Electric energy meter data processing method, device and terminal based on machine vision

Abstract

The present invention provides a method, device and terminal for processing electric energy meter data based on machine vision. The method comprises: collecting video images; analyzing the video images to determine a first estimated amount of electricity corresponding to the existing electrical equipment and a second estimated amount of electricity corresponding to the electricity consumption change factor; determining a user electricity consumption factor table corresponding to the smart electric energy meter according to the first estimated amount of electricity and the second estimated amount of electricity; determining a corresponding electricity consumption behavior optimization table according to the user electricity consumption factor table and the preset electricity consumption factor table. The present invention can provide users with which electricity consumption factor their own electricity consumption behavior comes from during the electricity consumption process according to the electricity consumption behavior optimization table, thereby guiding users to form an effective power saving awareness.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing in the Internet of Things, and in particular to a method, device and terminal for processing electric energy meter data based on machine vision. Background Art

[0002] Since the full launch of the smart energy meter replacement work, the coverage rate of smart energy meters in various provinces and cities in my country has continued to increase, and the current smart meter coverage rate has reached more than 70%. However, with the large-scale popularization of smart energy meters, there have been disputes that have never stopped. For example, after replacing the smart energy meter, the metered electricity consumption is much higher than before. From a technical perspective, the smart energy meter provided by the power supply department is more sensitive, and its accuracy can be accurate to two decimal places, so many subtle currents in the home can also be recorded by the smart energy meter, and the number will be very considerable after long-term accumulation. As the replacement of smart energy meters gradually matures, the concept of increasing metered electricity consumption has gradually been accepted due to the accuracy of smart energy meters.

[0003] However, the inventors found that users still lack subjective initiative in developing good electricity usage habits, and they actually waste electricity without realizing it. As a result, electricity users still cannot fully understand their electricity usage and are full of doubts. Therefore, there is an urgent need for a device to provide users with proactive power-saving behavior judgments and suggestions to guide users to develop good user behaviors. Summary of the invention

[0004] The embodiments of the present invention provide a method, device and terminal for processing electric energy meter data based on machine vision, so as to solve the problem that no device in the prior art provides users with proactive power-saving behavior judgment and suggestions.

[0005] In a first aspect, an embodiment of the present invention provides a method for processing electric energy meter data based on machine vision, which is applied to a third-party smart device, wherein the smart device is installed in a preset area centered on the smart electric energy meter, and comprises:

[0006] Collect video images;

[0007] Analyze the video image to determine a first estimated amount of electricity corresponding to the existing electrical equipment and a second estimated amount of electricity corresponding to the electricity consumption change factor;

[0008] Determine a user power consumption factor table corresponding to the smart electric energy meter according to the first estimated power and the second estimated power;

[0009] According to the user power consumption factor table and the preset power consumption factor table, a corresponding power consumption behavior optimization table is determined.

[0010] In a possible implementation, analyzing the video image to determine a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to the power consumption change factor includes:

[0011] Analyzing the objects in the video image to obtain a human behavior data set and a visual target recognition result set;

[0012] The power consumption of the electrical equipment is estimated according to the human behavior data set and the visual target recognition result set to determine a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to the power consumption change factor.

[0013] In a possible implementation, estimating the power consumption of the electrical equipment according to the human behavior data set and the visual target recognition result set, and determining a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to the power consumption change factor, includes:

[0014] Determining whether there is a power consumption change factor based on the human behavior data set and the visual target recognition result set;

[0015] When there are factors that change the electricity consumption, determine the factors that cause the change in electricity consumption;

[0016] According to the historical data and the factors causing the change in power consumption, a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to each factor causing the change in power consumption are determined.

[0017] In a possible implementation, the factors causing the change in electricity consumption include at least: an increase in electrical equipment, an increase in population, a temperature change, and a change in working hours.

[0018] In a possible implementation manner, after the video image is captured, the method further includes:

[0019] Obtaining a current reading of the smart electric energy meter;

[0020] After determining the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to each factor causing the power consumption change according to the historical data and the factors causing the power consumption change, the method further includes:

[0021] According to the historical data and the factors causing the change in power consumption, the power consumption fluctuation value corresponding to the existing electrical equipment and the power consumption fluctuation value corresponding to each factor causing the change in power consumption are determined.

[0022] In a possible implementation manner, before determining the user power consumption factor table corresponding to the smart power meter according to the first estimated power and the second estimated power, the method further includes:

[0023] The sum of the first estimated power, the second estimated power, the power consumption fluctuation value corresponding to the existing electrical equipment, and the power consumption fluctuation values ​​corresponding to the factors causing the power consumption change is taken as the total power consumption of the user;

[0024] Calculating the difference between the total amount of electricity used by the user and the current reading of the smart energy meter;

[0025] detecting whether the difference is within a preset range;

[0026] When the difference is within a preset range, a step is executed to determine a user power consumption factor table corresponding to the smart power meter according to the first estimated power and the second estimated power.

[0027] In a possible implementation manner, after detecting whether the difference is within a preset range, the method further includes:

[0028] When the difference is not within the preset range, jump to the step of "determining the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to each factor causing the change in power consumption based on the historical data and the factors causing the change in power consumption" to re-determine the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to each factor causing the change in power consumption until the difference between the recalculated total power consumption of the user and the current reading of the smart power meter is within the preset range.

[0029] In a second aspect, an embodiment of the present invention provides an electric energy meter data processing device based on machine vision, comprising:

[0030] An acquisition module, used for acquiring video images;

[0031] An analysis module is used to analyze the video image to determine a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to the power consumption change factor;

[0032] a processing module, configured to determine a user power consumption factor table corresponding to the smart electric energy meter according to the first estimated power consumption and the second estimated power consumption;

[0033] The processing module is further used to determine a corresponding power usage behavior optimization table according to the user power usage factor table and a preset power usage factor table.

[0034] In a third aspect, an embodiment of the present invention provides a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the machine vision-based electric energy meter data processing method as described in the first aspect or any possible implementation method of the first aspect are implemented.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the machine vision-based electricity meter data processing method as described in the first aspect or any possible implementation method of the first aspect are implemented.

[0036] The embodiments of the present invention provide an electric energy meter data processing method, device and terminal based on machine vision, which collect video images; analyze the video images to determine a first estimated power corresponding to the existing electrical equipment and a second estimated power corresponding to the power consumption change factor; determine a user power consumption factor table corresponding to the smart electric energy meter according to the first estimated power and the second estimated power; determine a corresponding power consumption behavior optimization table according to the user power consumption factor table and a preset power consumption factor table, so that the user power consumption factor table can be obtained according to the collected video images, and the power consumption behavior optimization table can be obtained based on the preset power consumption factor table, so that the user can clearly understand which power consumption factor his power consumption behavior comes from during the power consumption process, thereby guiding the user to form an effective power saving awareness. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 This is an application scenario diagram of the electric energy meter data processing method based on machine vision provided by an embodiment of the present invention;

[0039] Figure 2 is a schematic diagram of the structure of an electric energy meter data processing device based on machine vision provided by an embodiment of the present invention;

[0040] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0043] Figure 1 The present invention provides a flowchart of an implementation method of an electric energy meter data processing method based on machine vision. The electric energy meter data processing method based on machine vision is applied to a third-party smart device, which is installed in a preset area centered on the smart electric energy meter. The electric energy meter data processing method based on machine vision is described in detail as follows:

[0044] Step 101, capturing video images.

[0045] The intelligent device collects video images based on visual acquisition methods to achieve video monitoring of the monitored area.

[0046] It should be noted that the monitoring area of ​​the smart device is the smart electricity meter and its surrounding area. According to the electricity usage scenario, the smart device can also be installed in the non-smart electricity meter area, which can also be understood as being far away from the smart electricity meter, such as being set up in the user's home, so as to better monitor the user's electricity usage behavior and provide the user with a better data basis for electricity-saving behavior.

[0047] In this embodiment, the video images may be collected in real time, or the video images may be collected at preset time intervals in order to reduce the processing amount of the video images. For example, the preset time interval may be 24 hours or 36 hours.

[0048] Step 102: Analyze the video image to determine a first estimated power quantity corresponding to the existing electrical equipment and a second estimated power quantity corresponding to the power consumption change factor.

[0049] In this step, the video images collected by the smart device can be analyzed, wherein the frequency of analysis can be determined according to actual needs, for example, once a month.

[0050] The existing electrical equipment here can be understood as existing electrical equipment that generates stable electricity consumption data, such as televisions, refrigerators, lighting lamps, etc. in users' homes.

[0051] The factors that affect electricity consumption here include at least: adding electrical equipment, increasing population, temperature changes, and changes in working hours. Adding electrical equipment will inevitably generate more electricity consumption. Similarly, increasing population will also increase electricity consumption. Temperature changes, such as too hot in summer or too cold in winter, will increase the use of air conditioners, which will generate additional electricity consumption. Changes in working hours may increase or decrease electricity consumption. For example, if users work overtime at home at night, the electricity consumption of heating and lighting equipment will increase. If users are away from home on business, the electricity consumption will decrease.

[0052] In this step, a human behavior data set and a visual target recognition result set are determined by analyzing the video images; the power consumption of electrical equipment is estimated based on the human behavior data set and the visual target recognition result set, and a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to the power consumption change factor are determined.

[0053] The video images can be used to collect images of the user's time of going out to work, the time of returning home, the number of people in the user's home, and the user carrying new electrical equipment. The human behavior data set obtained may include the user's working time data set, whether the number of people has changed, and whether new electrical equipment has been added.

[0054] The visual target recognition result set may include targets identified within the monitoring area of ​​the smart device. For example, if the smart device is set in the user's home, the targets may be a TV and a living room light. The visual target recognition result set may include data such as the TV's power-on and power-off times, the living room light's power-on and power-off times, and other data.

[0055] Optionally, estimating the power consumption of the electrical equipment according to the human behavior data set and the visual target recognition result set, and determining a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to the power consumption change factor may include:

[0056] Determine whether there is a factor of power consumption change based on the human behavior data set and the visual target recognition result set;

[0057] When there are factors that change the electricity consumption, determine the factors that cause the change in electricity consumption;

[0058] According to the historical data and the factors causing the change in power consumption, the first estimated power consumption corresponding to the existing electrical equipment and the second estimated power consumption corresponding to each factor causing the change in power consumption are determined.

[0059] Here, the data in the human behavior data set can be used to determine whether there are new population, new electrical equipment, and whether the user's electricity usage time has changed. The visual target recognition result set can be used to determine whether the visual target's electricity usage time has changed. Therefore, the first estimated electricity corresponding to the existing electrical equipment and the second estimated electricity corresponding to each factor that causes the change in electricity consumption are determined based on historical data, that is, the electricity consumption data of each electrical equipment in the same time period stored previously and the determined factors that cause the change in electricity consumption.

[0060] Optionally, the power consumption fluctuation value corresponding to the existing electrical equipment and the power consumption fluctuation value corresponding to each factor causing the power consumption change can also be determined based on historical data and factors causing power consumption changes. In this way, the video image can be analyzed to obtain multiple subdivided power consumption behaviors or processes. It is necessary to verify whether the various data generated by the above subdivision process are correct. Only on the basis that the above data are correct can step 103 be continued.

[0061] After acquiring the video image of the monitoring area, it also includes: acquiring the current reading of the smart energy meter; we use the current reading of the smart energy meter to verify whether the above-mentioned estimated power consumption is correct.

[0062] Optionally, the sum of the first estimated power, the second estimated power, the power consumption fluctuation value corresponding to the existing electrical equipment, and the power consumption fluctuation value corresponding to each factor causing the power consumption change is taken as the total power consumption of the user;

[0063] Calculate the difference between the total amount of electricity used by the user and the current reading of the smart energy meter;

[0064] Whether the detection difference is within the preset range;

[0065] When the difference is within the preset range, it means that each estimated power consumption is equal to or slightly different from the current reading of the smart power meter, which means that the estimated power consumption is correct, and step 103 is continued.

[0066] When the difference is not within the preset range, it means that the above-mentioned estimated power consumption is incorrect, and jump to the step "determine the first estimated power consumption corresponding to the existing electrical equipment and the second estimated power consumption corresponding to each factor causing the change in power consumption based on historical data and factors causing the change in power consumption" to re-determine the first estimated power consumption corresponding to the existing electrical equipment and the second estimated power consumption corresponding to each factor causing the change in power consumption until the difference between the recalculated total power consumption of the user and the current reading of the smart power meter is within the preset range.

[0067] Optionally, the above-mentioned process of obtaining the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to the power consumption change factor through the human behavior data set and the visual target recognition result, as well as the power consumption fluctuation value corresponding to the existing electrical equipment and the power consumption fluctuation value corresponding to each factor causing the power consumption change, can be performed by neural network feature analysis and dimensionality reduction mapping to train a mapping data model with a high confidence, such as a power consumption estimation mathematical model. The power consumption estimation mathematical model can provide power consumption estimates for different households.

[0068] Step 103: Determine a user power consumption factor table corresponding to the smart power meter according to the first estimated power consumption and the second estimated power consumption.

[0069] Optionally, this step can determine the user electricity consumption factor table corresponding to the smart power meter based on the first estimated electricity quantity and the second estimated electricity quantity, as well as the electricity consumption fluctuation values ​​corresponding to the existing electrical equipment and the electricity consumption fluctuation values ​​corresponding to various factors causing the changes in electricity consumption, that is, various electricity consumption factors and the corresponding estimated electricity quantities are provided to the user in the form of a table, so that the user can understand his or her own electricity consumption behavior.

[0070] Step 104: Determine a corresponding power usage behavior optimization table according to the user power usage factor table and the preset power usage factor table.

[0071] The preset electricity consumption factor table may be a typical electricity consumption behavior table, that is, an average value of behaviors of multiple household users, to obtain an electricity consumption table that conforms to the general electricity consumption behavior and corresponds to a more energy-saving electricity consumption behavior.

[0072] The electricity usage behavior optimization table may include the data contents of the user electricity usage factor table and the preset electricity usage factor table, as well as the difference between the electricity consumption in the user electricity usage factor table and the electricity consumption in the preset electricity usage factor table. The electricity usage behavior optimization table can enable users to clearly understand which electricity consumption factor their electricity consumption behavior comes from during the electricity usage process, thereby forming an effective electricity saving awareness.

[0073] The above-mentioned electric energy meter data processing method based on machine vision collects video images; analyzes the video images to determine the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to the power change factor; determines the user power factor table corresponding to the smart electric energy meter based on the first estimated power and the second estimated power; determines the corresponding power consumption behavior optimization table based on the user power factor table and the preset power factor table, so that the user power factor table can be obtained based on the collected video images, and the power consumption behavior optimization table can be obtained based on the preset power factor table, so that the user can clearly understand which power factor his power consumption behavior comes from during the power consumption process, thereby guiding the user to form an effective power saving awareness. And by detecting the total power consumption of the user obtained by analyzing the video image and the current reading of the smart electric energy meter, the accuracy of the power estimated by the smart device for each power factor can be determined.

[0074] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0075] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0076] Figure 2 The following is a schematic diagram of the structure of a data processing device for electric energy meters based on machine vision provided by an embodiment of the present invention. For the sake of convenience, only the parts related to the embodiment of the present invention are shown, which are described in detail as follows:

[0077] like Figure 2 As shown, the electric energy meter data processing device based on machine vision includes: a collection module 201, an analysis module 202 and a processing module 203;

[0078] The acquisition module 201 is used to acquire video images;

[0079] An analysis module 202 is used to analyze the video image to determine a first estimated power quantity corresponding to the existing electrical equipment and a second estimated power quantity corresponding to the power consumption change factor;

[0080] The processing module 203 is used to determine a user power consumption factor table corresponding to the smart electric energy meter according to the first estimated power and the second estimated power;

[0081] The processing module 203 is further used to determine a corresponding power usage behavior optimization table according to the user power usage factor table and the preset power usage factor table.

[0082] In a possible implementation, when the analysis module 202 analyzes the video image to determine the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to the power consumption change factor, it is used to:

[0083] Analyze the objects in the video image to obtain a human behavior data set and a visual target recognition result set;

[0084] The power consumption of electrical equipment is estimated based on the human behavior data set and the visual target recognition result set to determine a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to the power consumption change factor.

[0085] In a possible implementation, the analysis module 202 estimates the power consumption of the electrical equipment according to the human behavior data set and the visual target recognition result set, and determines the first estimated power consumption corresponding to the existing electrical equipment and the second estimated power consumption corresponding to the power consumption change factor, for:

[0086] Determine whether there is a factor of power consumption change based on the human behavior data set and the visual target recognition result set;

[0087] When there are factors that change the electricity consumption, determine the factors that cause the change in electricity consumption;

[0088] According to the historical data and the factors causing the change in power consumption, the first estimated power consumption corresponding to the existing electrical equipment and the second estimated power consumption corresponding to each factor causing the change in power consumption are determined.

[0089] In a possible implementation, factors causing changes in electricity consumption include at least: an increase in electrical equipment, an increase in population, temperature changes, and changes in working hours.

[0090] In a possible implementation, after the acquisition module 201 acquires the video image, the acquisition module 201 is further configured to:

[0091] Get the current reading of the smart energy meter;

[0092] After the analysis module 202 determines the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to each factor causing the power consumption change according to the historical data and the factors causing the power consumption change, the analysis module 202 is further used to:

[0093] Based on historical data and factors that cause changes in electricity consumption, determine the electricity consumption fluctuation values ​​corresponding to the existing electrical equipment and the electricity consumption fluctuation values ​​corresponding to each factor that causes changes in electricity consumption.

[0094] In a possible implementation, before the processing module 203 determines the user power consumption factor table corresponding to the smart power meter according to the first estimated power and the second estimated power, the processing module 203 is further configured to:

[0095] The sum of the first estimated power, the second estimated power, the power consumption fluctuation value corresponding to the existing electrical equipment, and the power consumption fluctuation value corresponding to each factor causing the power consumption change is taken as the total power consumption of the user;

[0096] Calculate the difference between the total amount of electricity used by the user and the current reading of the smart energy meter;

[0097] Whether the detection difference is within the preset range;

[0098] When the difference is within a preset range, the execution step is to determine a user power consumption factor table corresponding to the smart power meter according to the first estimated power and the second estimated power.

[0099] The processing module 203 is also used to: when the difference is not within a preset range, jump to the step of "determining the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to each factor causing the change in power consumption based on historical data and factors causing the change in power consumption" to re-determine the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to each factor causing the change in power consumption, until the difference between the recalculated total power consumption of the user and the current reading of the smart power meter is within a preset range.

[0100] The above-mentioned electric energy meter data processing device based on machine vision collects video images through the acquisition module; the analysis module analyzes the video images to determine the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to the power change factor; based on the first estimated power and the second estimated power, the processing module determines the user power factor table corresponding to the smart electric energy meter; based on the user power factor table and the preset power factor table, the processing module determines the corresponding power behavior optimization table, so that the user power factor table can be obtained based on the collected video images, and the power behavior optimization table can be obtained based on the preset power factor table, so that the user can clearly understand which power factor his power consumption behavior comes from during the power consumption process, thereby guiding the user to form an effective power saving awareness. And by detecting the total power consumption of the user obtained by analyzing the video image and the current reading of the smart electric energy meter, the accuracy of the power estimated by the smart device for each power factor can be determined.

[0101] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. Figure 3 As shown, the terminal 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the above-mentioned embodiments of the electric energy meter data processing method based on machine vision are implemented, for example Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 2 Functionality of modules / units 201 to 203 shown.

[0102] Exemplarily, the computer program 32 may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 32 in the terminal 3. For example, the computer program 32 may be divided into Figure 2 Modules / units 201 to 203 are shown.

[0103] The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of terminal 3 and does not constitute a limitation on terminal 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0104] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0105] The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or memory of the terminal 3. The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal 3. Further, the memory 31 may also include both an internal storage unit of the terminal 3 and an external storage device. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0106] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0107] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0108] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0109] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0112] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned embodiments of the electric energy meter data processing method based on machine vision can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0113] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for processing electric energy meter data based on machine vision, characterized in that: Applied to a third-party smart device, the smart device is installed in a preset area centered on a smart electric energy meter, and the electric energy meter data processing method based on machine vision includes: Collect video images; Analyze the video image to determine a first estimated amount of electricity corresponding to the existing electrical equipment and a second estimated amount of electricity corresponding to the electricity consumption change factor; Determine a user power consumption factor table corresponding to the smart electric energy meter according to the first estimated power and the second estimated power; Determine a corresponding power consumption behavior optimization table according to the user power consumption factor table and the preset power consumption factor table; The analyzing the video image to determine a first estimated power corresponding to the existing electrical equipment and a second estimated power corresponding to the power consumption change factor includes: Analyzing the objects in the video image to obtain a human behavior data set and a visual target recognition result set; Estimating the power consumption of electrical equipment according to the human behavior data set and the visual target recognition result set, and determining a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to the power consumption change factor; The estimating the power consumption of the electrical equipment according to the human behavior data set and the visual target recognition result set, and determining a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to the power consumption change factor, includes: Determining whether there is a power consumption change factor based on the human behavior data set and the visual target recognition result set; When there are factors that change the electricity consumption, determine the factors that cause the change in electricity consumption; According to the historical data and the factors causing the change in power consumption, a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to each factor causing the change in power consumption are determined.

2. The method for processing electric energy meter data based on machine vision according to claim 1, characterized in that: The factors causing the change in electricity consumption include at least: increase in electrical equipment, increase in population, temperature change and change in working hours.

3. The method for processing electric energy meter data based on machine vision according to claim 1, characterized in that: After the video image is collected, the method further includes: Obtaining a current reading of the smart electric energy meter; After determining the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to each factor causing the power consumption change according to the historical data and the factors causing the power consumption change, the method further includes: According to the historical data and the factors causing the change in power consumption, the power consumption fluctuation value corresponding to the existing electrical equipment and the power consumption fluctuation value corresponding to each factor causing the change in power consumption are determined.

4. The method for processing electric energy meter data based on machine vision according to claim 3, characterized in that: Before determining the user power consumption factor table corresponding to the smart electric energy meter according to the first estimated power and the second estimated power, the method further includes: The sum of the first estimated power, the second estimated power, the power consumption fluctuation value corresponding to the existing electrical equipment, and the power consumption fluctuation values ​​corresponding to the factors causing the power consumption change is taken as the total power consumption of the user; Calculating the difference between the total amount of electricity used by the user and the current reading of the smart energy meter; detecting whether the difference is within a preset range; When the difference is within a preset range, a step is executed to determine a user power consumption factor table corresponding to the smart power meter according to the first estimated power and the second estimated power.

5. The method for processing electric energy meter data based on machine vision according to claim 4, characterized in that: After detecting whether the difference is within a preset range, the method further includes: When the difference is not within the preset range, jump to step "determine the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to each factor causing the change in power consumption based on the historical data and the factors causing the change in power consumption" to re-determine the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to each factor causing the change in power consumption until the difference between the recalculated total power consumption of the user and the current reading of the smart power meter is within the preset range.

6. A data processing device for electric energy meters based on machine vision, characterized in that: include: An acquisition module, used for acquiring video images; An analysis module is used to analyze the video image to determine a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to the power consumption change factor; a processing module, configured to determine a user power consumption factor table corresponding to the smart electric energy meter according to the first estimated power consumption and the second estimated power consumption; The processing module is further used to determine a corresponding power consumption behavior optimization table according to the user power consumption factor table and the preset power consumption factor table; When the analysis module analyzes the video image to determine the first estimated power corresponding to the existing electrical equipment and the second estimated power corresponding to the power consumption change factor, it is used to: Analyzing the objects in the video image to obtain a human behavior data set and a visual target recognition result set; Estimating the power consumption of electrical equipment according to the human behavior data set and the visual target recognition result set, and determining a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to the power consumption change factor; The analysis module estimates the power consumption of the electrical equipment according to the human behavior data set and the visual target recognition result set, and determines the first estimated power consumption corresponding to the existing electrical equipment and the second estimated power consumption corresponding to the power consumption change factor, for: Determining whether there is a power consumption change factor based on the human behavior data set and the visual target recognition result set; When there are factors that change the electricity consumption, determine the factors that cause the change in electricity consumption; According to the historical data and the factors causing the change in power consumption, a first estimated power consumption corresponding to the existing electrical equipment and a second estimated power consumption corresponding to each factor causing the change in power consumption are determined.

7. A terminal comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory, characterized in that: When the processor executes the computer program, the steps of the electric energy meter data processing method based on machine vision as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the electric energy meter data processing method based on machine vision as described in any one of claims 1 to 5 are implemented.

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