CN112948942A - Intelligent electricity-saving control method and system for building construction electricity consumption - Google Patents
Intelligent electricity-saving control method and system for building construction electricity consumption Download PDFInfo
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Abstract
The intelligent power-saving control method and system for building construction power consumption sequentially determine building construction power consumption data from each group of power consumption data records, then determine energy-saving data characteristics corresponding to the building construction power consumption data and update a characteristic clustering model, then obtain daytime building construction power consumption data and nighttime building construction power consumption data, further determine building construction progress information of the nighttime building construction power consumption data in parallel when power consumption behavior analysis is carried out on each group of power consumption data records according to the daytime building construction power consumption data, recognize the building construction progress information to obtain construction power consumption demand information, and finally store the construction power consumption demand information. Therefore, the construction power consumption demand information can be accurately determined in real time based on the construction power consumption data in the daytime and at night, so that the power saving control is performed according to the stored construction power consumption demand information, and the construction accidents caused by the conflict between the subsequent power saving measures and the actual construction state are avoided.
Description
Technical Field
The application relates to the technical field of building construction and energy conservation and environmental protection, in particular to an intelligent electricity-saving control method and system for electricity consumption in building construction.
Background
Along with the enhancement of energy-saving and environment-friendly consciousness, the electricity utilization standard of building construction is effectively improved. At present, the electric energy waste condition of most building construction sites is improved. However, on the premise of realizing the electricity saving of the electricity for building construction, how to avoid the construction accident caused by the conflict between the electricity saving measures and the actual construction state is a technical problem to be solved at present.
Disclosure of Invention
The specification provides an intelligent electricity-saving control method and system for building construction electricity, and aims to solve or partially solve the technical problems in the prior art.
The specification discloses an intelligent electricity-saving control method for building construction electricity, which comprises the following steps:
determining building construction electricity utilization data from each group of electricity utilization data records in sequence according to a data identification model pre-generated based on the electric energy consumption distribution information of each group of electricity utilization data records and electricity utilization peak-valley information corresponding to each group of electricity utilization data records;
determining energy-saving data characteristics corresponding to the building construction electricity consumption data based on the acquired pre-established characteristic clustering model and the electricity consumption behavior data recorded by each group of electricity consumption data, and updating the characteristic clustering model based on the energy-saving data characteristics;
marking the building construction power consumption data based on the updated feature clustering model to obtain daytime building construction power consumption data and nighttime building construction power consumption data;
determining the construction progress information of the night construction electricity consumption data in parallel when each group of electricity consumption data records are subjected to electricity consumption behavior analysis according to the daytime construction electricity consumption data, and identifying the construction progress information to obtain construction electricity consumption demand information;
and storing the construction power demand information and deleting the night building construction power data.
Preferably, the method further comprises:
and when building construction electricity consumption data recorded by other electricity consumption data are marked, updating the characteristic clustering model by using the stored construction electricity consumption demand information.
Preferably, the updating of the feature clustering model by using the stored construction power demand information includes:
recording a time sequence loss coefficient of a time sequence feature in the feature clustering model according to the power utilization data corresponding to the construction power utilization demand information, and extracting a model configuration parameter and clustering label distribution information of the feature clustering model; respectively constructing a parameter list corresponding to the model configuration parameters and a label list corresponding to the clustering label distribution information;
determining first electric data record description information of the parameter list and second electric data record description information corresponding to the tag list, determining data record correlation between the first electric data record description information and the second electric data record description information, and determining the proportion of the quantity of target correlation in the data record correlation, wherein the quantity of the target correlation is used for representing that the power consumption fluctuation values of the first electric data record description information and the second electric data record description information in a time period corresponding to the same electric data record are the same;
determining a model training distribution curve corresponding to the feature clustering model based on the ratio, extracting a plurality of curve segments with multi-dimensional feature labels from the model training distribution curve, and calculating the continuity between every two curve segments; marking the curve segment with the continuity larger than the set distance, and determining the curve segment with the maximum marking times as a target curve segment;
listing curve characteristic sequences corresponding to the target curve segments, and generating model updating indication information corresponding to the curve characteristic sequences; calculating a matching coefficient of the model updating indication information and the clustering label distribution information, and determining a recording position in each group of electricity consumption data records corresponding to non-electricity-consumption-demanding electricity consumption data between the model updating indication information and the clustering label distribution information according to the matching coefficient; and adding the construction power demand information to the recording positions in each group of power consumption data records in a target format in a model updating parameter set corresponding to the feature clustering model to update the feature clustering model.
Preferably, marking the building construction power consumption data based on the updated feature clustering model to obtain daytime building construction power consumption data and nighttime building construction power consumption data, and the method includes:
determining a plurality of first electricity weight values of the building construction electricity data based on the updated feature clustering model;
marking the building construction electricity consumption data according to the plurality of first electricity consumption weight values to obtain a first mark set and a second mark set; the first marker set is used for representing the daytime building construction electricity data, and the second marker set is used for representing the nighttime building construction electricity data;
calculating a correlation coefficient of the first set of labels and the second set of labels; when the correlation coefficient is larger than a set coefficient, weighting a first electricity weight value of the building construction electricity data based on the determined first construction electricity type data of the first mark set and second construction electricity type data of the second mark set to obtain a plurality of second electricity weight values; executing steps corresponding to marking the building construction electricity utilization data according to the first electricity weight values to obtain a first mark set and a second mark set on the basis of the second electricity weight values until the calculated correlation coefficient is smaller than or equal to the set coefficient; and determining a first target mark set corresponding to the correlation coefficient smaller than or equal to the set coefficient as daytime building construction electricity data, and determining a second target mark set corresponding to the correlation coefficient smaller than or equal to the set coefficient as nighttime building construction electricity data.
Preferably, the determining the building construction electricity consumption data from each group of electricity consumption data records in sequence according to the pre-generated data identification model based on the electricity consumption distribution information of each group of electricity consumption data records and the electricity consumption peak-valley information corresponding to each group of electricity consumption data records includes:
determining a recording format parameter of the power consumption data record corresponding to the power consumption peak valley information corresponding to each group of power consumption data record from the power consumption distribution information of each group of power consumption data record;
generating a parameter identification path corresponding to a recording format parameter of the electricity data record based on the order priority of the data identification model;
calculating a path matching rate between a recording format parameter of the electricity consumption data record and the parameter identification path; determining the building construction electricity utilization data from each group of electricity utilization data records according to the sequence from high to low of the sequence priority when the path matching rate is greater than the preset threshold; and when the path matching rate is less than or equal to a preset threshold value, determining the building construction electricity utilization data from each group of electricity utilization data records according to the sequence from low to high of the sequence priority.
Preferably, the determining, based on the acquired pre-established feature clustering model and the electricity consumption behavior data recorded by each group of electricity consumption data, an energy saving data feature corresponding to the building construction electricity consumption data, and updating the feature clustering model based on the energy saving data feature includes:
acquiring a plurality of construction equipment parameter information pairs in the electricity consumption behavior data and extracting target construction equipment parameter information of equipment parameter adjustment existing in the corresponding electricity consumption data records in each construction equipment parameter information pair; wherein, each target construction equipment parameter information has different parameter updating frequency values;
after the parameter information of each target construction device is arranged according to the size sequence of the corresponding energy-saving grade of the parameter information of each construction device, calculating the difference value of the parameter updating frequency values between every two adjacent pieces of parameter information of the target construction device; judging whether the difference value of the parameter updating frequency values between every two adjacent pieces of target construction equipment parameter information reaches a preset difference value or not; if the preset difference is reached, determining that the associated equipment parameters exist between the two adjacent pieces of target construction equipment parameter information, and if the preset difference is not reached, determining that the associated equipment parameters do not exist between the two adjacent pieces of target construction equipment parameter information;
drawing an energy-saving data record table of the electricity consumption behavior data according to the target construction equipment parameter information with the associated equipment parameters, extracting a plurality of energy-saving record data from the energy-saving data record table and integrating the energy-saving record data into the energy-saving data characteristics;
and updating the characteristic clustering model based on the priority of the energy-saving record data corresponding to the energy-saving data characteristics on the time sequence.
Preferably, when power consumption behavior analysis is performed on each group of power consumption data records according to the daytime construction power consumption data, building construction progress information of the nighttime building construction power consumption data is determined in parallel, and the building construction progress information is identified to obtain construction power consumption demand information, and the method includes the following steps:
when power consumption behavior analysis is carried out on each group of power consumption data records according to the daytime building construction power consumption data, power consumption behavior safety data of the nighttime building construction power consumption data and construction time period data of the nighttime building construction power consumption data are determined in parallel;
performing characteristic extraction on the night building construction electricity utilization data according to the electricity utilization behavior safety data and the construction period data to obtain building construction progress information of the night building construction electricity utilization data;
and inputting the building construction progress information into a preset convolutional neural network in an information distribution format and acquiring construction power consumption demand information output by the preset convolutional neural network.
The specification discloses an intelligent electricity-saving control system for building construction electricity, which comprises intelligent electricity-saving control equipment and construction equipment which are communicated with each other; wherein the intelligent power-saving control device is configured to:
determining building construction electricity utilization data from each group of electricity utilization data records in sequence according to a data identification model pre-generated based on the electric energy consumption distribution information of each group of electricity utilization data records and electricity utilization peak-valley information corresponding to each group of electricity utilization data records;
determining energy-saving data characteristics corresponding to the building construction electricity consumption data based on the acquired pre-established characteristic clustering model and the electricity consumption behavior data recorded by each group of electricity consumption data, and updating the characteristic clustering model based on the energy-saving data characteristics;
marking the building construction power consumption data based on the updated feature clustering model to obtain daytime building construction power consumption data and nighttime building construction power consumption data;
determining the construction progress information of the night construction electricity consumption data in parallel when each group of electricity consumption data records are subjected to electricity consumption behavior analysis according to the daytime construction electricity consumption data, and identifying the construction progress information to obtain construction electricity consumption demand information;
storing the construction power demand information and deleting the night building construction power data; and controlling the power utilization state of the construction equipment in the target construction area based on the stored construction power utilization demand information.
Through one or more technical schemes of this description, this description has following beneficial effect or advantage: firstly, building construction electricity utilization data are determined from each group of electricity utilization data records in sequence, secondly, energy-saving data characteristics corresponding to the building construction electricity utilization data are determined based on the acquired pre-established characteristic clustering model and the electricity utilization behavior data of each group of electricity utilization data records, and the characteristic clustering model is updated, then classifying the building construction electricity data based on the updated feature clustering model to obtain daytime building construction electricity data and nighttime building construction electricity data, further, when the electricity consumption behavior analysis is carried out on each group of electricity consumption data records according to the daytime building construction electricity consumption data, the building construction progress information of the nighttime building construction electricity consumption data is determined in parallel, and identifying the construction progress information to obtain construction power demand information, and finally storing the construction power demand information and deleting the construction power data at night. Therefore, the construction power consumption demand information can be accurately determined in real time based on the construction power consumption data in the daytime and at night, so that the power saving control is performed according to the stored construction power consumption demand information, and the construction accidents caused by the conflict between the subsequent power saving measures and the actual construction state are avoided.
The above description is only an outline of the technical solution of the present specification, and the embodiments of the present specification are described below in order to make the technical means of the present specification more clearly understood, and the present specification and other objects, features, and advantages of the present specification can be more clearly understood.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the specification. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flow chart illustrating an intelligent electricity-saving control method for building construction electricity according to an embodiment of the present disclosure.
Fig. 2 shows a block diagram of a building construction electricity intelligent electricity-saving control device according to an embodiment of the present specification.
Fig. 3 shows a schematic diagram of an intelligent power-saving control device according to one embodiment of the present description.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Referring to fig. 1, a flow chart of an intelligent electricity-saving control method for building construction electricity is provided, the method can be applied to an intelligent electricity-saving control device, and further, the method can include the contents described in the following steps S110 to S150.
And step S110, sequentially determining building construction electricity consumption data from each group of electricity consumption data records according to the electricity consumption distribution information based on each group of electricity consumption data records and a data identification model pre-generated by electricity consumption peak-valley information corresponding to each group of electricity consumption data records.
Step S120, determining energy-saving data characteristics corresponding to the building construction electricity consumption data based on the acquired pre-established characteristic clustering model and the electricity consumption behavior data recorded by each group of electricity consumption data, and updating the characteristic clustering model based on the energy-saving data characteristics.
And S130, marking the building construction power consumption data based on the updated feature clustering model to obtain daytime building construction power consumption data and nighttime building construction power consumption data.
Step S140, determining the construction progress information of the night construction electricity consumption data in parallel when performing electricity consumption behavior analysis on each group of electricity consumption data records according to the daytime construction electricity consumption data, and identifying the construction progress information to obtain construction electricity consumption demand information.
And S150, storing the construction power demand information and deleting the night building construction power data.
In specific implementation, through the steps S110 to S150, building construction electricity data are determined from each group of electricity data records in sequence, secondly, determining energy-saving data characteristics corresponding to the building construction electricity consumption data based on the acquired pre-established characteristic clustering model and the electricity consumption behavior data recorded by each group of electricity consumption data and updating the characteristic clustering model, then classifying the building construction electricity data based on the updated feature clustering model to obtain daytime building construction electricity data and nighttime building construction electricity data, further, when the electricity consumption behavior analysis is carried out on each group of electricity consumption data records according to the daytime building construction electricity consumption data, the building construction progress information of the nighttime building construction electricity consumption data is determined in parallel, and identifying the construction progress information to obtain construction power demand information, and finally storing the construction power demand information and deleting the construction power data at night. Therefore, the construction power consumption demand information can be accurately determined in real time based on the construction power consumption data in the daytime and at night, so that the power saving control is performed according to the stored construction power consumption demand information, and the construction accidents caused by the conflict between the subsequent power saving measures and the actual construction state are avoided.
Optionally, in order to improve the real-time performance and accuracy of updating the feature clustering model, on the basis of the above steps S110 to S150, the following step S160 is further included: and when building construction electricity consumption data recorded by other electricity consumption data are marked, updating the characteristic clustering model by using the stored construction electricity consumption demand information. Therefore, the characteristic clustering model can be accurately updated in real time by using the stored construction power demand information.
Further, step S160 may also include what is described in the following steps S161-S164 when implemented.
Step S161, extracting model configuration parameters and clustering label distribution information of the feature clustering model according to a time sequence loss coefficient of the power consumption data recording time sequence features corresponding to the construction power consumption demand information in the feature clustering model; and respectively constructing a parameter list corresponding to the model configuration parameters and a label list corresponding to the clustering label distribution information.
Step S162, determining first electrical data record description information of the parameter list and second electrical data record description information corresponding to the tag list, determining a data record correlation between the first electrical data record description information and the second electrical data record description information, and determining a ratio of the quantity of the target correlation in the data record correlation, which is used for representing that the power consumption fluctuation values of the first electrical data record description information and the second electrical data record description information in a period corresponding to the same electrical data record are the same.
Step S163, determining a model training distribution curve corresponding to the feature clustering model based on the ratio, extracting a plurality of curve segments with multi-dimensional feature labels from the model training distribution curve, and calculating the continuity between every two curve segments; and marking the curve segment with the continuity larger than the set distance, and determining the curve segment with the maximum marking times as the target curve segment.
Step S164, listing curve characteristic sequences corresponding to the target curve segments, and generating model updating indication information corresponding to the curve characteristic sequences; calculating a matching coefficient of the model updating indication information and the clustering label distribution information, and determining a recording position in each group of electricity consumption data records corresponding to non-electricity-consumption-demanding electricity consumption data between the model updating indication information and the clustering label distribution information according to the matching coefficient; and adding the construction power demand information to the recording positions in each group of power consumption data records in a target format in a model updating parameter set corresponding to the feature clustering model to update the feature clustering model.
It is understood that, when the contents described in the above steps S161 to S164 are applied, timeliness of the feature clustering model after updating can be ensured.
In one possible embodiment, in order to accurately determine the electricity data for building construction and avoid the absence of the electricity data for building construction, the data identification model pre-generated based on the power consumption distribution information of each group of electricity data records and the electricity peak-valley information corresponding to each group of electricity data records, which is described in step S110, sequentially determines the electricity data for building construction from each group of electricity data records, and further includes the contents described in the following steps S111 to S113.
And step S111, determining the recording format parameters of the power consumption data records corresponding to the power consumption peak and valley information corresponding to each group of power consumption data records from the power consumption distribution information of each group of power consumption data records.
And step S112, generating a parameter identification path corresponding to the recording format parameter of the electricity consumption data record based on the sequence priority of the data identification model.
Step S113, calculating a path matching rate between the recording format parameters of the electricity consumption data records and the parameter identification paths; determining the building construction electricity utilization data from each group of electricity utilization data records according to the sequence from high to low of the sequence priority when the path matching rate is greater than the preset threshold; and when the path matching rate is less than or equal to a preset threshold value, determining the building construction electricity utilization data from each group of electricity utilization data records according to the sequence from low to high of the sequence priority.
By implementing the steps S111 to S113, the building construction electricity data can be accurately determined, and the building construction electricity data is prevented from missing.
In an alternative embodiment, in order to ensure that the feature clustering model does not have the problem of increased partition error when the feature clustering model is updated, in step S120, the energy-saving data feature corresponding to the building construction electricity data is determined based on the acquired pre-established feature clustering model and the electricity consumption behavior data recorded by each group of electricity consumption data, and the feature clustering model is updated based on the energy-saving data feature, which may exemplarily include the contents described in the following steps S121 to S124.
Step S121, acquiring a plurality of construction equipment parameter information pairs in the electricity consumption behavior data and extracting target construction equipment parameter information of equipment parameter adjustment existing in the corresponding electricity consumption data records of each construction equipment parameter information pair; wherein each target construction equipment parameter information has a different parameter update frequency value.
Step S122, after arranging each piece of target construction equipment parameter information according to the size sequence of the corresponding energy-saving grade of each piece of construction equipment parameter information, calculating the difference value of the parameter updating frequency values between every two adjacent pieces of target construction equipment parameter information; judging whether the difference value of the parameter updating frequency values between every two adjacent pieces of target construction equipment parameter information reaches a preset difference value or not; and if the preset difference is not reached, determining that no associated equipment parameter exists between the two adjacent pieces of target construction equipment parameter information.
Step S123, drawing an energy-saving data record table of the electricity consumption behavior data according to the target construction equipment parameter information with the associated equipment parameters, extracting a plurality of energy-saving record data from the energy-saving data record table, and integrating the energy-saving record data into the energy-saving data characteristics.
Step S124, updating the feature clustering model based on the priority of the energy saving recording data corresponding to the energy saving data features in the time sequence.
Based on the contents described in the above steps S121 to S124, it can be ensured that the feature clustering model does not have the problem of increased partitioning error when the feature clustering model is updated.
In an implementation manner, the building construction progress information of the nighttime building construction electricity consumption data is determined in parallel when the electricity consumption behavior analysis is performed on each group of electricity consumption data records according to the daytime building construction electricity consumption data in the step S140, and the building construction progress information is identified to obtain the construction electricity consumption demand information, which can be further implemented through the following steps S141 to S143.
Step S141, when power consumption behavior analysis is carried out on each group of power consumption data records according to the daytime construction power consumption data, power consumption behavior safety data of the nighttime construction power consumption data and construction time period data of the nighttime construction power consumption data are determined in parallel.
And S142, performing characteristic extraction on the night building construction electricity utilization data according to the electricity utilization behavior safety data and the construction period data to obtain building construction progress information of the night building construction electricity utilization data.
And S143, inputting the construction progress information into a preset convolutional neural network in an information distribution format and acquiring construction power demand information output by the preset convolutional neural network.
Therefore, the construction electricity demand information can be accurately determined according to the steps S141 to S143, so as to ensure the time-sequence synchronism of the construction electricity demand information.
Based on the same inventive concept as the above embodiment, please refer to fig. 2 in combination, which is a functional block diagram of an intelligent electricity-saving control device 200 for building construction electricity, the intelligent electricity-saving control device 200 for building construction electricity includes:
the data determining module 210 is configured to sequentially determine building construction electricity consumption data from each group of electricity consumption data records according to a data identification model pre-generated based on the electricity consumption distribution information of each group of electricity consumption data records and electricity consumption peak-valley information corresponding to each group of electricity consumption data records;
the model updating module 220 is configured to determine energy-saving data features corresponding to the building construction electricity consumption data based on the acquired pre-established feature clustering model and the electricity consumption behavior data recorded in each group of electricity consumption data, and update the feature clustering model based on the energy-saving data features;
the data marking module 230 is configured to mark the building construction electricity consumption data based on the updated feature clustering model to obtain daytime building construction electricity consumption data and nighttime building construction electricity consumption data;
the information identification module 240 is used for determining the building construction progress information of the night building construction electricity consumption data in parallel when each group of electricity consumption data records are subjected to electricity consumption behavior analysis according to the daytime building construction electricity consumption data, and identifying the building construction progress information to obtain construction electricity consumption demand information;
and an information storage module 250, configured to store the construction power demand information and delete the night building construction power data.
Based on the same inventive concept as the previous embodiment, the invention also provides an intelligent electricity-saving control system for building construction electricity, which comprises intelligent electricity-saving control equipment and construction equipment which are communicated with each other; wherein the intelligent power-saving control device is configured to:
determining building construction electricity utilization data from each group of electricity utilization data records in sequence according to a data identification model pre-generated based on the electric energy consumption distribution information of each group of electricity utilization data records and electricity utilization peak-valley information corresponding to each group of electricity utilization data records;
determining energy-saving data characteristics corresponding to the building construction electricity consumption data based on the acquired pre-established characteristic clustering model and the electricity consumption behavior data recorded by each group of electricity consumption data, and updating the characteristic clustering model based on the energy-saving data characteristics;
marking the building construction power consumption data based on the updated feature clustering model to obtain daytime building construction power consumption data and nighttime building construction power consumption data;
determining the construction progress information of the night construction electricity consumption data in parallel when each group of electricity consumption data records are subjected to electricity consumption behavior analysis according to the daytime construction electricity consumption data, and identifying the construction progress information to obtain construction electricity consumption demand information;
storing the construction power demand information and deleting the night building construction power data; and controlling the power utilization state of the construction equipment in the target construction area based on the stored construction power utilization demand information.
Based on the same inventive concept as in the previous embodiments, the present specification further provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of any of the methods described above.
Based on the same inventive concept as the previous embodiment, as shown in fig. 3, the embodiment of the present specification further provides an intelligent power-saving control device 300, which includes a memory 310, a processor 320 and a computer program stored in the memory 310 and executable on the processor 320, wherein the processor 320 implements the steps of any one of the methods described above when executing the program.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, this description is not intended for any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present specification and that specific languages are described above to disclose the best modes of the specification.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present description may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the specification, various features of the specification are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, the present specification as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this specification.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the description and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of this description may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components of a gateway, proxy server, system in accordance with embodiments of the present description. The present description may also be embodied as an apparatus or device program (e.g., computer program and computer program product) for performing a portion or all of the methods described herein. Such programs implementing the description may be stored on a computer-readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the specification, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The description may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (8)
1. An intelligent electricity-saving control method for building construction electricity utilization is characterized by comprising the following steps:
determining building construction electricity utilization data from each group of electricity utilization data records in sequence according to a data identification model pre-generated based on the electric energy consumption distribution information of each group of electricity utilization data records and electricity utilization peak-valley information corresponding to each group of electricity utilization data records;
determining energy-saving data characteristics corresponding to the building construction electricity consumption data based on the acquired pre-established characteristic clustering model and the electricity consumption behavior data recorded by each group of electricity consumption data, and updating the characteristic clustering model based on the energy-saving data characteristics;
marking the building construction power consumption data based on the updated feature clustering model to obtain daytime building construction power consumption data and nighttime building construction power consumption data;
determining the construction progress information of the night construction electricity consumption data in parallel when each group of electricity consumption data records are subjected to electricity consumption behavior analysis according to the daytime construction electricity consumption data, and identifying the construction progress information to obtain construction electricity consumption demand information;
storing the construction power demand information and deleting the night building construction power data; and controlling the power utilization state of the construction equipment in the target construction area based on the stored construction power utilization demand information.
2. The method of claim 1, wherein the method further comprises:
and when building construction electricity consumption data recorded by other electricity consumption data are marked, updating the characteristic clustering model by using the stored construction electricity consumption demand information.
3. The method of claim 2, wherein updating the feature clustering model with the stored construction electricity demand information comprises:
recording a time sequence loss coefficient of a time sequence feature in the feature clustering model according to the power utilization data corresponding to the construction power utilization demand information, and extracting a model configuration parameter and clustering label distribution information of the feature clustering model; respectively constructing a parameter list corresponding to the model configuration parameters and a label list corresponding to the clustering label distribution information;
determining first electric data record description information of the parameter list and second electric data record description information corresponding to the tag list, determining data record correlation between the first electric data record description information and the second electric data record description information, and determining the proportion of the quantity of target correlation in the data record correlation, wherein the quantity of the target correlation is used for representing that the power consumption fluctuation values of the first electric data record description information and the second electric data record description information in a time period corresponding to the same electric data record are the same;
determining a model training distribution curve corresponding to the feature clustering model based on the ratio, extracting a plurality of curve segments with multi-dimensional feature labels from the model training distribution curve, and calculating the continuity between every two curve segments; marking the curve segment with the continuity larger than the set distance, and determining the curve segment with the maximum marking times as a target curve segment;
listing curve characteristic sequences corresponding to the target curve segments, and generating model updating indication information corresponding to the curve characteristic sequences; calculating a matching coefficient of the model updating indication information and the clustering label distribution information, and determining a recording position in each group of electricity consumption data records corresponding to non-electricity-consumption-demanding electricity consumption data between the model updating indication information and the clustering label distribution information according to the matching coefficient; and adding the construction power demand information to the recording positions in each group of power consumption data records in a target format in a model updating parameter set corresponding to the feature clustering model to update the feature clustering model.
4. The method of any one of claims 1-3, wherein tagging the building construction electricity data based on the updated feature clustering model results in daytime building construction electricity data and nighttime building construction electricity data, comprising:
determining a plurality of first electricity weight values of the building construction electricity data based on the updated feature clustering model;
marking the building construction electricity consumption data according to the plurality of first electricity consumption weight values to obtain a first mark set and a second mark set; the first marker set is used for representing the daytime building construction electricity data, and the second marker set is used for representing the nighttime building construction electricity data;
calculating a correlation coefficient of the first set of labels and the second set of labels; when the correlation coefficient is larger than a set coefficient, weighting a first electricity weight value of the building construction electricity data based on the determined first construction electricity type data of the first mark set and second construction electricity type data of the second mark set to obtain a plurality of second electricity weight values; executing steps corresponding to marking the building construction electricity utilization data according to the first electricity weight values to obtain a first mark set and a second mark set on the basis of the second electricity weight values until the calculated correlation coefficient is smaller than or equal to the set coefficient; and determining a first target mark set corresponding to the correlation coefficient smaller than or equal to the set coefficient as daytime building construction electricity data, and determining a second target mark set corresponding to the correlation coefficient smaller than or equal to the set coefficient as nighttime building construction electricity data.
5. The method of claim 1, wherein determining the construction electricity consumption data from each set of electricity consumption data records in turn based on a pre-generated data recognition model based on the electricity consumption distribution information for each set of electricity consumption data records and the electricity consumption peak-to-valley information corresponding to each set of electricity consumption data records comprises:
determining a recording format parameter of the power consumption data record corresponding to the power consumption peak valley information corresponding to each group of power consumption data record from the power consumption distribution information of each group of power consumption data record;
generating a parameter identification path corresponding to a recording format parameter of the electricity data record based on the order priority of the data identification model;
calculating a path matching rate between a recording format parameter of the electricity consumption data record and the parameter identification path; determining the building construction electricity utilization data from each group of electricity utilization data records according to the sequence from high to low of the sequence priority when the path matching rate is greater than the preset threshold; and when the path matching rate is less than or equal to a preset threshold value, determining the building construction electricity utilization data from each group of electricity utilization data records according to the sequence from low to high of the sequence priority.
6. The method of claim 1, wherein determining energy-saving data characteristics corresponding to the building construction electricity consumption data based on the acquired pre-established characteristic clustering model and the electricity consumption behavior data of each group of electricity consumption data records, and updating the characteristic clustering model based on the energy-saving data characteristics comprises:
acquiring a plurality of construction equipment parameter information pairs in the electricity consumption behavior data and extracting target construction equipment parameter information of equipment parameter adjustment existing in the corresponding electricity consumption data records in each construction equipment parameter information pair; wherein, each target construction equipment parameter information has different parameter updating frequency values;
after the parameter information of each target construction device is arranged according to the size sequence of the corresponding energy-saving grade of the parameter information of each construction device, calculating the difference value of the parameter updating frequency values between every two adjacent pieces of parameter information of the target construction device; judging whether the difference value of the parameter updating frequency values between every two adjacent pieces of target construction equipment parameter information reaches a preset difference value or not; if the preset difference is reached, determining that the associated equipment parameters exist between the two adjacent pieces of target construction equipment parameter information, and if the preset difference is not reached, determining that the associated equipment parameters do not exist between the two adjacent pieces of target construction equipment parameter information;
drawing an energy-saving data record table of the electricity consumption behavior data according to the target construction equipment parameter information with the associated equipment parameters, extracting a plurality of energy-saving record data from the energy-saving data record table and integrating the energy-saving record data into the energy-saving data characteristics;
and updating the characteristic clustering model based on the priority of the energy-saving record data corresponding to the energy-saving data characteristics on the time sequence.
7. The method of claim 1, wherein determining building construction progress information of the nighttime building construction electricity data in parallel when analyzing electricity consumption behavior of each group of electricity consumption data records according to the daytime building construction electricity data, and identifying the building construction progress information to obtain construction electricity demand information comprises:
when power consumption behavior analysis is carried out on each group of power consumption data records according to the daytime building construction power consumption data, power consumption behavior safety data of the nighttime building construction power consumption data and construction time period data of the nighttime building construction power consumption data are determined in parallel;
performing characteristic extraction on the night building construction electricity utilization data according to the electricity utilization behavior safety data and the construction period data to obtain building construction progress information of the night building construction electricity utilization data;
and inputting the building construction progress information into a preset convolutional neural network in an information distribution format and acquiring construction power consumption demand information output by the preset convolutional neural network.
8. An intelligent electricity-saving control system for building construction electricity consumption is characterized by comprising intelligent electricity-saving control equipment and construction equipment which are communicated with each other; wherein the intelligent power-saving control device is configured to:
determining building construction electricity utilization data from each group of electricity utilization data records in sequence according to a data identification model pre-generated based on the electric energy consumption distribution information of each group of electricity utilization data records and electricity utilization peak-valley information corresponding to each group of electricity utilization data records;
determining energy-saving data characteristics corresponding to the building construction electricity consumption data based on the acquired pre-established characteristic clustering model and the electricity consumption behavior data recorded by each group of electricity consumption data, and updating the characteristic clustering model based on the energy-saving data characteristics;
marking the building construction power consumption data based on the updated feature clustering model to obtain daytime building construction power consumption data and nighttime building construction power consumption data;
determining the construction progress information of the night construction electricity consumption data in parallel when each group of electricity consumption data records are subjected to electricity consumption behavior analysis according to the daytime construction electricity consumption data, and identifying the construction progress information to obtain construction electricity consumption demand information;
storing the construction power demand information and deleting the night building construction power data; and controlling the power utilization state of the construction equipment in the target construction area based on the stored construction power utilization demand information.
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