Energy consumption anomaly detection method and device, equipment, storage medium and program product

Abstract

The invention relates to an energy consumption anomaly detection method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining the energy consumption data and operation information of to-be-detected equipment based on a sensor installed on the to-be-detected equipment; determining working condition energy consumption data of the to-be-detected equipment under each working condition based on the energy consumption data and the operation information; and comparing the working condition energy consumption data with the energy consumption threshold value under the corresponding working condition, and when the target working condition energy consumption data in the working condition energy consumption data is greater than the target energy consumption threshold value under the corresponding working condition, sending out abnormity reminding information. The energy consumption data and the operation information of the to-be-detected equipment are acquired through the sensor, so that the working condition energy consumption data of the to-be-detected equipment under various working conditions are obtained, the reminding is given out when the energy consumption is abnormal, the energy consumption data of the to-be-detected equipment can be accurately collected and processed, the energy consumption abnormal condition can be found in time, and the early warning is given out; workers can take energy-saving measures conveniently, energy waste is reduced, and operation cost is reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of equipment management, and particularly to a method, device, equipment, storage medium and program product for detecting abnormal energy consumption. Background Art

[0002] In mining operations, there are a large number of mining equipment such as excavators, loaders, wide-body mining trucks, auxiliary vehicles, etc., which are used extremely frequently in daily mining operations. The energy consumption of mining equipment is an important factor affecting the operating costs of mines. However, the current supervision of the energy consumption of mining equipment mainly relies on manual recording and statistics, which is not only inefficient and difficult to meet the requirements of large-scale and high-intensity mining operations, but also can only obtain relatively rough energy consumption data with large errors, and cannot truly and comprehensively reflect the actual energy consumption of mining equipment. As a result, enterprises are difficult to effectively analyze and manage the energy consumption of mining equipment. For mining equipment with abnormal energy consumption, it is often impossible to detect it in time and take corresponding measures, resulting in waste of energy and increased operating costs. Therefore, how to improve the efficiency and accuracy of detecting abnormal energy consumption of mining equipment is a technical problem to be solved. Summary of the Invention

[0003] To solve the above technical problems, the present disclosure provides a method, device, equipment, storage medium and program product for detecting abnormal energy consumption.

[0004] The first aspect of the embodiments of the present disclosure provides a method for detecting abnormal energy consumption, the method comprising:

[0005] Based on sensors installed on the device to be detected, obtaining energy consumption data and operation information of the device to be detected;

[0006] Based on the energy consumption data and the operation information, determining the energy consumption data of the device to be detected under each working condition;

[0007] Comparing the energy consumption data of the working conditions with the energy consumption thresholds corresponding to the respective working conditions, and when the target energy consumption data of the working conditions is greater than the target energy consumption threshold corresponding to the respective working conditions, sending out an abnormal reminder message.

[0008] The second aspect of the embodiments of the present disclosure provides a device for detecting abnormal energy consumption, the device comprising:

[0009] An obtaining module, configured to obtain energy consumption data and operation information of the device to be detected based on sensors installed on the device to be detected;

[0010] A first determining module, configured to determine the energy consumption data of the device to be detected under each working condition based on the energy consumption data and the operation information;

[0011] A reminder module for comparing the energy consumption data of the working conditions with the energy consumption thresholds under the corresponding working conditions respectively, and sending an abnormal reminder message when the target working condition energy consumption data in the energy consumption data of the working conditions is greater than the target energy consumption threshold under the corresponding working condition.

[0012] The third aspect of the embodiments of the present disclosure provides a computer device, including a memory, a processor, and a computer program. Among them, the computer program is stored in the memory, and when the computer program is executed by the processor, it implements the energy consumption anomaly detection method as described in the first aspect above.

[0013] The fourth aspect of the embodiments of the present disclosure provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by the processor, it implements the energy consumption anomaly detection method as described in the first aspect above.

[0014] The fifth aspect of the embodiments of the present disclosure provides a computer program product, including a computer program, and when the computer program is executed by the processor, it implements the energy consumption anomaly detection method as described in the first aspect above.

[0015] The technical solutions provided by the embodiments of the present disclosure have the following advantages compared with the prior art:

[0016] In the energy consumption anomaly detection method, device, equipment, storage medium and program product provided by the embodiments of the present disclosure, based on the sensors installed on the device to be detected, the energy consumption data and operation information of the device to be detected are obtained. Based on the energy consumption data and operation information, the energy consumption data of the device to be detected under each working condition is determined. The energy consumption data of the working conditions is compared with the energy consumption thresholds under the corresponding working conditions respectively, and when the target working condition energy consumption data in the energy consumption data of the working conditions is greater than the target energy consumption threshold under the corresponding working condition, an abnormal reminder message is sent. It can obtain the energy consumption data and operation information of the device to be detected through sensors, so as to obtain accurate energy consumption data of the device to be detected under each working condition. Based on this energy consumption data of the working conditions, energy consumption anomalies can be found in time and warnings can be issued, which is convenient for staff to take energy-saving measures, reduce energy waste, reduce the operating costs of enterprises, and at the same time contribute to promoting the development of mines towards intelligent and green directions, and improving the overall management level and competitiveness of mines. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0018] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 is a flowchart of a method for detecting abnormal energy consumption provided by an embodiment of the present disclosure;

[0020] Figure 2 is a flowchart of a method for obtaining energy consumption data and job information provided by an embodiment of the present disclosure;

[0021] Figure 3 is a flowchart of a method for correcting remaining fuel quantity data provided by an embodiment of the present disclosure;

[0022] Figure 4 is a flowchart of another method for correcting remaining fuel quantity data provided by an embodiment of the present disclosure;

[0023] Figure 5 is a schematic structural diagram of a device for detecting abnormal energy consumption provided by an embodiment of the present disclosure;

[0024] Figure 6 is a schematic structural diagram of a computer device provided by an embodiment of the present disclosure. Detailed Embodiments

[0025] In order to better understand the above objects, features, and advantages of the present disclosure, the following will further describe the solutions of the present disclosure. It should be noted that, without conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other.

[0026] In the following description, many specific details are set forth to fully understand the present disclosure, but the present disclosure can also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all the embodiments.

[0027] It should be understood that the various steps recorded in the method embodiments of the present disclosure can be executed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this regard.

[0028] Figure 1It is a flowchart of an energy consumption anomaly detection method provided by an embodiment of the present disclosure. This method can be executed by an energy consumption anomaly detection device, which can be implemented in software and / or hardware, and can be configured in an electronic device, such as a server or a terminal. Specifically, the terminal includes a mobile phone, a computer, a tablet computer, etc. As Figure 1 shown, the energy consumption anomaly detection method provided in this embodiment includes the following steps:

[0029] S101. Based on the sensors installed on the device to be detected, obtain the energy consumption data and operation information of the device to be detected.

[0030] The device to be detected in the embodiment of the present disclosure can be understood as a mining device that needs to monitor and analyze the energy consumption during the working process. For example, the device to be detected can be an excavator, a loader, a wide-body mining truck, an auxiliary vehicle, etc., which is not limited herein.

[0031] The sensors in the embodiment of the present disclosure can be understood as sensors pre-installed on the device to be detected according to the data types of the energy consumption data and operation information to be obtained. For example, the sensors can include an oil level sensor, a power sensor, a positioning sensor, a load sensor, an acceleration sensor, an engine speed sensor, etc., which is not limited herein.

[0032] The energy consumption data in the embodiment of the present disclosure can be understood as data used to characterize the energy consumption of the device to be detected at each moment. For example, the energy consumption data can include remaining oil quantity data, remaining power data, etc., which is not limited herein.

[0033] The operation information in the embodiment of the present disclosure can be understood as the operation parameters at each moment recorded during the working process of the device to be detected. For example, the operation information can include positioning information, elevation information, load, vertical acceleration, engine speed, etc., which is not limited herein.

[0034] In the embodiment of the present disclosure, the energy consumption anomaly detection device can obtain the energy consumption data and operation information of the device to be detected through the sensors pre-installed on the device to be detected.

[0035] In an exemplary implementation manner of the embodiment of the present disclosure, the energy consumption anomaly detection device can periodically send a data acquisition request to the sensor, obtain the recorded data returned by the sensor, and perform preliminary verification and sorting on the recorded data based on the reasonable value range of various types of data to obtain the energy consumption data and operation information of the device to be detected.

[0036] S102. Based on the energy consumption data and operation information, determine the energy consumption data of the device to be detected under each working condition.

[0037] The operating conditions in the embodiments of the present disclosure can be understood as the working conditions of the device to be detected under various states and conditions during the working process. By way of example, the operating conditions of the device to be detected may include idle operating conditions, heavy-load operation conditions, no-load operation conditions, heavy-load climbing conditions, no-load climbing conditions, etc., which are not limited herein.

[0038] The operating condition energy consumption data in the embodiments of the present disclosure can be understood as the energy consumption of the device to be detected per unit driving distance under specific operating conditions.

[0039] In the embodiments of the present disclosure, after obtaining the energy consumption data and operation information of the device to be detected, the energy consumption anomaly detection device can analyze the operation information to determine the operating conditions of the device to be detected at each moment, sort out the working periods corresponding to each operating condition, and classify and summarize the energy consumption data under each operating condition according to the time information in the energy consumption data. Based on the summarized energy consumption data, the operating condition energy consumption data of the device to be detected under each operating condition is calculated.

[0040] S103. Compare the operating condition energy consumption data with the energy consumption thresholds corresponding to the respective operating conditions, and when the target operating condition energy consumption data in the operating condition energy consumption data is greater than the target energy consumption threshold corresponding to the operating condition, send an anomaly reminder message.

[0041] The energy consumption threshold in the embodiments of the present disclosure can be understood as the upper limit value of the energy consumption data determined according to the device parameters and historical energy consumption data of the device to be detected, representing the maximum value within the normal range of the energy consumption data.

[0042] The target operating condition energy consumption data in the embodiments of the present disclosure can be any operating condition energy consumption data in the operating condition energy consumption data under each operating condition that is greater than the target energy consumption threshold corresponding to the operating condition. The target energy consumption threshold can be understood as the energy consumption threshold corresponding to the operating condition to which the target operating condition energy consumption data belongs.

[0043] The anomaly reminder message in the embodiments of the present disclosure can be understood as a message used to remind the staff of the existence of an energy consumption anomaly. Optionally, the anomaly reminder message may include the device identifier of the device to be detected, the target operating condition energy consumption data, the target operating condition corresponding to the target operating condition energy consumption data, etc., which are not limited herein.

[0044] In the embodiments of the present disclosure, after obtaining the operating condition energy consumption data of the device to be detected under each operating condition, the energy consumption anomaly detection device can obtain the energy consumption thresholds of the device to be detected under each operating condition, and for each operating condition, compare the operating condition energy consumption data under the operating condition with the energy consumption threshold corresponding to the operating condition. If there is at least one operating condition energy consumption data among the operating condition energy consumption data under each operating condition that is greater than the energy consumption threshold corresponding to the operating condition, then determine the operating condition energy consumption data as the target operating condition energy consumption data, determine the energy consumption threshold corresponding to the operating condition as the target energy consumption threshold, and send an anomaly reminder message to a pre-set device or account.

[0045] In an exemplary implementation of the embodiments of the present disclosure, when the energy consumption anomaly detection device determines that there is target working condition energy consumption data in the working condition energy consumption data under each working condition that is greater than the target energy consumption threshold under the corresponding working condition, it can calculate the ratio of the target working condition energy consumption data to the target energy consumption threshold, and determine the urgency level of the abnormal reminder information according to the ratio. For example, if the ratio is less than or equal to 1.1, an abnormal reminder information marked as general is sent; if the ratio is greater than 1.1 and less than or equal to 1.5, an abnormal reminder information marked as urgent is sent; if the ratio is greater than 1.5, an abnormal reminder information marked as extremely urgent is sent.

[0046] In the embodiments of the present disclosure, based on the sensors installed on the device to be detected, the energy consumption data and operation information of the device to be detected are obtained. Based on the energy consumption data and operation information, the working condition energy consumption data of the device to be detected under each working condition is determined. The working condition energy consumption data is compared with the energy consumption threshold under the corresponding working condition respectively. When the target working condition energy consumption data in the working condition energy consumption data is greater than the target energy consumption threshold under the corresponding working condition, an abnormal reminder information is sent. It can obtain the energy consumption data and operation information of the device to be detected through the sensors, so as to obtain the accurate working condition energy consumption data of the device to be detected under each working condition. Based on this working condition energy consumption data, the energy consumption anomaly situation can be discovered in time and a warning can be issued, which is convenient for the staff to take energy-saving measures, reduce energy waste, and reduce the operation cost of the enterprise. At the same time, it helps to promote the development of the mine towards the direction of intelligentization and greenization, and improve the overall management level and competitiveness of the mine.

[0047] Figure 2 is a flowchart of a method for obtaining energy consumption data and operation information provided by the embodiments of the present disclosure. As Figure 2 shown, based on the above embodiments, the energy consumption data and operation information can be obtained through the following method.

[0048] S201. Obtain the initial energy consumption data and initial operation information collected by the sensors installed on the device to be detected.

[0049] In the embodiments of the present disclosure, the energy consumption anomaly detection device can obtain the initial energy consumption data and initial operation information collected by the sensors through the communication connection with the sensors.

[0050] S202. Preprocess the initial energy consumption data and initial operation information to obtain the energy consumption data and operation information. The preprocessing includes at least one of data denoising preprocessing, missing value filling preprocessing, and format conversion preprocessing.

[0051] In the embodiments of the present disclosure, after obtaining the initial energy consumption data and the initial operation information, the energy consumption anomaly detection device may preprocess the initial energy consumption data and the initial operation information. Specifically, at least one preprocessing operation of denoising preprocessing, filling missing values preprocessing, and format conversion preprocessing may be performed on the initial energy consumption data and the initial operation information, and the processed initial energy consumption data and initial operation information are determined as energy consumption data and operation information.

[0052] In an exemplary implementation manner of the embodiments of the present disclosure, the energy consumption anomaly detection device may perform denoising preprocessing on the initial energy consumption data and the initial operation information through a median filtering algorithm, use the method of linear interpolation to fill in the missing values in the denoised initial energy consumption data and initial operation information, and perform format conversion on the filled initial energy consumption data and initial operation information to obtain the energy consumption data and operation information.

[0053] The embodiments of the present disclosure obtain the initial energy consumption data and the initial operation information collected by the sensors installed on the device to be detected, preprocess the initial energy consumption data and the initial operation information to obtain the energy consumption data and operation information. The preprocessing includes at least one of data denoising preprocessing, filling missing values preprocessing, and format conversion preprocessing, which can improve the accuracy and reliability of the energy consumption data and operation information and facilitate subsequent energy consumption calculation and analysis.

[0054] Figure 3 is a flowchart of a method for correcting the remaining fuel quantity data provided by the embodiments of the present disclosure. As Figure 3 shown, on the basis of the above embodiments, the remaining fuel quantity data can be corrected by the following method. Among them, the energy consumption data includes the remaining fuel quantity data, and the operation information includes the elevation information.

[0055] S301. Based on the elevation information, determine the climbing period and climbing angle of the device to be detected.

[0056] The elevation information in the embodiments of the present disclosure can be directly obtained through sensors or obtained from positioning information.

[0057] In the embodiments of the present disclosure, after obtaining the elevation information of the device to be detected, the energy consumption anomaly detection device may determine the climbing period and height difference of the device to be detected according to the change of the elevation information, and combine the driving distance of the device to be detected during the climbing period to determine the climbing angle of the device to be detected.

[0058] S302. Based on the climbing angle, correct the first remaining fuel quantity data corresponding to the climbing period in the remaining fuel quantity data.

[0059] In an embodiment of the present disclosure, after determining the climbing period and climbing angle, the energy consumption anomaly detection device may extract first remaining fuel quantity data corresponding to the climbing period from the remaining fuel quantity data, and then correct the first remaining fuel quantity data according to the climbing angle.

[0060] In an exemplary implementation manner of an embodiment of the present disclosure, the energy consumption anomaly detection device may input the climbing angle and the first remaining fuel quantity data into a pre-trained fuel quantity correction model to obtain the corrected first remaining fuel quantity data output by the fuel quantity correction model.

[0061] In another exemplary implementation manner of an embodiment of the present disclosure, the energy consumption anomaly detection device may determine the weight corresponding to the first remaining fuel quantity data during the climbing process according to the climbing angle, and correct the first remaining fuel quantity data in combination with the remaining fuel quantity data before starting to climb and after ending the climb.

[0062] In an embodiment of the present disclosure, by determining the climbing period and climbing angle of the device to be detected based on the elevation information, and correcting the first remaining fuel quantity data corresponding to the climbing period in the remaining fuel quantity data based on the climbing angle, the error of the remaining fuel quantity data recorded during the climbing process can be corrected, thereby improving the accuracy of subsequent energy consumption anomaly detection.

[0063] Figure 4 It is a flowchart of another method for correcting the remaining fuel quantity data provided by an embodiment of the present disclosure. As Figure 4 shown, based on the above embodiment, the remaining fuel quantity data can be corrected by the following method, where the operation information includes the vertical acceleration.

[0064] S401. Determine the bumpy period of the device to be detected based on the vertical acceleration.

[0065] In an embodiment of the present disclosure, after obtaining the vertical acceleration of the device to be detected, the energy consumption anomaly detection device may compare the vertical acceleration with a preset acceleration threshold, and determine the period when the vertical acceleration is greater than the acceleration threshold as the bumpy period.

[0066] S402. Correct the second remaining fuel quantity data corresponding to the bumpy period in the remaining fuel quantity data by using low-pass filtering and mean filtering.

[0067] In an embodiment of the present disclosure, after determining the bumpy period of the device to be detected, the energy consumption anomaly detection device may extract the second remaining fuel quantity data corresponding to the bumpy period from the remaining fuel quantity data, and then correct the second remaining fuel quantity data by at least one of low-pass filtering and mean filtering.

[0068] In an exemplary implementation of the embodiments of the present disclosure, the energy consumption anomaly detection device may adopt a low-pass filtering method. Based on a preset cut-off frequency, noise components higher than the cut-off frequency (i.e., high-frequency fluctuations of the second remaining fuel quantity data caused by jolts) are filtered out from the second remaining fuel quantity data, and the low-frequency effective fuel quantity signal is retained. Then, the mean filtering method is applied to average the second remaining fuel quantity data after low-pass filtering within a unit time (such as every 10 consecutive data points), removing the interfering signals of random mutations and further smoothing the second remaining fuel quantity data.

[0069] In the embodiments of the present disclosure, by determining the jolting period of the device to be detected based on the vertical acceleration and using low-pass filtering and mean filtering to correct the second remaining fuel quantity data corresponding to the jolting period in the remaining fuel quantity data, the error of the remaining fuel quantity data recorded during the jolting process can be corrected, thereby improving the accuracy of subsequent energy consumption anomaly detection.

[0070] In some embodiments, after executing S102, for each working condition, the energy consumption anomaly detection device may determine the pre-acquired reference energy consumption data of the device to be detected under the working condition as the prior value, and determine the working condition energy consumption data under the working condition as the observed value, and use the Kalman filter to update the working condition energy consumption data.

[0071] Specifically, after determining the working condition energy consumption data of the device to be detected under each working condition, the energy consumption anomaly detection device may, according to the pre-acquired reference energy consumption data of the device to be detected under each working condition, or according to the reference energy consumption data of each working condition calculated based on the pre-acquired device parameters of the device to be detected, for each working condition, determine the reference energy consumption data under the working condition as the prior value, determine the working condition energy consumption data obtained based on the sensor as the observed value, and use the Kalman filter to update the working condition energy consumption data. Among them, the energy consumption anomaly detection device may use the prior value to predict the estimated value of the current state during the prediction stage, and calculate the Kalman gain according to the observed value to correct the predicted value during the update stage, realizing the update of the working condition energy consumption data, further improving the accuracy of the working condition energy consumption data, and thereby improving the accuracy of subsequent energy consumption anomaly detection.

[0072] In some other embodiments, after executing S103, the energy consumption anomaly detection device may store the working condition energy consumption data and / or the anomaly reminder information in a database.

[0073] Specifically, after the energy consumption anomaly detection device detects that there is target working condition energy consumption data greater than the target energy consumption threshold under the corresponding working condition and issues an anomaly reminder message, it can classify and store at least one of the working condition energy consumption data and the anomaly reminder message into a pre-established database according to information such as the device type of the device to be detected and the data time. Among them, the database can adopt a relational database, and store the basic device information, working condition energy consumption data, anomaly reminder information, etc. of the device to be detected through multiple data tables respectively, so as to facilitate querying, statistics and analysis of the data through the database management system, provide strong data support for the maintenance, upgrade and optimization of operation parameters of the device, and improve the energy utilization efficiency of the device.

[0074] Figure 5 is a schematic structural diagram of an energy consumption anomaly detection device provided by an embodiment of the present disclosure. As Figure 5 shown, the energy consumption anomaly detection device 500 includes: an acquisition module 510, a first determination module 520, and a reminder module 530. Among them, the acquisition module 510 is used to acquire the energy consumption data and operation information of the device to be detected based on the sensors installed on the device to be detected; the first determination module 520 is used to determine the working condition energy consumption data of the device to be detected under each working condition based on the energy consumption data and the operation information; the reminder module 530 is used to compare the working condition energy consumption data with the energy consumption threshold under the corresponding working condition respectively, and issue an anomaly reminder message when the target working condition energy consumption data in the working condition energy consumption data is greater than the target energy consumption threshold under the corresponding working condition.

[0075] Optionally, the acquisition module 510 includes: an acquisition unit, configured to acquire the initial energy consumption data and initial operation information collected by the sensors installed on the device to be detected; a preprocessing unit, configured to preprocess the initial energy consumption data and the initial operation information to obtain the energy consumption data and the operation information, and the preprocessing includes at least one of data denoising preprocessing, filling missing value preprocessing, and format conversion preprocessing.

[0076] Optionally, the energy consumption data includes remaining fuel quantity data, and the operation information includes elevation information. The energy consumption anomaly detection device 500 further includes: a second determination module, configured to determine the climbing period and climbing angle of the device to be detected based on the elevation information; a first correction module, configured to correct the first remaining fuel quantity data corresponding to the climbing period in the remaining fuel quantity data based on the climbing angle.

[0077] Optionally, the operation information includes vertical acceleration, and the energy consumption anomaly detection device 500 further includes: a third determination module, configured to determine a bump period of the device to be detected based on the vertical acceleration; a second correction module, configured to correct the second remaining fuel quantity data corresponding to the bump period in the remaining fuel quantity data by using low-pass filtering and mean filtering.

[0078] Optionally, the energy consumption anomaly detection device 500 further includes: an update module, configured to, for each working condition, determine the pre-acquired reference energy consumption data of the device to be detected under the working condition as a prior value, determine the working condition energy consumption data under the working condition as an observed value, and update the working condition energy consumption data by using Kalman filtering.

[0079] Optionally, the energy consumption anomaly detection device 500 further includes: a storage module, configured to store the working condition energy consumption data and / or the anomaly reminder information into a database.

[0080] The energy consumption anomaly detection device provided in this embodiment can execute the method described in any of the above embodiments, and its execution manner and beneficial effects are similar, which will not be elaborated here.

[0081] Figure 6 It is a schematic structural diagram of a computer device provided by an embodiment of the present disclosure.

[0082] As Figure 6 shown, the computer device may include a processor 610 and a memory 620 storing computer program instructions.

[0083] Specifically, the above-mentioned processor 610 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0084] The memory 620 may include a mass memory for information or instructions. By way of example and not limitation, the memory 620 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 620 may include removable or non-removable (or fixed) media. Where appropriate, the memory 620 may be internal or external to the integrated gateway device. In a particular embodiment, the memory 620 is a non-volatile solid-state memory. In a particular embodiment, the memory 620 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0085] The processor 610 reads and executes the computer program instructions stored in the memory 620 to perform the steps of the energy consumption anomaly detection method provided by the embodiments of the present disclosure.

[0086] In one example, the computer device may further include a transceiver 630 and a bus 640. Among them, as Figure 6 shown, the processor 610, the memory 620, and the transceiver 630 are connected through the bus 640 and complete communication with each other.

[0087] The bus 640 includes hardware, software, or both. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side BUS (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable bus or a combination of two or more of these. Where appropriate, the bus 640 may include one or more buses. Although embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0088] Embodiments of the present disclosure also provide a computer-readable storage medium that may store a computer program, which when executed by a processor, causes the processor to implement the energy consumption anomaly detection method provided by the embodiments of the present disclosure.

[0089] The above storage medium may include, for example, a memory 620 for computer program instructions, and the above instructions may be executed by a processor 610 of the energy consumption anomaly detection device to complete the energy consumption anomaly detection method provided by the embodiments of the present disclosure. Optionally, the storage medium may be a non-transitory computer-readable storage medium. For example, the non-transitory computer-readable storage medium may be ROM, random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc. The above computer program may be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of the present disclosure. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0090] The embodiments of the present disclosure also provide a computer program product, including a computer program, which when executed by a processor, enables the processor to implement the energy consumption anomaly detection method provided by the embodiments of the present disclosure.

[0091] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0092] The above description is only a specific implementation manner of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments described herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An energy consumption anomaly detection method, characterized in that, The method includes: Based on the sensors installed on the device to be detected, obtaining the energy consumption data and operation information of the device to be detected; Based on the energy consumption data and the operation information, determining the energy consumption data of the device to be detected under each working condition; Comparing the energy consumption data of each working condition with the energy consumption threshold corresponding to the working condition respectively, and when the target energy consumption data in the energy consumption data of the working condition is greater than the target energy consumption threshold corresponding to the working condition, sending an abnormal reminder message.

2. The method according to claim 1, wherein The obtaining, based on the sensors installed on the device to be detected, the energy consumption data and operation information of the device to be detected includes: Obtaining the initial energy consumption data and initial operation information collected by the sensors installed on the device to be detected; Performing preprocessing on the initial energy consumption data and the initial operation information to obtain the energy consumption data and the operation information, where the preprocessing includes at least one of data denoising preprocessing, missing value filling preprocessing, and format conversion preprocessing.

3. The method according to claim 1, wherein The energy consumption data includes remaining fuel quantity data, and the operation information includes elevation information. Before determining the energy consumption data of the device to be detected under each working condition based on the energy consumption data and the operation information, the method further includes: Based on the elevation information, determining the climbing period and climbing angle of the device to be detected; Based on the climbing angle, correcting the first remaining fuel quantity data corresponding to the climbing period in the remaining fuel quantity data.

4. The method according to claim 3, wherein The operation information includes vertical acceleration. Before determining the energy consumption data of the device to be detected under each working condition based on the energy consumption data and the operation information, the method further includes: Based on the vertical acceleration, determining the bumpy period of the device to be detected; Using low-pass filtering and mean filtering to correct the second remaining fuel quantity data corresponding to the bumpy period in the remaining fuel quantity data.

5. The method according to claim 1, wherein After determining the energy consumption data of the device to be detected under each working condition based on the energy consumption data and the operation information, the method further includes: For each working condition, determining the pre-acquired reference energy consumption data of the device to be detected under the working condition as the prior value, determining the energy consumption data of the working condition as the observed value, and using Kalman filtering to update the energy consumption data of the working condition.

6. The method according to claim 1, characterized in that, After comparing the energy consumption data of each working condition with the energy consumption threshold corresponding to the working condition respectively, and when the target energy consumption data in the energy consumption data of the working condition is greater than the target energy consumption threshold corresponding to the working condition, sending an abnormal reminder message, the method further includes: Storing the energy consumption data and / or the abnormal reminder message into a database.

7. An abnormal energy consumption detection device, characterized in that, The device includes: An obtaining module, configured to obtain the energy consumption data and operation information of the device to be detected based on the sensors installed on the device to be detected; A first determining module, configured to determine the energy consumption data of the device to be detected under each working condition based on the energy consumption data and the operation information; A reminder module, configured to compare the energy consumption data of each working condition with the energy consumption threshold corresponding to the working condition respectively, and when the target energy consumption data in the energy consumption data of the working condition is greater than the target energy consumption threshold corresponding to the working condition, sending an abnormal reminder message.

8. A computer device, characterized in that, Comprising: A memory; A processor; And a computer program; wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the energy consumption anomaly detection method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, A computer program is stored in the storage medium, and when the computer program is executed by a processor, the energy consumption anomaly detection method according to any one of claims 1-6 is implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the energy consumption anomaly detection method according to any one of claims 1-6 is implemented.

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