Method and device for measuring fuel consumption and carbon emission based on vibration sensing

Abstract

The embodiment of the present application relates to a kind of based on vibration sensing's oil consumption carbon emission metering method and device, comprising: the running information of X, Y, Z three axes of target mechanical equipment in specific node position is collected;The standard form data is obtained by data processing to the running information;The mechanical equipment type and power state of the target mechanical equipment are identified based on the standard form data and type feature library and power feature library;The carbon emission-time curve corresponding to the target mechanical equipment is determined based on the mechanical equipment type and power state of the target mechanical equipment identified;The working time of the target mechanical equipment is obtained by integral calculation based on the carbon emission-time curve target carbon emission amount.It is thus possible to simply and quickly and accurately measure the carbon emission in the process of building engineering construction.

Description

Technical Field

[0001] This invention relates to the field of carbon metering technology for fuel-powered machinery and equipment during the construction of building projects, and particularly to a method and device for measuring fuel consumption and carbon emissions based on vibration sensing. Background Technology

[0002] Under the dual-carbon context, the construction industry accounts for a high proportion of carbon emissions, approximately half of all carbon emissions. Therefore, reducing carbon emissions in the construction industry is a crucial support for efforts to address dual-carbon issues.

[0003] Due to the complexity of the entire construction process, especially the construction process itself, it is difficult to accurately measure the carbon emissions generated during construction. Therefore, how to accurately measure the carbon emissions generated by construction fuel-powered machinery in real time has become an urgent problem to be solved. Summary of the Invention

[0004] In view of this, in order to solve the above-mentioned technical problems or some of the technical problems, the present invention provides a method and device for measuring fuel consumption and carbon emissions based on vibration sensing.

[0005] In a first aspect, embodiments of the present invention provide a method for measuring fuel consumption and carbon emissions based on vibration sensing, comprising:

[0006] Collect the X, Y, and Z axis operating information of the target mechanical equipment at specific node positions;

[0007] The operational information is processed to obtain data in a standard format.

[0008] Based on the standard form data, the mechanical equipment type and power status of the target mechanical equipment are identified by matching with the type feature library and the power feature library.

[0009] The carbon emission-time curve corresponding to the target mechanical equipment is determined based on the identified mechanical equipment type and power status;

[0010] The target carbon emissions are calculated by integrating the working time of the target mechanical equipment based on the carbon emission-time curve.

[0011] In one possible implementation, the method further includes:

[0012] Based on a preset time frequency, a piezoelectric triaxial accelerometer is used to collect the vibration acceleration of the X, Y, and Z axes of the target mechanical equipment at a specific node location, as well as the node battery power information and the node internal temperature information.

[0013] In one possible implementation, the method further includes:

[0014] The vibration acceleration along the X, Y, and Z axes, the battery charge information at the nodes, and the internal temperature information at the nodes are processed to obtain first and second vibration standard form data. The data processing includes at least noise reduction, format conversion, and data filling.

[0015] In one possible implementation, the method further includes:

[0016] Based on the first vibration standard form data and the type feature library, the mechanical equipment type of the target mechanical equipment is determined by matching and identifying them.

[0017] After determining the mechanical equipment type of the target mechanical equipment, the power state of the target mechanical equipment type is determined by matching and identifying the second vibration standard form data with the power feature library.

[0018] In one possible implementation, the method further includes:

[0019] Extract the peak value of the vibration amplitude of the Z-axis, the median value of the vibration amplitude of the Z-axis, the waveform of the Z-axis amplitude, and the median value of the weighted vibration amplitude of the XY-axis from the operating information of the X, Y, and Z axes;

[0020] The peak value of the Z-axis vibration amplitude, the median value of the Z-axis vibration amplitude, the Z-axis amplitude waveform, and the median value of the weighted vibration amplitude of the XY axes are matched with the corresponding parameter ranges in the type feature library;

[0021] When the matching degree meets the preset threshold, the mechanical equipment type of the target mechanical equipment is determined;

[0022] The determined mechanical equipment type is verified by using a paddle code pre-placed at the specific node position.

[0023] In one possible implementation, the method further includes:

[0024] After determining the mechanical equipment type of the target mechanical equipment, the vibration waveforms in the power feature library are hierarchically divided according to vibration characteristics based on a preset power state range.

[0025] The vibration waveform after the vibration feature hierarchy is divided is changed into a square wave;

[0026] Based on the square wave, the power state of the target mechanical equipment is determined by matching and identifying the second vibration standard form data.

[0027] In one possible implementation, the method further includes:

[0028] Based on the mechanical equipment type and power status of the target mechanical equipment, determine the fuel consumption data under different power statuses;

[0029] The fuel consumption-time curve for a preset time period is determined based on fuel consumption data under different power conditions.

[0030] The carbon emission-time curve corresponding to the target mechanical equipment is determined based on the fuel consumption-time curve and the carbon emission factor of the fuel type.

[0031] Secondly, embodiments of the present invention provide a fuel consumption and carbon emission metering device based on vibration sensing, comprising:

[0032] The data acquisition module is used to collect the X, Y, and Z axis operating information of the target mechanical equipment at specific node positions;

[0033] The data processing module is used to process the operational information to obtain data in a standard format.

[0034] The matching and identification module is used to match and identify the mechanical equipment type and power status of the target mechanical equipment based on the standard form data and the type feature library and power feature library;

[0035] The determination module is used to determine the carbon emission-time curve corresponding to the target mechanical equipment based on the identified mechanical equipment type and power status;

[0036] The metering module is used to calculate the target carbon emissions by integrating the working time of the target mechanical equipment based on the carbon emission-time curve.

[0037] Thirdly, embodiments of the present invention provide a computer device, including: a processor and a memory, wherein the processor is configured to execute a vibration-sensing-based fuel consumption and carbon emission measurement program stored in the memory, so as to implement the vibration-sensing-based fuel consumption and carbon emission measurement method described in the first aspect above.

[0038] Fourthly, embodiments of the present invention provide a storage medium, comprising: the storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the vibration sensing-based fuel consumption and carbon emission measurement method described in the first aspect above.

[0039] The vibration-sensing-based fuel consumption and carbon emission measurement scheme provided in this invention collects the X, Y, and Z axis operating information of a target mechanical device at a specific node location; processes the operating information to obtain standard data; matches the standard data with a type feature library and a power feature library to identify the mechanical device type and power status; determines the corresponding carbon emission-time curve based on the identified mechanical device type and power status; and calculates the target carbon emission amount by integrating the operating time of the target mechanical device based on the carbon emission-time curve. This scheme, by collecting the triaxial acceleration information of the target device and combining it with a device type feature library and a partial load power feature library generated from historical test data, determines the device's start / stop state, device type, and power status in real time, plots the device power-time status diagram, calculates the device fuel consumption-time status diagram based on the device power-fuel consumption relationship, and calculates the device carbon emission intensity and cumulative carbon emission amount using the fuel consumption carbon emission factor. Thus, it achieves simple, fast, and accurate indirect carbon emission measurement through the collection of device vibration information. Attached Figure Description

[0040] Figure 1 A schematic flowchart of a method for measuring fuel consumption and carbon emissions based on vibration sensing, provided in an embodiment of the present invention;

[0041] Figure 2 A schematic flowchart of another method for measuring fuel consumption and carbon emissions based on vibration sensing provided in an embodiment of the present invention;

[0042] Figure 3 A logic diagram for identifying mechanical equipment type is provided in an embodiment of the present invention;

[0043] Figure 4 A logic diagram for identifying the power and start / stop of mechanical equipment is provided in an embodiment of the present invention;

[0044] Figure 5 A schematic diagram of a fuel consumption and carbon emission metering device based on vibration sensing provided in an embodiment of the present invention;

[0045] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0048] Example 1

[0049] Figure 1 A schematic flowchart of a vibration-sensing-based fuel consumption and carbon emission measurement method provided in an embodiment of the present invention is shown below. Figure 1 As shown, the method specifically includes:

[0050] S11. Collect the X, Y, and Z axis operating information of the target mechanical equipment at a specific node position.

[0051] To achieve automated measurement of carbon emissions during construction projects and ensure accurate recording of carbon emission data from fuel-consuming machinery and other equipment, this invention proposes an indirect fuel consumption measurement method for carbon emissions in the second scope. This method improves the accuracy of carbon emission accounting during the construction process and helps promote the construction industry's entry into the carbon trading market.

[0052] Specifically, in combination Figure 2 As shown in the flowchart, the fuel consumption carbon emission measurement method based on vibration sensing provided in this embodiment of the invention consists of two parts: basic information collection and indirect calculation and measurement of equipment carbon emissions. By collecting relevant data such as triaxial vibration acceleration, temperature and battery power of the equipment, the carbon emissions of the equipment are indirectly calculated and measured using intelligent identification methods, thereby realizing remote, real-time and automated measurement of carbon emissions of fuel-consuming machinery.

[0053] First, passive wireless terminal nodes are used to collect the X, Y, and Z axis operating information of the target mechanical equipment at specific node locations. This operating information includes at least vibration acceleration, node battery level, and internal temperature information, and is transmitted wirelessly to an edge computing device (e.g., a computer). The minimum frequency for vibration information acquisition and uploading is 10 seconds per instance. The vibration information acquisition locations for different mechanical devices are relatively fixed; when the location changes significantly, the vibration information needs to be corrected for the changed location.

[0054] It should be noted that the vibration acquisition terminal in this embodiment of the invention is fixed by strong magnetic adsorption, realizing a rigid connection between the acquisition node and the mechanical equipment, which is convenient for installation; it adopts LORA local area networking, which enables wireless communication within the boundary of the engineering construction project without increasing power consumption, making the deployment more flexible.

[0055] S12. The operation information is processed to obtain standard data.

[0056] The vibration acceleration along the X, Y, and Z axes, the battery charge information at each node, and the internal temperature information at each node are processed to obtain first vibration standard form (standard form 1) data and second vibration standard form (standard form 2) data. The data processing includes at least noise reduction, format conversion, and data filling. The first vibration standard form data is used to match and identify the type of the target mechanical equipment against a type feature library; the second vibration standard form data is used to match and identify the power status of the target mechanical equipment against a power feature library.

[0057] S13. Based on the standard form data, match it with the type feature library and the power feature library to identify the mechanical equipment type and power status of the target mechanical equipment.

[0058] The mechanical equipment type of the target machine is determined by matching the first vibration standard form data with the type feature library. Then, after determining the mechanical equipment type, the power status of the target machine is determined by matching the second vibration standard form data with the power feature library. The identified mechanical equipment type and power status, combined with start / stop judgments, are converted into carbon emission intensity and cumulative carbon emissions, and uploaded to the system management platform.

[0059] Specifically, the process extracts the peak value, median value, and waveform of the Z-axis vibration amplitude, as well as the median weighted vibration amplitude of the X and Y axes, from the X, Y, and Z axes. These parameters are then matched against corresponding parameter ranges in a type feature library. When the matching degree meets a preset threshold, the mechanical equipment type of the target machine is determined. The determined mechanical equipment type is then verified using a pre-placed paddle code at the specific node position. The detailed execution logic is attached. Figure 3 As shown.

[0060] Furthermore, after determining the mechanical equipment type of the target mechanical equipment, the vibration waveforms in the power feature library are hierarchically divided according to vibration characteristics based on the preset power state range; the vibration waveforms after the vibration feature hierarchical division are changed into square waves; the second vibration standard form data is matched and identified based on the square waves to determine the power state of the mechanical equipment type of the target mechanical equipment.

[0061] It should be noted that when the vibration amplitude in the X, Y, and Z axes is all less than the set lower limit, the machine is determined to be in a stopped state. The lower limit varies depending on the type of machine, and this determination must be made after the machine type determination. The specific execution logic is attached. Figure 4 As shown.

[0062] S14. Determine the carbon emission-time curve corresponding to the target mechanical equipment based on the identified mechanical equipment type and power status.

[0063] S15. The target carbon emissions are obtained by integrating the working time of the target mechanical equipment based on the carbon emission-time curve.

[0064] After completing the identification of the type of mechanical equipment and the identification of some power states, the fuel consumption-time curve is calculated based on the fuel consumption of the machine under different power states. Then, the fuel consumption-time curve is obtained by multiplying it by the carbon emission factor of diesel or gasoline. This carbon emission-time curve is the carbon emission intensity curve of the machine at any time. The cumulative carbon emission is obtained by integrating over time.

[0065] It should be noted that this invention can also be used in conjunction with power sub-metering terminals, building material automatic reading terminals, and a complete data integration management platform to achieve carbon emission monitoring, statistics, analysis, and optimization throughout the entire process of engineering construction projects, providing a data foundation for enterprises' dual control of carbon emissions.

[0066] The vibration sensing-based fuel consumption and carbon emission measurement method provided in this invention collects the X, Y, and Z axis operating information of a target mechanical device at a specific node position; processes the operating information to obtain standard data; matches the standard data with a type feature library and a power feature library to identify the mechanical device type and power status of the target mechanical device; determines the carbon emission-time curve corresponding to the target mechanical device based on the identified mechanical device type and power status; and calculates the target carbon emission amount by integrating the operating time of the target mechanical device based on the carbon emission-time curve. This method collects triaxial acceleration information of the target equipment, combines it with equipment type feature library and partial load power feature library generated from historical test data, and determines the equipment's start-up / shutdown status, equipment type, and power status in real time. It then plots the equipment power-time status diagram, calculates the equipment fuel consumption-time status diagram based on the equipment power-fuel consumption relationship, and calculates the equipment carbon emission intensity and cumulative carbon emissions using the fuel consumption carbon emission factor. Furthermore, by collecting equipment vibration information, it achieves automated measurement of carbon emissions from building construction, ensures accurate recording of carbon emission data for fuel-consuming machinery and other equipment, improves the accuracy of carbon emission accounting during the building construction process, and helps promote the construction industry's entry into the carbon trading market.

[0067] Example 2

[0068] Combination Figure 2 As shown in the flowchart, this invention has developed a carbon metering device for remote real-time monitoring of mechanical carbon emissions based on a piezoelectric triaxial accelerometer. By processing vibration information into a standard form and intelligently matching it with a type feature library and a power feature library, the device identifies the type and power status of mechanical equipment. This enables functions such as second-level sensing of machine start / stop, automatic identification of machine type, and monitoring of machine power status. The data is then converted into carbon emission data and uploaded to the carbon metering platform via the Internet of Things, achieving real-time, refined monitoring and statistical management of carbon emissions from construction machinery.

[0069] First, passive wireless terminal nodes are used to collect the X, Y, and Z axis operating information of the target mechanical equipment at specific node locations. This operating information includes at least vibration acceleration, node battery level, and internal temperature information, and is transmitted wirelessly to an edge computing device (e.g., a computer). The minimum frequency for vibration information acquisition and uploading is 10 seconds per instance. The vibration information acquisition locations for different mechanical devices are relatively fixed; when the location changes significantly, the vibration information needs to be corrected for the changed location.

[0070] Furthermore, the vibration acceleration along the X, Y, and Z axes, the battery charge information at each node, and the internal temperature information at each node are processed to obtain first vibration standard form (standard form 1) data and second vibration standard form (standard form 2) data. The data processing includes at least noise reduction, format conversion, and data padding. The first vibration standard form data is used for matching and identification with a type feature library to determine the mechanical equipment type of the target machine; the second vibration standard form data is used for matching and identification with a power feature library to determine the power state of the target machine's mechanical equipment type.

[0071] Furthermore, preliminary identification is performed using vibration standard form 1 and a type feature library, and the machine type is verified using manual levers when placing the data acquisition nodes, ensuring the accuracy of machine type identification in the early stages when data accumulation is insufficient. The method for identifying machine type using vibration standard form 1 and the type feature library mainly involves extracting four key parameters from the triaxial vibration information: peak Z-axis vibration amplitude, median Z-axis vibration amplitude, Z-axis amplitude waveform, and median XY-axis weighted vibration amplitude. These parameters are then matched with the corresponding parameter ranges in the type feature library. When the matching degree meets the set requirements, the machine type is determined. Verification is then performed using the lever code set when placing the data acquisition nodes. If the requirements are not met, the type feature library is corrected and the parameter weights are adjusted. Actual usage data is used to improve the accuracy of machine type identification. The specific execution logic is shown in the attached figure. Figure 3 As shown in the figure. The manual dial is set at the vibration acquisition node and consists of two decimal digit dials. When placing the acquisition node, the digital code is used to correspond to the machine type, and the dial is moved to the corresponding position to accurately collect the machine type information. When the data accumulates to the point where the recognition accuracy meets the requirements, the manual dial can be canceled.

[0072] Furthermore, partial power state identification is performed using vibration standard form 2 and a power feature library. Specifically, after determining the machine type, vibration characteristics can be divided into four levels: 0-20%, 20%-45%, 45%-75%, and 75%-100%, where the 100% power state represents the historical peak value. After the division, the vibration waveform is shaped into a square wave, thereby completing the partial power state matching for this type of machine. The specific execution logic is shown in the appendix. Figure 4 As shown.

[0073] When the vibration amplitude in the X, Y, and Z axes is all less than the set lower limit, the machine is determined to be in a stopped state. The lower limit value varies depending on the type of machine, and this judgment must be made after the machine type determination. The specific execution logic is shown in the attached figure. Figure 4 As shown.

[0074] After identifying the type of machinery and its partial power status, the fuel consumption-time curve (fuel consumption-time square wave) is calculated based on the fuel consumption under different power statuses of the machinery. This curve is then multiplied by the carbon emission factor of diesel or gasoline to obtain the carbon emission-time curve (carbon emission intensity-time square wave), which represents the carbon emission intensity curve of the machinery at any given time. Integrating over time yields the cumulative carbon emissions. The carbon emission factor of diesel or gasoline is known prior to actual production operations.

[0075] Taking a real-world application project as an example, this method, combined with electricity sub-metering terminals, building material automatic reading terminals, and a complete data integration management platform, can provide a 1.84% carbon reduction for engineering construction projects, with a cumulative carbon reduction of 3882.4 tons throughout the entire process, generating significant environmental benefits. The application of this method effectively reduces the human resource investment in carbon inventory for engineering construction projects, and the real-time monitoring results provide convenient construction management tools for project sites, resulting in significant project service benefits. Furthermore, enterprise managers can utilize the online-deployed SaaS service to gain an overview of the carbon emissions of all their engineering construction projects, effectively strengthening the enterprise's remote monitoring capabilities and enabling comprehensive planning and optimization of total corporate carbon emissions from a holistic perspective, thus contributing to corporate carbon reduction and ESG disclosure.

[0076] The vibration sensing-based fuel consumption and carbon emission measurement method provided in this invention collects the X, Y, and Z axis operating information of a target mechanical device at a specific node position; processes the operating information to obtain standard data; matches the standard data with a type feature library and a power feature library to identify the mechanical device type and power status of the target mechanical device; determines the carbon emission-time curve corresponding to the target mechanical device based on the identified mechanical device type and power status; and calculates the target carbon emission amount by integrating the operating time of the target mechanical device based on the carbon emission-time curve. This method collects triaxial acceleration information of the target equipment, combines it with equipment type feature library and partial load power feature library generated from historical test data, and determines the equipment's start-up / shutdown status, equipment type, and power status in real time. It then plots the equipment power-time status diagram, calculates the equipment fuel consumption-time status diagram based on the equipment power-fuel consumption relationship, and calculates the equipment carbon emission intensity and cumulative carbon emissions using the fuel consumption carbon emission factor. Furthermore, by collecting equipment vibration information, it achieves automated measurement of carbon emissions from building construction, ensures accurate recording of carbon emission data for fuel-consuming machinery and other equipment, improves the accuracy of carbon emission accounting during the building construction process, and helps promote the construction industry's entry into the carbon trading market.

[0077] Example 3

[0078] Figure 5A schematic diagram of a vibration-sensing-based fuel consumption and carbon emission metering device provided in this embodiment of the invention specifically includes:

[0079] The data acquisition module 501 is used to acquire the X, Y, and Z axis operating information of the target mechanical equipment at a specific node position. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0080] The data processing module 502 is used to process the operational information to obtain data in a standard format. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0081] The matching and identification module 503 is used to match and identify the mechanical equipment type and power status of the target mechanical equipment based on the standard form data and the type feature library and power feature library. For detailed explanation, please refer to the relevant descriptions in the above method embodiments, which will not be repeated here.

[0082] The determination module 504 is used to determine the carbon emission-time curve corresponding to the identified target mechanical equipment based on the mechanical equipment type and power status of the target mechanical equipment. For detailed explanation, please refer to the relevant descriptions in the above method embodiments, which will not be repeated here.

[0083] The metering module 505 is used to calculate the target carbon emissions by integrating the working time of the target mechanical equipment based on the carbon emission-time curve. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0084] The vibration-sensing-based fuel consumption and carbon emission metering device provided in this embodiment can be as follows: Figure 5 The vibration-sensing-based fuel consumption and carbon emission metering device shown can perform functions such as... Figure 1-2 All steps of the vibration sensing-based fuel consumption and carbon emission measurement method are implemented to achieve... Figure 1-2 The technical effects of the vibration-sensing-based fuel consumption and carbon emission measurement method shown are detailed in the attached diagram. Figure 1-2 The relevant descriptions are presented concisely and will not be elaborated upon here.

[0085] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Figure 6 The computer device 600 shown includes at least one processor 601, a memory 602, at least one network interface 604, and other user interfaces 603. The various components in the computer device 600 are coupled together via a bus system 605. It is understood that the bus system 605 is used to implement communication between these components. In addition to a data bus, the bus system 605 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 6 The general designated all buses as Bus System 605.

[0086] The user interface 603 may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen).

[0087] It is understood that the memory 602 in this embodiment of the invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 602 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0088] In some implementations, memory 602 stores elements, executable units or data structures, or subsets thereof, or extended sets thereof: operating system 6021 and application program 6022.

[0089] The operating system 6021 includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application program 6022 includes various applications, such as a media player and a browser, used to implement various application functions. The program implementing the method of this embodiment can be included in the application program 6022.

[0090] In this embodiment of the invention, by calling the program or instructions stored in memory 602, specifically the program or instructions stored in application program 6022, processor 601 executes the method steps provided in each method embodiment, including, for example:

[0091] The system collects the X, Y, and Z axis operating information of the target mechanical equipment at specific node positions; processes the operating information to obtain standard format data; matches the standard format data with a type feature library and a power feature library to identify the mechanical equipment type and power status of the target mechanical equipment; determines the carbon emission-time curve corresponding to the target mechanical equipment based on the identified mechanical equipment type and power status; and calculates the target carbon emission amount by integrating the working time of the target mechanical equipment based on the carbon emission-time curve.

[0092] In one possible implementation, the vibration acceleration of the target mechanical equipment along the X, Y, and Z axes, the battery level of the node, and the internal temperature of the node are collected by a piezoelectric triaxial accelerometer at a specific node location based on a preset time frequency.

[0093] In one possible implementation, the vibration acceleration of the X, Y, and Z axes, the node battery power information, and the node internal temperature information are processed to obtain first vibration standard form data and second vibration standard form data. The data processing includes at least noise reduction, format conversion, and data padding.

[0094] In one possible implementation, the mechanical equipment type of the target mechanical equipment is determined by matching and identifying the first vibration standard form data with a type feature library; after determining the mechanical equipment type of the target mechanical equipment, the power state of the mechanical equipment type of the target mechanical equipment is determined by matching and identifying the second vibration standard form data with a power feature library.

[0095] In one possible implementation, the peak value of the Z-axis vibration amplitude, the median value of the Z-axis vibration amplitude, the Z-axis amplitude waveform, and the median value of the weighted vibration amplitude of the X and Y axes are extracted from the operating information of the X, Y, and Z axes. The peak value of the Z-axis vibration amplitude, the median value of the Z-axis vibration amplitude, the Z-axis amplitude waveform, and the median value of the weighted vibration amplitude of the X and Y axes are matched with the corresponding parameter ranges in the type feature library. When the matching degree meets a preset threshold, the mechanical equipment type of the target mechanical equipment is determined. The determined mechanical equipment type is verified by a paddle code pre-placed at the specific node position.

[0096] In one possible implementation, after determining the mechanical equipment type of the target mechanical equipment, the vibration waveforms in the power feature library are hierarchically divided according to vibration characteristics based on a preset power state range; the vibration waveforms after the vibration feature hierarchical division are changed into square waves; the second vibration standard form data is matched and identified based on the square waves to determine the power state of the mechanical equipment type of the target mechanical equipment.

[0097] In one possible implementation, fuel consumption data under different power states is determined based on the mechanical equipment type and power status of the target mechanical equipment; fuel consumption-time curves within a preset time period are determined based on the fuel consumption data under different power states; and carbon emission-time curves corresponding to the target mechanical equipment are determined based on the fuel consumption-time curves and the carbon emission factors of the fuel type.

[0098] The methods disclosed in the above embodiments of the present invention can be applied to processor 601, or implemented by processor 601. Processor 601 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 601 or by instructions in the form of software. The processor 601 may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software units in the decoding processor. The software units may be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 602. Processor 601 reads the information in memory 602 and, in conjunction with its hardware, completes the steps of the above method.

[0099] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or combinations thereof.

[0100] For software implementation, the techniques described herein can be implemented by units that perform the functions described herein. The software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or external to the processor.

[0101] The computer device provided in this embodiment may be as follows: Figure 6 The computer device shown can perform, for example Figure 1-2 All steps of the vibration sensing-based fuel consumption and carbon emission measurement method are implemented to achieve... Figure 1-2 The technical effects of the vibration-sensing-based fuel consumption and carbon emission measurement method shown are detailed in the attached diagram. Figure 1-2 The relevant descriptions are presented concisely and will not be elaborated upon here.

[0102] This invention also provides a storage medium (computer-readable storage medium). This storage medium stores one or more programs. The storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; the memory may also include combinations of the above types of memory.

[0103] When one or more programs in the storage medium can be executed by one or more processors to implement the above-mentioned vibration sensing-based fuel consumption and carbon emission measurement method executed on the computer device side.

[0104] The processor is used to execute a vibration-sensing-based fuel consumption and carbon emission measurement program stored in the memory to implement the following steps of the vibration-sensing-based fuel consumption and carbon emission measurement method executed on the computer device side:

[0105] The system collects the X, Y, and Z axis operating information of the target mechanical equipment at specific node positions; processes the operating information to obtain standard format data; matches the standard format data with a type feature library and a power feature library to identify the mechanical equipment type and power status of the target mechanical equipment; determines the carbon emission-time curve corresponding to the target mechanical equipment based on the identified mechanical equipment type and power status; and calculates the target carbon emission amount by integrating the working time of the target mechanical equipment based on the carbon emission-time curve.

[0106] In one possible implementation, the vibration acceleration of the target mechanical equipment along the X, Y, and Z axes, the battery level of the node, and the internal temperature of the node are collected by a piezoelectric triaxial accelerometer at a specific node location based on a preset time frequency.

[0107] In one possible implementation, the vibration acceleration of the X, Y, and Z axes, the node battery power information, and the node internal temperature information are processed to obtain first vibration standard form data and second vibration standard form data. The data processing includes at least noise reduction, format conversion, and data padding.

[0108] In one possible implementation, the mechanical equipment type of the target mechanical equipment is determined by matching and identifying the first vibration standard form data with a type feature library; after determining the mechanical equipment type of the target mechanical equipment, the power state of the mechanical equipment type of the target mechanical equipment is determined by matching and identifying the second vibration standard form data with a power feature library.

[0109] In one possible implementation, the peak value of the Z-axis vibration amplitude, the median value of the Z-axis vibration amplitude, the Z-axis amplitude waveform, and the median value of the weighted vibration amplitude of the X and Y axes are extracted from the operating information of the X, Y, and Z axes. The peak value of the Z-axis vibration amplitude, the median value of the Z-axis vibration amplitude, the Z-axis amplitude waveform, and the median value of the weighted vibration amplitude of the X and Y axes are matched with the corresponding parameter ranges in the type feature library. When the matching degree meets a preset threshold, the mechanical equipment type of the target mechanical equipment is determined. The determined mechanical equipment type is verified by a paddle code pre-placed at the specific node position.

[0110] In one possible implementation, after determining the mechanical equipment type of the target mechanical equipment, the vibration waveforms in the power feature library are hierarchically divided according to vibration characteristics based on a preset power state range; the vibration waveforms after the vibration feature hierarchical division are changed into square waves; the second vibration standard form data is matched and identified based on the square waves to determine the power state of the mechanical equipment type of the target mechanical equipment.

[0111] In one possible implementation, fuel consumption data under different power states is determined based on the mechanical equipment type and power status of the target mechanical equipment; fuel consumption-time curves within a preset time period are determined based on the fuel consumption data under different power states; and carbon emission-time curves corresponding to the target mechanical equipment are determined based on the fuel consumption-time curves and the carbon emission factors of the fuel type.

[0112] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0113] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0114] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for measuring fuel consumption and carbon emissions based on vibration sensing, characterized in that, include: Collect the X, Y, and Z axis operation information of the target mechanical equipment at a specific node position. The operation information includes at least vibration acceleration, node battery power information, and node internal temperature information. The vibration acceleration along the X, Y, and Z axes, the battery charge information at each node, and the internal temperature information at each node are processed to obtain first and second standard vibration data. The data processing includes at least noise reduction, format conversion, and data padding. The standard data includes both the first and second standard vibration data. The first standard vibration data is used to match and identify the target mechanical equipment type against a type feature library. The second standard vibration data is used to match and identify the target mechanical equipment type against a power feature library to determine its power state. Based on the standard form data, the mechanical equipment type and power status of the target mechanical equipment are identified by matching with the type feature library and the power feature library. Based on the first vibration standard form data and the type feature library, the mechanical equipment type of the target mechanical equipment is determined by matching and identification, including: Extract the peak value of the vibration amplitude of the Z-axis, the median value of the vibration amplitude of the Z-axis, the waveform of the Z-axis amplitude, and the median value of the weighted vibration amplitude of the XY-axis from the operating information of the X, Y, and Z axes; The peak value of the Z-axis vibration amplitude, the median value of the Z-axis vibration amplitude, the Z-axis amplitude waveform, and the median value of the weighted vibration amplitude of the XY axes are matched with the corresponding parameter ranges in the type feature library; When the matching degree meets the preset threshold, the mechanical equipment type of the target mechanical equipment is determined; After determining the mechanical equipment type of the target mechanical equipment, the power state of the mechanical equipment type of the target mechanical equipment is determined by matching and identifying the second vibration standard form data with the power feature library. Based on the mechanical equipment type and power status of the target mechanical equipment, determine the fuel consumption data under different power statuses; The fuel consumption-time curve for a preset time period is determined based on fuel consumption data under different power conditions. The carbon emission-time curve corresponding to the target mechanical equipment is determined based on the fuel consumption-time curve and the carbon emission factor of the fuel type. The target carbon emissions are obtained by integrating the working time of the target mechanical equipment based on the carbon emission-time curve.

2. The method according to claim 1, characterized in that, The acquisition of the target mechanical equipment's X, Y, and Z axis operating information at specific node positions includes: The vibration acceleration of the target mechanical equipment at a specific node position is collected by a piezoelectric triaxial accelerometer based on a preset time frequency.

3. The method according to claim 1, characterized in that, The method further includes: The determined mechanical equipment type is verified by using a paddle code pre-placed at the specific node position.

4. The method according to claim 3, characterized in that, After determining the mechanical equipment type of the target mechanical equipment, the step of matching and identifying the power state of the mechanical equipment type of the target mechanical equipment based on the second vibration standard form data and the power feature library includes: After determining the mechanical equipment type of the target mechanical equipment, the vibration waveforms in the power feature library are hierarchically divided according to vibration characteristics based on a preset power state range. The vibration waveform after the vibration feature hierarchy is divided is changed into a square wave; Based on the square wave, the power state of the target mechanical equipment is determined by matching and identifying the second vibration standard form data.

5. A fuel consumption and carbon emission metering device based on vibration sensing, characterized in that, include: The data acquisition module is used to collect the X, Y, and Z axis operation information of the target mechanical equipment at a specific node position. The operation information includes at least vibration acceleration, node battery power information, and node internal temperature information. The data processing module is used to process the vibration acceleration, node battery power information, and node internal temperature information along the X, Y, and Z axes to obtain a first vibration standard form data and a second vibration standard form data. The data processing includes at least noise reduction, format conversion, and data padding. The standard form data includes the first vibration standard form data and the second vibration standard form data. The first vibration standard form data is used to match and identify the target mechanical equipment type against a type feature library; the second vibration standard form data is used to match and identify the target mechanical equipment type against a power feature library to determine the power state of the target mechanical equipment. The matching and identification module is used to match and identify the mechanical equipment type and power status of the target mechanical equipment based on the standard form data and the type feature library and the power feature library; to determine the mechanical equipment type of the target mechanical equipment based on the first vibration standard form data and the type feature library, including: extracting the peak value of the Z-axis vibration amplitude, the median value of the Z-axis vibration amplitude, the Z-axis amplitude waveform, and the median value of the XY-axis weighted vibration amplitude from the X, Y, and Z axis operating information; matching the peak value of the Z-axis vibration amplitude, the median value of the Z-axis vibration amplitude, the Z-axis amplitude waveform, and the median value of the XY-axis weighted vibration amplitude with the corresponding parameter ranges in the type feature library; when the matching degree meets a preset threshold, the mechanical equipment type of the target mechanical equipment is determined; after determining the mechanical equipment type of the target mechanical equipment, the power status of the target mechanical equipment type is determined based on the second vibration standard form data and the power feature library. The determination module is used to determine fuel consumption data under different power states based on the mechanical equipment type and power state of the target mechanical equipment; determine the fuel consumption-time curve within a preset time period based on the fuel consumption data under different power states; and determine the carbon emission-time curve corresponding to the target mechanical equipment based on the fuel consumption-time curve and the carbon emission factor of the fuel type. The metering module is used to calculate the target carbon emissions by integrating the working time of the target mechanical equipment based on the carbon emission-time curve.

6. A computer device, characterized in that, include: A processor and a memory, the processor being configured to execute a vibration-sensing-based fuel consumption and carbon emission measurement program stored in the memory, to implement the vibration-sensing-based fuel consumption and carbon emission measurement method according to any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the vibration sensing-based fuel consumption and carbon emission measurement method according to any one of claims 1 to 4.

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