset risk threshold.

[0017] Preferably, the construction efficiency evaluation algorithm performs the following steps: determining whether the construction at each site meets the resource utilization rate standard; marking the ratio of the number of substandard sites to the total number of sites as an inefficient construction coefficient; extracting the deviation between the actual resource consumption and the planned resource consumption at each substandard site and marking it as a resource dissipation degree; calculating the average of the resource dissipation degrees of all 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.

[0018] Preferably, the judgment logic for the resource utilization rate 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 determined to be a non-compliant construction site.

[0019] Preferably, the server is communicatively connected to the equipment performance diagnosis module. When the cross-site resource analysis module generates a global normal signal, the server forwards the global normal signal to the equipment performance diagnosis module. After receiving the signal, the equipment performance diagnosis module analyzes the operating status of all construction machinery used in photovoltaic construction sites within the specified construction period, generates an equipment performance failure signal based on the analysis results, and transmits it to the resource early warning module via the server.

[0020] Preferably, the equipment performance diagnosis module performs the following: deploying several sensor nodes on the key components of the construction machinery, collecting real-time working parameters of each node during construction, calculating the average of the parameters of all nodes to obtain a benchmark performance value, and marking the absolute deviation between the real-time parameters of each node and the benchmark performance value as the node anomaly degree; if the node anomaly degree exceeds a preset anomaly threshold, it is marked as a defective node.

[0021] The total runtime of the construction machinery within the cumulative monitoring period is recorded. The number of times each sensor node is marked as a defective node is counted and marked as the node anomaly frequency. The ratio of the node anomaly frequency to the total runtime is marked as the node failure ratio. Nodes are classified into high-risk, medium-risk, or low-risk defective nodes according to the classification judgment rules. If a high-risk defective node exists, an equipment performance failure signal is generated. If there is no high-risk defective node, 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.

[0022] Preferably, the classification judgment rule is as follows: 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 judged as a medium-risk defect node.

[0023] Compared with the prior art, the beneficial effects of the present invention are:

[0024] Through the coordinated operation of multiple modules, refined management of resources throughout the entire photovoltaic construction process is achieved. The resource monitoring module continuously tracks the three stages of the construction process, incorporating key aspects of each stage into the monitoring scope, ensuring that various situations during material preparation, component installation, and grid connection commissioning can be captured in a timely manner. During the material preparation stage, real-time monitoring of the storage environment can prevent material problems caused by environmental factors from affecting subsequent construction; during the component installation stage, continuous monitoring of workstation construction can promptly detect abnormalities in the construction rhythm; and during the grid connection commissioning stage, system performance testing and tracking help to identify potential problems during the commissioning process as early as possible.

[0025] When anomalies occur at any stage, the anomaly signals generated by the resource monitoring module are simultaneously transmitted to the dynamic configuration optimization module and the resource early warning module, forming a dual mechanism for anomaly response. The resource early warning module's visual prompts and audible / visual alarms allow relevant personnel to quickly become aware of the anomaly, facilitating timely intervention. Upon receiving anomaly signals, the dynamic configuration optimization module adaptively adjusts the resource allocation plan for the corresponding construction site. This adjustment, based on real-time anomaly conditions, better aligns with actual construction needs and effectively alleviates resource allocation imbalances.

[0026] The configuration efficiency monitoring module quantitatively evaluates the effectiveness of resource regulation by recording the start point and actual duration of regulation. The accumulation of qualified and unqualified counts provides a direct basis for optimizing resource management strategies. This closed-loop monitoring and evaluation mechanism clearly demonstrates the effectiveness of resource regulation measures, helps to continuously improve resource allocation models, makes resource utilization more rational during photovoltaic construction, reduces resource waste and idleness, and makes the connection between different construction stages smoother, thereby improving the continuity and stability of the construction process. Attached Figure Description

[0027] Figure 1 This is a schematic diagram illustrating the working principle of the photovoltaic construction resource dynamic optimization allocation management platform described in this invention.

[0028] Figure 2 A flowchart for generating abnormal signals in the resource monitoring module;

[0029] Figure 3 A flowchart for global signal generation in the cross-site resource analysis module;

[0030] Figure 4 A flowchart for calculating the risk value in the cross-site resource analysis module;

[0031] Figure 5 This is a flowchart triggered by the equipment performance diagnosis module. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see Figure 1 This invention provides a dynamic optimization and allocation management platform for photovoltaic construction resources. The platform includes a processor, a resource monitoring module, a dynamic configuration optimization module, a configuration efficiency monitoring module, and a resource early warning module. Specific implementation details are as follows:

[0034] The resource monitoring module continuously tracks the construction progress of multiple photovoltaic construction sites. The construction process is divided into three stages: material preparation, component installation, and grid connection commissioning. In the material preparation stage, photovoltaic panels and supporting electrical equipment are transported to the designated site warehouse and the storage environment is monitored. Once the storage environment parameters meet the preset preparation standards, the component installation stage begins. The photovoltaic array is mechanically fixed and electrically connected at the designated workstation. After continuous construction reaches the preset working hours, the grid connection commissioning stage begins to perform system performance testing.

[0035] The resource monitoring module analyzes and generates abnormal signals such as material preparation anomalies, component installation anomalies, or grid connection commissioning anomalies, and transmits these signals to the dynamic configuration optimization module and the resource early warning module via a processor. The resource early warning module provides visual prompts and activates audible and visual alarms upon receiving abnormal signals, while the dynamic configuration optimization module adaptively adjusts the resource allocation plan for the corresponding construction site upon receiving an abnormal signal.

[0036] The configuration efficiency monitoring module collects the moment when the dynamic configuration optimization module responds to an abnormal signal as the starting point for control, and continuously monitors until the construction process returns to normal to determine the actual control duration. When the actual control duration is less than the preset control time limit, the resource control is deemed qualified and the qualified count is accumulated; when the actual control duration exceeds the preset control time limit, the resource control is deemed unqualified and the unqualified count is accumulated.

[0037] Example 1: See Figure 2 The resource monitoring module's operating logic is set with targeted monitoring dimensions and anomaly judgment mechanisms according to different stages of construction, so as to achieve precise control over the entire photovoltaic construction process.

[0038] During the material preparation phase, the resource monitoring module collects three core parameters in real time: temperature fluctuation, humidity deviation, and material turnover rate through temperature and humidity sensors, material identification equipment, and a turnover record system deployed in the construction site warehouse. Temperature fluctuation is the temperature difference between two consecutive hours within the warehouse. The standard preparation temperature is pre-set based on the storage requirements of the photovoltaic panels and supporting electrical equipment. Temperature anomaly is the absolute difference between the temperature fluctuation and the standard preparation temperature. Humidity deviation is the deviation of the actual measured humidity from the standard humidity, and humidity deviation anomaly is the percentage of this deviation relative to the standard humidity. Material turnover rate is the total amount of photovoltaic panels and supporting electrical equipment entering and leaving the warehouse per unit time. Turnover rate anomaly is the ratio of the difference between the actual turnover rate and the planned turnover rate to the planned turnover rate. When the temperature anomaly exceeds the set range, the humidity deviation anomaly reaches a certain proportion, or the turnover rate anomaly exceeds the limit proportion, the resource monitoring module immediately generates a material preparation anomaly signal.

[0039] During the component installation phase, the resource monitoring module shifts its focus to manpower allocation, equipment operating status, and engineering vehicle dispatch efficiency. The number of construction personnel is obtained through a facial recognition attendance system or workstation clock-in records. The standard configuration number is determined based on the construction area, component installation density, and a preset man-machine ratio. The manpower deviation value is the difference between the actual configuration number and the standard configuration number. The equipment failure rate is the proportion of the number of failures of construction machinery (such as bracket installation machines and cable laying equipment) per unit time to the total number of operations. The baseline failure rate is set based on equipment model, service life, and historical operating data. The equipment anomaly rate is the difference between the equipment failure rate and the baseline failure rate. The engineering vehicle dispatch delay rate is the proportion of the delay time between the actual arrival time and the planned arrival time to the planned travel time. The dispatch delay anomaly rate 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 personnel, or the equipment anomaly rate exceeds a specific proportion, or the dispatch delay anomaly rate reaches a set proportion, the resource monitoring module generates a component installation anomaly signal.

[0040] During the grid connection commissioning phase, the resource monitoring module focuses on grid connection efficiency, equipment output stability, and detection accuracy. Grid connection delay is calculated by comparing the actual connection time with the planned connection time fed back by the grid dispatch system; the grid connection delay is the ratio of this delay to the planned connection time. Inverter output fluctuation is the maximum fluctuation amplitude 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. Testing equipment calibration error is the deviation between the measured value of the testing 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 a set ratio, or the output fluctuation anomaly reaches a specific value, or the calibration error anomaly exceeds the limit range, the resource monitoring module generates a grid connection commissioning anomaly signal.

[0041] All abnormal signals generated by the resource monitoring module at each stage are sent to the processor in real time via a data transmission protocol. The processor then synchronously forwards these signals to the dynamic configuration optimization module and the resource early warning module. The dynamic configuration optimization module activates corresponding resource adjustment strategies based on the type and severity of the abnormal signal. For example, if temperature anomalies occur during the material preparation stage, it triggers parameter adjustment commands for the warehouse air conditioning system; if manpower discrepancies occur during the component installation stage, it initiates a cross-shift personnel allocation process; and if output fluctuations occur during the grid connection and commissioning stage, it adjusts the inverter's operating parameters. Upon receiving an abnormal signal, the resource early warning module displays the anomaly type, occurrence time, and affected construction sites on the monitoring center's screen. Simultaneously, it activates the audible and visual alarm devices installed in the site duty room and monitoring center to alert relevant personnel for timely handling.

[0042] Throughout the monitoring process, the resource monitoring module maintains high-frequency data acquisition and analysis. The acquisition interval is set according to the importance of each construction stage, with shorter intervals during the material preparation and grid connection commissioning stages, and relatively longer intervals during the component installation stage. This ensures monitoring accuracy while avoiding excessive system load due to data redundancy. The anomaly detection thresholds for each stage can be dynamically adjusted based on factors such as the climate conditions of the construction site, the technical level of the construction team, and the age of the equipment. Adjustment commands are issued by the processor based on historical operating data and input from management personnel.

[0043] Example 2: See Figure 3 The processor-connected construction smoothness assessment module and cross-site resource analysis module work together to form a multi-dimensional control mechanism for the overall and local photovoltaic construction.

[0044] The construction smoothness assessment module operates on a target construction cycle as the time unit, with the cycle length flexibly set according to the scale of the construction project. Within the cycle, the module continuously counts the frequency of various abnormal signals, including material preparation anomalies, component installation anomalies, and grid connection commissioning anomalies, merging the total number of these signals and marking them as an anomaly frequency index. Simultaneously, the module extracts qualified and unqualified counts from the configuration performance monitoring module, calculates the quotient between the unqualified and qualified counts, and defines this quotient as the control failure coefficient. Subsequently, the module uses a weighted fusion algorithm to process the anomaly frequency index and the control failure coefficient, assigning fixed weights to the two parameters and generating a construction smoothness evaluation value through numerical calculation. When the construction smoothness evaluation value exceeds a preset smoothness threshold, the module generates a construction anomaly warning signal and transmits it to the resource early warning module via a processor. Upon receiving the signal, the resource early warning module marks the corresponding construction project with a prominent color on the monitoring interface and updates the anomaly status list.

[0045] The cross-site resource analysis module begins operation with a set monitoring period. This period can be chosen to match the target construction period of the construction smoothness assessment module, or it can be set independently according to management needs. Within the monitoring period, the module aggregates resource scheduling records from all photovoltaic construction sites through a data interface, including specific information such as manpower allocation, equipment allocation, and material transfer. It also collects response records from each site to abnormal signals, covering the time of occurrence, handling measures, and recovery status. The module performs multi-dimensional data analysis on these records, including the timeliness of resource scheduling, the coordination of abnormal responses, and the balance of cross-site resource flow. Based on the analysis results, the module generates a global early warning signal or a global normal signal.

[0046] When a global early warning signal is generated, it is transmitted to the resource early warning module via the processor, triggering a cross-site resource allocation instruction. The allocation instruction is formulated based on resource redundancy and shortage. For example, if a site experiences a shortage of manpower during the component installation phase, while another site has surplus manpower at the same time, the instruction will specify the allocation of the corresponding number of construction workers from the latter to the former, and specify the allocation time window and transportation arrangements. If a site experiences frequent equipment failures and lacks its own backup equipment, the instruction will arrange to allocate the same type of equipment from a site with sufficient equipment reserves, and simultaneously coordinate transport vehicles and loading and unloading personnel.

[0047] When the cross-site resource analysis module generates a global normal signal, it does not trigger resource allocation instructions, but it synchronizes the signal to the system backend as reference data for subsequent resource planning. The module also periodically archives resource scheduling and anomaly response data within the monitoring period to form a historical database. The data in the database is arranged in chronological order and includes the global signal type, resource allocation details, and anomaly handling results for each period, facilitating subsequent traceability and analysis.

[0048] The construction smoothness assessment module and the cross-site resource analysis module maintain data communication. The number of construction anomaly warning signals generated by the construction smoothness assessment module serves as one of the input parameters for the cross-site resource analysis module, affecting the generation of global early warning signals or global normal signals. Simultaneously, resource scheduling records from the cross-site resource analysis module are also fed back to the construction smoothness assessment module to correct the calculation of anomaly frequency indicators and control failure coefficients for the next cycle, forming a closed-loop management system.

[0049] During data processing, both modules employ encrypted transmission protocols to ensure the security of resource information and construction data, preventing information leakage or tampering. The module's operational status is monitored in real-time by the processor. In the event of data transmission interruption or computational error, the processor generates a module fault alert and sends it to the system maintenance terminal, prompting maintenance personnel to investigate. Furthermore, the modules support manual intervention, allowing administrators to adjust monitoring cycle parameters, analysis dimension weights, and other settings to meet the management needs of different construction phases.

[0050] 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. It generates a global risk value through a resource aggregation analysis model, and then determines whether to activate a global early warning signal.

[0051] After the cross-site resource analysis module is activated, the periodic efficiency index is first calculated using a construction efficiency evaluation algorithm. The algorithm determines whether each construction site meets the resource utilization rate standard, involving an analysis of the matching degree between resource input and output at each site. The ratio of the number of substandard sites to the total number of sites is marked as the inefficient construction coefficient. For example, in a monitoring period, if there are 30 photovoltaic construction sites, and 8 of them fail to meet the resource utilization rate standard, the inefficient construction coefficient is the ratio of 8 to 30. For each substandard site, the deviation between actual and planned resource consumption is extracted; this deviation is the resource dissipation rate, encompassing the consumption deviations of various resources such as manpower, equipment, and materials. The arithmetic mean of the resource dissipation rates for all sites is then calculated to obtain the periodic dissipation index. During the calculation, the resource dissipation rate of each site must be included in the averaging calculation with equal weight, without considering differences in site size or resource input.

[0052] After obtaining the inefficient construction coefficient and the periodic dissipation index, both are input into the efficiency calculation model to generate the periodic efficiency index. The efficiency calculation model adopts a linear combination method, multiplying the inefficient construction coefficient and the periodic dissipation index by their respective coefficients and then summing them. The coefficients are set according to the focus of resource management. For example, if more attention is paid to the overall efficiency of resource utilization, the proportion of the periodic dissipation index coefficient can be appropriately increased.

[0053] While calculating the periodic performance indicators, the cross-site resource analysis module collects the number of construction anomaly warning signals generated by the construction smoothness assessment module. The ratio of this number to the total number of construction projects within the monitoring period is marked as the percentage of anomaly projects. For example, if there are 25 construction projects within the monitoring period, and 6 of them generate construction anomaly warning signals, then the percentage of anomaly projects is the ratio of 6 to 25.

[0054] The cycle performance index and the proportion of abnormal projects are input into a resource aggregation analysis model, which generates a global risk value through a pre-defined algorithm. In the algorithm, the cycle performance index and the proportion of abnormal projects are assigned different weights, determined based on their historical impact on the overall construction risk. For example, if historical data shows that the proportion of abnormal projects has a greater impact on the overall risk, a higher weight can be assigned. The formula for calculating the global risk value is:

[0055]

[0056] in, Represents the overall risk value. Represents a cyclical performance indicator. This represents the percentage of abnormal items. and These represent the weighting coefficients for the periodic performance indicators and the proportion of abnormal projects, respectively. .

[0057] After generating a global risk value, the resource aggregation analysis model compares it with a preset risk threshold. The preset risk threshold is set based on industry standards, corporate management objectives, and historical construction risk data. For example, it uses the maximum risk value within a 90% confidence interval, taking into account construction risk records from the past three years, as a reference threshold. When the global risk value exceeds the preset risk threshold, the cross-site resource analysis module activates a global early warning signal; when 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 early warning signal.

[0058] After a global early warning signal is generated, it is transmitted to the resource early warning module via the processor, triggering the formulation and execution of cross-site resource allocation instructions. The allocation instructions cover information such as resource type, allocation quantity, origin and destination sites, transportation route, and completion time limit. For example, five experienced construction workers may be allocated from a site with high resource utilization and no abnormal projects to a site with a higher proportion of abnormal projects, while two backup inverters may be allocated to deal with possible equipment failures.

[0059] Throughout the entire processing flow, the cross-site resource analysis module continuously receives real-time data from each construction site and dynamically updates periodic performance indicators, the proportion of abnormal projects, and the global risk value. The update frequency is determined based on the construction progress; for example, it can be updated every 4 hours during peak construction periods and every 12 hours during periods of slower construction. If the updated values ​​cause the global risk value to exceed a preset risk threshold, the module will immediately adjust the type of signal generated and simultaneously trigger corresponding subsequent operations.

[0060] In addition, the module archives the processing results for each monitoring period. The archived content includes the calculation process of the periodic performance indicators, the specific composition of the proportion of abnormal items, the change curve of the global risk value, and the types of signals generated. This archived data can be retrieved through a query interface for reviewing and optimizing resource management strategies, as well as providing a reference for parameter setting in subsequent monitoring periods.

[0061] Example 4: See Figure 5 The determination of resource utilization rate standards and the operation of equipment performance diagnosis module form a linkage mechanism, covering the entire process of resource input rationality assessment and equipment status monitoring.

[0062] The logic for determining the resource utilization rate compliance 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 components installed, the preset construction period, and the average work efficiency per person. The actual human resource input is collected in real time through the construction site attendance system, including the number of workers on duty each day, the actual working hours, and the personnel allocation for each type of work. The ratio of the actual human resource input to the planned human resource input is converted into a percentage, which yields the manpower matching rate. For example, if a construction site plans to employ 30 workers, and 27 workers actually arrive on a certain workday, then the manpower matching rate for that day is 90%. When the manpower matching rate is lower than the preset matching threshold, the construction site is determined to be a substandard site. The preset matching threshold is dynamically adjusted according to the construction stage. The threshold can be appropriately lowered during the material preparation and grid connection commissioning stages, while the threshold is set relatively higher during the component installation stage because the manpower demand is more intensive.

[0063] The server and equipment performance diagnostic module establish a communication connection via a wired network. Data transmission employs 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 through a preset data interface. Upon receiving the signal, the equipment performance diagnostic module initiates an analysis of the operational status of all construction machinery used at all photovoltaic construction sites within the specified construction period. This construction machinery encompasses various construction and testing equipment, including bracket installation machines, photovoltaic panel lifting platforms, cable laying equipment, and grid connection testing instruments.

[0064] The equipment performance diagnostic module analyzes data including equipment runtime, fault records, maintenance frequency, and key parameter fluctuations within a specified construction period. The module accesses the equipment management systems at each construction site, extracts this data, and categorizes and summarizes it. For example, for bracket installation machines, it collects total operating hours, cumulative downtime due to faults, and replacement records of different components within the specified construction period; for grid-connected testing instruments, it collects calibration records, measurement error range, and changes in battery life for each use.

[0065] During the analysis, the module compares various types of data, using the equipment's original technical parameters and the average operating level of similar equipment as benchmarks. For example, it compares the actual operating speed of a photovoltaic panel lifting platform at a construction site with the factory-set standard speed to calculate the deviation range; it 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 multi-dimensional comparisons, the module identifies equipment whose operating status deviates from the normal range and generates a signal indicating that the equipment's performance is substandard.

[0066] The equipment performance failure signal includes information such as the equipment number, the construction site, the type of abnormal parameter, and its specific value. This information is transmitted to the resource early warning module via the server. Upon receiving the signal, the resource early warning module records the relevant information in the equipment anomaly log and marks it on the equipment management interface of the monitoring terminal. The marking method uses color differentiation: yellow for minor anomalies, orange for moderate anomalies, and red for severe anomalies, facilitating quick identification of key areas of concern by management personnel.

[0067] The following is a summary table of operational status analysis data for some construction machinery within a specified construction period:

[0068]

[0069] Based on the above data and preset judgment rules, the equipment performance diagnosis module determines whether to generate a signal indicating that the equipment performance is unqualified. For example, if the deviation range of the key parameters of the grid-connected test instrument exceeds ±10%, or if the number of downtimes due to failure of the bracket installation machine exceeds 5 within the specified construction period, it is judged as unqualified, and a corresponding signal is generated.

[0070] After completing the analysis, the equipment performance diagnosis module compiles the operational status assessment results of all equipment into a report. The report includes a list of qualified equipment, a list of unqualified equipment, and details of abnormal parameters. This report is then fed back to the equipment management department via the server as reference material for equipment maintenance, replacement, and allocation. Simultaneously, the module archives all types of data generated during the analysis process, with the archiving period consistent with the specified project duration, facilitating subsequent traceability and comparison with historical data.

[0071] Example 5: The equipment performance diagnosis module achieves accurate assessment of equipment operating status through real-time monitoring and multi-dimensional analysis of key components of engineering machinery.

[0072] The operation of the equipment performance diagnostic module begins with the deployment of sensor nodes. Several sensor nodes are installed on key components of the construction machinery, including core components that directly affect equipment performance, such as engines, hydraulic pumps, transmission gears, braking systems, and control systems. The type of sensor node is determined by the monitoring parameters; for example, temperature sensors collect component operating temperatures, pressure sensors monitor hydraulic system pressure, vibration sensors record component vibration frequencies, and current sensors measure motor operating current. The sensor nodes establish a connection with the equipment performance diagnostic module via wireless transmission. The transmission frequency is set according to the importance of the component; the data transmission interval for core components is shorter, while the transmission interval for non-core components can be appropriately extended.

[0073] During construction, each sensor node continuously collects real-time operating parameters and sends the data to the equipment performance diagnosis module in real time. Upon receiving the data, the module first calculates the average of the parameters from all nodes to obtain a baseline performance value. The baseline performance value calculation covers the parameters of all sensor nodes within the same time period. For example, if five temperature sensors collect readings of 80℃, 82℃, 79℃, 81℃, and 83℃ within a minute, the baseline performance value for that time period is 81℃. Subsequently, the module calculates the absolute deviation between the real-time parameters of each node and the baseline performance value, marking this deviation as the node's anomaly degree. For example, if a temperature sensor's real-time parameter is 85℃ and the baseline performance value is 81℃, then the node's anomaly degree is 4℃. When the node's anomaly degree exceeds a preset anomaly threshold, the module marks the node as a defective node. The preset anomaly threshold is set according to the sensor type and the characteristics of the monitored parameters; for example, the anomaly threshold for a temperature sensor might be set to ±5℃, and the anomaly threshold for a vibration sensor might be set to ±0.5mm / s.

[0074] The equipment performance diagnosis module accumulates the total operating time of the construction machinery within the monitoring period. Total operating time is the cumulative working time from start-up to shutdown, excluding downtime for rest or maintenance. Simultaneously, the module counts the number of times each sensor node is marked as a defective node, labeling this as the node anomaly frequency. The node failure rate 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 6 times within the monitoring period, and the total operating time of the equipment is 300 hours, then the failure rate of that node is 6 times / 300 hours.

[0075] The module classifies nodes into different levels using a tiered judgment rule. If the node failure rate exceeds the preset upper limit, it is judged as a high-risk defect node; if the node failure rate is below the preset lower limit, it is judged as a low-risk defect node; if the node failure rate is within the range between the preset upper and lower limits, it is judged as a medium-risk defect 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.

[0076] If high-risk defect nodes exist, the equipment performance diagnosis module directly generates an equipment performance failure signal. If no high-risk defect nodes exist, the ratio of the number of medium-risk defect nodes to the number of low-risk defect nodes is calculated, and this ratio is marked as the equipment risk value. When the equipment risk value exceeds a preset risk threshold, the module generates an equipment performance failure signal. The equipment performance failure signal includes information such as equipment number, defect node location, risk level, and abnormal parameters. It is transmitted to the resource early warning module via the server. The resource early warning module updates the equipment status list based on the signal content and triggers the corresponding prompt mechanism.

[0077] During operation, the equipment performance diagnostic module stores all monitoring data and analysis results in real time. The stored data includes the original parameters of each sensor node, baseline performance values, node anomaly rates, defective node marker records, node failure rates, and the type of signal ultimately generated. This data is archived chronologically to form an equipment operation file, facilitating subsequent retrieval of the equipment's historical status and performance trends.

[0078] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0079] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic optimization allocation management platform for photovoltaic construction resources, characterized in that, It includes a processor, a resource monitoring module, a dynamic configuration optimization module, a configuration performance 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 the material preparation stage, the component installation stage, and the grid connection and commissioning stage. In the material preparation stage, the photovoltaic panels and supporting electrical equipment are transported to the designated construction site warehouse and the storage environment is monitored. When the storage environment parameters reach the preset preparation standards, the component installation stage begins. The mechanical fixing and electrical connection of the photovoltaic array are completed at the designated work station. After the construction continues for the preset working hours, the grid connection and commissioning stage begins to perform system performance testing. The resource monitoring module analyzes and generates abnormal signals such as material preparation abnormality signals, component installation abnormality signals, or grid connection debugging abnormality signals, and transmits the abnormal signals to the dynamic configuration optimization module and the resource early warning module via the processor. The resource early warning module provides visual prompts for received abnormal signals and activates audio-visual alarms. The dynamic configuration optimization module performs adaptive adjustments to the resource allocation scheme of the corresponding construction site when it receives an abnormal signal. The configuration efficiency monitoring module collects the moment when the dynamic configuration optimization module responds to the abnormal signal as the starting point of the control, and continuously monitors 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 deemed qualified and the qualified count is accumulated. When the actual control time exceeds the preset control time limit, the resource control is deemed unqualified and the unqualified count is accumulated. The processor is connected to a construction smoothness assessment module, which performs the following actions: statistically analyzing the frequency of various abnormal signals occurring within the target construction period and marking them as abnormal frequency indicators; simultaneously acquiring the qualified and unqualified counts recorded by the configuration performance monitoring module, and marking the quotient of the unqualified counts and qualified counts as the control failure coefficient; processing the abnormal frequency indicators and control failure coefficients through a weighted fusion algorithm to generate a construction smoothness evaluation value; and generating a construction abnormality warning signal and transmitting it to the resource early warning module when the construction smoothness evaluation value exceeds a preset smoothness threshold.

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

3. The photovoltaic construction resource dynamic optimization allocation management platform according to claim 1, characterized in that, The processor is connected to a cross-site resource analysis module, which performs the following actions: setting a monitoring period, summarizing resource scheduling records and abnormal response records of all photovoltaic construction sites within the period, and generating a global early warning signal or a global normal signal through multi-dimensional data analysis; the global early warning signal is transmitted to the resource early warning module via the processor and triggers a cross-site resource allocation command.

4. The photovoltaic construction resource dynamic optimization allocation management platform according to claim 3, characterized in that, The processing flow of the cross-site resource analysis module includes: calculating the periodic efficiency index through the construction efficiency evaluation algorithm, and simultaneously collecting the number of construction anomaly warning signals generated by the construction smoothness evaluation module, marking the ratio of the number of signals to the total number of construction projects within the period as the percentage of abnormal projects; inputting the periodic efficiency index and the percentage of abnormal projects into the resource aggregation analysis model to generate a global risk value; The global early warning signal is activated when the global risk value exceeds the preset risk threshold.

5. The photovoltaic construction resource dynamic optimization allocation management platform according to claim 4, characterized in that, The construction efficiency evaluation algorithm is executed as follows: it determines whether the construction of each construction site meets the resource utilization rate standard, marks the ratio of the number of substandard construction sites to the total number of construction sites as the inefficient construction coefficient, extracts the deviation between the actual resource consumption and the planned resource consumption of each substandard construction site and marks it as the resource dissipation degree, calculates the average of the resource dissipation degrees of all construction sites to obtain the periodic dissipation index, and inputs the inefficient construction coefficient and the periodic dissipation index into the efficiency calculation model to generate the periodic efficiency index.

6. The photovoltaic construction resource dynamic optimization allocation management platform according to claim 5, characterized in that, The logic for determining the resource utilization rate 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 determined to be a non-compliant construction site.

7. The photovoltaic construction resource dynamic optimization allocation management platform according to claim 3, characterized in that, The server is communicatively connected to the equipment performance diagnosis module. When the cross-site resource analysis module generates a global normal signal, the server forwards the global normal signal to the equipment performance diagnosis module. After receiving the signal, the equipment performance diagnosis module analyzes the operating status of all construction machinery used in photovoltaic construction sites within the specified construction period, generates an equipment performance failure signal based on the analysis results, and transmits it to the resource early warning module via the server.

8. The photovoltaic construction resource dynamic optimization allocation management platform according to claim 7, characterized in that, The equipment performance diagnosis module performs the following: deploying several sensor nodes on the key components of the engineering machinery, collecting the real-time working parameters of each node during construction, calculating the average value of the parameters of all nodes to obtain the benchmark performance value, and marking the absolute deviation between the real-time parameters of each node and the benchmark performance value as the node anomaly degree. If the anomaly degree of a node exceeds the preset anomaly threshold, it is marked as a defective node. The total runtime of the construction machinery within the cumulative monitoring period is recorded. The number of times each sensor node is marked as a defective node is counted and marked as the node anomaly frequency. The ratio of the node anomaly frequency to the total runtime is marked as the node failure ratio. The nodes are classified into high-risk defective nodes, medium-risk defective nodes, or low-risk defective nodes according to the classification judgment rules. If a high-risk defect node exists, a signal indicating that the equipment performance is unqualified will be generated. If there are no high-risk defect nodes, the ratio of the number of medium-risk defect nodes to the number of low-risk defect 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.

9. The photovoltaic construction resource dynamic optimization allocation management platform according to claim 8, characterized in that, The classification judgment rule is defined as follows: if the node failure rate exceeds the preset upper limit of the failure rate, it is judged as a high-risk defect node; if the node failure rate is lower than the preset lower limit of the failure rate, 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 defect node.

Images