A large-span cableway construction energy efficiency management system based on edge computing

Abstract

The application discloses a kind of based on edge computing's large-span cableway construction energy efficiency management system, it is related to construction energy efficiency intelligent distribution technical field, by constructing three-dimensional energy efficiency benchmark matrix integration multi-source data, and introduce the construction utility resource allocation model based on linear utility coefficient and virtual resource budget;With iterative optimization module, through loop calculation and dynamic adjustment shadow price, until total energy efficiency demand and total supply balance, finally output optimal first allocation index, the system realizes closed loop from data perception to accurate control, can be divided into energy efficiency level according to second energy efficiency index and execute differentiated equipment power regulation strategy, to significantly improve the energy efficiency level, resource utilization and construction period guarantee capability of large-span cableway construction.

Description

Technical Field

[0001] This invention relates to the field of intelligent energy efficiency allocation technology in construction, and in particular to an energy efficiency management system for long-span cableway construction based on edge computing. Background Technology

[0002] As a key transportation infrastructure project spanning complex terrains such as mountains and canyons, long-span cableway projects are characterized by numerous high-altitude operations, high technical difficulty, long construction periods, wide distribution of energy equipment, and high total energy consumption. Therefore, how to achieve energy conservation and consumption reduction and improve energy efficiency management in such large-scale projects has become one of the core issues that urgently need to be addressed in the field of engineering construction.

[0003] Currently, traditional energy efficiency management in cableway construction mainly relies on manual recording, regular inspections, and experience-based adjustments. This model has several inherent flaws: First, at the data acquisition level, energy consumption data is often obtained through manual meter reading or isolated sensors, resulting in significant lag and incompleteness. The data dimensions are limited, typically only recording total electricity or fuel consumption, failing to accurately correlate energy consumption with specific equipment, processes, and environmental factors, leading to low data value. Second, at the data analysis level, there is a lack of a unified and scientific energy efficiency assessment index system. Management decisions are mostly based on macroscopic, static comparisons of total energy consumption, failing to dynamically measure energy costs per unit of construction progress and making it difficult to identify energy efficiency bottlenecks and improvement potential in local areas. Finally, at the resource management level, energy resource allocation often relies on the subjective experience of managers, adopting an extensive management model of average distribution. This model cannot respond to the dynamic needs of different construction zones due to differences in progress, environmental changes, and equipment status, easily leading to resource misallocation, resulting in resource surplus and waste in some areas, while critical areas suffer delays due to resource shortages.

[0004] With the development of IoT and cloud computing technologies, some research attempts to introduce intelligent methods into construction management. However, for cableway construction sites located in remote areas, directly uploading massive amounts of sensor data to the cloud for processing faces challenges such as insufficient network bandwidth, high transmission latency, and poor real-time performance, making it difficult to meet the needs of real-time monitoring and rapid decision-making during the construction process. In addition, some existing energy efficiency management systems focus on monitoring and display, and their analytical models are too simplified, failing to consider energy efficiency, progress, environment, and other multi-dimensional factors within a unified optimization framework for collaborative evaluation, and thus unable to generate precise and differentiated energy efficiency management strategies. Therefore, current technology lacks an energy efficiency management system that can adapt to the characteristics of long-span cableway construction, integrating real-time accurate sensing, dynamic energy efficiency assessment, and intelligent optimization decision-making. There is an urgent need for a solution based on edge computing architecture to bring computing power down to the construction site, enabling on-site processing and real-time analysis of multi-source energy efficiency data, so as to comprehensively improve the energy efficiency level and economic benefits of long-span cableway construction. Summary of the Invention

[0005] The technical problem solved by this invention is that the existing technology only focuses on the absolute energy consumption of the construction area and ignores the dynamic evaluation target of construction progress and construction energy efficiency cost. Traditional experience allocation cannot achieve the global optimal energy efficiency allocation of construction zones, and it is difficult to accurately measure and intervene in the energy efficiency problem of construction progress in a timely manner.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] An edge computing-based energy efficiency management system for long-span cableway construction includes a construction energy efficiency data acquisition module, an energy efficiency analysis module, an iterative optimization module, and a classification module.

[0008] The construction energy efficiency acquisition module is used to collect cableway construction energy efficiency data, classification data, and electrical energy base cost to construct a three-dimensional energy efficiency benchmark matrix.

[0009] The energy efficiency analysis module is used to calculate the first energy efficiency index through cableway construction energy efficiency data and a three-dimensional energy efficiency benchmark matrix, allocate energy efficiency to the construction area according to the first energy efficiency index and the expected construction period, and generate a set of utility parameters and a virtual budget allocation vector.

[0010] The iterative optimization module is used to construct a construction utility resource allocation model, perform iterative calculations on the utility parameter set and the virtual budget allocation vector, and generate a first allocation index.

[0011] The grading module is used to calculate the second energy efficiency index based on the first allocation index of each construction zone, and to grade the construction zones according to the second energy efficiency index, thereby generating the optimal energy efficiency management decision.

[0012] Preferably, the construction energy efficiency acquisition module includes a sensor acquisition unit and an initialization energy efficiency unit;

[0013] The sensor acquisition unit includes:

[0014] Multimodal sensors are deployed in the cableway construction area to collect cableway construction energy efficiency data. The multimodal sensors send the cableway construction energy efficiency data to the nearest edge node through a lightweight communication protocol. The edge node is used to receive and store the cableway construction energy efficiency data.

[0015] The construction progress of each cableway construction area is statistically analyzed to obtain construction progress data, which is used to represent the cumulative completion percentage of different construction areas.

[0016] The multimodal sensors include an energy meter, an equipment operation sensor, a GPS positioning device, an environmental sensor, and a load sensor;

[0017] The cableway construction energy efficiency data includes timestamps, equipment IDs, energy consumption values, operating time, operating status, location information, equipment load rates, and environmental parameters.

[0018] The environmental parameters include temperature, humidity, and wind speed;

[0019] The energy consumption value includes the value of building material consumption and the value of energy resource consumption.

[0020] Preferably, the initial energy efficiency unit includes:

[0021] Extract energy efficiency data from cableway construction, classify equipment IDs as core equipment, and obtain power equipment data, which includes core equipment IDs and non-core equipment IDs.

[0022] An environmental correction factor is set based on the environmental parameters of the day. The processing logic for the environmental correction factor is as follows:

[0023] The initial environmental correction factor is 0. When the temperature is greater than the preset temperature threshold, the environmental correction factor increases by 0.03 for every 1 degree Celsius increase in temperature. When the humidity is greater than the preset humidity threshold, the environmental correction factor increases by 0.05.

[0024] The load correction factor is set based on the equipment load rate of the day. The processing logic for the load correction factor is as follows:

[0025] The initial load correction factor is 0. When the equipment load rate is less than the preset load rate, the load correction factor increases by 0.15.

[0026] Based on environmental correction factors and load correction factors, the basic cost of electricity is calculated. The formula for calculating the basic cost of electricity is as follows:

[0027] ;

[0028] in, For the basic cost of electricity, This is the environmental correction factor. This is the load correction factor. Basic electricity price;

[0029] A three-dimensional energy efficiency benchmark matrix is ​​constructed using cableway construction energy efficiency data, classification data, and basic electricity costs. Each row of the three-dimensional energy efficiency benchmark matrix represents a construction zone, each column represents an energy type, and each element of the three-dimensional energy efficiency benchmark matrix represents the initial energy efficiency cost of the energy type required for the current construction zone.

[0030] Preferably, the energy efficiency analysis module includes an energy efficiency index calculation unit and a virtual budget construction unit;

[0031] The energy efficiency index calculation unit includes:

[0032] Extract the three-dimensional energy efficiency benchmark matrix and construction progress data, calculate the first energy efficiency index, and the processing logic for the first energy efficiency index is as follows:

[0033] Extract the data from each row of the three-dimensional energy efficiency benchmark matrix and the construction progress data, and calculate the construction energy efficiency index for each construction zone.

[0034]

[0035] in, Here, i represents the construction energy efficiency index for construction zone i, and i is the label for construction zone i. ), For the first The electrical base cost of the j-th electrical equipment in each construction zone, where j is the data label of the electrical equipment. For the first Energy consumption of the j-th electrical equipment in each construction zone. Let i represent the construction progress data for the i-th construction zone. For the estimated construction period;

[0036] Statistical analysis of construction energy efficiency indicators for each construction zone was conducted to obtain the primary energy efficiency indicator. .

[0037] Preferably, the virtual budget construction unit includes:

[0038] Based on the primary energy efficiency index and the estimated construction period, a resource allocation model is constructed. The processing logic of the resource allocation model is as follows:

[0039] Set a linear utility coefficient for each construction zone i That is, the reciprocal of the construction energy efficiency index. ;

[0040] Calculate the virtual resource budget for each construction zone i. The expression for calculating the virtual resource budget is:

[0041] ;

[0042] in, The preset schedule for construction zone i This represents the current actual progress, i.e., the construction progress data;

[0043] Save the linear utility coefficients of each construction zone as a set of utility parameters, and save the virtual resource budget of each construction zone as a virtual budget allocation vector.

[0044] Preferably, the iterative optimization module includes a supply and demand iterative unit and an optimal allocation unit;

[0045] The supply and demand iteration unit includes:

[0046] The arithmetic mean of the basic costs of the corresponding energy types in the three-dimensional energy efficiency benchmark matrix is ​​calculated as the initial shadow price p. Using the construction utility resource allocation model, iterative calculations are performed for each construction zone. The processing logic of the construction utility resource allocation model is as follows:

[0047] Preset the total energy efficiency supply for construction, initialize the shadow prices p of various energy efficiency resources, initialize the iteration counter k=0, and for each construction zone i, calculate the current shadow price. and virtual resource budget The energy efficiency resource index values ​​for each construction zone are calculated iteratively. The calculation expression for the energy efficiency resource index values ​​is as follows:

[0048] ;

[0049] in, To calculate the total energy efficiency resource demand for construction zone i in the k-th iteration, we need to determine the energy efficiency resource allocation. The linear utility coefficient;

[0050] The total energy efficiency resource demand is obtained by summing the energy efficiency resource demand of each interval. The absolute difference between the total energy efficiency demand and the total energy efficiency supply is compared. The shadow price is iteratively updated based on the gradient ascent method. The iteration is terminated when the absolute difference is less than the preset convergence threshold.

[0051] Preferably, the optimal allocation unit includes:

[0052] When the supply and demand iteration unit converges, record the currently converged shadow price. Calculate the optimal energy efficiency index value for each construction zone based on shadow prices. The optimal energy efficiency index of all construction zones is saved as the first allocation index.

[0053] Preferably, the classification module includes an energy efficiency index calculation unit and a decision generation unit;

[0054] The energy efficiency index calculation unit includes:

[0055] Extract the initial energy efficiency cost of each construction zone from the three-dimensional energy efficiency benchmark matrix, and calculate the second energy efficiency index. The calculation expression for the second energy efficiency index is as follows:

[0056] ;

[0057] in, This is the second energy efficiency indicator for construction zone i. The first allocation index for construction zone i. For virtual resource budgeting, This is the environmental correction factor. This is the load correction factor. The initial energy efficiency cost of the electrical energy required for construction zone i;

[0058] The logic for dividing the second energy efficiency index into three intervals—the first interval, the second interval, and the third interval—by setting a first threshold and a second threshold is as follows:

[0059] When the sample corresponding to the second energy efficiency index is greater than the first threshold, it is set as the first index range;

[0060] When the sample corresponding to the second energy efficiency index is between the first threshold and the second threshold, it is set as the second index interval;

[0061] When the sample corresponding to the second energy efficiency index is less than the second threshold, it is set to the third index range;

[0062] The number of samples in the first indicator interval is greater than the number of samples in the second indicator interval, which is greater than the number of samples in the third indicator interval.

[0063] Preferably, the decision generation unit includes:

[0064] The first indicator range is divided into Level 1 energy efficiency zones, corresponding to the construction areas with the highest second energy efficiency index. The second indicator range is divided into Level 2 energy efficiency zones, corresponding to the construction areas with the middle second energy efficiency index. The third energy efficiency index is divided into Level 3 energy efficiency zones, corresponding to the construction areas with the lowest second energy efficiency index.

[0065] Preferably, the processing methods corresponding to the Level 1, Level 2, and Level 3 energy efficiency zones are as follows:

[0066] Adjust the power of core and non-core equipment in the first-level energy efficiency zone to 100%;

[0067] Adjust the power of core equipment corresponding to the Level 2 energy efficiency zone to 100%, and adjust the power of non-core equipment to 50%.

[0068] Adjust the power of core equipment corresponding to the Level 3 energy efficiency zone to 80%, and shut down non-core equipment.

[0069] The beneficial effects of this invention are as follows: By collecting resource energy efficiency data from historical construction sections through a construction energy efficiency acquisition module and constructing a three-dimensional matrix based on physical adjustments such as temperature and humidity, the energy efficiency of historical construction can be comprehensively reflected. Furthermore, by incorporating the construction progress of each construction section and introducing a virtual resource budget, construction progress and resources are dynamically linked, providing correlation parameters for subsequent iterative optimization mechanisms. The key to this invention lies in constructing a self-optimizing intelligent energy efficiency management system by introducing an economically based resource allocation model and a dynamic iterative optimization mechanism. The introduction of the linear utility coefficient and the iterative optimization output mechanism are the innovations of this invention. This invention establishes a value orientation for resource allocation, fundamentally solving the problem of resource misallocation. The traditional method cannot quantify and assess the differences in benefits generated by resource investment in different areas, resulting in resources often being evenly distributed or invested in inefficient construction areas. This invention creatively defines the reciprocal of the first energy efficiency index as the utility coefficient, indicating that the lower the energy efficiency level (higher E value) of a construction zone, the lower the utility improvement (linear utility coefficient value) that a unit of resource investment can bring. This enables the system to automatically identify the necessary zones for resource allocation. The system will instinctively tilt resources towards zones with high utilization coefficient (i.e., high energy efficiency), allowing resources to create greater marginal value. This not only greatly improves the overall utilization efficiency of resources, but also forms a positive incentive, guiding the entire construction system towards a more efficient state from a mechanistic perspective. Attached Figure Description

[0070] Figure 1 A basic flowchart of a large-span cableway construction energy efficiency management system based on edge computing is provided as an embodiment of the present invention;

[0071] Figure 2 This is a schematic diagram illustrating the basic process of constructing a three-dimensional energy efficiency benchmark matrix. Detailed Implementation

[0072] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0073] Example, refer to Figure 1 This paper presents an energy efficiency management system for long-span cableway construction based on edge computing, including a construction energy efficiency acquisition module, an energy efficiency analysis module, an iterative optimization module, and a classification module.

[0074] The construction energy efficiency data acquisition module is used to collect cableway construction energy efficiency data, classification data, and electrical energy base costs to construct a three-dimensional energy efficiency benchmark matrix.

[0075] The energy efficiency analysis module is used to calculate the first energy efficiency index using cableway construction energy efficiency data and a three-dimensional energy efficiency benchmark matrix, allocate energy efficiency to the construction area based on the first energy efficiency index and the expected construction period, and generate a set of utility parameters and a virtual budget allocation vector.

[0076] The iterative optimization module is used to construct a construction utility resource allocation model, perform iterative calculations on the utility parameter set and the virtual budget allocation vector, and generate the first allocation index.

[0077] The grading module is used to calculate the second energy efficiency index based on the first allocation index of each construction zone, and to grade the construction zones according to the second energy efficiency index, thereby generating the optimal energy efficiency management decision.

[0078] The construction energy efficiency data acquisition module includes a sensor acquisition unit and an initialization energy efficiency unit;

[0079] The sensor acquisition unit includes:

[0080] Multimodal sensors are deployed in the cableway construction area to collect cableway construction energy efficiency data. The multimodal sensors send the cableway construction energy efficiency data to the nearest edge node through a lightweight communication protocol. The edge node is used to receive and store the cableway construction energy efficiency data.

[0081] The construction progress of each cableway construction area is statistically analyzed to obtain construction progress data, which is used to represent the cumulative completion percentage of different construction areas.

[0082] Multimodal sensors include energy meters, equipment operation sensors, GPS positioning devices, environmental sensors, and load sensors;

[0083] Cableway construction energy efficiency data includes timestamps, equipment IDs, energy consumption values, operating time, operating status, location information, equipment load rates, and environmental parameters.

[0084] Environmental parameters include temperature, humidity, and wind speed;

[0085] Energy consumption value includes the value of building material consumption and the value of energy resource consumption.

[0086] Within each construction zone, a systematic deployment of various multimodal sensors is implemented: energy meters and equipment operation sensors are installed on large equipment to monitor energy consumption and operational status in real time; GPS positioning devices are installed on key construction machinery such as winches and traction machines to accurately record their working positions and ensure accurate data correlation with the construction zone; environmental sensors are installed at key locations within the construction area to collect temperature, humidity, and wind speed data. For example, in the cable-laying area, wind speed is a key factor affecting construction energy efficiency; load sensors are installed on lifting and tensioning equipment to measure equipment load rates and determine whether the equipment is operating within its high-efficiency range. Data including timestamps, equipment IDs, energy consumption values, operating time, operating status, location information, equipment load rates, and environmental parameters are collected as cableway construction energy efficiency data. This data is transmitted via lightweight communication. The protocol is sent to the edge nodes at the construction site. The edge nodes are responsible for receiving, storing, and initially aggregating the data. Lightweight communication protocols such as MQTT or CoAP can be used. At the same time, the edge nodes collect construction progress data for each construction area, i.e., the cumulative completion percentage, for subsequent energy efficiency analysis. This unit is used to ensure real-time and comprehensive coverage of the construction area, build a distributed data acquisition network, and use an edge computing framework to achieve low-latency data processing, providing real-time and reliable data input for the entire system. The power meter records the power consumption for the whole day, and the environmental sensors detect the temperature, wind speed, and humidity of the day, which together affect the equipment's energy consumption, i.e., power consumption. In this embodiment, a 5-day time window is used to collect daily power consumption and other cableway construction energy efficiency data. All data are preprocessed at the edge nodes to reduce data transmission volume and improve response speed.

[0087] The initial energy efficiency unit includes:

[0088] Extract energy efficiency data from cableway construction, classify equipment IDs as core equipment, and obtain power equipment data, which includes core equipment IDs and non-core equipment IDs.

[0089] The environmental correction factor is set based on the environmental parameters of the day. The processing logic for the environmental correction factor is as follows:

[0090] The initial environmental correction factor is 0. When the temperature is greater than the preset temperature threshold, the environmental correction factor increases by 0.03 for every 1 degree Celsius increase in temperature. When the humidity is greater than the preset humidity threshold, the environmental correction factor increases by 0.05.

[0091] The load correction factor is set based on the equipment load rate of the day. The processing logic for the load correction factor is as follows:

[0092] The initial load correction factor is 0. When the equipment load rate is less than the preset load rate, the load correction factor increases by 0.15.

[0093] Based on environmental and load correction factors, the cost of electricity base is calculated. The formula for calculating the cost of electricity base is as follows:

[0094] ;

[0095] in, For the basic cost of electricity, This is the environmental correction factor. This is the load correction factor. Basic electricity price;

[0096] A three-dimensional energy efficiency benchmark matrix was constructed using cableway construction energy efficiency data, electrical equipment data, and electrical infrastructure costs, with reference to... Figure 2 The rows of the three-dimensional energy efficiency benchmark matrix represent construction zones, and the columns represent energy types. Each element of the three-dimensional energy efficiency benchmark matrix represents the initial energy efficiency cost of the energy type required for the current construction zone.

[0097] The original cableway construction energy efficiency data is extracted from the edge nodes. The equipment is classified into core and non-core according to the equipment ID. Core equipment is equipment that is directly related to the progress of the main construction line and has high power and cannot be easily stopped, such as cable traction machine, main winch and tower crane. Non-core equipment is auxiliary equipment, such as area lighting system, small electric welding machine and on-site ventilation equipment.

[0098] Read the historical average values ​​of environmental parameters for the day. Set the preset temperature threshold to 30 degrees Celsius and the humidity threshold to 80%. Initialize the environmental correction factor a=0. If the average temperature for the day is 35 degrees Celsius, which is higher than the preset temperature threshold, then a increases. If the average humidity of the day is 85%, then 'a' is increased by 0.05, so the final environmental correction factor is 0.2. The average equipment load rate of the core equipment is calculated for the day, with a preset load rate threshold of 60%. Analysis of the core equipment data reveals that the average load rate is only 50%, lower than the preset load rate. Therefore, the load correction factor b = 0.15 is used as a penalty for inefficient load. The local grid's base electricity price is then obtained. (For example, =1.0 yuan / kWh), combined with environmental correction factor and load correction factor, the basic cost of electricity W is 1.35 yuan / kWh. Compared with the basic electricity price, it can more realistically reflect the actual cost of each kilowatt-hour consumed under the current harsh working conditions of high temperature, high humidity and insufficient equipment load. Based on all the previous data, a core three-dimensional energy efficiency benchmark matrix is ​​constructed. The matrix rows are different construction zones, and the matrix columns are different electrical equipment. The elements are the initial energy efficiency costs of a certain electrical equipment in a certain zone. The initial energy efficiency costs are obtained by statistically analyzing the overall electricity costs of the equipment on that day.

[0099] The innovation of this embodiment lies in quantifying objective environmental conditions and subjective operating status into coefficients that can be used in calculations. This makes the energy efficiency cost more realistic and accurate, and also provides a fair correction standard for subsequent energy efficiency assessments. It avoids the bias caused by using a fixed benchmark price for assessment in harsh environments or inefficient operating conditions. Based on the quantification of influencing factors, it identifies key external and internal factors that affect energy efficiency, thereby dynamically compensating for theoretical costs and guiding more reasonable optimization decisions.

[0100] The energy efficiency analysis module includes an energy efficiency index calculation unit and a virtual budget construction unit;

[0101] The energy efficiency index calculation unit includes:

[0102] Extract the three-dimensional energy efficiency benchmark matrix and construction progress data, calculate the first energy efficiency index, and the processing logic for the first energy efficiency index is as follows:

[0103] Extract the data from each row of the three-dimensional energy efficiency benchmark matrix and the construction progress data, and calculate the construction energy efficiency index for each construction zone. The calculation expression for the construction energy efficiency index is as follows:

[0104]

[0105] in, Here, i represents the construction energy efficiency index for construction zone i, and i is the label for construction zone i. ), For the first The electrical base cost of the j-th electrical equipment in each construction zone, where j is the data label of the electrical equipment. For the first Energy consumption of the j-th electrical equipment in each construction zone. Let i represent the construction progress data for the i-th construction zone. For the estimated construction period;

[0106] Statistical analysis of construction energy efficiency indicators for each construction zone was conducted to obtain the primary energy efficiency indicator. .

[0107] In a specific embodiment of the present invention, the energy efficiency index calculation unit receives a three-dimensional energy efficiency benchmark matrix from the initialization energy efficiency unit. Its core task is to quantify the energy efficiency level of each construction zone, calculate the first energy efficiency index E, and for each construction zone i (e.g., P1, P2, P3), extract the basic energy cost of all corresponding electrical equipment. Simultaneously, it obtains the construction progress data (cumulative completion percentage) and the project's estimated total duration T from the database for that zone. Applying the given construction energy efficiency index formula, it calculates the construction energy efficiency index, where the numerator represents the total electrical energy consumption cost of that zone during the statistical period. This cost is the energy consumption cost of all electrical equipment j within that zone, i.e., the corrected unit cost. Actual consumption The summation of these values ​​reflects the inputs of that region, while the denominator represents the planned schedule equivalent for that region. This is the current progress. It is the total construction period. The time required to proceed at the planned rate measures the productivity of that partition; the slower the progress, the smaller this value. The physical meaning of this is the energy efficiency cost consumed per unit of planned schedule equivalent. The higher the value, the greater the energy cost for that partition to achieve its current progress in the future, meaning lower energy efficiency; specific examples are as follows:

[0108] The estimated construction period is T = 100 days, and zone P1 is the western tower base area:

[0109] Construction progress =0.5 (50% complete), this section has two core pieces of equipment: the main winch and the lighting system;

[0110] Main winch: =1.35 yuan / kWh, =500kWh, the cost at this time is 675 yuan;

[0111] Lighting system: =1.35 yuan / kWh, =100kWh, the cost at this time is 135 yuan;

[0112] Total energy cost ∑=675+135=810 yuan;

[0113] =810 / (0.5×100)=810 / 50=16.2;

[0114] Zone P2 is the mid-span cable work area:

[0115] Construction progress =0.8 (80% complete), this section has one core piece of equipment: cable traction machine;

[0116] Cable traction machine: =1.35 yuan / kWh, =600kWh, at this time, the total energy cost ∑=810 yuan;

[0117] =810 / (0.8×100)=810 / 80=10.125;

[0118] The system iterates through all construction zones, calculates each construction energy efficiency index, and summarizes them into the first energy efficiency index. This example shows that the energy efficiency of zone 1 is much lower than that of zone 2.

[0119] This embodiment serves as a core unit in a data processing workflow. Through the output of the preceding modules, it calculates a unified quantitative measurement standard that enables horizontal comparison of energy efficiency levels across different construction zones. The calculated first energy efficiency index provides a direct driving force for subsequent optimization and iteration processes, influencing the priority of subsequent resource allocation. The quantitative system provided by the energy efficiency index calculation unit serves as a feasible quantitative standard to diagnose and locate energy efficiency problems, providing direction and basis for system optimization.

[0120] The virtual budget building block includes:

[0121] Based on the primary energy efficiency index and the estimated construction period, a resource allocation model is constructed. The processing logic of the resource allocation model is as follows:

[0122] Set a linear utility coefficient for each construction zone i That is, the reciprocal of the construction energy efficiency index. Calculate the virtual resource budget for each construction zone. The expression for calculating the virtual resource budget is:

[0123] ;

[0124] in, The preset schedule for construction zone i This represents the current actual progress, i.e., the construction progress data;

[0125] Save the linear utility coefficients of each construction zone as a set of utility parameters, and save the virtual resource budget of each construction zone as a virtual budget allocation vector.

[0126] By iterating through the first energy efficiency index, the linear utility coefficient of each construction zone i is calculated. = This linear utility coefficient establishes a core economic model; the worse the first energy efficiency index Ei (the higher the value), the higher the utility coefficient of the corresponding partition. The smaller the value, the lower the utility improvement brought by the unit resource of the partition. The virtual resource budget quantifies the progress deficit of each partition relative to the plan and transforms it into the demand and usage of additional resources by obtaining the preset planned progress and the current actual progress of each construction partition.

[0127] This embodiment, as a core innovation of the present invention, transforms the first energy efficiency index from a negative index ( The higher the value, the worse the energy efficiency of the construction zone (which is converted into a positive indicator: linear utility coefficient). (linear utility coefficient) The higher the linear utility coefficient, the better the resource utility. This avoids extensive allocation in subsequent optimization models and achieves precise allocation based on value-driven principles. The lower the linear utility coefficient of a construction zone, the lower its potential and efficiency in further executing construction tasks, thus achieving rationalization of subsequent overall energy efficiency allocation. The virtual resource budget quantifies the schedule deviation in project schedule management into a key input parameter in the resource optimization model, realizing the linkage between schedule and resources. Even if a zone has acceptable energy efficiency but is seriously behind schedule, it will still receive more resource budget to ensure the overall project schedule. Conversely, a zone that is ahead of schedule can appropriately relinquish resources. The introduction of the linear utility coefficient brings efficiency orientation to resource allocation. It ensures that valuable energy efficiency resources are not continuously wasted in bottomless inefficient areas, but are used to amplify the advantages of efficient areas, thereby raising the overall energy efficiency level of the project. The introduction of the virtual resource budget brings schedule guarantee for resource allocation. It ensures that the resource allocation model is closely linked to the timely completion of the project, preventing the system from sacrificing the overall schedule in pursuit of extreme local energy efficiency, reflecting the idea of ​​global optimization.

[0128] The iterative optimization module includes a supply and demand iteration unit and an optimal allocation unit;

[0129] The supply and demand iteration unit includes:

[0130] The arithmetic mean of the base costs for the corresponding energy types in the three-dimensional energy efficiency benchmark matrix is ​​calculated as the initial shadow price p. Using the construction utility resource allocation model, iterative calculations are performed for each construction zone. The processing logic of the construction utility resource allocation model is as follows:

[0131] Preset the total energy efficiency supply for construction, initialize the shadow prices p of various energy efficiency resources, initialize the iteration counter k=0, and for each construction zone i, calculate the current shadow price. and virtual resource budget The energy efficiency resource index values ​​for each construction zone are calculated iteratively. The expression for calculating the energy efficiency resource index values ​​is as follows:

[0132] ;

[0133] in, To calculate the total energy efficiency resource demand for construction zone i in the k-th iteration, we need to determine the energy efficiency resource allocation. The linear utility coefficient;

[0134] The total energy efficiency resource demand is obtained by summing the energy efficiency resource demand of each interval. The absolute difference between the total energy efficiency demand and the total energy efficiency supply is compared. The shadow price is iteratively updated based on the gradient ascent method. The iteration is terminated when the absolute difference is less than the preset convergence threshold.

[0135] Receive the utility parameter set and virtual budget allocation vector from the virtual budget construction unit. Based on the overall energy budget of the project, set the total energy efficiency resource available for allocation within five days to S=1000 kWh. Extract the basic energy cost W of all electrical equipment in the three-dimensional energy efficiency benchmark matrix and calculate its arithmetic mean as the initial shadow price. Initialize the iteration counter k=0, start a loop, calculate the resource requirements for each partition, and for each construction partition i, calculate the resource requirements based on the current shadow price. Linear utility coefficient and virtual budget Calculate its energy efficiency resource demand at this price. The total energy efficiency resource demand at the current price is obtained by summing the energy efficiency resource demands of all construction zones. The absolute difference between total demand and total supply is calculated, and the shadow price is updated using the gradient ascent method. When demand is less than supply, the price should decrease to stimulate demand; when demand exceeds supply, the price should increase to suppress demand. This process is repeated iteratively. The cycle continues to adjust until the absolute difference is less than 1, at which point the aggregate demand and aggregate supply are almost equal.

[0136] This embodiment, as one of the core innovations of this invention, initializes the shadow price as an average value, providing a reasonable initial guess for the iterative process. This significantly reduces the number of iterations, improves computational efficiency, and constructs a construction utility resource allocation model and iteration. The aim is to create a dynamic simulated market, solving for the optimal value through calculation and feedback. This is the core algorithm for achieving globally optimal resource allocation. This innovative approach can adaptively respond to different utilities and demands in each partition and find an equilibrium point under overall supply constraints. Through the Walrasian equilibrium principle, the shadow price is introduced as a reconciler between the utility coefficient and the virtual budget. When aggregate demand is much less than aggregate supply, the shadow price decreases, making... Construction zones with smaller products (i.e., less efficient zones) can allocate more resources. When aggregate demand approaches or exceeds aggregate supply, shadow prices rise, causing... Construction zones with smaller product cannot obtain resources. Instead, resources are allocated to the most efficient and urgently needed construction zones. This embodiment achieves a dynamic resource allocation that balances fairness and efficiency. Through an iterative optimization mechanism, it replaces the resource allocation method based on construction experience, realizing data-driven resource allocation. By simulating market price adjustment mechanism, it efficiently ensures the complex resource planning problem of construction progress and energy efficiency under the condition of limited total resources. The output of the resource demand of each zone is the most critical step in the supply and demand iteration.

[0137] The optimal allocation unit includes:

[0138] When the supply and demand iteration unit converges, record the currently converged shadow price. Calculate the optimal energy efficiency index value for each construction zone based on shadow prices. The optimal energy efficiency index of all construction zones is saved as the first allocation index.

[0139] It receives the convergence status from the supply and demand iteration unit and outputs the final first allocation index of the entire optimization process. The first allocation index serves as the action plan that will affect the actual resource scheduling.

[0140] The rating module includes an energy efficiency index calculation unit and a decision generation unit;

[0141] The energy efficiency index calculation unit includes:

[0142] Extract the initial energy efficiency cost of each construction zone from the three-dimensional energy efficiency benchmark matrix, and calculate the second energy efficiency index. The calculation expression for the second energy efficiency index is as follows:

[0143] ;

[0144] in, This is the second energy efficiency indicator for construction zone i. The first allocation index for construction zone i. For virtual resource budgeting, This is the environmental correction factor. This is the load correction factor. The initial energy efficiency cost of the electrical energy required for construction zone i;

[0145] The logic for dividing the second energy efficiency index into three intervals—the first interval, the second interval, and the third interval—by setting a first threshold and a second threshold is as follows:

[0146] When the sample corresponding to the second energy efficiency indicator is greater than the first threshold, it is set as the first indicator range;

[0147] When the sample corresponding to the second energy efficiency indicator is between the first threshold and the second threshold, it is set as the second indicator range;

[0148] When the sample corresponding to the second energy efficiency indicator is less than the second threshold, it is set to the third indicator range;

[0149] The number of samples within the first indicator interval is greater than the number of samples within the second indicator interval, which in turn is greater than the number of samples within the third indicator interval.

[0150] By calculating the second energy efficiency index, a comprehensive efficiency evaluation index was created. Its purpose is to consider the overall efficiency of system resource allocation optimization under actual working conditions and schedule pressures. The numerator of the second energy efficiency index calculation formula is... The denominator represents the amount of resources that the partition should receive after complex system optimization. This represents the overall difficulty level faced by this partition, therefore The physical meaning of "individual difficulty" is the amount of resources the system determines should be allocated. The higher the value, the greater the potential of the current construction zone to overcome difficulties and utilize resources efficiently, and the higher its comprehensive energy efficiency level. A first threshold of 0.06 and a second threshold of 0.03 are set for continuous energy efficiency indicators. Discretization into management levels, by setting thresholds, complex management objects are divided into typical construction energy efficiency categories. Targeted and standardized management measures are implemented for each category. In the example, the first allocation indicator and virtual resource budget calculated for partition P2 are both negative, but the calculated second energy efficiency indicator is still positive and higher than that of partition P1, demonstrating the robustness of the formula design. When a partition's schedule is ahead of schedule and the system determines that it should be weakened, the second energy efficiency indicator... If the result is still positive, it means that the current construction zone is energy efficient, ahead of schedule, and can contribute resources. It should receive a high energy efficiency rating. The formula solves the awkward situation in this case by using the mathematical property that the numerator and denominator have the same sign.

[0151] The decision generation unit includes:

[0152] The first indicator range is divided into Level 1 energy efficiency zones, corresponding to the construction areas with the highest second energy efficiency index. The second indicator range is divided into Level 2 energy efficiency zones, corresponding to the construction areas with the middle second energy efficiency index. The third energy efficiency index is divided into Level 3 energy efficiency zones, corresponding to the construction areas with the lowest second energy efficiency index.

[0153] In this embodiment, based on the divided index range, a clear energy efficiency management level label is assigned to each construction zone, a standardized equipment power adjustment strategy is preset for each energy efficiency level, and the strategy is automatically sent to the equipment controller of the corresponding zone.

[0154] The processing methods for Level 1, Level 2, and Level 3 energy efficiency zones are as follows:

[0155] Adjust the power of core and non-core equipment in the first-level energy efficiency zone to 100%;

[0156] Adjust the power of core equipment corresponding to the Level 2 energy efficiency zone to 100%, and adjust the power of non-core equipment to 50%.

[0157] Adjust the power of core equipment corresponding to the Level 3 energy efficiency zone to 80%, and shut down non-core equipment.

[0158] Level 1 Energy Efficiency Zone: Because this zone has the highest overall energy efficiency, the system allows it to operate at full capacity, which is both a reward for its high efficiency and a way to contribute more to the overall project's progress and output by ensuring its full operation.

[0159] Level 2 Energy Efficiency Zone: This zone has moderate energy efficiency. Core operations must be guaranteed, so core equipment runs at full power. However, non-core equipment (such as lighting and auxiliary power) is moderately restricted to force energy conservation. The purpose is to ensure the operation of core equipment, reduce the energy consumption of non-core equipment, and improve overall energy efficiency while ensuring critical progress.

[0160] Level 3 Energy Efficiency Zone: This zone has the lowest energy efficiency and serious resource waste or management problems. The system takes strict control measures to limit the power of its core equipment to force it to improve its operating efficiency and directly stop unnecessary energy consumption. The purpose is to force energy efficiency rectification and avoid further waste of resources.

[0161] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0162] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A long-span cableway construction energy efficiency management system based on edge computing, characterized in that, It includes a construction energy efficiency data acquisition module, an energy efficiency analysis module, an iterative optimization module, and a classification module: The construction energy efficiency acquisition module is used to collect cableway construction energy efficiency data, classification data, and electrical energy base cost to construct a three-dimensional energy efficiency benchmark matrix. The energy efficiency analysis module is used to calculate the first energy efficiency index through cableway construction energy efficiency data and a three-dimensional energy efficiency benchmark matrix, allocate energy efficiency to the construction area according to the first energy efficiency index and the expected construction period, and generate a set of utility parameters and a virtual budget allocation vector. The iterative optimization module is used to construct a construction utility resource allocation model, perform iterative calculations on the utility parameter set and the virtual budget allocation vector, and generate a first allocation index. The grading module is used to calculate the second energy efficiency index based on the first allocation index of each construction zone, and to grade the construction zones based on the second energy efficiency index to generate the optimal energy efficiency management decision. The construction energy efficiency acquisition module includes a sensor acquisition unit and an initialization energy efficiency unit; The initialization energy efficiency unit includes: Extract energy efficiency data from cableway construction, classify equipment IDs as core equipment, and obtain power equipment data, which includes core equipment IDs and non-core equipment IDs. An environmental correction factor is set based on the environmental parameters of the day. The processing logic for the environmental correction factor is as follows: The initial environmental correction factor is 0. When the temperature is greater than the preset temperature threshold, the environmental correction factor increases by 0.03 for every 1 degree Celsius increase in temperature. When the humidity is greater than the preset humidity threshold, the environmental correction factor increases by 0.

05. The load correction factor is set based on the equipment load rate of the day. The processing logic for the load correction factor is as follows: The initial load correction factor is 0. When the equipment load rate is less than the preset load rate, the load correction factor increases by 0.

15. Based on environmental correction factors and load correction factors, the basic cost of electricity is calculated. The formula for calculating the basic cost of electricity is as follows: ； in, For the basic cost of electricity, This is the environmental correction factor. This is the load correction factor. Basic electricity price; A three-dimensional energy efficiency benchmark matrix is ​​constructed using cableway construction energy efficiency data, classification data, and basic electricity cost. Each row of the three-dimensional energy efficiency benchmark matrix represents a construction zone, and each element of the three-dimensional energy efficiency benchmark matrix represents the initial energy efficiency cost of the electricity required for the current construction zone. The energy efficiency analysis module includes an energy efficiency index calculation unit and a virtual budget construction unit; The energy efficiency index calculation unit includes: Extract the three-dimensional energy efficiency benchmark matrix and construction progress data, calculate the first energy efficiency index, and the processing logic for the first energy efficiency index is as follows: Extract the data from each row of the three-dimensional energy efficiency benchmark matrix and the construction progress data, and calculate the construction energy efficiency index for each construction zone. ； in, Here, i represents the construction energy efficiency index for construction zone i, and i is the label for construction zone i. ), For the first The electrical base cost of the j-th electrical equipment in each construction zone, where j is the data label of the electrical equipment. For the first Energy consumption of the j-th electrical equipment in each construction zone. Let i represent the construction progress data for the i-th construction zone. For the estimated construction period; Statistical analysis of construction energy efficiency indicators for each construction zone was conducted to obtain the primary energy efficiency indicator. ; The virtual budget construction unit includes: Based on the primary energy efficiency index and the estimated construction period, a resource allocation model is constructed. The processing logic of the resource allocation model is as follows: Set a linear utility coefficient for each construction zone i That is, the reciprocal of the construction energy efficiency index. ; Calculate the virtual resource budget for each construction zone i. The expression for calculating the virtual resource budget is: ； in, The preset schedule for construction zone i, This represents the current actual progress, i.e., the construction progress data; Save the linear utility coefficients of each construction zone as a set of utility parameters, and save the virtual resource budget of each construction zone as a virtual budget allocation vector; The iterative optimization module includes a supply and demand iterative unit and an optimal allocation unit; The supply and demand iteration unit includes: The arithmetic mean of the basic costs of the corresponding energy types in the three-dimensional energy efficiency benchmark matrix is ​​calculated as the initial shadow price p. Using the construction utility resource allocation model, iterative calculations are performed for each construction zone. The processing logic of the construction utility resource allocation model is as follows: Preset the total energy efficiency supply for construction, initialize the shadow prices p of various energy efficiency resources, initialize the iteration counter k=0, and for each construction zone i, calculate the current shadow price. and virtual resource budget The energy efficiency resource index values ​​for each construction zone are calculated iteratively. The calculation expression for the energy efficiency resource index values ​​is as follows: ； in, To calculate the total energy efficiency resource demand for construction zone i in the k-th iteration, we need to determine the energy efficiency resource allocation. The linear utility coefficient; The total energy efficiency resource demand is obtained by summing the energy efficiency resource demand of each interval. The absolute difference between the total energy efficiency demand and the total energy efficiency supply is compared. The shadow price is iteratively updated based on the gradient ascent method. The iteration terminates when the absolute difference is less than a preset convergence threshold, and the result is output. ; The optimal allocation unit includes: When the supply and demand iteration unit converges, record the currently converged shadow price. Calculate the optimal energy efficiency index value for each construction zone based on shadow prices. Save the optimal energy efficiency index of all construction zones as the first allocation index; The classification module includes an energy efficiency index calculation unit and a decision generation unit; The energy efficiency index calculation unit includes: Extract the initial energy efficiency cost of each construction zone from the three-dimensional energy efficiency benchmark matrix, and calculate the second energy efficiency index. The calculation expression for the second energy efficiency index is as follows: ； in, This is the second energy efficiency indicator for construction zone i. The first allocation index for construction zone i. For virtual resource budgeting, This is the environmental correction factor. This is the load correction factor. The initial energy efficiency cost of the electrical energy required for construction zone i; The logic for dividing the second energy efficiency index into three intervals—the first interval, the second interval, and the third interval—by setting a first threshold and a second threshold is as follows: When the sample corresponding to the second energy efficiency index is greater than the first threshold, it is set as the first index range; When the sample corresponding to the second energy efficiency index is between the first threshold and the second threshold, it is set as the second index interval; When the sample corresponding to the second energy efficiency index is less than the second threshold, it is set to the third index range; The number of samples within the first indicator interval is greater than the number of samples within the second indicator interval, which is greater than the number of samples within the third indicator interval. The decision generation unit includes: The optimal energy efficiency management decision is set up by dividing the first indicator range into first-level energy efficiency zones, corresponding to the construction areas with the highest second energy efficiency index; dividing the second indicator range into second-level energy efficiency zones, corresponding to the construction areas with the middle second energy efficiency index; and dividing the third energy efficiency index into third-level energy efficiency zones, corresponding to the construction areas with the lowest second energy efficiency index.

2. The energy efficiency management system for long-span cableway construction based on edge computing as described in claim 1, characterized in that, The sensor acquisition unit includes: Multimodal sensors are deployed in the cableway construction area to collect cableway construction energy efficiency data. The multimodal sensors send the cableway construction energy efficiency data to the nearest edge node through a lightweight communication protocol. The edge node is used to receive and store the cableway construction energy efficiency data. The construction progress of each cableway construction area is statistically analyzed to obtain construction progress data, which is used to represent the cumulative completion percentage of different construction areas. The multimodal sensors include an energy meter, an equipment operation sensor, a GPS positioning device, an environmental sensor, and a load sensor; The cableway construction energy efficiency data includes timestamps, equipment IDs, energy consumption values, operating time, operating status, location information, equipment load rates, and environmental parameters. The environmental parameters include temperature, humidity, and wind speed; The energy consumption value includes the value of building material consumption and the value of energy resource consumption.

3. The energy efficiency management system for long-span cableway construction based on edge computing as described in claim 1, characterized in that, The processing methods for the Level 1, Level 2, and Level 3 energy efficiency zones are as follows: Adjust the power of core and non-core equipment in the first-level energy efficiency zone to 100%; Adjust the power of core equipment corresponding to the Level 2 energy efficiency zone to 100%, and adjust the power of non-core equipment to 50%. Adjust the power of core equipment corresponding to the Level 3 energy efficiency zone to 80%, and shut down non-core equipment.

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