CN118095907A - Energy consumption monitoring and predicting method and device, storage medium and electronic equipment - Google Patents
Energy consumption monitoring and predicting method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
In the method for monitoring and predicting the energy consumption provided by the specification, the order quantity of a warehouse, the attribute of each area, the environmental data of each area and the actual energy consumption data of the warehouse collected by energy consumption monitoring equipment are input into a pre-trained energy consumption monitoring model to obtain ideal energy consumption data of the warehouse output by the model, and the advantages and disadvantages of the energy consumption management condition of the warehouse are monitored according to the ideal energy consumption data and the actual energy consumption data of the warehouse. According to the method, the energy consumption management condition of the warehouse can be monitored more accurately according to the actual energy consumption data of the warehouse, the order quantity of the warehouse, the attribute of each area and the environmental data of each area.
Description
Technical Field
The present disclosure relates to the field of unmanned devices, and in particular, to an energy consumption monitoring and predicting method, an apparatus, a storage medium, and an electronic device.
Background
Along with the continuous development of internet technology and logistics industry, the application of warehouse is more and more extensive, and along with the warehouse entering an automatic period, the problem of energy consumption of the warehouse is also widely concerned.
The storage cost is high and can influence the whole cost of enterprises, the actual energy consumption of the warehouse is not only related to the energy consumption of the warehouse, but also can be increased because of different objective factors such as order quantity, local air temperature, area and the like, the objective factors such as the order quantity, the local air temperature, the area and the like are generally called energy consumption influencing factors of the warehouse, the size of the warehouse influenced by the energy consumption factors is used as an index for measuring the quality of energy consumption management, if the energy consumption influencing factors of one warehouse influence the energy consumption of the warehouse too much, the actual energy consumption of the warehouse is generally larger than the energy consumption value of the warehouse, namely, the warehouse is influenced by the energy consumption influencing factors, unnecessary energy consumption waste is caused, the energy consumption management of the warehouse is judged to be poor, if the energy consumption management of the warehouse needs to be modified for saving the cost (namely, the waste caused by the energy consumption influencing factors of the warehouse is reduced), and if the energy consumption management of the warehouse is monitored, the energy consumption management of the warehouse can be timely carried out, so that the cost of the warehouse is effectively saved, and how to monitor the quality of the energy consumption management of the warehouse is a problem to be solved.
The specification provides a method for monitoring energy consumption, which solves the problem of how to monitor the advantages and disadvantages of energy consumption management of a warehouse.
Disclosure of Invention
The present disclosure provides an energy consumption monitoring and predicting method, an apparatus, a storage medium, and an electronic device, so as to at least partially solve the foregoing problems in the prior art.
The technical scheme adopted in the specification is as follows:
The specification provides a method of energy consumption monitoring, the method comprising:
the monitoring equipment collects actual energy consumption data of the warehouse within a preset time period and sends the actual energy consumption data to an energy consumption management platform;
The energy consumption management platform receives the actual energy consumption data and inquires the order quantity of the warehouse in the preset duration; acquiring attributes of each area and environment data of the area in the preset time length aiming at each area divided in advance in the warehouse;
The energy consumption management platform inputs a pre-trained energy consumption monitoring model based on actual energy consumption data of the warehouse in a preset time period, order quantity of the warehouse in the preset time period, attributes of all areas in the warehouse and environment data of all areas in the preset time period, and ideal energy consumption data of the warehouse, which is output by the energy consumption monitoring model, are obtained;
And determining an energy consumption management representation value of the warehouse according to the ideal energy consumption data and the actual energy consumption data, wherein the energy consumption management representation value is used for representing the energy consumption management quality of the warehouse.
Optionally, the method further comprises:
determining contribution degrees of the order quantity, the attribute and the environment data to ideal energy consumption data of the warehouse respectively;
and determining influencing factors of actual energy consumption data of the warehouse according to the order quantity, the attribute and the contribution degree of the environmental data to the ideal energy consumption data of the warehouse.
Optionally, the method further comprises:
The energy consumption management platform ranks the energy consumption management of each warehouse according to the energy consumption management characterization value of each warehouse to obtain the energy consumption management rank of each warehouse;
And the energy consumption management platform sends the energy consumption management rank of each warehouse and the influencing factors of the actual energy consumption data of each warehouse to a user, so that the user manages the energy consumption of each warehouse according to the energy consumption management rank of each warehouse and the influencing factors of the actual energy consumption data of each warehouse.
Optionally, the attribute includes at least one of area and number of devices;
The environmental data includes at least one of warehouse temperature, warehouse humidity.
Optionally, training the energy consumption monitoring model specifically includes:
Determining at least one warehouse as a sample warehouse in all warehouses in advance;
The energy consumption management platform queries actual energy consumption data of the sample warehouse in a historic preset time period and order quantity of the sample warehouse in the historic preset time period; and acquiring, for each region in the sample warehouse, an attribute of the region and environmental data of the region within the historical preset time period; taking actual energy consumption data of the sample warehouse in the historic preset time period as labels of the sample warehouse;
The energy consumption management platform inputs actual energy consumption data of the sample warehouse in the historical preset time period, order quantity of the warehouse in the historical preset time period, attributes of all areas in the warehouse and environment data of all areas in the historical preset time period into an energy consumption monitoring model to be trained, and ideal energy consumption data of the sample warehouse is obtained through the model;
And training the energy consumption monitoring model to be trained according to the ideal energy consumption data of the sample warehouse and the labels of the sample warehouse to obtain the energy consumption monitoring model.
The specification provides a method for predicting energy consumption, which divides a warehouse into areas with different functions in advance; the method comprises the following steps:
The monitoring equipment collects actual energy consumption data of the warehouse in a first preset time period and sends the actual energy consumption data of the warehouse in the first preset time period to the energy consumption management platform;
The energy consumption management platform receives actual energy consumption data of the warehouse in a first preset time period and order quantity of the warehouse in the first preset time period; and acquiring the attribute of each area and the environmental data of the area in the first preset time period aiming at each area divided in advance in the warehouse;
The energy consumption management platform inputs actual energy consumption data of the warehouse in the first preset time period, order quantity of the warehouse in the first preset time period, attributes of all areas in the warehouse and environment data of all areas in the first preset time period into a pre-trained energy consumption prediction model to obtain energy consumption data of the warehouse in a second preset time period predicted by the energy consumption prediction model.
Optionally, the attribute includes at least one of area and number of devices;
The environmental data includes at least one of warehouse temperature, warehouse humidity.
Optionally, training the energy consumption prediction model specifically includes:
the energy consumption management platform inquires actual energy consumption data of the warehouse in a third preset time period and order quantity of the warehouse in the third preset time period; and acquiring the attribute of each region and the environmental data of the region in the third preset time period for each region in the warehouse; taking the actual energy consumption data of the warehouse in a fourth preset time period as the label of the warehouse;
the energy consumption management platform inputs actual energy consumption data of the warehouse in the third preset time period, order quantity of the warehouse in the third preset time period, attributes of all areas in the warehouse and environment data of all areas in the third preset time period into an energy consumption prediction model to be trained, and energy consumption data of the warehouse, predicted by the energy consumption prediction model to be trained, in a fourth preset time period is obtained;
And training the energy consumption prediction model to be trained according to the energy consumption data of the warehouse in the fourth preset time period and the labeling of the warehouse to obtain the energy consumption prediction model.
The present specification provides a computer readable storage medium storing a computer program which when executed by a processor implements the above-described energy consumption monitoring and prediction method.
The present specification provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above-described energy consumption monitoring and prediction method when executing the program.
The above-mentioned at least one technical scheme that this specification adopted can reach following beneficial effect:
In the energy consumption monitoring and predicting method provided by the specification, the warehouse order quantity, the attribute of each region, the environmental data of each region and the actual energy consumption data of the warehouse collected by the energy consumption monitoring equipment are input into a pre-trained energy consumption monitoring model, the ideal energy consumption data of the warehouse output by the model is obtained, and the advantages and disadvantages of the warehouse energy consumption management condition are monitored according to the ideal energy consumption data and the actual energy consumption data of the warehouse.
According to the method, the energy consumption management condition of the warehouse can be monitored more accurately according to the actual energy consumption data of the warehouse, the order quantity of the warehouse, the attribute of each area and the environmental data of each area.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification, illustrate and explain the exemplary embodiments of the present specification and their description, are not intended to limit the specification unduly. In the drawings:
FIG. 1 is a schematic flow chart of an energy consumption monitoring method in the present specification;
FIG. 2 is a schematic flow chart of training an energy consumption monitoring model in the present specification;
FIG. 3 is a schematic flow chart of an energy consumption prediction method in the present specification;
FIG. 4 is a schematic flow chart of training an energy consumption prediction model in the present specification;
Fig. 5 is a schematic view of an electronic device corresponding to fig. 1 or fig. 2 provided in the present specification.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present specification more apparent, the technical solutions of the present specification will be clearly and completely described below with reference to specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present application based on the embodiments herein.
The following describes in detail the technical solutions provided by the embodiments of the present specification with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an energy consumption monitoring method provided in the present specification, which specifically includes the following steps:
S100: the monitoring equipment collects actual energy consumption data of the warehouse within a preset time period and sends the actual energy consumption data to the energy consumption management platform.
The technical scheme of the application is used for monitoring the energy consumption management condition of the warehouse, and the warehouse can be divided into a plurality of areas according to functions, for example, the warehouse is divided into a refrigerating area, an aquatic product area, a freezing area, an office area and the like. The areas and the number of the equipment in different areas may be different, the environments in which the equipment are located may also be different, for example, the temperature requirement of a freezing area is-18 ℃ to-25 ℃ in general, the temperature requirement of a refrigerating area is 5 ℃ to-5 ℃, a plurality of fish tanks are arranged in an aquatic product area, and the areas of the different areas are different. The data such as the area, the number of devices and the like between different areas in the warehouse are generally called as the attribute of the different areas of the warehouse, and the temperature and the humidity are the data of the environment in which the warehouse is located. The energy source in the energy consumption can be electric energy, water energy or other energy sources.
The amount of orders in the warehouse, the attributes of each area and the environmental data all affect the energy consumption of the warehouse. For example, if the freezing area of a warehouse only needs-20 ℃ to achieve the freezing and fresh-keeping function, and warehouse management personnel set the freezing area to-25 ℃, unnecessary energy consumption is caused, or the warehouse management personnel enter and exit the refrigerator for many times but forget to close the door, so that the energy consumption of the refrigerator is increased for maintaining low temperature, or the energy consumption is increased due to the fact that the order quantity is too large, the energy consumption data collected by the monitoring equipment are the actual energy consumption generated when each area of the warehouse has the attribute and receives the order in the environment, and the monitoring equipment sends the collected energy consumption data of preset time to the energy consumption management platform, and the preset time can be any time, such as one day or one month.
S102: the energy consumption management platform receives the actual energy consumption data and inquires the order quantity of the warehouse in the preset duration; acquiring attributes of each area and environment data of the area in the preset time length aiming at each area divided in advance in the warehouse;
after receiving the actual energy consumption data, the energy consumption management platform can perform statistical management on the energy consumption data, for example, statistics on the energy consumption data of the warehouse within one month, and also can perform statistical calculation on the energy consumption data, for example, calculate the average energy consumption data of the warehouse within the past year per month.
As can be seen from the above, the actual energy consumption data is the actual energy consumption generated when the warehouse receives an order in the environment with the attribute in each area, so the energy consumption management of the monitoring warehouse is the energy consumption of the monitoring warehouse affected by the order quantity, the attribute in each area and the environment in each area.
Specifically, to monitor the energy consumption management quality of the warehouse, besides acquiring the actual energy consumption data of the warehouse within a preset time period, the order quantity of the warehouse, the attribute of each area and the environmental data of each area within the preset time period need to be acquired, and for convenience of description, the order quantity of the warehouse, the attribute of each area and the environmental data of each area are hereinafter collectively referred to as objective factors of the warehouse.
Specifically, the objective factors of the warehouse may be recorded in a file, a database or a storage medium having a storage function in advance, and if the objective factors of the warehouse change, the recorded data may be updated in time. When the energy consumption management platform needs to monitor the energy consumption management condition of the warehouse, the energy consumption management platform can inquire from a file/database/storage medium recording objective factors of the warehouse.
S104: the energy consumption management platform inputs a pre-trained energy consumption monitoring model based on actual energy consumption data of the warehouse in a preset time period, order quantity of the warehouse in the preset time period, attributes of all areas in the warehouse and environment data of all areas in the preset time period, and ideal energy consumption data of the warehouse, which are output by the energy consumption monitoring model, are obtained.
After acquiring actual energy consumption data and objective factors (namely order quantity, attribute of each area and environmental data of each area) of the warehouse in a preset time period, the energy consumption management platform inputs the actual energy consumption data and the objective factors of the actual energy consumption data of the warehouse in the preset time period into a pre-trained energy consumption monitoring model, the energy consumption monitoring model fits the actual energy consumption data of the warehouse in the preset time period and the objective factors of the actual energy consumption data of the warehouse to obtain ideal energy consumption data of the warehouse, wherein the ideal energy consumption data of the warehouse refers to ideal energy consumption data of the warehouse after the influence of the objective factors is eliminated in the preset time period, namely, the energy consumption data of the warehouse under optimal energy consumption management (the energy consumption of the warehouse is least influenced by the objective factors).
S106: and determining an energy consumption management representation value of the warehouse according to the ideal energy consumption data and the actual energy consumption data, wherein the energy consumption management representation value is used for representing the energy consumption management quality of the warehouse.
The ideal energy consumption data of the warehouse in the preset time period, which is output by the energy consumption monitoring model, are determined, and according to the difference between the ideal energy consumption data and the actual energy consumption data of the warehouse in the preset time period, the energy consumption management representation value of the warehouse can be determined, and the energy consumption management representation value can be used for representing the energy consumption management quality value of the warehouse.
According to the method, the actual energy consumption data of the warehouse and objective factors influencing the actual energy consumption of the warehouse are input into the energy consumption monitoring model, the ideal energy consumption data of the warehouse, under the optimal management (namely, the influence of the objective factors is eliminated), is obtained through fitting of the energy consumption monitoring model, and the advantages and disadvantages of the energy consumption management condition of the warehouse are determined according to the ideal energy consumption data and the actual energy consumption data of the warehouse, so that the energy consumption management condition of the warehouse is accurately monitored.
As described above, when monitoring the energy consumption management representation value of the warehouse in the preset time period, the method can also monitor the energy consumption management condition of the warehouse in the preset time period according to the preset period, that is, the method shown in fig. 1 is adopted every other preset period, if the preset time period is one day, the preset time period can also be one day, that is, the energy consumption management representation value of one day is output every day according to the actual energy consumption data of the warehouse in the same day and the objective factor of the same day, so as to represent the advantages and disadvantages of the energy consumption management condition in the warehouse in the same day. That is, the preset period of monitoring the energy consumption management condition of the warehouse may be the same as the preset period described in step S100.
Optionally, the energy consumption management characterization value of the warehouse can be determined through the difference value between the ideal energy consumption data and the actual energy consumption data of the warehouse within the preset duration, the smaller the difference value is, the smaller the energy consumption management characterization value of the warehouse is, the better the energy consumption management of the warehouse is, the worse the energy consumption management of the warehouse is, the energy consumption management characterization value of the warehouse can also be determined through the ratio between the ideal energy consumption data and the actual energy consumption data of the warehouse within the preset duration, the closer the ratio is to 1, the smaller the energy consumption management characterization value of the warehouse is, the better the energy consumption management of the warehouse is, and the worse the energy consumption management of the warehouse is.
Optionally, in order to better manage the energy consumption situation of the warehouse, after the ideal energy consumption data of the warehouse is determined by using an energy consumption monitoring model, the contribution degrees of the order quantity, the attribute and the environment data to the ideal energy consumption data of the warehouse are respectively determined, and specifically, deepLIFT, SHAP or other algorithms may be used to respectively determine the contribution degrees of the order quantity, the attribute and the environment data to the ideal energy consumption data of the warehouse.
And then determining influencing factors of the actual energy consumption data of the warehouse according to the order quantity, the attribute and the contribution degree of the environmental data to the ideal energy consumption data of the warehouse.
Specifically, n objective factors (i.e., the order quantity, the attribute, and the environmental data) having the greatest contribution degree may be determined as influencing factors of actual energy consumption data of the warehouse, where n is a natural number not less than 1.
Further, for the energy consumption management situation of a plurality of warehouses, after the energy consumption representation values of the warehouses are respectively determined by the method, the energy consumption management of the warehouses can be ranked according to the energy consumption representation values among the warehouses, and the warehouses with the ranked ranks are modified, namely, the influence of factors of the warehouses on the actual energy consumption of the warehouses is reduced, so that the energy consumption is further saved. If the influence factors of the actual energy consumption data of the warehouse are determined, energy consumption management optimization can be performed on the warehouse according to the influence factors.
Specifically, the energy consumption management platform ranks the energy consumption management of each warehouse according to the energy consumption management representation value of each warehouse, obtains the energy consumption management rank of each warehouse, and then sends the energy consumption management rank of each warehouse and the influencing factors of the actual energy consumption data of each warehouse to a user, so that the user manages the energy consumption of each warehouse according to the energy consumption management rank of each warehouse and the influencing factors of the actual energy consumption data of each warehouse.
As described in step S104, the energy consumption monitoring model is pre-trained, and the training step is shown in fig. 2, and includes:
s200: at least one warehouse is determined in advance among all the warehouses as a sample warehouse.
And selecting m warehouses with optimal energy consumption management conditions from all warehouses in advance as the sample warehouse according to the energy consumption management conditions of the warehouses, wherein m is a natural number which is not less than 1 and not more than the total number of the warehouses.
S202: the energy consumption management platform queries actual energy consumption data of the sample warehouse in a historic preset time period and order quantity of the sample warehouse in the historic preset time period; and acquiring, for each region in the sample warehouse, an attribute of the region and environmental data of the region within the historical preset time period; and taking the actual energy consumption data of the sample warehouse in the historical preset time period as the label of the sample warehouse.
Because the method aims at fitting the ideal energy consumption of the warehouse through the energy consumption monitoring model, which is influenced by objective factors (namely the order quantity, the attribute and the environmental data), the actual energy consumption data, the order quantity, the attribute of each area and the environmental data of each area of the sample warehouse in the historic preset time period need to be acquired. Because the data used in training is historical energy consumption data, the energy consumption management platform can directly inquire.
And because the predetermined sample warehouse is m warehouses with optimal energy consumption management in all warehouses, that is, the actual energy consumption of the sample warehouse is extremely little influenced by objective factors (namely, the order quantity, the attribute and the environmental data), the actual energy consumption data of the sample warehouse is the label of the sample warehouse.
S204: the energy consumption management platform inputs actual energy consumption data of the sample warehouse in the historical preset time period, order quantity of the warehouse in the historical preset time period, attributes of all areas in the warehouse and environment data of all areas in the historical preset time period into an energy consumption monitoring model to be trained, and ideal energy consumption data of the sample warehouse are obtained through the model.
S206: and training the energy consumption monitoring model to be trained according to the ideal energy consumption data of the sample warehouse and the labels of the sample warehouse to obtain the energy consumption monitoring model.
It should be noted that, the selection of the sample warehouse is relative (i.e. a plurality of warehouses with optimal energy consumption management conditions are selected from all warehouses as the sample warehouse), and the energy consumption management of the warehouses is not constant, such that when the energy consumption management conditions of the warehouses are monitored by the method, the situation that the energy consumption management characterization value of the non-sample warehouse is not worse than that of the sample warehouse may occur, in this case, the non-sample warehouse with the energy consumption management characterization value not worse than that of the sample warehouse is determined as the sample warehouse, and then the energy consumption monitoring model is trained according to the re-determined sample warehouse.
In addition, the energy consumption of many warehouses may need to be paid in advance to use the energy source, that is, the energy consumption of the warehouses is paid in advance, if the energy consumption of the warehouses cannot be accurately predicted, the prediction of the energy consumption of the warehouses is inaccurate, if the predicted cost is lower than the actual cost, the energy supply of the warehouses may be interrupted, and the loss is caused, and if the predicted cost of the energy consumption is higher than the actual cost, the monitoring of the excessive cost is troublesome, so that the energy consumption of the warehouses needs to be accurately predicted, but the energy consumption of the warehouses is related to not only the energy consumption of the warehouses but also objective factors (namely, the order amount, the attribute and the environmental data) of the warehouses, so how to accurately predict the future energy consumption of the warehouses is also an urgent problem.
Fig. 3 is a schematic flow chart of an energy consumption prediction method provided in the present specification, specifically including the following steps:
s300: the monitoring equipment collects actual energy consumption data of the warehouse in a first preset time period and sends the actual energy consumption data of the warehouse in the first preset time period to the energy consumption management platform.
The energy consumption prediction method is used for predicting the future energy consumption of the warehouse, specifically, the energy consumption data of another time period in the future is predicted according to the data of a certain time period in the history, so that the monitoring equipment firstly collects the actual energy consumption data of the warehouse in a first preset time period and sends the actual energy consumption data to the energy consumption management platform. The energy consumption management platform and the energy consumption management platform in the energy consumption monitoring method provided by fig. 1 may be the same energy consumption management platform or may not be the same platform, but at least have the functions of receiving, counting and calculating the warehouse energy consumption data and inquiring the order quantity, the attribute of each region and the environmental data of each region, as the energy consumption management platform in the energy consumption monitoring method provided by fig. 1.
S302: the energy consumption management platform receives actual energy consumption data of the warehouse in a first preset time period and order quantity of the warehouse in the first preset time period; and acquiring the attribute of each area and the environmental data of the area in the first preset time period aiming at each area divided in advance in the warehouse;
Because the actual energy consumption of the warehouse is affected by factors, in order to accurately predict the actual energy consumption of the warehouse in the future, the influence of the factors on the actual energy consumption of the warehouse must be considered, and when the data input into the energy consumption prediction model are obtained, the order quantity of the warehouse in the preset time period, the attribute of each area and the environmental data of each area need to be obtained, and in particular, the energy consumption management platform only needs to inquire from a file/database/storage medium recording the factors of the warehouse.
S304: the energy consumption management platform inputs actual energy consumption data of the warehouse in the first preset time period, order quantity of the warehouse in the first preset time period, attributes of all areas in the warehouse and environment data of all areas in the first preset time period into a pre-trained energy consumption prediction model to obtain energy consumption data of the warehouse in a second preset time period predicted by the energy consumption prediction model.
Inputting actual energy consumption data of the warehouse in a first preset time period and objective factors thereof into a model, and fitting to obtain predicted energy consumption of the warehouse in a second time period.
According to the method, future energy consumption data of the warehouse are predicted according to the historical actual energy consumption data of the warehouse, the predicted future energy consumption data are actual energy consumption data under the influence of objective factors, and the prediction result of the method is objective and accurate.
Optionally, the attribute includes at least one of an area and a number of devices, and the environmental data includes at least one of a warehouse temperature and a warehouse humidity.
Further, the method for predicting the energy consumption of the future time period of the warehouse according to the method as described above may further predict the energy consumption of the future time period according to the actual energy consumption of the warehouse and the objective factor in the period by using the method as shown in fig. 3 every other preset period, and if the duration of the first preset time period is one month, the preset period may also be one month, that is, each month predicts the energy consumption of the future time period (may be the next month of the month) according to the actual energy consumption of the warehouse in the month and the objective factor. That is, the preset period of the prediction warehouse energy within the future period of time may be the same as the first preset period of time described in step S300.
As described in step S304, the energy consumption prediction model is pre-trained, and the training step is shown in fig. 4, and includes:
S400: the energy consumption management platform inquires actual energy consumption data of the warehouse in a third preset time period and order quantity of the warehouse in the third preset time period; and acquiring the attribute of each region and the environmental data of the region in the third preset time period for each region in the warehouse; and taking the actual energy consumption data of the warehouse in a fourth preset time period as the label of the warehouse.
Because the data used in training is historical energy consumption data, the energy consumption management platform can directly inquire. The energy consumption data of the fourth time period is the energy consumption data of the future time period predicted by the energy consumption prediction model according to the energy consumption data of the third time period and objective factors thereof, so that the actual energy consumption data of the warehouse in the fourth preset time period is used as the label of the warehouse.
The third time period and the fourth time period are both historical time periods with respect to the first time period, and the fourth time period is a future time period thereof with respect to the third time period.
S402: the energy consumption management platform inputs actual energy consumption data of the warehouse in the third preset time period, order quantity of the warehouse in the third preset time period, attributes of all areas in the warehouse and environment data of all areas in the third preset time period into an energy consumption prediction model to be trained, and energy consumption data of the warehouse in a fourth preset time period, which is predicted by the energy consumption prediction model to be trained, is obtained.
S404: and training the energy consumption prediction model to be trained according to the energy consumption data of the warehouse in the fourth preset time period and the labeling of the warehouse to obtain the energy consumption prediction model.
The energy consumption monitoring and predicting method provided above for one or more embodiments of the present specification also provides a computer readable storage medium storing a computer program, where the computer program may be used to perform the energy consumption monitoring method provided above in fig. 1 or the energy consumption predicting method described above in fig. 3.
The present specification also provides a schematic structural diagram of the electronic device shown in fig. 5. At the hardware level, the unmanned device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, as described in fig. 5, although other hardware required by other services is possible. The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to implement the energy consumption monitoring method described in fig. 1 or the energy consumption prediction method described in fig. 3. Of course, other implementations, such as logic devices or combinations of hardware and software, are not excluded from the present description, that is, the execution subject of the following processing flows is not limited to each logic unit, but may be hardware or logic devices.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable GATE ARRAY, FPGA)) is an integrated circuit whose logic functions are determined by user programming of the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented with "logic compiler (logic compiler)" software, which is similar to the software compiler used in program development and writing, and the original code before being compiled is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but HDL is not just one, but a plurality of kinds, such as ABEL(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby Hardware Description Language), and VHDL (Very-High-SPEED INTEGRATED Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application SPECIFIC INTEGRATED Circuits (ASICs), programmable logic controllers, and embedded microcontrollers, examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the present specification may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present disclosure and is not intended to limit the disclosure. Various modifications and alterations to this specification will become apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, or the like, which are within the spirit and principles of the present description, are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A method of energy consumption monitoring, the method comprising:
The monitoring equipment collects actual energy consumption data of the warehouse within a preset time period and sends the actual energy consumption data to the energy consumption management platform;
The energy consumption management platform receives the actual energy consumption data and inquires the order quantity of the warehouse in the preset duration; acquiring attributes of each area and environment data of the area in the preset time length aiming at each area divided in advance in the warehouse;
The energy consumption management platform obtains ideal energy consumption data of the warehouse, which is output by the energy consumption monitoring model, based on actual energy consumption data of the warehouse in a preset time period, order quantity of the warehouse in the preset time period, attributes of all areas in the warehouse and environment data of all areas in the preset time period;
And determining an energy consumption management representation value of the warehouse according to the ideal energy consumption data and the actual energy consumption data, wherein the energy consumption management representation value is used for representing the energy consumption management quality of the warehouse.
2. The method of claim 1, wherein the method further comprises:
determining contribution degrees of the order quantity, the attribute and the environment data to ideal energy consumption data of the warehouse respectively;
and determining influencing factors of actual energy consumption data of the warehouse according to the order quantity, the attribute and the contribution degree of the environmental data to the ideal energy consumption data of the warehouse.
3. The method of claim 2, wherein the method further comprises:
The energy consumption management platform ranks the energy consumption management of each warehouse according to the energy consumption management characterization value of each warehouse to obtain the energy consumption management rank of each warehouse;
And the energy consumption management platform sends the energy consumption management rank of each warehouse and the influencing factors of the actual energy consumption data of each warehouse to a user, so that the user manages the energy consumption of each warehouse according to the energy consumption management rank of each warehouse and the influencing factors of the actual energy consumption data of each warehouse.
4. The method of claim 1 or 2, wherein the attribute comprises at least one of area, number of devices;
The environmental data includes at least one of warehouse temperature, warehouse humidity.
5. The method according to claim 1 or 2, characterized by training an energy consumption monitoring model, comprising in particular:
Determining at least one warehouse as a sample warehouse in all warehouses in advance;
The energy consumption management platform queries actual energy consumption data of the sample warehouse in a historic preset time period and order quantity of the sample warehouse in the historic preset time period; and acquiring, for each region in the sample warehouse, an attribute of the region and environmental data of the region within the historical preset time period; taking actual energy consumption data of the sample warehouse in the historic preset time period as labels of the sample warehouse;
The energy consumption management platform inputs actual energy consumption data of the sample warehouse in the historical preset time period, order quantity of the warehouse in the historical preset time period, attributes of all areas in the warehouse and environment data of all areas in the historical preset time period into an energy consumption monitoring model to be trained, and ideal energy consumption data of the sample warehouse is obtained through the model;
And training the energy consumption monitoring model to be trained according to the ideal energy consumption data of the sample warehouse and the labels of the sample warehouse to obtain the energy consumption monitoring model.
6. A method of energy consumption prediction, the method comprising:
The monitoring equipment collects actual energy consumption data of the warehouse in a first preset time period and sends the actual energy consumption data of the warehouse in the first preset time period to the energy consumption management platform;
The energy consumption management platform receives actual energy consumption data of the warehouse in a first preset time period and order quantity of the warehouse in the first preset time period; and acquiring the attribute of each area and the environmental data of the area in the first preset time period aiming at each area divided in advance in the warehouse;
The energy consumption management platform inputs actual energy consumption data of the warehouse in the first preset time period, order quantity of the warehouse in the first preset time period, attributes of all areas in the warehouse and environment data of all areas in the first preset time period into a pre-trained energy consumption prediction model to obtain energy consumption data of the warehouse in a second preset time period predicted by the energy consumption prediction model.
7. The method of claim 6, wherein the attribute comprises at least one of area, number of devices;
The environmental data includes at least one of warehouse temperature, warehouse humidity.
8. The method of claim 6, wherein training the energy consumption prediction model comprises:
the energy consumption management platform inquires actual energy consumption data of the warehouse in a third preset time period and order quantity of the warehouse in the third preset time period; and acquiring the attribute of each region and the environmental data of the region in the third preset time period for each region in the warehouse; taking the actual energy consumption data of the warehouse in a fourth preset time period as the label of the warehouse;
the energy consumption management platform inputs actual energy consumption data of the warehouse in the third preset time period, order quantity of the warehouse in the third preset time period, attributes of all areas in the warehouse and environment data of all areas in the third preset time period into an energy consumption prediction model to be trained, and energy consumption data of the warehouse, predicted by the energy consumption prediction model to be trained, in a fourth preset time period is obtained;
And training the energy consumption prediction model to be trained according to the energy consumption data of the warehouse in the fourth preset time period and the labeling of the warehouse to obtain the energy consumption prediction model.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-8.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of the preceding claims 1-8 when executing the program.
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