A method and system for energy consumption management of building construction equipment

Abstract

This invention provides a method and system for energy consumption management of construction equipment, comprising: acquiring real-time construction data and real-time energy consumption data corresponding to each piece of construction equipment at the construction site, uploading them to a data aggregation node for periodic optimization to obtain corresponding real-time construction information and real-time energy consumption information; generating corresponding estimated energy consumption information by simulating the construction process of each piece of construction equipment at the construction site; screening high-energy-consuming construction equipment at the construction site; performing historical energy consumption analysis on each high-energy-consuming construction equipment to determine the reasons for high energy consumption of the high-energy-consuming construction equipment; optimizing the energy consumption management plan at the construction site; and transmitting the data to the manager's page of each piece of construction equipment for display, thereby effectively improving the efficiency of energy consumption management at the construction site, reducing unnecessary energy costs, and facilitating green and refined management of the construction process.

Description

Technical Field

[0001] This invention relates to the field of building construction equipment management technology, and in particular to a method and system for energy consumption management of building construction equipment. Background Technology

[0002] As a major energy-consuming sector, the construction industry's equipment energy management level directly impacts project cost control and the achievement of green construction goals. Currently, my country's construction industry generally suffers from high equipment energy consumption, inefficient management, and insufficient exploration of energy-saving potential, specifically manifested in the following aspects:

[0003] First, data collection is untimely and inaccurate, making it impossible to monitor the energy consumption status of equipment in real time;

[0004] Second, the lack of systematic analysis of energy consumption data makes it difficult to identify energy consumption anomalies and energy-saving potential.

[0005] Third, the lack of coordination between energy management and construction progress means that energy-saving measures may affect construction efficiency.

[0006] Fourth, energy management between devices is independent, making it impossible to achieve collaborative energy saving among multiple devices.

[0007] Because equipment energy consumption is a costly and difficult problem to solve, many construction sites lack a systematic energy consumption management system. Existing management models are mostly based on post-event statistics rather than in-process control. By the time energy consumption exceeds limits, energy waste has already occurred. Furthermore, there is a lack of coordinated scheduling between the energy supply system and equipment energy demand at construction sites, and unreasonable equipment operation arrangements during peak and off-peak electricity periods further exacerbate energy waste. Therefore, how to effectively manage the power consumption of various equipment at construction sites has become an urgent problem to be solved.

[0008] Therefore, the present invention provides a method and system for energy consumption management of building construction equipment. Summary of the Invention

[0009] This invention provides an energy consumption management method and system for building construction equipment, which can realize real-time monitoring, intelligent analysis and dynamic optimization of energy consumption of construction equipment, improve energy utilization efficiency and reduce construction costs.

[0010] This invention provides a method for energy consumption management of building construction equipment, comprising:

[0011] Step 1: Obtain the real-time construction data and real-time energy consumption data for each construction equipment at the construction site, and upload them to the data aggregation node for regular optimization to obtain the corresponding real-time construction information and real-time energy consumption information.

[0012] Step 2: Based on the real-time construction information, simulate the construction process corresponding to each building construction equipment at the construction site to generate corresponding estimated energy consumption information, and filter out the high-energy-consuming building construction equipment at the construction site;

[0013] Step 3: Perform historical energy consumption analysis on each of the aforementioned high-energy-consuming building construction equipment to determine the reasons for the high energy consumption of the equipment.

[0014] Step 4: Optimize the energy management plan for the construction site based on the reasons for high energy consumption of each of the high-energy-consuming construction equipment, and transmit the plan to the manager's page of each of the construction equipment for display.

[0015] In one feasible embodiment, step 1 includes:

[0016] Step 11: Configure corresponding sensing and data acquisition devices for each of the building construction equipment, and at the same time, set up a network at the construction site, and connect each of the sensing and data acquisition devices to the field network to collect data, so as to obtain the real-time construction data and real-time energy consumption data of each of the building construction equipment at the construction site.

[0017] Step 12: Perform data regression training on the real-time construction data corresponding to each of the construction equipment at different times to obtain several linear construction rules corresponding to each of the construction equipment. Use each of the linear construction rules as the basis for structural training to perform logical training on the real-time construction data.

[0018] Step 13: Generate a logical decision tree for each construction equipment under different linear dimensions based on several logical training results corresponding to each construction equipment. Use the decision tree to analyze the outliers corresponding to the construction equipment and use the corresponding linear law to analyze the operating cycle characteristics of the construction equipment.

[0019] Step 14: Generate real-time construction information corresponding to each construction equipment using several logical decision trees and the operating cycle features. At the same time, sort the real-time energy consumption data corresponding to each construction equipment to generate real-time energy consumption information corresponding to each construction equipment.

[0020] In one feasible approach, the process of generating real-time construction information corresponding to each construction equipment using a plurality of logical decision trees and the operational cycle features includes:

[0021] Each decision tree is recursively split to obtain several tree branch features corresponding to the construction equipment, and several node features contained in each tree branch feature are identified.

[0022] Based on the operating cycle, a feature generation rule corresponding to the construction equipment is constructed, and a corresponding rule interval is matched for each tree feature to generate the operating feature structure corresponding to the construction equipment.

[0023] Treat nodes with the same characteristics as the same node class;

[0024] Based on the feature generation rules, the generation rules corresponding to each node class are analyzed to obtain the node sorting within each rule interval;

[0025] The structural points of the operational feature structure are rearranged according to the node sorting to obtain the real-time construction information of the corresponding building construction equipment.

[0026] In one feasible embodiment, step 2 includes:

[0027] Step 21: Obtain several construction functions corresponding to the real-time construction information, construct real-time operation interaction information between different building construction equipment in the construction site based on the construction functions, and generate a comprehensive construction scene of the construction site;

[0028] Step 22: Run the comprehensive construction scenario, identify the real-time parameters of each construction equipment by combining the basic equipment parameters corresponding to each construction equipment, and derive the estimated energy consumption information of the construction equipment in the comprehensive construction scenario using the real-time equipment parameters.

[0029] Step 23: Compare the energy consumption difference between the real-time energy consumption information and the estimated energy consumption information corresponding to each of the building construction equipment, and generate an energy consumption difference sequence corresponding to each of the building construction equipment. When the energy consumption difference sequence contains a dangerous energy consumption difference that does not meet the prescribed energy consumption standards, the corresponding building construction equipment is regarded as a high-energy-consuming building construction equipment.

[0030] One feasible approach also includes:

[0031] Each of the energy consumption difference sequences is first screened to obtain the abnormal energy consumption difference that is higher than the specified energy consumption difference in each of the energy consumption difference sequences.

[0032] Each of the energy consumption difference sequences is screened a second time, and based on the sequence position of each abnormal energy consumption difference in the corresponding energy consumption difference sequence, a dangerous energy consumption difference sequence with continuous abnormal characteristics is obtained.

[0033] The construction equipment corresponding to the dangerous energy consumption difference sequence is regarded as high-energy-consuming construction equipment.

[0034] At the same time, based on the continuous value corresponding to each of the continuous abnormal features, a corresponding energy consumption abnormal weight is set for the corresponding high-energy-consuming building construction equipment.

[0035] In one feasible embodiment, step 3 includes:

[0036] Step 31: Based on the real-time energy consumption information of the high-energy-consuming building construction equipment at different historical moments, perform historical energy consumption analysis on the corresponding high-energy-consuming building construction equipment to determine the historical energy consumption value of the high-energy-consuming building construction equipment at different historical moments.

[0037] Step 32: Filter several equivalent historical energy consumption values ​​that are consistent with the current energy consumption value of the high-energy-consuming building construction equipment, and identify the equipment operation status corresponding to each equivalent historical energy consumption value in the corresponding real-time construction information at the corresponding historical time, and construct several high-energy-consuming items corresponding to the high-energy-consuming building construction equipment;

[0038] Step 33: Analyze the operation of each high-energy-consuming item using the corresponding estimated energy consumption information, determine several types of on-site high-energy-consuming items corresponding to the high-energy-consuming building equipment, simulate the energy consumption process corresponding to each on-site high-energy-consuming item, and determine the high energy consumption cause of the high-energy-consuming building construction equipment.

[0039] In one feasible embodiment, step 4 includes:

[0040] Step 41: Obtain the energy consumption management plan for the construction site, and determine the latest update time of the energy consumption management plan. In the energy consumption management plan, find several relevant management conditions corresponding to each of the high energy consumption reasons.

[0041] Step 42: Simulate the management execution process corresponding to several related management conditions for each of the high energy consumption causes, and derive the energy consumption management characteristics of each of the related management conditions for the high energy consumption causes;

[0042] Step 43: Screen inefficient related management conditions whose energy consumption management feature values ​​are lower than the specified management feature values, and use the corresponding high energy consumption reasons to iteratively optimize the inefficient related management conditions until the screening of energy consumption management feature values ​​no longer filters energy consumption management feature values;

[0043] Step 44: Update the energy consumption management scheme of the construction site according to the optimized relevant management conditions and transmit it to each manager's page for display. At the same time, generate an updated management scheme for each of the building construction equipment and transmit it to the manager's page of the corresponding building construction equipment for display.

[0044] One feasible approach also includes:

[0045] Each type of construction equipment has several high energy consumption causes, and the number of times each high energy consumption cause is presented is counted.

[0046] Based on the number of presentations, the repetitive energy-consuming operations at the construction site are deduced;

[0047] The frequency of reminders for the energy management scheme should be optimized based on the repetitive energy-consuming operations.

[0048] This invention provides an energy consumption management system for building construction equipment, comprising:

[0049] The real-time analysis module is used to acquire real-time construction data and real-time energy consumption data for each construction equipment on the construction site, and upload them to the data aggregation node for periodic optimization to obtain the corresponding real-time construction information and real-time energy consumption information.

[0050] The construction simulation module is used to simulate the construction process of each building construction equipment in the construction site based on the real-time construction information, generate corresponding estimated energy consumption information, and filter high-energy-consuming building construction equipment in the construction site.

[0051] The energy consumption analysis module is used to perform historical energy consumption analysis on each of the high-energy-consuming building construction equipment to determine the reasons for the high energy consumption of the high-energy-consuming building construction equipment.

[0052] The scheme optimization module is used to optimize the energy consumption management scheme of the construction site according to the high energy consumption reasons corresponding to each of the high energy-consuming building construction equipment, and transmit it to the manager page of each of the building construction equipment for display.

[0053] In one implementable manner, the real-time analysis module includes:

[0054] The data analysis unit is used to configure corresponding sensing and acquisition devices for each of the building construction equipment, and to set up a network for the construction site, and to connect each of the sensing and acquisition devices to the site network to collect data, so as to obtain the real-time construction data and real-time energy consumption data of each of the building construction equipment at the construction site.

[0055] The logic analysis unit is used to perform data regression training on the real-time construction data corresponding to each of the construction equipment at different times to obtain several linear construction rules corresponding to each of the construction equipment. The real-time construction data is then logically trained based on each of the linear construction rules.

[0056] The pattern analysis unit is used to generate a logical decision tree for each construction equipment under different linear dimensions based on several logical training results corresponding to each construction equipment, analyze the outliers corresponding to the construction equipment using the decision tree, and analyze the operating cycle characteristics of the construction equipment using the corresponding linear pattern.

[0057] The information determination unit is used to generate real-time construction information corresponding to each construction equipment by utilizing several logical decision trees and the operating cycle features corresponding to each construction equipment, and simultaneously sorting the real-time energy consumption data corresponding to each construction equipment to generate real-time energy consumption information corresponding to each construction equipment.

[0058] The beneficial effects of the above technical solution are as follows: In order to promptly manage the idle energy consumption of equipment caused by unreasonable scheduling and improve the ability to deeply analyze the construction site environment, firstly, by collecting real-time construction and energy consumption data of equipment and optimizing it regularly through data aggregation nodes, the generated real-time construction and energy consumption information is ensured to be true and reliable. This avoids the problem of errors caused by relying on manual records or unprocessed raw data, which leads to deviations in subsequent energy consumption analysis. This lays a precise data foundation for full-process management. Then, based on the real-time construction information, the construction process is simulated to generate estimated energy consumption. By comparing real-time energy consumption with estimated energy consumption, high-energy-consuming equipment is screened out, accurately identifying equipment with real energy consumption anomalies and reducing ineffective management targets. Furthermore, historical energy consumption analysis is used to trace the causes of high energy consumption, deeply mining the causes of energy consumption of the high-energy-consuming equipment, providing clear targets for subsequent optimization. Finally, based on the specific causes, a customized energy consumption management plan is customized and pushed to the manager's page in real time, realizing rapid implementation and visualized management of the plan. This avoids energy waste caused by delayed management information or untimely communication of the plan, effectively improving the efficiency of energy consumption management at the construction site, reducing unnecessary energy costs, and at the same time contributing to the green and refined management of the construction process.

[0059] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0060] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0061] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0062] Figure 1 This is a schematic diagram of the workflow of an energy consumption management method for building construction equipment according to an embodiment of the present invention;

[0063] Figure 2 This is a schematic diagram of the composition of an energy consumption management system for building construction equipment according to an embodiment of the present invention. Detailed Implementation

[0064] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0065] Example 1: This example provides a method for energy consumption management of building construction equipment, such as... Figure 1 As shown, it includes:

[0066] Step 1: Obtain the real-time construction data and real-time energy consumption data for each construction equipment at the construction site, and upload them to the data aggregation node for regular optimization to obtain the corresponding real-time construction information and real-time energy consumption information.

[0067] Step 2: Based on the real-time construction information, simulate the construction process corresponding to each building construction equipment at the construction site to generate corresponding estimated energy consumption information, and filter out the high-energy-consuming building construction equipment at the construction site;

[0068] Step 3: Perform historical energy consumption analysis on each of the aforementioned high-energy-consuming building construction equipment to determine the reasons for the high energy consumption of the equipment.

[0069] Step 4: Optimize the energy management plan for the construction site based on the reasons for high energy consumption of each of the high-energy-consuming construction equipment, and transmit the plan to the manager's page of each of the construction equipment for display.

[0070] In this example, construction equipment refers to the equipment used when carrying out construction work;

[0071] In this example, real-time construction data refers to the data generated by the construction equipment during the construction process, and real-time energy consumption data refers to the data on the electrical energy consumed by the construction equipment during the construction process.

[0072] In this example, the data aggregation node represents a network node used for data collection, integration, and processing;

[0073] In this example, periodic optimization refers to the process of cleaning and integrating real-time construction data and real-time energy consumption data in 30-minute cycles.

[0074] In this example, the estimated energy consumption information represents the energy consumption required for construction equipment to complete its work through simulation.

[0075] In this example, the energy management scheme refers to the implementation plan used to manage the energy consumption of building construction equipment at the construction site;

[0076] In this example, an administrator can manage one or more construction equipment, and each administrator is equipped with a management terminal, on which the administrator's page is displayed.

[0077] The working principle and beneficial effects of the above technical solution are as follows: To promptly address the energy consumption of idle equipment caused by unreasonable scheduling and improve the ability to deeply analyze the construction site environment, the solution first collects real-time construction and energy consumption data from equipment and optimizes it periodically through data aggregation nodes. This ensures that the generated real-time construction and energy consumption information is accurate and reliable, avoiding errors caused by traditional reliance on manual recording or unprocessed raw data, which can lead to biases in subsequent energy consumption analysis. This lays a precise data foundation for full-process management. Then, based on the actual real-time construction information, the solution simulates the construction process to generate estimated energy consumption. By comparing real-time energy consumption with estimated energy consumption, high-energy-consuming equipment is identified, accurately pinpointing equipment with truly abnormal energy consumption and reducing ineffective management targets. Furthermore, historical energy consumption analysis is used to trace the causes of high energy consumption, deeply exploring the reasons for the high-energy-consuming equipment and providing clear targets for subsequent optimization. Finally, an energy consumption management plan is customized based on the specific causes and pushed to the manager's page in real time, enabling rapid implementation and visualized management of the plan. This avoids energy waste caused by delayed management information or untimely communication of the plan, effectively improving the efficiency of energy consumption management at the construction site, reducing unnecessary energy costs, and simultaneously contributing to the green and refined management of the construction process.

[0078] Example 2: Based on Example 1, the energy consumption management method for building construction equipment, step 1 includes:

[0079] Step 11: Configure corresponding sensing and data acquisition devices for each of the building construction equipment, and at the same time, set up a network at the construction site, and connect each of the sensing and data acquisition devices to the field network to collect data, so as to obtain the real-time construction data and real-time energy consumption data of each of the building construction equipment at the construction site.

[0080] Step 12: Perform data regression training on the real-time construction data corresponding to each of the construction equipment at different times to obtain several linear construction rules corresponding to each of the construction equipment. Use each of the linear construction rules as the basis for structural training to perform logical training on the real-time construction data.

[0081] Step 13: Generate a logical decision tree for each construction equipment under different linear dimensions based on several logical training results corresponding to each construction equipment. Use the decision tree to analyze the outliers corresponding to the construction equipment and use the corresponding linear law to analyze the operating cycle characteristics of the construction equipment.

[0082] Step 14: Generate real-time construction information corresponding to each construction equipment using several logical decision trees and the operating cycle features. At the same time, sort the real-time energy consumption data corresponding to each construction equipment to generate real-time energy consumption information corresponding to each construction equipment.

[0083] In this example, the sensing and acquisition device refers to the sensor used to collect data from construction equipment;

[0084] In this example, the field network refers to a dedicated network covering the construction site;

[0085] In this example, data regression training represents the training process for analyzing the linear relationships contained in real-time construction data;

[0086] In this example, the structure training basis represents the generative basis of the decision tree constructed according to linear laws;

[0087] In this example, the linear dimension includes: execution on-site work dimension, standby dimension, energy consumption dimension, and work error dimension.

[0088] The working principle and beneficial effects of the above technical solution are as follows: To provide high-quality data support for subsequent energy consumption management, firstly, each piece of construction equipment is equipped with a dedicated sensor acquisition device, and a data acquisition network is constructed by combining it with the network layout of the construction site. This not only captures the construction data of the equipment in real time but also ensures the comprehensiveness and timeliness of data collection. Then, regression training is performed on the real-time construction data at different times to obtain the linear law of construction. Based on this, logical training is carried out to extract the inherent correlation of equipment construction data and further strengthen the causal logic between data. This avoids the disorder of the original data and transforms fragmented information. The data is transformed into a quantifiable and analyzable pattern model. Then, based on the logical training results, a multi-linear-dimensional logical decision tree is generated. Combined with the linear pattern analysis of the operating cycle characteristics, the purpose of anomaly screening and pattern analysis is achieved. Through the logical decision tree, outliers in construction data can be accurately identified from multiple dimensions, eliminating abnormal interference caused by data noise or equipment failure, and ensuring that the analysis results are not affected by abnormal data. Finally, the logical decision tree and operating cycle characteristics are integrated to generate real-time construction information, and real-time energy consumption data is sorted to generate real-time energy consumption information, which facilitates the rapid location of peak energy consumption periods or abnormal energy consumption points, thereby improving the scientific nature and accuracy of energy consumption management from the source.

[0089] Example 3: Based on Example 2, the energy consumption management method for construction equipment, which generates real-time construction information corresponding to each construction equipment by utilizing several logical decision trees and the operating cycle characteristics, includes:

[0090] Each decision tree is recursively split to obtain several tree branch features corresponding to the construction equipment, and several node features contained in each tree branch feature are identified.

[0091] Based on the operating cycle, a feature generation rule corresponding to the construction equipment is constructed, and a corresponding rule interval is matched for each tree feature to generate the operating feature structure corresponding to the construction equipment.

[0092] Treat nodes with the same characteristics as the same node class;

[0093] Based on the feature generation rules, the generation rules corresponding to each node class are analyzed to obtain the node sorting within each rule interval;

[0094] The structural points of the operational feature structure are rearranged according to the node sorting to obtain the real-time construction information of the corresponding building construction equipment.

[0095] In this example, recursive splitting represents the process of dividing a decision tree into independent tree branches;

[0096] In this example, the feature generation rule represents the pattern of various features generated by construction equipment during operation;

[0097] In this example, the regular interval represents the range of a tree feature within the feature generation regularity;

[0098] In this example, there is a one-to-one correspondence between structural points and nodes.

[0099] The working principle and beneficial effects of the above technical solution are as follows: Recursively splitting the decision tree to obtain tree branches and node features can deconstruct the internal structure of equipment construction data from multiple dimensions, avoiding information omissions caused by single-dimensional analysis. Furthermore, combining the operation cycle to construct feature generation rules and match rule intervals, so that node features accurately correspond to the actual operation stage of the equipment, ensuring that the feature structure fits the real working conditions of the equipment. Then, the same node features are classified to reduce data redundancy and strengthen feature correlation, which facilitates the rapid identification of core construction features. By determining the node order through rule analysis and rearranging the operation feature structure, the real-time construction information is presented in an orderly manner according to the equipment operation logic, which not only eliminates the disorder of the original data, but also intuitively reflects the evolution of the construction status.

[0100] Example 4: Based on Example 1, the energy consumption management method for building construction equipment, step 2 includes:

[0101] Step 21: Obtain several construction functions corresponding to the real-time construction information, construct real-time operation interaction information between different building construction equipment in the construction site based on the construction functions, and generate a comprehensive construction scene of the construction site;

[0102] Step 22: Run the comprehensive construction scenario, identify the real-time parameters of each construction equipment by combining the basic equipment parameters corresponding to each construction equipment, and derive the estimated energy consumption information of the construction equipment in the comprehensive construction scenario using the real-time equipment parameters.

[0103] Step 23: Compare the energy consumption difference between the real-time energy consumption information and the estimated energy consumption information corresponding to each of the building construction equipment, and generate an energy consumption difference sequence corresponding to each of the building construction equipment. When the energy consumption difference sequence contains a dangerous energy consumption difference that does not meet the prescribed energy consumption standards, the corresponding building construction equipment is regarded as a high-energy-consuming building construction equipment.

[0104] In this example, construction action refers to the action produced by construction equipment when performing construction tasks;

[0105] In this example, the real-time interactive information refers to the information generated by the interaction between different construction equipment when they perform the same task.

[0106] In this example, the basic equipment parameters represent the initial parameters of the construction equipment, such as equipment power and rated energy consumption.

[0107] In this example, failure to meet the prescribed energy consumption standard means that several high energy consumption differences appear consecutively in the energy consumption difference sequence.

[0108] The working principle and beneficial effects of the above technical solution are as follows: First, real-time operational interaction information between equipment is generated during construction, constructing a comprehensive construction scenario to restore the operating status of equipment in actual collaborative operations. This avoids the bias in energy consumption judgment caused by isolated analysis of a single piece of equipment, providing a scenario basis that fits the site for subsequent energy consumption estimation. Then, the comprehensive construction scenario is run, and real-time parameters are identified by combining basic equipment parameters to derive estimated energy consumption information, ensuring that the estimated energy consumption is closer to the actual energy consumption of the equipment during construction. This provides a reliable energy consumption benchmark for subsequent comparison and screening. Finally, a sequence is generated by comparing the difference between real-time and estimated energy consumption. High-energy-consuming equipment is screened based on the dangerous energy consumption difference, solving the problem of the crudeness of traditional reliance on experience judgment or screening based on a single energy consumption value. This avoids misjudging normal energy-consuming equipment due to failure to consider differences in construction scenarios. At the same time, the energy consumption difference sequence can trace the duration and fluctuation pattern of abnormal energy consumption, providing a clear direction for subsequent analysis of the causes of high energy consumption and improving the pertinence and efficiency of energy consumption management.

[0109] Example 5: Based on Example 4, the energy consumption management method for building construction equipment further includes:

[0110] Each of the energy consumption difference sequences is first screened to obtain the abnormal energy consumption difference that is higher than the specified energy consumption difference in each of the energy consumption difference sequences.

[0111] Each of the energy consumption difference sequences is screened a second time, and based on the sequence position of each abnormal energy consumption difference in the corresponding energy consumption difference sequence, a dangerous energy consumption difference sequence with continuous abnormal characteristics is obtained.

[0112] The construction equipment corresponding to the dangerous energy consumption difference sequence is regarded as high-energy-consuming construction equipment.

[0113] At the same time, based on the continuous value corresponding to each of the continuous abnormal features, a corresponding energy consumption abnormal weight is set for the corresponding high-energy-consuming building construction equipment.

[0114] In this example, the first screening indicates that abnormal energy consumption differences exceeding the specified energy consumption difference are selected. The specified energy consumption difference is 1% of the rated energy consumption of the construction equipment.

[0115] In this example, the second screening means filtering out the sequence of consecutive abnormally high energy consumption differences;

[0116] In this example, the higher the continuous value, the greater the weight of the energy consumption anomaly.

[0117] The working principle and beneficial effects of the above technical solution are as follows: In order to quickly identify high-energy-consuming building construction equipment, normal energy consumption data that meets the standards is eliminated through two screenings. Then, corresponding weights are set for the screened high-energy-consuming building construction equipment. Compared with focusing on only a single anomaly, this is more in line with the actual needs of the construction scenario, effectively eliminates misjudgments caused by accidental factors, and ensures that the screened high-energy-consuming equipment are all objects with real energy consumption anomalies.

[0118] Example 6: Based on Example 1, the energy consumption management method for building construction equipment, step 3 includes:

[0119] Step 31: Based on the real-time energy consumption information of the high-energy-consuming building construction equipment at different historical moments, perform historical energy consumption analysis on the corresponding high-energy-consuming building construction equipment to determine the historical energy consumption value of the high-energy-consuming building construction equipment at different historical moments.

[0120] Step 32: Filter several equivalent historical energy consumption values ​​that are consistent with the current energy consumption value of the high-energy-consuming building construction equipment, and identify the equipment operation status corresponding to each equivalent historical energy consumption value in the corresponding real-time construction information at the corresponding historical time, and construct several high-energy-consuming items corresponding to the high-energy-consuming building construction equipment;

[0121] Step 33: Analyze the operation of each high-energy-consuming item using the corresponding estimated energy consumption information, determine several types of on-site high-energy-consuming items corresponding to the high-energy-consuming building equipment, simulate the energy consumption process corresponding to each on-site high-energy-consuming item, and determine the high energy consumption cause of the high-energy-consuming building construction equipment.

[0122] In this example, high-energy-consuming items refer to work items that generate additional energy consumption from high-energy-consuming building construction equipment, such as standby and leakage current.

[0123] In this example, the high energy consumption items on site refer to the high energy consumption items currently exhibited by high energy-consuming building equipment.

[0124] The working principle and beneficial effects of the above technical solution are as follows: First, based on real-time energy consumption information at different historical moments, historical energy consumption values ​​are analyzed. A complete energy consumption change trajectory is constructed through longitudinal comparison, providing a historical reference system for subsequent cause analysis and avoiding the one-sidedness of cause judgment due to single data. Then, equivalent historical energy consumption values ​​that are consistent with the current energy consumption value are selected. Combined with real-time construction information at the corresponding moment, the equipment operating status is identified. High energy consumption items that match the current high energy consumption status can be extracted from historical cases, giving the cause analysis of high energy consumption a specific direction and avoiding blind investigation. Furthermore, combined with estimated energy consumption information, the operation of high energy consumption items is analyzed. Focusing on the actual working conditions on site, the high energy consumption items on site are identified and the energy consumption process is simulated. Interference items in historical data that do not match the current scenario can be eliminated. Finally, targeted high energy consumption causes are accurately identified. In this way, an effective management plan is formulated with a scientific basis, avoiding the problem of ineffective optimization measures caused by the generality of traditional cause analysis, and significantly improving the accuracy and efficiency of energy consumption management and rectification.

[0125] Example 7: Based on Example 1, the energy consumption management method for building construction equipment, step 4 includes:

[0126] Step 41: Obtain the energy consumption management plan for the construction site, and determine the latest update time of the energy consumption management plan. In the energy consumption management plan, find several relevant management conditions corresponding to each of the high energy consumption reasons.

[0127] Step 42: Simulate the management execution process corresponding to several related management conditions for each of the high energy consumption causes, and derive the energy consumption management characteristics of each of the related management conditions for the high energy consumption causes;

[0128] Step 43: Screen inefficient related management conditions whose energy consumption management feature values ​​are lower than the specified management feature values, and use the corresponding high energy consumption reasons to iteratively optimize the inefficient related management conditions until the screening of energy consumption management feature values ​​no longer filters energy consumption management feature values;

[0129] Step 44: Update the energy consumption management scheme of the construction site according to the optimized relevant management conditions and transmit it to each manager's page for display. At the same time, generate an updated management scheme for each of the building construction equipment and transmit it to the manager's page of the corresponding building construction equipment for display.

[0130] In this example, the relevant management conditions refer to the conditions included in the energy consumption management plan used to manage the causes of high energy consumption;

[0131] In this example, iterative optimization refers to the process of fine-tuning the energy management scheme multiple times, such as shortening the maintenance cycle and refining the operating procedures.

[0132] The working principle and beneficial effects of the above technical solution are as follows: In order to achieve targeted solutions to high energy consumption problems and significantly improve the effectiveness of energy consumption management through precise optimization and efficient implementation of energy consumption management solutions, the following steps are taken: First, the latest management solutions and causes of high energy consumption are combined to identify relevant management conditions, ensuring that optimization is based on the current effective management framework and avoiding optimization deviations caused by using outdated solutions. At the same time, the key control points directly related to each cause of high energy consumption in the solution are identified, providing precise targets for subsequent optimization. Then, the management execution process is simulated to deduce management characteristics, which can predict in advance the actual control effect of existing management conditions on high energy consumption causes and avoid resource waste caused by blindly applying solutions. Then, inefficient management conditions are selected and iteratively optimized, and continuous adjustments are made until the management targets are met. Finally, the solution is updated, synchronized to the manager's page, and a device-specific solution is generated, realizing the visualization and differentiated push of management solutions. This ensures that managers can quickly obtain and implement appropriate control measures and avoid inadequate implementation due to information delays or unclear solutions.

[0133] Example 8: Based on Example 1, the energy consumption management method for building construction equipment further includes:

[0134] Each type of construction equipment has several high energy consumption causes, and the number of times each high energy consumption cause is presented is counted.

[0135] Based on the number of presentations, the repetitive energy-consuming operations at the construction site are deduced;

[0136] The frequency of reminders for the energy management scheme should be optimized based on the repetitive energy-consuming operations.

[0137] The working principle and beneficial effects of the above technical solution are as follows: When the same energy consumption cause occurs repeatedly, it indicates that the on-site staff are often negligent and need to be reminded multiple times to reduce the number of errors.

[0138] Example 9: This example provides an energy management system for building construction equipment, such as... Figure 2 As shown, it includes:

[0139] The real-time analysis module is used to acquire real-time construction data and real-time energy consumption data for each construction equipment on the construction site, and upload them to the data aggregation node for periodic optimization to obtain the corresponding real-time construction information and real-time energy consumption information.

[0140] The construction simulation module is used to simulate the construction process of each building construction equipment in the construction site based on the real-time construction information, generate corresponding estimated energy consumption information, and filter high-energy-consuming building construction equipment in the construction site.

[0141] The energy consumption analysis module is used to perform historical energy consumption analysis on each of the high-energy-consuming building construction equipment to determine the reasons for the high energy consumption of the high-energy-consuming building construction equipment.

[0142] The scheme optimization module is used to optimize the energy consumption management scheme of the construction site according to the high energy consumption reasons corresponding to each of the high energy-consuming building construction equipment, and transmit it to the manager page of each of the building construction equipment for display.

[0143] In this example, construction equipment refers to the equipment used when carrying out construction work;

[0144] In this example, real-time construction data refers to the data generated by the construction equipment during the construction process, and real-time energy consumption data refers to the data on the electrical energy consumed by the construction equipment during the construction process.

[0145] In this example, the data aggregation node represents a network node used for data collection, integration, and processing;

[0146] In this example, periodic optimization refers to the process of cleaning and integrating real-time construction data and real-time energy consumption data in 30-minute cycles.

[0147] In this example, the estimated energy consumption information represents the energy consumption required for construction equipment to complete its work through simulation.

[0148] In this example, the energy management scheme refers to the implementation plan used to manage the energy consumption of building construction equipment at the construction site;

[0149] In this example, an administrator can manage one or more construction equipment, and each administrator is equipped with a management terminal, on which the administrator's page is displayed.

[0150] The working principle and beneficial effects of the above technical solution are as follows: To promptly address the energy consumption of idle equipment caused by unreasonable scheduling and improve the ability to deeply analyze the construction site environment, the solution first collects real-time construction and energy consumption data from equipment and optimizes it periodically through data aggregation nodes. This ensures that the generated real-time construction and energy consumption information is accurate and reliable, avoiding errors caused by traditional reliance on manual recording or unprocessed raw data, which can lead to biases in subsequent energy consumption analysis. This lays a precise data foundation for full-process management. Then, based on the actual real-time construction information, the solution simulates the construction process to generate estimated energy consumption. By comparing real-time energy consumption with estimated energy consumption, high-energy-consuming equipment is identified, accurately pinpointing equipment with truly abnormal energy consumption and reducing ineffective management targets. Furthermore, historical energy consumption analysis is used to trace the causes of high energy consumption, deeply exploring the reasons for the high-energy-consuming equipment and providing clear targets for subsequent optimization. Finally, an energy consumption management plan is customized based on the specific causes and pushed to the manager's page in real time, enabling rapid implementation and visualized management of the plan. This avoids energy waste caused by delayed management information or untimely communication of the plan, effectively improving the efficiency of energy consumption management at the construction site, reducing unnecessary energy costs, and simultaneously contributing to the green and refined management of the construction process.

[0151] Example 10: Based on Example 9, the energy consumption management system for building construction equipment, wherein the real-time analysis module includes:

[0152] The data analysis unit is used to configure corresponding sensing and acquisition devices for each of the building construction equipment, and to set up a network for the construction site, and to connect each of the sensing and acquisition devices to the site network to collect data, so as to obtain the real-time construction data and real-time energy consumption data of each of the building construction equipment at the construction site.

[0153] The logic analysis unit is used to perform data regression training on the real-time construction data corresponding to each of the construction equipment at different times to obtain several linear construction rules corresponding to each of the construction equipment. The real-time construction data is then logically trained based on each of the linear construction rules.

[0154] The pattern analysis unit is used to generate a logical decision tree for each construction equipment under different linear dimensions based on several logical training results corresponding to each construction equipment, analyze the outliers corresponding to the construction equipment using the decision tree, and analyze the operating cycle characteristics of the construction equipment using the corresponding linear pattern.

[0155] The information determination unit is used to generate real-time construction information corresponding to each construction equipment by utilizing several logical decision trees and the operating cycle features corresponding to each construction equipment, and simultaneously sorting the real-time energy consumption data corresponding to each construction equipment to generate real-time energy consumption information corresponding to each construction equipment.

[0156] In this example, the sensing and acquisition device refers to the sensor used to collect data from construction equipment;

[0157] In this example, the field network refers to a dedicated network covering the construction site;

[0158] In this example, data regression training represents the training process for analyzing the linear relationships contained in real-time construction data;

[0159] In this example, the structure training basis represents the generative basis of the decision tree constructed according to linear laws;

[0160] In this example, the linear dimension includes: execution on-site work dimension, standby dimension, energy consumption dimension, and work error dimension.

[0161] The working principle and beneficial effects of the above technical solution are as follows: To provide high-quality data support for subsequent energy consumption management, firstly, each piece of construction equipment is equipped with a dedicated sensor acquisition device, and a data acquisition network is constructed by combining it with the network layout of the construction site. This not only captures the construction data of the equipment in real time but also ensures the comprehensiveness and timeliness of data collection. Then, regression training is performed on the real-time construction data at different times to obtain the linear law of construction. Based on this, logical training is carried out to extract the inherent correlation of equipment construction data and further strengthen the causal logic between data. This avoids the disorder of the original data and transforms fragmented information. The data is transformed into a quantifiable and analyzable pattern model. Then, based on the logical training results, a multi-linear-dimensional logical decision tree is generated. Combined with the linear pattern analysis of the operating cycle characteristics, the purpose of anomaly screening and pattern analysis is achieved. Through the logical decision tree, outliers in construction data can be accurately identified from multiple dimensions, eliminating abnormal interference caused by data noise or equipment failure, and ensuring that the analysis results are not affected by abnormal data. Finally, the logical decision tree and operating cycle characteristics are integrated to generate real-time construction information, and real-time energy consumption data is sorted to generate real-time energy consumption information, which facilitates the rapid location of peak energy consumption periods or abnormal energy consumption points, thereby improving the scientific nature and accuracy of energy consumption management from the source.

[0162] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for energy consumption management of a construction equipment, characterized by, include: Step 1: Obtain the real-time construction data and real-time energy consumption data for each construction equipment at the construction site, and upload them to the data aggregation node for regular optimization to obtain the corresponding real-time construction information and real-time energy consumption information. Step 2: Based on the real-time construction information, simulate the construction process corresponding to each building construction equipment in the construction site to generate corresponding estimated energy consumption information, and filter the high-energy-consuming building construction equipment in the construction site; Step 3: Perform historical energy consumption analysis on each of the aforementioned high-energy-consuming building construction equipment to determine the reasons for the high energy consumption of the equipment. Step 4: Optimize the energy management plan for the construction site according to the reasons for high energy consumption of each of the high-energy-consuming building construction equipment, and transmit it to the manager page of each of the building construction equipment for display; Step 1 includes: Step 11: Configure corresponding sensing and data acquisition devices for each of the building construction equipment, and at the same time, set up a network at the construction site, and connect each of the sensing and data acquisition devices to the field network to collect data, so as to obtain the real-time construction data and real-time energy consumption data of each of the building construction equipment at the construction site. Step 12: Perform data regression training on the real-time construction data corresponding to each of the construction equipment at different times to obtain several linear construction rules corresponding to each of the construction equipment. Use each of the linear construction rules as the basis for structural training to perform logical training on the real-time construction data. Step 13: Generate a logical decision tree for each construction equipment under different linear dimensions based on several logical training results corresponding to each construction equipment. Use the decision tree to analyze the outliers corresponding to the construction equipment and use the corresponding linear law to analyze the operating cycle characteristics of the construction equipment. Step 14: Generate real-time construction information corresponding to each construction equipment using several logical decision trees and the operating cycle features. At the same time, sort the real-time energy consumption data corresponding to each construction equipment to generate real-time energy consumption information corresponding to each construction equipment.

2. The energy consumption management method for a construction machine according to Claim 1, wherein The process of generating real-time construction information corresponding to each construction equipment using several logical decision trees and the operating cycle features includes: Each decision tree is recursively split to obtain several tree branch features corresponding to the construction equipment, and several node features contained in each tree branch feature are identified. Based on the operating cycle, a feature generation rule corresponding to the construction equipment is constructed, and a corresponding rule interval is matched for each tree feature to generate the operating feature structure corresponding to the construction equipment. Treat nodes with the same characteristics as the same node class; Based on the feature generation rules, the generation rules corresponding to each node class are analyzed to obtain the node sorting within each rule interval; The structural points of the operational feature structure are rearranged according to the node sorting to obtain the real-time construction information of the corresponding building construction equipment.

3. The energy consumption management method for building construction equipment as described in claim 1, characterized in that, Step 2 includes: Step 21: Obtain several construction functions corresponding to the real-time construction information, construct real-time operation interaction information between different building construction equipment in the construction site based on the construction functions, and generate a comprehensive construction scene of the construction site; Step 22: Run the comprehensive construction scenario, identify the real-time parameters of each construction equipment by combining the basic equipment parameters corresponding to each construction equipment, and derive the estimated energy consumption information of the construction equipment in the comprehensive construction scenario using the real-time equipment parameters. Step 23: Compare the energy consumption difference between the real-time energy consumption information and the estimated energy consumption information corresponding to each of the building construction equipment, and generate an energy consumption difference sequence corresponding to each of the building construction equipment. When the energy consumption difference sequence contains a dangerous energy consumption difference that does not meet the prescribed energy consumption standards, the corresponding building construction equipment is regarded as a high-energy-consuming building construction equipment.

4. The energy consumption management method for building construction equipment as described in claim 3, characterized in that, Also includes: Each of the energy consumption difference sequences is first screened to obtain the abnormal energy consumption difference that is higher than the specified energy consumption difference in each of the energy consumption difference sequences. Each of the energy consumption difference sequences is screened a second time, and based on the sequence position of each abnormal energy consumption difference in the corresponding energy consumption difference sequence, a dangerous energy consumption difference sequence with continuous abnormal characteristics is obtained. The construction equipment corresponding to the dangerous energy consumption difference sequence is regarded as high-energy-consuming construction equipment. At the same time, based on the continuous value corresponding to each of the continuous abnormal features, a corresponding energy consumption abnormal weight is set for the corresponding high-energy-consuming building construction equipment.

5. The energy consumption management method for building construction equipment as described in claim 1, characterized in that, Step 3 includes: Step 31: Based on the real-time energy consumption information of the high-energy-consuming building construction equipment at different historical moments, perform historical energy consumption analysis on the corresponding high-energy-consuming building construction equipment to determine the historical energy consumption value of the high-energy-consuming building construction equipment at different historical moments. Step 32: Filter several equivalent historical energy consumption values ​​that are consistent with the current energy consumption value of the high-energy-consuming building construction equipment, and identify the equipment operation status corresponding to each equivalent historical energy consumption value in the corresponding real-time construction information at the corresponding historical time, and construct several high-energy-consuming items corresponding to the high-energy-consuming building construction equipment; Step 33: Analyze the operation of each high-energy-consuming item using the estimated energy consumption information to identify several types of on-site high-energy-consuming items corresponding to the high-energy-consuming building construction equipment. Simulate the energy consumption process corresponding to each on-site high-energy-consuming item to determine the high energy consumption cause of the high-energy-consuming building construction equipment.

6. The energy consumption management method for building construction equipment as described in claim 1, characterized in that, Step 4 includes: Step 41: Obtain the energy consumption management plan for the construction site, and determine the latest update time of the energy consumption management plan. In the energy consumption management plan, find several relevant management conditions corresponding to each of the high energy consumption reasons. Step 42: Simulate the management execution process corresponding to several related management conditions for each of the high energy consumption causes, and derive the energy consumption management characteristics of each of the related management conditions for the high energy consumption causes; Step 43: Screen inefficient management conditions whose energy consumption management characteristic values ​​are lower than the specified management characteristic values, and iteratively optimize the inefficient management conditions using the corresponding reasons for high energy consumption; Step 44: Update the energy consumption management scheme of the construction site according to the optimized relevant management conditions and transmit it to each manager's page for display. At the same time, generate an updated management scheme for each of the building construction equipment and transmit it to the manager's page of the corresponding building construction equipment for display.

7. The energy consumption management method for building construction equipment as described in claim 1, characterized in that, Also includes: Each type of construction equipment has several high energy consumption causes, and the number of times each high energy consumption cause is presented is counted. Based on the number of presentations, the repetitive energy-consuming operations at the construction site are deduced; The frequency of reminders for the energy management scheme should be optimized based on the repetitive energy-consuming operations.

8. An energy consumption management system for building construction equipment, characterized in that, include: The real-time analysis module is used to acquire real-time construction data and real-time energy consumption data for each construction equipment on the construction site, and upload them to the data aggregation node for periodic optimization to obtain the corresponding real-time construction information and real-time energy consumption information. The construction simulation module is used to simulate the construction process of each building construction equipment in the construction site based on the real-time construction information, generate corresponding estimated energy consumption information, and filter high-energy-consuming building construction equipment in the construction site. The energy consumption analysis module is used to perform historical energy consumption analysis on each of the high-energy-consuming building construction equipment to determine the reasons for the high energy consumption of the high-energy-consuming building construction equipment. The scheme optimization module is used to optimize the energy consumption management scheme of the construction site according to the high energy consumption reasons corresponding to each of the high energy-consuming building construction equipment, and transmit it to the manager page of each of the building construction equipment for display. The real-time analysis module includes: The data analysis unit is used to configure corresponding sensing and acquisition devices for each of the building construction equipment, and to set up a network for the construction site, and to connect each of the sensing and acquisition devices to the site network to collect data, so as to obtain the real-time construction data and real-time energy consumption data of each of the building construction equipment at the construction site. The logic analysis unit is used to perform data regression training on the real-time construction data corresponding to each of the construction equipment at different times to obtain several linear construction rules corresponding to each of the construction equipment. The real-time construction data is then logically trained based on each of the linear construction rules. The pattern analysis unit is used to generate a logical decision tree for each construction equipment under different linear dimensions based on several logical training results corresponding to each construction equipment, analyze the outliers corresponding to the construction equipment using the decision tree, and analyze the operating cycle characteristics of the construction equipment using the corresponding linear pattern. The information determination unit is used to generate real-time construction information corresponding to each construction equipment by using several logical decision trees and the operating cycle features corresponding to each construction equipment, and to sort the real-time energy consumption data corresponding to each construction equipment to generate real-time energy consumption information corresponding to each construction equipment.

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