Energy management method and device for building site, electronic equipment and storage medium

Abstract

The invention relates to an energy management method and device for a building site, electronic equipment and a storage medium, and the method comprises the steps: collecting the multi-source energy use data of a target building site; performing data processing on the multi-source energy use data to obtain energy convergence data meeting a preset condition; analyzing the energy convergence data in combination with a data source of the target building site to obtain various key index data of the energy convergence data; performing early warning matching on each kind of key index data in the multiple kinds of key index data to obtain a matching result; and generating a corresponding energy intervention strategy according to the matching result, and intervening the energy usage amount of at least one data source of the target building site based on the energy intervention strategy. Therefore, the problems that in related technologies, energy resource detection is difficult to balance cost, timeliness and data multisource and is difficult to find and process in time, meanwhile, due to the fact that the relations among different buildings, energy and carbon emission are different, macroscopic regulation and control indexes are difficult to achieve through unified quota management, and the energy resource detection efficiency is improved are solved. And manual management and control or equipment updating are difficult to effectively consider the office environment and economic benefits.

Description

Technical Field

[0001] The present application relates to the technical field of resource management, and in particular to an energy management method, device, electronic equipment and storage medium for a construction site. Background Art

[0002] Among the relevant technologies, when data collection is carried out in the mode of "issuing forms, reporting offline, and manually summarizing", the links involved are complex and involve many people, resulting in the lack of traceability of the data, and the disadvantages of vague and empirical supervision are highlighted; when the current supervision is used to promote phased work through notification documents such as annual performance statistics, creation of demonstration units, and reporting of typical cases, buildings at all levels fill in and implement according to the requirements of the documents, which is passive supervision and cannot accurately identify problems in the process of energy resource consumption and operation management, and it is difficult to support the rapid resolution of abnormal energy and carbon indicators; at the same time, most buildings have not yet started the construction of energy resource monitoring systems, and some buildings with systems focus on electricity and energy management. Due to construction time, construction goals and other reasons, it is difficult to ensure the standardization and integrity of the data, and it is impossible to support carbon management services such as carbon metering and carbon inventory. In addition, the relationship between different buildings and buildings / energy / carbon emissions may not be exactly the same. Buildings are usually managed by superior authorities on an annual basis. If a certain energy and carbon indicator is blindly issued, it will objectively lead to organizations choosing human management methods that reduce comfort (such as significantly reducing the use time of air conditioning, changing the set temperature) or replacing large facilities and equipment in order to ensure that macro-control indicators are met. These methods may not necessarily bring a good office environment and economical investment returns.

[0003] To sum up, among the relevant technologies, the energy resource detection system has not yet been popularized, data collection still requires manual participation, the cost is high, the validity of the data is questionable, and it is difficult to timely discover problems in the energy resource consumption and operation management process. The energy resource detection system mostly focuses on electricity and energy management, and is limited by building factors, making it difficult to ensure the standardization and integrity of the data. At the same time, due to the differences in the relationship between different buildings, energy and carbon emissions, unified quota management is difficult to achieve macro-control indicators, and manual control or equipment updates are difficult to effectively take into account both the office environment and economic benefits, which urgently needs to be improved. Summary of the invention

[0004] The present application provides an energy management method, device, electronic device and storage medium for a building site to solve technical problems in related technologies, such as the difficulty in balancing cost, timeliness and multi-source data in energy resource detection, and the difficulty in timely discovering and handling problems. At the same time, due to differences in the relationship between different buildings, energy and carbon emissions, unified quota management is difficult to achieve macro-control indicators, and manual control or equipment updates are difficult to effectively balance office environment and economic benefits.

[0005] A first aspect of the present application provides an energy management method for a construction site, comprising the following steps: collecting multi-source energy usage data of a target construction site; performing data processing on the multi-source energy usage data to obtain energy aggregation data that meets preset conditions; analyzing the energy aggregation data in combination with a data source of the target construction site to obtain a plurality of key indicator data of the energy aggregation data; performing early warning matching on each of the plurality of key indicator data to obtain a matching result; generating a corresponding energy intervention strategy according to the matching result, and intervening in the energy usage of at least one data source of the target construction site based on the energy intervention strategy.

[0006] Through the above technical solution, the existing sensors at the target building site can be reused for multi-source data collection, reducing equipment costs and labor costs, and multi-source data can be fused to extract a variety of key indicator data, thereby achieving more targeted energy early warning and intervention based on a variety of key indicator data, achieving the goal of energy conservation and carbon reduction based on dynamic supervision, and effectively taking into account the economic benefits of the office environment.

[0007] Optionally, in one embodiment of the present application, generating a corresponding energy intervention strategy based on the matching result includes: inputting the multi-source energy usage data into a pre-constructed carbon emission prediction model to obtain the carbon emission prediction result of the target building site, wherein the carbon emission prediction model is trained by historical multi-source energy usage data and historical carbon emission data; inputting the matching result and the carbon emission prediction result into a pre-constructed carbon emission index allocation model to obtain the carbon emission index corresponding to each data source, wherein the carbon emission index allocation model is updated by historical energy intervention results; and obtaining the energy intervention strategy based on the carbon emission index of each data source.

[0008] Through the above technical solution, the carbon emission prediction model can be used to predict carbon emissions based on the current monitoring data, and the carbon emission indicator allocation model can be used to allocate indicators based on the carbon emission prediction results and matching results. The carbon emission indicator allocation model can be dynamically adjusted according to the intervention results of each carbon emission intervention, so that the final energy intervention strategy can be more in line with carbon emission needs.

[0009] Optionally, in one embodiment of the present application, before performing early warning matching on each of the multiple key indicator data respectively, it also includes: obtaining basic data of multiple buildings that meet preset relevant conditions; standardizing the basic data to obtain processed sample data; obtaining standard information of the target building site, and constructing data weights based on the standard information; and constructing a scoring model using the sample data and the data weights to perform early warning matching using the scoring model.

[0010] Through the above technical solution, basic data can be used as samples, and weights can be constructed with standard information to obtain a scoring model that can be used to evaluate target site energy consumption, so that the scoring can take into account both office needs and environmental standards.

[0011] Optionally, in one embodiment of the present application, the early warning matching is performed on each of the multiple key indicator data respectively to obtain a matching result, including: inputting each of the key indicator data into the scoring model to obtain a comprehensive score of the target building site, and using the comprehensive score as the matching result; or performing early warning matching on each of the key indicator data and a plurality of pre-constructed key indicator thresholds respectively to screen out at least one key indicator data that meets a preset exceeding condition, and using the at least one key indicator data as the matching result.

[0012] Through the above technical solution, early warning matching can be achieved in a variety of ways. On the one hand, the energy consumption score of the target building site can be calculated through the scoring model to more intuitively determine whether the target building site meets the early warning standards. On the other hand, threshold warnings can be performed on each key indicator data to clarify the specific key indicators that exceed the standard, which is convenient for subsequent strategic intervention.

[0013] Optionally, in one embodiment of the present application, the early warning matching is performed on each of the multiple key indicator data respectively to obtain a matching result, including: determining whether the target construction site meets the preset special conditions; if the target construction site meets the preset special conditions, then based on the special factors of the target construction site, adding corresponding adjustment factors to the scoring model to obtain an updated scoring model; and using the updated scoring model to calculate the comprehensive score.

[0014] Through the above technical solution, the scoring model can be adjusted according to the actual situation of the target building site, so that the results calculated by the scoring model can effectively reflect the actual situation of the target building site, so that the subsequent energy intervention strategy can effectively achieve energy saving and carbon reduction.

[0015] Optionally, in one embodiment of the present application, it also includes: after executing the energy intervention strategy for a preset period of time, obtaining the current multi-source energy usage data of the target building site; using the current multi-source energy usage data to obtain a new matching result; using the new matching result to update the data weight of the scoring model; or using the new matching result to update the energy intervention strategy.

[0016] Through the above technical solution, feedback adjustment can be carried out according to the execution results of the energy intervention strategy, and the energy intervention strategy or scoring model can be continuously improved, so as to achieve adaptive adjustment of the solution, so that the energy intervention strategy or scoring model can always meet the actual situation of the target building site.

[0017] A second aspect of the present application provides an energy management device for a construction site, comprising: a collection module for collecting multi-source energy usage data of a target construction site; a first processing module for processing the multi-source energy usage data to obtain energy aggregation data that meets preset conditions; an analysis module for analyzing the energy aggregation data in combination with the data source of the target construction site to obtain a variety of key indicator data of the energy aggregation data; a first matching module for performing early warning matching on each of the multiple key indicator data respectively to obtain a matching result; and a management module for generating a corresponding energy intervention strategy according to the matching result, and intervening in the energy usage of at least one data source of the target construction site based on the energy intervention strategy.

[0018] Optionally, in one embodiment of the present application, the management module includes: a prediction unit, used to input the multi-source energy usage data into a pre-built carbon emission prediction model to obtain the carbon emission prediction result of the target building site, wherein the carbon emission prediction model is trained by historical multi-source energy usage data and historical carbon emission data; a first calculation unit, used to input the matching result and the carbon emission prediction result into a pre-built carbon emission index allocation model to obtain the carbon emission index corresponding to each data source, wherein the carbon emission index allocation model is updated by historical energy intervention results; an acquisition unit, used to obtain the energy intervention strategy based on the carbon emission index of each data source.

[0019] Optionally, in one embodiment of the present application, it also includes: a first acquisition module, used to obtain basic data of multiple buildings that meet preset relevant conditions; a second processing module, used to standardize the basic data to obtain processed sample data; a construction module, used to obtain standard information of the target building site and construct data weights based on the standard information; a reconstruction module, used to construct a scoring model using the sample data and the data weights, so as to use the scoring model for early warning matching.

[0020] Optionally, in one embodiment of the present application, the first matching module includes: a second calculation unit, used to input each of the key indicator data into the scoring model to obtain a comprehensive score of the target building site, and use the comprehensive score as the matching result; or a matching unit, used to perform early warning matching on each of the key indicator data with a plurality of pre-constructed key indicator thresholds, respectively, to screen out at least one key indicator data that meets a preset exceeding standard condition, and use the at least one key indicator data as the matching result.

[0021] Optionally, in one embodiment of the present application, the first matching module includes: a judgment unit, used to judge whether the target construction site meets preset special conditions; an updating unit, used to add corresponding adjustment factors to the scoring model based on special factors of the target construction site when the target construction site meets the preset special conditions, so as to obtain an updated scoring model; and a third calculation unit, used to calculate the comprehensive score using the updated scoring model.

[0022] Optionally, in one embodiment of the present application, it also includes: a second acquisition module, used to obtain the current multi-source energy usage data of the target building site after executing the energy intervention strategy for a preset period of time; a second matching module, used to obtain a new matching result using the current multi-source energy usage data; a first update module, used to update the data weight of the scoring model using the new matching result; or a second update module, used to update the energy intervention strategy using the new matching result.

[0023] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the energy management method for a building site as described in the above embodiment.

[0024] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the energy management method for a building site as described in the above embodiment.

[0025] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned energy management method for a building site.

[0026] The embodiment of the present application can collect multi-source energy usage data at the target building site, and fuse the multi-source energy usage data to obtain energy convergence data, and then analyze the energy convergence data in combination with the data source of the target building site, determine multiple key indicator data, and perform early warning matching, so as to generate a corresponding energy intervention strategy according to the matching result, and intervene in the energy usage of at least one data source at the target building site based on the energy intervention strategy, starting from the actual situation of each building, through monitoring and analyzing multi-organization multi-source data, carry out dynamic energy carbon quota management, effectively find out the weak points and intervene in time, which is conducive to unified and standardized management on the one hand, and can also achieve energy conservation and carbon reduction in a real sense on the other hand. Thus, it solves the technical problems in the related technology that energy resource detection is difficult to balance cost, timeliness and multi-source of data, and it is difficult to find and deal with problems in time. At the same time, due to the differences in the relationship between different buildings, energy and carbon emissions, unified quota management is difficult to achieve macro-control indicators, and manual control or equipment update is difficult to effectively take into account the office environment and economic benefits.

[0027] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0029] Figure 1 A flowchart of an energy management method for a construction site provided according to an embodiment of the present application;

[0030] Figure 2a A schematic diagram of the principle of an energy management method for a construction site provided according to an embodiment of the present application;

[0031] Figure 2b A partially enlarged schematic diagram of the principle of an energy management method for a construction site provided according to an embodiment of the present application;

[0032] Figure 3 A schematic diagram of a display interface of a server platform provided according to an embodiment of the present application;

[0033] Figure 4 A schematic diagram of an energy carbon management machine provided according to an embodiment of the present application;

[0034] Figure 5 A schematic diagram of multi-source data fusion provided according to an embodiment of the present application;

[0035] Figure 6 A schematic diagram of the principle of a carbon emission prediction model provided according to an embodiment of the present application;

[0036] Figure 7 A schematic diagram of a model optimization principle provided according to an embodiment of the present application;

[0037] Figure 8 A schematic diagram of feedback and supervision principles provided according to an embodiment of the present application;

[0038] Fig. 9 A schematic diagram of a flow chart of dynamic quota management provided according to an embodiment of the present application;

[0039] Fig.10 A schematic diagram of the structure of an energy management device for a building site provided according to an embodiment of the present application;

[0040] Fig.11 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application.

[0041] Among them, 10-energy management device at the building site, 100-collection module, 200-first processing module, 300-analysis module, 400-first matching module, 500-management module; 1101-memory, 1102-processor, 1103-communication interface. DETAILED DESCRIPTION

[0042] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0043] The following describes the energy management method, device, electronic device and storage medium of the building site of the embodiment of the present application with reference to the accompanying drawings. In view of the related technologies mentioned in the above background technology, the data collection link is costly and complex, which easily leads to doubts about the validity of the data, and the passive supervision is adopted, which makes it difficult to accurately discover the defects in the energy resource consumption and operation management process. The present application provides an energy management method for the building site, in which the multi-source energy use data of the target building site can be collected, and the multi-source energy use data can be integrated to obtain energy convergence data, and then the energy convergence data can be analyzed in combination with the data source of the target building site, and multiple key indicator data can be determined, and early warning matching can be performed, so as to generate a corresponding energy intervention strategy according to the matching result, and intervene in the energy usage of at least one data source of the target building site based on the energy intervention strategy, starting from the actual situation of each building, by monitoring and analyzing multi-organization multi-source data, dynamic energy carbon quota management is carried out, and weak points are effectively found and timely intervention is carried out, which is conducive to unified and standardized management on the one hand, and can also achieve energy saving and carbon reduction in a real sense on the other hand. This solves the technical problems in related technologies, such as the difficulty in balancing cost, timeliness and data multi-source in energy resource detection, and the difficulty in timely discovering and handling problems. At the same time, due to the differences in the relationship between different buildings, energy and carbon emissions, unified quota management is difficult to achieve macro-control indicators, and manual control or equipment updates are difficult to effectively balance the office environment and economic benefits.

[0044] Specifically, Figure 1 A schematic flow chart of a construction site energy management method provided in an embodiment of the present application.

[0045] like Figure 1 As shown, the energy management method for the building site includes the following steps:

[0046] In step S101 , multi-source energy usage data of a target building site is collected.

[0047] It is understandable that due to the complex links and large number of personnel involved in data collection work in the mode of "issuing forms, reporting offline, and manual summary", the data is not traceable, and the disadvantages of vague and empirical supervision are highlighted; and the construction of energy resource monitoring systems that reduce personnel participation is not broad enough. Some buildings with systems focus on electricity and energy management. Due to construction time and construction goals, it is difficult to ensure the standardization and integrity of the data, and it is impossible to support carbon management services such as carbon metering and carbon inventory. It makes it difficult for related technologies to accurately grasp the energy status of the building site, and thus it is difficult to achieve effective supervision of carbon emissions.

[0048] In order to solve the above problems, the embodiments of the present application can utilize the electricity meters, water meters, flow meters, temperature sensors and other equipment deployed at the building site, and through streaming multi-source data acquisition (agent) #n, #b and #a and other different types of acquisition terminals, collect various types of energy usage data in the building in real time and accurately, that is, multi-source energy usage data, without the need for additional deployment, reducing costs, and these agents support multiple communication protocols to ensure the comprehensiveness, reliability and timeliness of data collection.

[0049] In step S102, data processing is performed on multi-source energy usage data to obtain energy aggregation data that meets preset conditions.

[0050] Furthermore, the embodiments of the present application can perform multi-source streaming data fusion and aggregation processing on the collected multi-source energy usage data.

[0051] For example, the embodiment of the present application can select data fusion technology that meets the needs of multi-source data processing, clean and integrate data from different sources to form a unified data format. Subsequently, the fused data is aggregated to form energy aggregation data, providing a solid data foundation for subsequent statistical analysis.

[0052] Among them, the preset conditions may include data format conditions, data validity conditions, etc., which can be specifically set by technical personnel in this field according to actual conditions and are not specifically limited here.

[0053] In step S103, the energy aggregation data is analyzed in combination with the data source of the target building site to obtain various key indicator data of the energy aggregation data.

[0054] As a possible implementation method, the embodiment of the present application can perform statistical mining and analysis on the energy aggregation data, and combine the data source of the target building site, such as the building to which the data belongs, the data source (water meter, electricity meter or temperature sensor), etc. to mark and classify different buildings and different building data, and form key indicator data items such as total energy, energy consumption per unit of building area, per capita energy consumption, per capita water consumption, special electricity consumption, total carbon emissions, per capita carbon emissions, etc. These key indicator data will serve as an important basis for energy consumption and carbon emission management.

[0055] In step S104, early warning matching is performed on each of the multiple key indicator data to obtain a matching result.

[0056] In order to achieve real-time monitoring of energy usage, the embodiment of the present application can perform early warning matching on each of the multiple key indicator data respectively, so as to perform energy early warning or energy strategy adjustment according to the matching results.

[0057] There are many ways of early warning matching. For example, threshold matching can be performed for each key indicator data one by one to determine whether any key indicator data exceeds the standard. A comprehensive score can be calculated using multiple key indicator data to determine whether an early warning is needed based on the comprehensive score. Anomalies can also be determined based on the fluctuation range of the key indicator data, etc. The specific settings can be made according to the actual situation of the target building site, and no specific restrictions are made here.

[0058] Optionally, in one embodiment of the present application, before performing early warning matching on each of the multiple key indicator data respectively, it also includes: obtaining basic data of multiple buildings that meet preset relevant conditions; standardizing the basic data to obtain processed sample data; obtaining standard information of the target building site, and constructing data weights based on the standard information; and constructing a scoring model using the sample data and data weights to perform early warning matching using the scoring model.

[0059] When the warning matching mechanism is a mechanism for performing warning matching using comprehensive scoring, the embodiment of the present application may include the following steps:

[0060] Step S1: Data collection and standardization.

[0061] The embodiment of the present application can collect basic information such as the building area, number of people, historical energy consumption and carbon emissions of all relevant buildings.

[0062] Since the building area and number of people in different buildings vary greatly, the embodiments of the present application can perform data standardization to standardize these data to the same scale. For example, Z-score standardization or maximum and minimum standardization methods of area and people can be used.

[0063] Step S2: Setting benchmarks and weights.

[0064] The embodiment of the present application can determine the average energy consumption and carbon emission intensity (such as carbon emissions / area, carbon emissions / personnel, etc.) for each administrative level, institution type, and building age as a baseline.

[0065] The embodiment of the present application can also set different weights for factors such as area, personnel, and historical carbon emissions according to policy goals (such as encouraging technological innovation, restricting high-emission units, and promoting the development of new areas). For example, if the policy focuses more on reducing carbon emissions per unit area, the weight of area can be higher.

[0066] Step S3: Construct a scoring model.

[0067] The embodiment of the present application can construct a comprehensive scoring model based on the standardized data and the set weights. The model can be a weighted sum model, in which each factor contributes to the total score according to its weight.

[0068] Taking carbon emission quota allocation as an example, the comprehensive score Si can be expressed as:

[0069] Si=w1·standardized area i +w2·Standardization staff i +w3·Standardized carbon emissions i ,

[0070] Among them, w1, w2, w3 are the weights of area, personnel and carbon emissions respectively, and w1+w2+w3=1.

[0071] Optionally, in one embodiment of the present application, early warning matching is performed on each of the multiple key indicator data to obtain a matching result, including: inputting each key indicator data into a scoring model to obtain a comprehensive score of the target building site, and using the comprehensive score as the matching result; or early warning matching is performed on each key indicator data with multiple pre-constructed key indicator thresholds to screen out at least one key indicator data that meets the preset exceeding standard condition, and using at least one key indicator data as the matching result.

[0072] In some embodiments, key indicator data may be input into a pre-built scoring model to calculate a comprehensive score, thereby allocating carbon emission indicators or energy intervention based on the comprehensive score.

[0073] For example, an embodiment of the present application may score the key indicator data of each building at the target building site individually, and then score the overall key indicator data of the target building site, thereby obtaining a comprehensive scoring result for each building and an overall comprehensive scoring result for the target building site, so as to subsequently allocate carbon emission indicators or perform energy intervention based on the proportion of the comprehensive score of each building.

[0074] In other embodiments, each key indicator data may be matched with a key indicator threshold, and key indicator data exceeding the key indicator threshold may be found, so as to perform carbon emission indicator allocation or energy intervention according to the key indicator data exceeding the threshold.

[0075] Among them, the embodiments of the present application can set the overall key indicator thresholds according to the actual conditions of the target construction site. For example, the key indicator thresholds can be set according to the fluctuation range of each key indicator that meets the emission standards of similar construction sites, and the overall key indicators can be matched to determine whether there are any key indicators that exceed the standards.

[0076] Similarly, the embodiments of the present application can also match the key indicator data of each building at the target construction site, thereby determining whether there are any buildings that exceed the standards, and determining the key indicators that exceed the standards in the buildings that exceed the standards.

[0077] Optionally, in one embodiment of the present application, early warning matching is performed on each of the multiple key indicator data to obtain a matching result, including: determining whether the target construction site meets the preset special conditions; if the target construction site meets the preset special conditions, then based on the special factors of the target construction site, adding corresponding adjustment factors to the scoring model to obtain an updated scoring model; and using the updated scoring model to calculate the comprehensive score.

[0078] It is understandable that there may be special circumstances at the target building site, such as technical modification plans, special institutional requirements, etc. The embodiment of the present application can add corresponding adjustment factors to the scoring model to update the scoring model, so that the embodiment of the present application can perform dynamic supervision of the target building site and further achieve the goal of energy conservation and carbon reduction.

[0079] In step S105, a corresponding energy intervention strategy is generated according to the matching result, and the energy usage of at least one data source at the target building site is intervened based on the energy intervention strategy.

[0080] In the actual implementation process, the embodiment of the present application can determine the energy intervention strategy of the target building site according to the matching results to intervene in the energy usage of at least one data source of the target building site.

[0081] For example, in the case of using a scoring model for early warning matching, the energy intervention strategy of an embodiment of the present application may include assigning lower carbon emission indicators to buildings with higher scores (if the goal is to reduce emissions), or conversely, if the policy encourages efficient use of resources, buildings with high scores may receive higher indicators.

[0082] For example, the energy intervention strategy of the embodiment of the present application can be a dynamic adjustment strategy. The embodiment of the present application can set an adjustment cycle (quarterly, monthly, weekly) according to the total amount of allocated indicators and the adjustable range, according to the actual performance of the building (such as emission reduction effects, technological progress, early warning conditions, etc.) and new policy goals, and dynamically adjust various energy carbon quota indicators, that is, determine a dynamically adjusted intervention strategy.

[0083] Optionally, in one embodiment of the present application, a corresponding energy intervention strategy is generated based on the matching results, including: inputting the multi-source energy usage data into a pre-built carbon emission prediction model to obtain the carbon emission prediction results for the target building site, wherein the carbon emission prediction model is trained by historical multi-source energy usage data and historical carbon emission data; inputting the matching results and the carbon emission prediction results into a pre-built carbon emission index allocation model to obtain the carbon emission index corresponding to each data source, wherein the carbon emission index allocation model is updated by the historical energy intervention results; and obtaining the energy intervention strategy based on the carbon emission index of each data source.

[0084] Among them, in order to dynamically establish the carbon emission indicators of the building, that is, to determine a more reasonable energy intervention strategy, the embodiment of the present application can introduce machine learning algorithms and optimization models to ensure the scientificity and fairness of the distribution.

[0085] Specifically, the embodiments of the present application can adopt a hybrid prediction model based on the combination of historical data and real-time monitoring data, combining the two algorithm architectures of Random Forest (Random Forest) and LSTM (Long Short-Term Memory) to capture the nonlinear relationships and periodic characteristics in time series, so as to more accurately predict future carbon emission trends and adjust the quota accordingly.

[0086] Furthermore, in terms of allocating carbon emission indicators, the embodiments of the present application can adopt an adaptive control strategy based on reinforcement learning to dynamically adjust the carbon emission limits of each building according to its actual performance (such as emission reduction effect, technological progress, etc.). In other words, the embodiments of the present application can combine the score matching results and the prediction results of carbon emissions to allocate indicators.

[0087] For example, the embodiment of the present application can classify buildings into several categories and set an initial emission limit for each category. Subsequently, the MDP (Markov Decision Process) is used to simulate the behavior choices and consequences of the buildings, and the reward mechanism is used to encourage the buildings to adopt a more environmentally friendly operation mode. When the building achieves a significant emission reduction target, it can obtain additional emission quotas as a reward; otherwise, it may face the risk of quota reduction.

[0088] By combining the above content, historical indicator allocation data samples and historical emission data, a carbon emission indicator allocation model can be constructed.

[0089] Among them, in order to ensure that the carbon emission index allocation model can be more adapted to the target building site, the model can be optimized and updated according to the intervention results obtained at the target building site after the previous round of intervention to achieve dynamic allocation of carbon emission indicators.

[0090] For example, the embodiment of the present application can automatically evaluate the effect of the current carbon emission index allocation once a quarter, that is, the intervention result of the energy intervention strategy, and recalculate the weight according to the latest policy goals and personal contribution factors. At the same time, with the help of genetic algorithms, the optimal solution is found, and the allocation plan is continuously iterated to ensure long-term stability and adaptability. In addition, by introducing blockchain technology to ensure the security and immutability of data, the reliability and fairness of the system are further improved.

[0091] The expression of genetic algorithm can be shown as follows:

[0092] First, calculate the maximized objective function F(x):

[0093]

[0094] Among them, w i represents the weight of the i-th factor, x i Indicates whether the factor is selected (0 or 1), that is

[0095] The constraints can be:

[0096]

[0097] Among them, c i represents the cost or resource consumption of each factor, and C represents the total resource limit.

[0098] Optionally, in one embodiment of the present application, it also includes: after executing the energy intervention strategy for a preset period of time, obtaining the current multi-source energy usage data of the target building site; using the current multi-source energy usage data to obtain new matching results; using the new matching results to update the data weights of the scoring model; or using the new matching results to update the energy intervention strategy.

[0099] As a possible implementation method, the embodiments of the present application may also establish a mechanism for feedback, supervision, and continuous optimization.

[0100] The embodiment of the present application can determine the execution effect of the energy intervention strategy based on the current multi-source energy usage data of the target building site after the energy intervention strategy has been executed for a certain period of time, and then adjust the scoring model data weights or the energy intervention strategy according to the execution effect to achieve the energy saving and carbon reduction goals of the target building site.

[0101] Combination Figures 2a to 7 As shown, the working principle of the energy management method for a building site of the embodiment of the present application is described in detail with an embodiment.

[0102] In the actual implementation process, the embodiments of this application can grasp the building energy consumption and carbon emission data in real time, dynamically and accurately based on the informatization and digitization of energy resources, realize the visual and controllable management of the energy resource consumption process, support quota targets, energy saving targets, energy consumption diagnosis, energy efficiency improvement, carbon neutrality demonstration and other management services, and gradually improve the level of building self-supervision and the linkage level of competent units. The construction of a dynamic supervision system is a comprehensive system approach, which mainly considers the following aspects:

[0103] (1) Informatization is a solid foundation for perception. Based on the Internet of Things technology, comprehensive monitoring and perception of various energy resource consumption, carbon emissions, new energy supply, temperature and humidity, video and other information can be achieved.

[0104] (2) Digitalization is the key guide and path. Using key technologies such as big data and artificial intelligence, we can achieve the organic integration of the physical space and digital space of energy resource consumption, and innovatively adopt the integrated hardware and software terminal design to save system construction costs and facilitate unified promotion and application.

[0105] (3) Greening is the ultimate goal of operation supervision. Based on digital transformation and services, we will eventually achieve refined management of energy resource consumption, technological transformation, energy efficiency optimization, carbon measurement and carbon neutrality services, and support normalized closed-loop supervision of the entire process of energy and carbon management.

[0106] Based on the above goals, the embodiments of the present application are not limited to collecting and calculating the consumption data of common energy sources such as electricity, gas, oil, steam, coal, and heat in buildings, but will also start from how to disassemble the building's quota indicators, and integrate the current multi-source data of the building to obtain the most real, effective and timely total energy consumption, energy consumption per unit building area, per capita energy consumption, energy consumption in specific areas (such as floors, departments, rooms), total carbon emissions, carbon emissions per unit building area, per capita carbon emissions, carbon emissions in specific areas (such as floors, departments, rooms) and other real-time data, and build a comprehensive scoring model to automatically weigh the quota indicators regularly, and dynamically carry out the issuance and supervision of quota indicators.

[0107] Specifically, the embodiment of the present application can rely on the structure shown in Figure 2 to achieve the advantages of real-time automatic collection, data quantification, edge computing, automatic balancing, prediction and early warning, lightweight integration, and easy promotion, effectively avoiding the traditional management method that relies entirely on manual or semi-automatic data collection and annual quota tracking, and helping buildings to significantly improve the statistical accuracy of energy carbon data and the level of quota management.

[0108] like Figure 2aAs shown, the structure involved in the embodiment of the present application may include an energy carbon management machine and a server platform (an energy carbon management network platform). The embodiment of the present application may set an energy carbon management machine in each building to realize data collection and energy management. Among them, the structure of the energy carbon management machine of building A may be as follows Figure 2b As shown, the display interface of the energy carbon management machine can be as follows Figure 3 shown.

[0109] Among them, the energy carbon management machine can be Figure 4 As shown, it can be applied to the building end, using a hardware and software integrated design to integrate the energy-saving supervision platform system, server, collector, and switch into an energy carbon management machine, and a supporting energy carbon management network platform. This design greatly reduces the construction cost, saving more than 50% of funds compared to traditional supervision systems, and "leveraging" large energy savings with small funds.

[0110] Note: According to market research, the average price of software with only energy resource consumption metering functions is about 45,000 yuan. The average price of software with air conditioning, lighting and other monitoring functions is about 120,000 yuan. In addition, the system needs to collect gateways, deploy servers, and operate computers when it is established. Considering only the core components of the above systems, a single building needs to invest 69,000 yuan / 144,000 yuan in two cases. The energy carbon management machine has more functions, saves most of the core components of the system, and the investment cost is about less than 50,000 yuan.

[0111] The energy and carbon management machine mainly considers energy, carbon emissions, and other three aspects. Energy functions include: energy consumption statistics, energy consumption analysis, energy efficiency analysis, energy consumption disclosure, energy consumption flow chart, clean energy supply, energy audit, energy-saving diagnosis, energy-saving control, technical transformation monitoring, quota management, safety alarm, etc.; carbon emission functions include: carbon accounting, carbon flow diagram, carbon quota, personal carbon points, personal carbon archives, zero-carbon conference, product carbon footprint, carbon inventory and carbon verification, carbon neutrality application, etc.; other functions include: data collection, data classification, data verification, data reporting, data reports, building information, personnel information, working hours, user permissions, video training, online Q&A, expert think tank, property assessment, patrol management, smart operation, system implementation, opinion collection, as well as carbon accounting model, energy carbon scheduling model, testing and certification services, etc. Considering that functional departments need to fully understand their own energy and carbon data and management status, in order to facilitate summary reporting and assist decision-making, a comprehensive situation visualization overview homepage should be provided as standard.

[0112] The server platform (one-network platform for energy and carbon management) can be set up at the superior competent unit end, based on the informatization and digitization of energy consumption, to grasp the building energy consumption and carbon emission information in real time, dynamically and accurately, and realize the visualization and controllable management of the dynamic process of energy consumption. The platform can support the building's own energy consumption targets, energy-saving targets, quota targets, carbon emission target management and energy consumption diagnosis, pollution reduction and carbon reduction and other management services, realize refined energy conservation, consumption reduction and emission reduction management, and improve energy utilization efficiency; support the superior competent unit to carry out energy conservation, consumption reduction and emission reduction supervision work in a normalized and refined manner, realize energy consumption forecasting and early warning, judge energy consumption and green and low-carbon operation trends, improve the level of supervision, and help achieve the "dual carbon" goals as scheduled.

[0113] Combined with the current status of building energy-saving information construction in various places, the embodiments of this application can be applied to a variety of office building scenarios. For areas where the structures involved in the embodiments of this application have not yet been built, management machines can be configured for each building or key unit, and the network can be successfully established to achieve dynamic and refined operation supervision of the building quota; for areas where the structures involved in the embodiments of this application have been built, management machines can be installed for some buildings with complex energy consumption and carbon emissions, and in-depth management of quotas can be carried out to further improve the dynamic supervision system.

[0114] Based on the above structure, the embodiment of the present application may include the following steps:

[0115] Step S1: Multi-source data fusion.

[0116] like Figure 5 As shown, multi-source data fusion can include: multi-source data collection, data fusion and aggregation, statistical analysis and labeling classification, real-time matching warning and data application optimization and other steps.

[0117] The embodiment of the present application can utilize the electric meter, water meter, flow meter, temperature sensor and other equipment deployed at the target building site, and collect various types of energy usage data in the building in real time and accurately through different types of collection terminals such as streaming multi-source data collection (agent) type #n, type #b and type #a. These agents support multiple communication protocols to ensure the comprehensiveness and reliability of data collection.

[0118] The embodiment of the present application can transmit the collected multi-source usage data through the data bus, thereby realizing multi-source streaming data fusion and aggregation processing, and using data fusion technology to clean and integrate data from different sources to form a unified data format. Subsequently, the fused data can be aggregated, for example, to a pre-built data aggregation library to provide a solid data foundation for subsequent statistical analysis.

[0119] With the support of the data aggregation library, the embodiment of the present application can use the aggregated data statistical mining and analysis function to perform in-depth statistical analysis on the aggregated data. According to the analysis results, different buildings and different building data are marked and classified to form key indicator data items such as total energy, energy consumption per unit building area, per capita energy consumption, per capita water consumption, special electricity consumption, total carbon emissions, and per capita carbon emissions. These indicator data will serve as an important basis for energy consumption and carbon emission management.

[0120] In order to achieve real-time monitoring of energy use, the embodiment of the present application can also perform real-time matching warning. By performing real-time analysis on the marked and classified data, when the data is found to be abnormal or exceeds the preset threshold, the warning mechanism is immediately triggered to send warning information to the management personnel. This helps to find problems in time, take effective measures to intervene, and avoid energy waste and excessive carbon emissions.

[0121] Finally, the embodiments of the present application can apply the processed energy consumption and carbon emission related quota data items to the daily management and decision-making of buildings. Through continuous monitoring and analysis of data, energy use strategies can be continuously adjusted and optimized to improve energy efficiency and reduce carbon emissions. At the same time, these data can also provide strong support for policy formulation, energy conservation and emission reduction target setting, etc.

[0122] Through the above-mentioned efficient and precise implementation steps, the embodiments of the present application can realize the comprehensive collection, fusion, aggregation, statistical analysis and optimized application of multi-source data, providing strong technical support for the energy consumption and carbon emission management of buildings.

[0123] Step S2: Dynamic quota management.

[0124] It is understandable that in order to achieve real-time monitoring and intervention of energy in the above steps, the embodiment of the present application can implement a dynamic quota management strategy.

[0125] When implementing dynamic quota management, the embodiment of the present application may include:

[0126] 1. Data collection and standardization.

[0127] Collect data: First, the embodiment of the present application can collect basic information such as the building area, number of people, historical energy consumption and carbon emissions of all relevant buildings.

[0128] Data standardization: Since the building area and number of people in different buildings vary greatly, the embodiments of the present application can standardize these data to the same scale. For example, the Z-score standardization or maximum and minimum standardization method of area and people can be used.

[0129] 2. Set benchmarks and weights.

[0130] Industry benchmark: The embodiment of the present application can determine the average energy consumption and carbon emission intensity (such as carbon emissions / area, carbon emissions / personnel, etc.) for each administrative level, institution type, and building age as a baseline.

[0131] Weight setting: The embodiment of the present application can set different weights for factors such as area, personnel, and historical carbon emissions according to policy goals (such as encouraging technological innovation, restricting high-emission units, and promoting the development of new areas). For example, if the policy focuses more on reducing carbon emissions per unit area, the weight of the area can be higher.

[0132] 3. Calculate the comprehensive score.

[0133] Constructing a scoring model: The embodiment of the present application can construct a comprehensive scoring model based on the standardized data and the set weights. The model can be a weighted sum model, in which each factor contributes to the total score according to its weight.

[0134] Taking carbon emission quota allocation as an example, the comprehensive score Si can be expressed as:

[0135] Si=w1·standardized area i +w2·Standardization staff i +w3·Standardized carbon emissions i .

[0136] Adjustment factors: Taking into account the special circumstances of the building (such as technical improvement plans, special institutional requirements, etc.), the embodiment of the present application can add adjustment factors to the comprehensive score.

[0137] 4. Allocate carbon emission indicators.

[0138] Score-based allocation: The present application embodiment can allocate carbon emission indicators based on the comprehensive score of the building. Buildings with higher scores may receive lower carbon emission indicators (if the goal is to reduce emissions), or conversely, if the policy encourages efficient use of resources, buildings with higher scores may receive higher indicators.

[0139] Dynamic adjustment: The embodiment of the present application can set an adjustment cycle (quarterly, monthly, weekly) according to the total amount of allocated indicators and the adjustable range, based on the actual performance of the building (such as emission reduction effects, technological progress, early warning conditions, etc.) and new policy goals, and dynamically adjust various energy carbon quota indicators.

[0140] Among them, allocating carbon emission indicators can also include: dynamically establishing carbon emission indicators of buildings, allocating carbon emission indicators, feedback and continuous optimization.

[0141] Dynamically establish the carbon emission index of a building Figure 6As shown, the embodiment of the present application can adopt a hybrid prediction model based on the combination of historical data and real-time monitoring data, combining the hybrid prediction model of random forest and LSTM algorithm architectures to capture the nonlinear relationship and periodic characteristics in the time series, so as to more accurately predict future carbon emission trends and adjust the quota accordingly.

[0142] Carbon emission quotas can be allocated as follows: Figure 7 As shown, in terms of allocating carbon emission indicators, the embodiment of the present application can adopt an adaptive control strategy based on reinforcement learning to dynamically adjust the carbon emission limit of each building according to its actual performance (such as emission reduction effect, technological progress, etc.). In other words, the embodiment of the present application can combine the score matching results and the prediction results of carbon emissions to allocate indicators.

[0143] For example, the embodiment of the present application can classify buildings into several categories and set an initial emission limit for each category. Subsequently, the MDP (Markov Decision Process) is used to simulate the behavior choices and consequences of the buildings, and the reward mechanism is used to encourage the buildings to adopt a more environmentally friendly operation mode. When the building achieves a significant emission reduction target, it can obtain additional emission quotas as a reward; otherwise, it may face the risk of quota reduction.

[0144] In terms of feedback, the embodiments of the present application can be managed in a closed loop, relying not only on static historical data analysis, but also on real-time monitoring and future predictions. The effect of the current carbon emission index allocation is automatically evaluated once a quarter, and the weights are recalculated based on the latest policy goals and personal contribution factors. At the same time, the optimal solution is found with the help of genetic algorithms, and the allocation plan is continuously iterated to ensure long-term stability and adaptability. In addition, the security and immutability of data are guaranteed by the introduction of blockchain technology, which further improves the reliability and fairness of the system.

[0145] 5. Feedback and supervision.

[0146] Establish a feedback mechanism: The embodiment of the present application can obtain feedback from the building on the allocated carbon emission indicators and make adjustments based on reasonable suggestions.

[0147] Supervision and Implementation: The embodiments of the present application can establish an effective supervision mechanism to ensure that the building operates on a daily basis in accordance with the allocated carbon emission indicators.

[0148] Among them, feedback and supervision can be Figure 8 As shown, the embodiment of the present application adopts a server platform to achieve two-way communication.

[0149] On the one hand, buildings can submit opinions and suggestions on the allocation of carbon emission indicators through the server platform, and the server platform will automatically classify and prioritize all feedback information and respond to it in a timely manner. On the other hand, the superior competent unit has the right to check the carbon emissions of subordinate buildings at any time, and once abnormal fluctuations or violations are found, the investigation procedure can be initiated immediately. In addition, the server platform also supports public inquiry functions, which enhances social supervision and improves the credibility of the entire system.

[0150] 6. Continuous optimization.

[0151] Data analysis: The embodiment of the present application can regularly analyze the effect of carbon emission index allocation and evaluate the degree of achievement of policy goals.

[0152] Policy adjustment: The embodiment of the present application can continuously optimize the allocation algorithm and weight setting of carbon emission indicators according to the analysis results and changes in policy objectives.

[0153] Automatic control: The embodiment of the present application can automatically perform strategic control on intelligent equipment and systems such as air conditioners, lighting, and power distribution circuit breakers in the building based on indicator analysis and optimization suggestions, thereby achieving energy conservation and carbon reduction.

[0154] Based on the above steps, the embodiment of the present application can be performed as follows during actual execution: Fig. 9 As shown, first configure the building type, quota used, calculation method, etc. of the target building site, and configure the functional equipment, third-party system communication parameters, etc., and after the configuration is completed, collect energy and carbon emission data to form the basic quota data.

[0155] Furthermore, the embodiment of the present application can determine whether it is necessary to report to the system platform online, that is, the server platform, to dynamically monitor the quota.

[0156] After reporting, in the embodiment of the present application, the superior competent unit can configure the comprehensive building factors, adjustment frequency, adjustment interval, etc., and when special circumstances require adjustment, the data configured by the superior competent unit can be modified through new construction. After the configuration is completed, the embodiment of the present application can send the configuration results to the energy carbon management machine at the building end of the target building site, and display the sent configuration data through the LCD screen, or manually enter it, and perform subsequent calculations of quota data of different dimensions. It should be noted that if the server platform is not reported, the embodiment of the present application can directly calculate the quota data of different dimensions based on the existing configuration.

[0157] Furthermore, the embodiments of the present application can perform quota data benchmarking analysis, provide optimization suggestions based on the analysis results, and determine whether the carbon management machine is needed to perform dynamic supervision and optimization control.

[0158] In the absence of the need for dynamic supervision and optimization control, management is carried out through relevant personnel, systems, etc., that is, optimization control is carried out according to the energy intervention strategy formulated by relevant personnel.

[0159] When dynamic supervision and optimization control is required, the embodiments of the present application can realize dynamic carbon emission indicator allocation in combination with dynamic carbon emission forecasts, comprehensive scores and matching results of key indicators. At the same time, combined with historical intervention effects, the embodiments of the present application can also adjust the energy intervention strategy, that is, update the indicator allocation, such as automatic control strategy adjustment of air conditioning, lighting, and power distribution circuit breakers.

[0160] The embodiment of the present application can continuously calculate quota data of different dimensions after policy intervention to achieve dynamic supervision and dynamic control.

[0161] In summary, the embodiment of the present application integrates the energy-saving supervision platform system, server, controller, collector, and switch, which can greatly reduce the construction cost and save more than 50% of funds compared with the traditional supervision system.

[0162] The accounting method involved in the embodiment of the present application can efficiently collect, fuse, aggregate and statistically analyze multi-source data, realize accurate management and optimization of energy consumption and carbon emissions, and can directly calculate different types of building carbon emission data on the equipment.

[0163] The dynamic quota management method of the embodiment of the present application can regularly allocate quotas and continuously optimize the allocation algorithm and weight setting of carbon emission indicators.

[0164] According to the energy management method for the building site proposed in the embodiment of the present application, the multi-source energy usage data of the target building site can be collected, and the multi-source energy usage data can be integrated to obtain energy convergence data, and then the energy convergence data can be analyzed in combination with the data source of the target building site, multiple key indicator data can be determined, and early warning matching can be performed, so as to generate a corresponding energy intervention strategy according to the matching result, and intervene in the energy usage of at least one data source of the target building site based on the energy intervention strategy, starting from the actual situation of each building, by monitoring and analyzing multi-organization multi-source data, dynamic energy carbon quota management can be carried out, and weak points can be effectively found and timely intervened, which is conducive to unified and standardized management on the one hand, and can also achieve energy conservation and carbon reduction in a real sense on the other hand. Thus, the technical problems in the related technology that energy resource detection is difficult to balance cost, timeliness and multi-source of data, and it is difficult to find and deal with problems in time, at the same time, due to the differences in the relationship between different buildings, energy and carbon emissions, unified quota management is difficult to achieve macro-control indicators, and manual control or equipment update is difficult to effectively take into account office environment and economic benefits.

[0165] Next, the energy management device for a construction site proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0166] Fig.10 It is a block diagram of an energy management device for a building site according to an embodiment of the present application.

[0167] like Fig.10 As shown, the energy management device 10 at the building site includes: a collection module 100, a first processing module 200, an analysis module 300, a first matching module 400 and a management module 500.

[0168] Specifically, the collection module 100 is used to collect multi-source energy usage data at the target building site.

[0169] The first processing module 200 is used to process the multi-source energy usage data to obtain energy aggregation data that meets preset conditions.

[0170] The analysis module 300 is used to analyze the energy aggregation data in combination with the data source of the target building site to obtain various key indicator data of the energy aggregation data.

[0171] The first matching module 400 is used to perform early warning matching on each type of key indicator data among the multiple key indicator data to obtain a matching result.

[0172] The management module 500 is used to generate a corresponding energy intervention strategy according to the matching result, and intervene in the energy usage of at least one data source at the target building site based on the energy intervention strategy.

[0173] Optionally, in one embodiment of the present application, the management module includes: a prediction unit, a first calculation unit and an acquisition unit.

[0174] Among them, the prediction unit is used to input the multi-source energy usage data into a pre-built carbon emission prediction model to obtain the carbon emission prediction results of the target building site, wherein the carbon emission prediction model is trained by historical multi-source energy usage data and historical carbon emission data.

[0175] The first calculation unit is used to input the matching results and the carbon emission prediction results into a pre-built carbon emission index allocation model to obtain the carbon emission index corresponding to each data source, wherein the carbon emission index allocation model is updated by historical energy intervention results.

[0176] The acquisition unit is used to obtain an energy intervention strategy based on the carbon emission index of each data source.

[0177] Optionally, in one embodiment of the present application, the energy management device 10 at the building site further includes: a first acquisition module, a second processing module, a construction module and a reconstruction module.

[0178] Among them, the first acquisition module is used to obtain basic data of multiple buildings that meet preset relevant conditions.

[0179] The second processing module is used to perform standardization processing on the basic data to obtain processed sample data.

[0180] A construction module is used to obtain standard information of the target building site and construct data weights based on the standard information.

[0181] The reconstruction module is used to construct a scoring model using sample data and data weights, so as to use the scoring model for early warning matching.

[0182] Optionally, in one embodiment of the present application, the first matching module 400 includes: a second calculation unit or a matching unit.

[0183] The second calculation unit is used to input each key indicator data into the scoring model to obtain a comprehensive score of the target building site, and use the comprehensive score as the matching result.

[0184] The matching unit is used to perform early warning matching on each key indicator data with a plurality of pre-built key indicator thresholds, so as to screen out at least one key indicator data that meets the preset exceeding standard condition, and take at least one key indicator data as the matching result.

[0185] Optionally, in one embodiment of the present application, the first matching module 400 includes: a judgment unit, an updating unit and a third calculation unit.

[0186] Among them, the judgment unit is used to judge whether the target building site meets the preset special conditions.

[0187] The updating unit is used to add corresponding adjustment factors to the scoring model based on special factors of the target building site when the target building site meets the preset special conditions, so as to obtain an updated scoring model.

[0188] The third calculation unit is used to calculate the comprehensive score using the updated scoring model.

[0189] Optionally, in one embodiment of the present application, the energy management device 10 at the building site further includes: a second acquisition module, a second matching module, a first update module or a second update module.

[0190] The second acquisition module is used to acquire the current multi-source energy usage data of the target building site after executing the energy intervention strategy for a preset time.

[0191] The second matching module is used to obtain a new matching result by using the current multi-source energy usage data.

[0192] The first updating module is used to update the data weight of the scoring model using the new matching results.

[0193] The second updating module is used to update the energy intervention strategy using the new matching results.

[0194] It should be noted that the above explanation of the embodiment of the energy management method for a construction site is also applicable to the energy management device for a construction site of this embodiment, and will not be repeated here.

[0195] According to the energy management device for the building site proposed in the embodiment of the present application, it is possible to collect multi-source energy usage data of the target building site, and fuse the multi-source energy usage data to obtain energy convergence data, and then analyze the energy convergence data in combination with the data source of the target building site, determine multiple key indicator data, and perform early warning matching, so as to generate a corresponding energy intervention strategy according to the matching result, and intervene in the energy usage of at least one data source of the target building site based on the energy intervention strategy, starting from the actual situation of each building, by monitoring and analyzing multi-organization multi-source data, carry out dynamic energy carbon quota management, effectively find out the weak points and intervene in time, which is conducive to unified and standardized management on the one hand, and can also achieve energy conservation and carbon reduction in a real sense on the other hand. Thus, it solves the technical problems in the related technology that energy resource detection is difficult to balance cost, timeliness and multi-source of data, and it is difficult to find and deal with problems in time. At the same time, due to the differences in the relationship between different buildings, energy and carbon emissions, unified quota management is difficult to achieve macro-control indicators, and manual control or equipment update is difficult to effectively take into account the office environment and economic benefits.

[0196] Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0197] A memory 1101 , a processor 1102 , and a computer program stored in the memory 1101 and executable on the processor 1102 .

[0198] When the processor 1102 executes the program, the energy management method for the building site provided in the above embodiment is implemented.

[0199] Furthermore, the electronic device further comprises:

[0200] The communication interface 1103 is used for communication between the memory 1101 and the processor 1102 .

[0201] The memory 1101 is used to store computer programs that can be executed on the processor 1102 .

[0202] The memory 1101 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0203] If the memory 1101, the processor 1102 and the communication interface 1103 are implemented independently, the communication interface 1103, the memory 1101 and the processor 1102 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.11 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0204] Optionally, in a specific implementation, if the memory 1101, the processor 1102 and the communication interface 1103 are integrated on a chip, the memory 1101, the processor 1102 and the communication interface 1103 can communicate with each other through an internal interface.

[0205] The processor 1102 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0206] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned energy management method for a building site is implemented.

[0207] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the energy management method for a building site provided by an embodiment of the present invention.

[0208] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0209] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0210] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0211] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0212] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0213] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0214] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0215] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for energy management at a construction site, characterized in that: The following steps are involved: Collect multi-source energy usage data at the target building site; Processing the multi-source energy usage data to obtain energy aggregation data that meets preset conditions; Analyzing the energy aggregation data in combination with the data source of the target building site to obtain multiple key indicator data of the energy aggregation data; Performing early warning matching on each of the multiple key indicator data respectively to obtain a matching result; A corresponding energy intervention strategy is generated according to the matching result, and energy usage of at least one data source at the target building site is intervened based on the energy intervention strategy.

2. The method according to claim 1, characterized in that Generating a corresponding energy intervention strategy according to the matching result includes: Inputting the multi-source energy usage data into a pre-built carbon emission prediction model to obtain a carbon emission prediction result of the target building site, wherein the carbon emission prediction model is trained by historical multi-source energy usage data and historical carbon emission data; Inputting the matching result and the carbon emission prediction result into a pre-built carbon emission index allocation model to obtain the carbon emission index corresponding to each data source, wherein the carbon emission index allocation model is updated by the historical energy intervention results; The energy intervention strategy is obtained based on the carbon emission index of each data source.

3. The method according to claim 1, characterized in that Before performing early warning matching on each of the multiple key indicator data, the method further includes: Obtain basic data of multiple buildings that meet preset relevant conditions; Performing standardization processing on the basic data to obtain processed sample data; Acquire standard information of the target building site, and construct data weights according to the standard information; A scoring model is constructed using the sample data and the data weight, so as to perform early warning matching using the scoring model.

4. The method according to claim 3, characterized in that The performing early warning matching on each of the multiple key indicator data to obtain a matching result includes: Inputting each key indicator data into the scoring model to obtain a comprehensive score of the target building site, and using the comprehensive score as the matching result; or Each of the key indicator data is matched with a plurality of pre-constructed key indicator thresholds for early warning, so as to screen out at least one key indicator data that meets the preset exceeding condition, and the at least one key indicator data is used as the matching result.

5. The method according to claim 4, characterized in that The performing early warning matching on each of the multiple key indicator data to obtain a matching result includes: Determining whether the target building site meets preset special conditions; If the target construction site meets the preset special conditions, then based on the special factors of the target construction site, a corresponding adjustment factor is added to the scoring model to obtain an updated scoring model; The updated scoring model is used to calculate the comprehensive score.

6. The method according to claim 3, characterized in that Also includes: After executing the energy intervention strategy for a preset time, obtaining current multi-source energy usage data of the target building site; Obtaining a new matching result using the current multi-source energy usage data; Using the new matching result to update the data weight of the scoring model; or The energy intervention strategy is updated using the new matching result.

7. An energy management device for a construction site, characterized in that: include: A collection module for collecting multi-source energy usage data at the target building site; A first processing module, used for processing the multi-source energy usage data to obtain energy aggregation data that meets preset conditions; An analysis module, used to analyze the energy aggregation data in combination with the data source of the target building site to obtain multiple key indicator data of the energy aggregation data; A matching module, used to perform early warning matching on each of the multiple key indicator data to obtain a matching result; A management module is used to generate a corresponding energy intervention strategy according to the matching result, and intervene in the energy usage of at least one data source at the target building site based on the energy intervention strategy.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the energy management method for a building site as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the energy management method for a building site as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed, it is used to implement the energy management method for a construction site as described in any one of claims 1-6.

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