Device running state diagnosis method and device, electronic device, and storage medium

Abstract

The application discloses a device operation state diagnosis method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining historical energy consumption data of a device to be diagnosed; the historical energy consumption data at least comprises historical date energy consumption data and feature data of the historical date; the feature data is weather and public holiday category; determining a candidate historical date from the historical date according to the feature data of the historical date and feature data of a diagnosis date; and diagnosing the operation state of the device according to the historical energy consumption data and the number of the candidate historical dates. Through analysis of the feature data of the historical date and the feature data of the diagnosis date, the application accurately determines the candidate historical date, so that the operation state of the device can be more accurately diagnosed according to the historical energy consumption data and the number of the candidate historical dates, and the problem that the device operation state diagnosis is inaccurate due to insufficient historical energy consumption data is solved.

Description

Technical Field

[0001] This invention relates to the field of equipment operation diagnosis in the building electromechanical field, and particularly to a method, apparatus, electronic device, and storage medium for diagnosing equipment operation status. Background Technology

[0002] For building electromechanical systems and equipment, energy consumption data can be used to determine whether they are operating according to the operating plan or whether there are any abnormalities in their operating patterns, thereby identifying problems and troubleshooting them in a timely manner.

[0003] As a reference for determining whether the actual energy consumption of equipment triggers an alarm or diagnoses an anomaly, clustering and classification algorithms in machine learning are generally used to calculate the reference value based on the equipment's historical energy consumption data from the past year. However, machine learning algorithms have high requirements for the amount of historical data. If the amount of historical energy consumption data is insufficient, a model with strong generalization ability cannot be obtained, which will affect the accuracy of the output reference value and thus affect the diagnosis of the equipment's operating status. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for diagnosing equipment operating status, in order to solve the problem of inaccurate diagnosis of equipment operating status due to insufficient historical energy consumption data.

[0005] According to one aspect of the present invention, a method for diagnosing the operating status of a device is provided, the method comprising:

[0006] Acquire historical energy consumption data of the device to be diagnosed; the historical energy consumption data includes at least historical date energy consumption data and characteristic data of that historical date; the characteristic data is weather and public holiday categories.

[0007] Candidate historical dates are determined from the historical dates based on the characteristic data of the historical dates and the characteristic data of the diagnostic dates;

[0008] The operating status of the device is diagnosed based on the historical energy consumption data and the number of candidate historical dates.

[0009] According to another aspect of the present invention, a device for diagnosing equipment operating status is provided, the device comprising:

[0010] The data acquisition module is used to acquire historical energy consumption data of the device to be diagnosed; the historical energy consumption data includes at least historical date energy consumption data and characteristic data of that historical date; the characteristic data is weather and public holiday categories;

[0011] A candidate historical date determination module is used to determine candidate historical dates from the historical dates based on the feature data of the historical dates and the feature data of the diagnostic dates;

[0012] The diagnostic module is used to diagnose the operating status of the device based on the historical energy consumption data and the number of candidate historical dates.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the device operation status diagnosis method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the device operation status diagnosis method according to any embodiment of the present invention.

[0018] The technical solution of this invention involves acquiring historical energy consumption data of the device to be diagnosed. This historical energy consumption data includes at least historical date energy consumption data and characteristic data for that historical date. The characteristic data includes weather and public holiday categories. Candidate historical dates are then determined from the historical dates based on the characteristic data of the historical dates and the characteristic data of the diagnosis date. Finally, the operating status of the device is diagnosed based on the historical energy consumption data and the number of candidate historical dates. This application accurately determines candidate historical dates through analysis of the characteristic data of historical dates and the characteristic data of the diagnosis date. This allows for a more accurate diagnosis of the device's operating status based on the historical energy consumption data and the number of candidate historical dates, solving the problem of inaccurate device operating status diagnosis due to insufficient historical energy consumption data.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a device operation status diagnosis method provided in Embodiment 1 of the present invention;

[0022] Figure 2 This is a flowchart of a device operation status diagnosis method provided in Embodiment 2 of the present invention;

[0023] Figure 3 This is a schematic diagram of the short-term algorithm reference value applicable to the embodiments of the present invention;

[0024] Figure 4 This is a schematic diagram of the reference values ​​for the ultra-short-term algorithm applicable to the embodiments of the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of a device for diagnosing equipment operating status according to Embodiment 3 of the present invention;

[0026] Figure 6 This is a schematic diagram of the structure of an electronic device that implements the device operation status diagnosis method of this invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," "third," "fourth," and "candidate," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1This is a flowchart of a device operation status diagnosis method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where device operation status is diagnosed. The method can be executed by a device operation status diagnosis device, which can be implemented in hardware and / or software. This device can be configured in an electronic device that has a device operation status diagnosis method. Figure 1 As shown, the method includes:

[0031] S110. Obtain historical energy consumption data of the device to be diagnosed; the historical energy consumption data includes at least historical date energy consumption data and characteristic data of the historical date; the characteristic data is weather and public holiday categories.

[0032] Historical date energy consumption data refers to the energy consumption of diagnostic equipment on historical dates. For example, if February 2nd is a historical date and the energy consumption on that day is 60, then 60 represents the energy consumption of the diagnostic equipment on that historical date. Weather conditions can include temperature and humidity. Building electromechanical systems are affected by weather or public holidays, resulting in different historical date energy consumption data. For instance, in office buildings, air conditioning is often turned down or shut off on weekends or national holidays to save energy, as people are generally not working. Therefore, the energy consumption at these times differs from that on normal working days. Furthermore, different weather conditions lead to different air conditioning temperature settings, thus affecting energy consumption. Therefore, it is necessary to record characteristic data for historical dates.

[0033] S120. Based on the feature data of the historical dates and the feature data of the diagnostic dates, determine candidate historical dates from the historical dates.

[0034] Specifically, to ensure accurate assessment of the diagnostic equipment's operational status on the diagnosis date, the characteristic data corresponding to historical dates in the historical energy consumption data are compared. Historical dates with similar characteristic data to the diagnosis date are identified as candidate historical dates. Optionally, candidate historical dates that match the diagnosis date under preset conditions are selected from the historical energy consumption data. These preset conditions are dates with a temperature difference within a preset range and the same public holiday category as the diagnosis date. This ensures the accuracy of the candidate historical dates, allowing subsequent diagnosis of the equipment's operational status based on the corresponding historical date's energy consumption data.

[0035] S130. Diagnose the operating status of the device based on the historical energy consumption data and the number of candidate historical dates.

[0036] This solution introduces statistical methods to construct a white-box algorithm (proposing short-term and ultra-short-term algorithms based on the white-box algorithm) to compensate for the low accuracy of equipment operation status diagnosis when data volume is insufficient. It also proposes rules for automatic algorithm switching to improve the accuracy of equipment diagnosis. Specifically, by determining the number of historical dates and candidate historical dates in the historical energy consumption data, the equipment operation diagnosis methods are divided into long-term, short-term, and ultra-short-term algorithms based on these numbers. This allows for algorithm switching based on the number of historical dates and candidate historical dates, and the calculation of energy consumption data for historical dates to accurately determine whether the equipment's operating status is normal.

[0037] Among them, the long-term algorithm is suitable for situations where there are a large number of historical dates in the historical energy consumption data, such as six months, one year, or longer. The short-term algorithm and the ultra-short-term algorithm are suitable for situations where there are fewer historical dates. If the long-term algorithm is used, it will lead to inaccurate diagnostic results. Therefore, when the amount of data is small, the short-term algorithm and the ultra-short-term algorithm are distinguished by the number of candidate historical dates. This allows for the calculation of the historical date energy consumption data in the historical energy consumption data, accurately determining whether the equipment is operating normally, in order to make up for the problem of low accuracy in diagnosing the equipment operating status when the amount of data is insufficient.

[0038] The technical solution of this invention involves acquiring historical energy consumption data of the device to be diagnosed. This historical energy consumption data includes at least historical date energy consumption data and characteristic data for that historical date. The characteristic data includes weather and public holiday categories. Candidate historical dates are then determined from the historical dates based on the characteristic data of the historical dates and the characteristic data of the diagnosis date. Finally, the operating status of the device is diagnosed based on the historical energy consumption data and the number of candidate historical dates. This application accurately determines candidate historical dates through analysis of the characteristic data of historical dates and the characteristic data of the diagnosis date. This allows for a more accurate diagnosis of the device's operating status based on the historical energy consumption data and the number of candidate historical dates, solving the problem of inaccurate device operating status diagnosis due to insufficient historical energy consumption data.

[0039] Example 2

[0040] Figure 2 This is a flowchart of a device operation status diagnosis method provided in Embodiment 2 of the present invention. This embodiment provides a detailed description of S130 in the above embodiment. Figure 2 As shown, the method includes:

[0041] S210. Determine the energy consumption data difference parameter in the historical date based on the historical energy consumption data, and determine the operating mode of the device based on the energy consumption data difference parameter.

[0042] Among them, the energy consumption data difference parameter is used to determine whether the equipment belongs to rigid mode or flexible mode. The operating mode includes rigid mode and flexible mode.

[0043] Specifically, historical energy consumption data is acquired, and the energy consumption data for historical dates within this data is analyzed and calculated to accurately determine the energy consumption data difference parameters. These parameters are then analyzed, such as by comparing them with empirical values, to accurately determine the operating mode of the equipment.

[0044] Optionally, an energy consumption data difference parameter is determined based on the historical energy consumption data, and the operating mode of the device is determined based on the energy consumption data difference parameter. One feasible approach is as follows:

[0045] Step A1: Add up a preset number of energy consumption data points from the historical date energy consumption data to obtain the total energy consumption value for that historical date; wherein, the historical date energy consumption data includes a preset number of energy consumption data points divided according to a preset time interval; for example, if the energy consumption data is recorded once every 15 minutes, then 96 energy consumption data points will be recorded in a day, and the sum of these 96 energy consumption data points is the total energy consumption value corresponding to that historical date.

[0046] Step A2: Based on the total energy consumption value, determine the standard deviation and average value of the historical date energy consumption data, and use the ratio of the standard deviation and the average value as the energy consumption data difference parameter; that is, the total energy consumption value corresponding to each historical date in the historical energy consumption data is calculated in the manner of step A1, and these total energy consumption data values ​​are calculated to accurately obtain the standard deviation and average value of the historical date energy consumption data, and then the ratio of the standard deviation and the average value is used as the energy consumption data difference parameter, thereby realizing the accurate determination of the energy consumption data difference parameter.

[0047] Step A3: If the energy consumption data difference parameter is greater than a preset difference value, the equipment operates in flexible mode; otherwise, it operates in rigid mode. The preset difference value is obtained based on the experience of the staff and is not specifically limited here. For example, if the preset difference value is 0.36, the energy consumption data difference parameter obtained in step 2 is compared with 0.36. If the energy consumption data difference parameter is greater than 0.36, the equipment operates in flexible mode; otherwise, it operates in rigid mode.

[0048] This solution calculates the total energy consumption value by adding a preset number of energy consumption data points from historical dates, thereby accurately determining the standard deviation and average value of the historical date energy consumption data. Then, by comparing the preset difference value with the ratio of the standard deviation and average value as the energy consumption data difference parameter, the operating mode of the equipment can be accurately obtained, so as to facilitate accurate diagnosis of the equipment's operating status in the future.

[0049] S220. If the device's operating mode is flexible mode and the number of historical dates meets the first preset condition, then the device's operating status is determined based on the pre-trained diagnostic model and the diagnostic energy consumption data of the diagnostic dates; the diagnostic model is trained based on the historical energy consumption data; the first preset condition is that the historical energy consumption time is greater than or equal to the first preset time and the historical energy consumption days are greater than or equal to the first preset number of days.

[0050] Among them, historical energy consumption time is the total historical operating time of the equipment, such as 6 months, 9 months or longer; historical energy consumption days refers to the number of days the equipment generates energy consumption within the historical energy consumption time. For example, if the equipment has been running for 6 months and generated energy consumption for 50 days, then those 50 days are the historical energy consumption days.

[0051] For example, if the first preset condition is that the historical energy consumption time span is more than 9 months (including 9 months) and the historical energy consumption days are more than 45 days (including 45 days), after comparing the energy consumption data difference parameter with the preset difference value and determining that the device's operating mode is elastic mode, and the number of historical dates meets the first preset condition, then the device's operating status is determined according to the pre-trained diagnostic model and the diagnostic energy consumption data of the diagnostic dates. This method can be called a long-term algorithm; if it does not meet the condition, then the device's operating status is determined according to a short-term algorithm or an ultra-short-term algorithm.

[0052] S230. If the device's operating mode is rigid mode and the number of historical dates meets the second preset condition, then the device's operating status is determined based on the pre-trained diagnostic model and the diagnostic energy consumption data of the diagnostic dates; the second preset condition is that the historical energy consumption time is greater than or equal to the second preset time and the historical energy consumption days are greater than or equal to the second preset number of days.

[0053] The second preset time is less than the first preset time, and the second preset number of days is less than the first preset number of days. This is because the energy consumption of the equipment in the elastic mode is more scattered than that in the rigid mode. Therefore, more data is needed to represent the operating status of the equipment as much as possible, so as to accurately diagnose the operating status of the equipment.

[0054] For example, if the second preset condition is set as a historical energy consumption period of more than 6 months (including 6 months) and a historical energy consumption day of more than 30 days (including 30 days), after comparing the energy consumption data difference parameter with the preset difference value and determining that the device's operating mode is rigid mode, and the number of historical dates meets the second preset condition, then the device's operating status is determined according to the pre-trained diagnostic model and the diagnostic energy consumption data of the diagnostic dates. This method can be called a long-term algorithm; if it does not meet the condition, then the device's operating status is determined according to a short-term algorithm or an ultra-short-term algorithm.

[0055] S240. If the number of historical dates does not meet the first or second preset condition, but the number of candidate historical dates meets the third preset condition, then a first number of first target historical dates are determined from the candidate historical dates, and the operating status of the device is diagnosed based on the first target data; the first target data is the energy consumption data of the historical date corresponding to the first target historical date; the third preset condition is that the number of historical energy consumption days is greater than or equal to the third preset number of days.

[0056] Specifically, after determining the operating mode of the equipment, the number of historical dates is analyzed. If the number of historical dates does not meet the first or second preset condition, but meets the third preset condition, it means that the short-term algorithm is satisfied. That is, a first number of first target historical dates are determined from the candidate historical dates, and the operating status of the equipment is diagnosed based on the first target data.

[0057] In one feasible embodiment, optionally, the operating status of the device is diagnosed based on the first target data, including steps B1-B4:

[0058] Step B1: Determine the confidence interval of the preset percentage of the first number of first target data corresponding to the target dimension using Chebyshev's inequality.

[0059] Step B2: Determine the target upper limit value corresponding to the target dimension based on the upper limit value of the confidence interval corresponding to the target dimension.

[0060] Step B3: Determine the target lower limit value corresponding to the target dimension based on the lower limit value of the confidence interval corresponding to the target dimension.

[0061] Step B4: Diagnose the operating status of the equipment based on the diagnostic energy consumption data of the diagnostic date and the comparison results of the target upper limit and target lower limit.

[0062] The target dimension is a preset number of divisions into which a day is divided according to a preset time interval. Each division is a dimension, and each dimension corresponds to an energy consumption data point. For example, if the preset time interval is 15 minutes, then a day will be divided into 96 dimensions.

[0063] Specifically, the confidence intervals for a preset percentage of the first number of first target data points corresponding to the target dimension are determined using Chebyshev's inequality; for example, a 95% confidence interval is used. Then, the maximum value between the first and second reference values ​​of the upper limit of the confidence interval corresponding to the target dimension is taken as the target upper limit value corresponding to the target dimension; the first reference value is the product of the upper limit of the confidence interval and the first empirical value, and the second reference value is the sum of the upper limit of the confidence interval and the second empirical value. The minimum value between the third and fourth reference values ​​of the lower limit of the confidence interval corresponding to the target dimension is taken as the target lower limit value corresponding to the target dimension; the third reference value is the product of the lower limit of the confidence interval and the third empirical value, and the fourth reference value is the sum of the lower limit of the confidence interval and the second empirical value. The first, second, and third empirical values ​​are determined based on the actual working conditions and the experience of the staff, and are not specifically limited here; for example, the first empirical value is 1.06, the second empirical value is 5, and the third empirical value is 0.94. If the target lower limit value is less than zero, then zero is taken as the target lower limit value. Finally, based on the comparison results of the diagnostic energy consumption data on the diagnosis date and the target upper and lower limits, the operating status of the equipment is diagnosed. If the diagnostic energy consumption data on the diagnosis date is between the target upper and lower limits, it indicates that the equipment is operating normally; otherwise, the equipment is operating abnormally and staff need to check it in a timely manner.

[0064] For example, Figure 3 This is a schematic diagram of the short-term algorithm reference values ​​applicable to embodiments of the present invention. The upper and lower limits of the reference values ​​in the diagram represent the target upper and lower limits of this application, respectively. The actual energy consumption is the energy consumption on the diagnosis date. The first quantity is 7, meaning 7 days are selected that are similar to the diagnosis date. The 95% confidence interval of the first target data within these 7 days is calculated. The upper and lower limits of the reference values ​​for each dimension are calculated separately, thus obtaining a curve used to determine the operating status of the device within one day on the diagnosis date. Figure 3 The actual energy consumption on the diagnosis date is compared with the upper and lower limits of the reference value to diagnose the operating status of the equipment.

[0065] This solution accurately determines the confidence interval of a preset percentage of the first number of first target data corresponding to the target dimension using Chebyshev's inequality; then, it determines the target upper limit value corresponding to the target dimension based on the upper limit value of the confidence interval corresponding to the target dimension; and determines the target lower limit value corresponding to the target dimension based on the lower limit value of the confidence interval corresponding to the target dimension; thereby achieving accurate diagnosis of the equipment's operating status based on the comparison results of the diagnostic energy consumption data on the diagnosis date and the target upper and lower limits.

[0066] S250. If the number of historical dates does not meet the first preset condition or the second preset condition, and the number of candidate historical dates does not meet the third preset condition, then a second number of second target historical dates are determined from the candidate historical dates, and the operating status of the device is diagnosed based on the second target data; the second target data is the historical date energy consumption data corresponding to the second target historical date; the first number is greater than the second number.

[0067] Specifically, after determining the operating mode of the equipment, the number of historical dates is analyzed. If the number of historical dates does not meet the first or second preset condition, and the number of historical dates does not meet the third preset condition, it means that the ultra-short-term algorithm is satisfied. That is, a second number of second target historical dates are determined from the candidate historical dates, and the operating status of the equipment is diagnosed based on the second target data.

[0068] Optionally, the second quantity is 1; the operating status of the device is diagnosed based on the second target data, including steps C1-C2:

[0069] Step C1: Determine the upper limit and lower limit of the target dimension based on the second target data corresponding to the target dimension.

[0070] Step C2: Diagnose the operating status of the equipment based on the diagnostic energy consumption data of the diagnostic date and the comparison results of the target upper limit and target lower limit.

[0071] Specifically, the second target data is obtained, and the maximum value between the product of the second target data corresponding to the target dimension and the first empirical value and the sum of the second target data and the second empirical value is taken as the target upper limit value of the target dimension; the target data is the historical energy consumption data corresponding to the historical date of the second target; the minimum value between the product of the second target data corresponding to the target dimension and the third empirical value and the sum of the second target data and the second empirical value is taken as the target lower limit value of the target dimension; if the target lower limit value is less than zero, then zero is taken as the target lower limit value; finally, based on the comparison results of the diagnostic energy consumption data on the diagnosis date and the target upper limit value and the target lower limit value, the operating status of the equipment is diagnosed. If the diagnostic energy consumption data on the diagnosis date is between the target upper limit value and the target lower limit value, it indicates that the operating status of the equipment is normal; otherwise, the operating status of the equipment is abnormal, and the staff needs to check it in time.

[0072] For example, Figure 4This is a schematic diagram of the ultra-short-term algorithm reference values ​​applicable to embodiments of the present invention. The upper and lower limits of the reference values ​​in the diagram represent the target upper and lower limits of this application, respectively. The actual energy consumption is the energy consumption on the diagnosis date. The first quantity is 1, meaning that a day similar to the diagnosis date is selected as 1 day. Then, the upper and lower limits of the reference values ​​for each dimension within a day are calculated, thereby obtaining a curve used to determine the operating status of the device within a day on the diagnosis date. Figure 4 The actual energy consumption on the diagnosis date is compared with the upper and lower limits of the reference value to diagnose the operating status of the equipment.

[0073] After acquiring the second target data, this solution accurately determines the target upper limit and target lower limit based on empirical values ​​according to the second target data corresponding to the target dimension. This enables accurate diagnosis of the equipment's operating status based on the comparison results of the diagnostic energy consumption data on the diagnosis date and the target upper limit and target lower limit.

[0074] The technical solution of this invention first acquires historical energy consumption data of the device to be diagnosed. This historical energy consumption data includes at least historical date energy consumption data and characteristic data for that historical date. The characteristic data includes weather and public holiday categories. Then, by calculating the historical energy consumption data, the energy consumption data difference parameters in the historical dates are accurately determined, thereby determining the device's operating mode based on these parameters. Further, the device's operating mode, the number of historical dates, and the number of candidate historical dates can be combined to determine whether to use a long-term algorithm, a short-term algorithm, or an ultra-short-term algorithm to diagnose the device's operating status. Specifically, when the number of historical dates meets a first or second preset condition, a long-term algorithm is used; when the number of historical dates does not meet the first or second preset condition, but the number of candidate historical dates meets a third preset condition, a short-term algorithm is used; when the number of historical dates does not meet the first or second preset condition, and the number of candidate historical dates does not meet the third preset condition, an ultra-short-term algorithm is used. This achieves algorithm switching based on data volume to diagnose the device's operating status based on data volume, solving the problem of inaccurate device operating status diagnosis due to insufficient historical energy consumption data, thereby improving the accuracy of device operating status diagnosis.

[0075] Example 3

[0076] Figure 5 This is a schematic diagram of a device for diagnosing the operating status of an equipment, provided in Embodiment 3 of the present invention. Figure 5 As shown, the device includes:

[0077] Data acquisition module 310 is used to acquire historical energy consumption data of the device to be diagnosed; the historical energy consumption data includes at least historical date energy consumption data and characteristic data of the historical date; the characteristic data is weather and public holiday categories;

[0078] The candidate historical date determination module 320 is used to determine candidate historical dates from the historical dates based on the feature data of the historical dates and the feature data of the diagnostic dates;

[0079] The diagnostic module 330 is used to diagnose the operating status of the device based on the historical energy consumption data and the number of candidate historical dates.

[0080] Optional diagnostic module, including:

[0081] The operation mode determination unit is specifically used to determine the energy consumption data difference parameters in the historical dates based on the historical energy consumption data, and to determine the operation mode of the equipment based on the energy consumption data difference parameters.

[0082] The first diagnostic unit is specifically used to determine the operating status of the device based on a pre-trained diagnostic model and the diagnostic energy consumption data of the diagnostic dates if the device's operating mode is elastic mode and the number of historical dates meets a first preset condition; the diagnostic model is trained based on the historical energy consumption data; the first preset condition is that the historical energy consumption time is greater than or equal to a first preset time and the historical energy consumption days are greater than or equal to a first preset number of days.

[0083] The second diagnostic unit is specifically used to determine the operating status of the device based on a pre-trained diagnostic model and the diagnostic energy consumption data of the diagnostic dates if the device's operating mode is rigid and the number of historical dates meets the second preset condition; the second preset condition is that the historical energy consumption time is greater than or equal to the second preset time and the historical energy consumption days are greater than or equal to the second preset number of days.

[0084] Optionally, the diagnostic module also includes:

[0085] The third diagnostic unit is specifically used to determine a first number of first target historical dates from the candidate historical dates if the number of historical dates does not meet the first or second preset condition, but the number of candidate historical dates meets the third preset condition, and to diagnose the operating status of the device based on the first target data; the first target data is the energy consumption data of the historical date corresponding to the first target historical date; the third preset condition is that the number of historical energy consumption days is greater than or equal to the third preset number of days.

[0086] The fourth diagnostic unit is specifically used to determine a second number of second target historical dates from the candidate historical dates if the number of historical dates does not meet the first preset condition or the second preset condition, and the number of candidate historical dates does not meet the third preset condition, and to diagnose the operating status of the device based on the second target data; the second target data is the historical date energy consumption data corresponding to the second target historical date; the first number is greater than the second number.

[0087] Optionally, the third diagnostic unit includes a first target upper and lower limit determination unit, specifically used for:

[0088] The confidence interval of a preset percentage of the first number of first target data corresponding to the target dimension is determined by Chebyshev's inequality; the target dimension is a preset number of division values ​​into which a day is divided according to a preset time interval, each division value is a dimension, and each dimension corresponds to an energy consumption data.

[0089] The target upper limit value corresponding to the target dimension is determined based on the upper limit value of the confidence interval corresponding to the target dimension.

[0090] Determine the target lower limit value corresponding to the target dimension based on the lower limit value of the confidence interval corresponding to the target dimension;

[0091] The operating status of the equipment is diagnosed based on the energy consumption data of the diagnosis date and the comparison results of the target upper limit and target lower limit.

[0092] Optionally, the second quantity is 1; optionally, the fourth diagnostic unit includes a second target upper and lower limit determination unit, specifically used for:

[0093] Determine the upper and lower limits of the target dimension based on the second target data corresponding to the target dimension.

[0094] The operating status of the equipment is diagnosed based on the energy consumption data of the diagnosis date and the comparison results of the target upper limit and target lower limit.

[0095] Optionally, the historical date energy consumption data includes a preset number of energy consumption data points divided according to a preset time interval;

[0096] Correspondingly, the operating mode determination unit is specifically used for:

[0097] The total energy consumption value for a given historical date is obtained by adding up a predetermined number of energy consumption data points from the historical date energy consumption data.

[0098] Based on the total energy consumption value, the standard deviation and mean of the energy consumption data for the historical dates are determined, and the ratio of the standard deviation to the mean is used as the energy consumption data difference parameter.

[0099] If the energy consumption data difference parameter is greater than the preset difference value, the operating mode of the device is flexible mode; otherwise, it is rigid mode.

[0100] Optional, a candidate historical date determination module, specifically used for:

[0101] In the historical energy consumption data, candidate historical dates that are the same as the diagnosis date under preset conditions are identified; the preset conditions are dates whose temperature difference is within a preset range and whose public holiday category is the same as the diagnosis date.

[0102] The device for diagnosing the operating status provided in this embodiment of the invention can execute the device operation status diagnosis method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0103] The acquisition, storage, use, and processing of data in this application comply with relevant national laws and regulations and do not violate public order and good morals.

[0104] Example 4

[0105] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0106] Figure 6 A schematic diagram of an electronic device that can be used to implement the device operation status diagnosis method of embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0107] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0108] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0109] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as device operating status diagnostic methods.

[0110] In some embodiments, the device operating status diagnostic method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the device operating status diagnostic method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the device operating status diagnostic method by any other suitable means (e.g., by means of firmware).

[0111] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0112] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0113] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0114] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0115] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0116] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0117] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0118] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for diagnosing the operating status of equipment, characterized in that, include: Acquire historical energy consumption data of the device to be diagnosed; the historical energy consumption data includes at least historical date energy consumption data and characteristic data of that historical date; the characteristic data is weather and public holiday categories. Candidate historical dates are determined from the historical dates based on the characteristic data of the historical dates and the characteristic data of the diagnostic dates; The operating status of the device is diagnosed based on the historical energy consumption data and the number of candidate historical dates; The diagnostic process for the operating status of the device, based on the historical energy consumption data and the number of candidate historical dates, includes: Based on the historical energy consumption data, determine the energy consumption data difference parameter for the historical dates, and determine the operating mode of the equipment based on the energy consumption data difference parameter; If the device operates in a flexible mode and the number of historical dates meets the first preset condition, then the operating status of the device is determined based on the pre-trained diagnostic model and the diagnostic energy consumption data of the diagnostic dates; the diagnostic model is trained based on the historical energy consumption data; the first preset condition is that the historical energy consumption time is greater than or equal to the first preset time and the historical energy consumption days are greater than or equal to the first preset number of days. If the device operates in a rigid mode and the number of historical dates meets the second preset condition, the device's operating status is determined based on the pre-trained diagnostic model and the diagnostic energy consumption data of the diagnostic dates; the second preset condition is that the historical energy consumption time is greater than or equal to the second preset time and the historical energy consumption days are greater than or equal to the second preset number of days. If the number of historical dates does not meet the first or second preset condition, but the number of candidate historical dates meets the third preset condition, then a first number of first target historical dates are determined from the candidate historical dates, and the operating status of the device is diagnosed based on the first target data; the first target data is the energy consumption data of the historical date corresponding to the first target historical date; the third preset condition is that the number of historical energy consumption days is greater than or equal to the third preset number of days. If the number of historical dates does not meet the first or second preset condition, and the number of candidate historical dates does not meet the third preset condition, then a second number of second target historical dates are determined from the candidate historical dates, and the operating status of the device is diagnosed based on the second target data; the second target data is the energy consumption data of the historical date corresponding to the second target historical date; the first number is greater than the second number.

2. The method according to claim 1, characterized in that, Diagnosing the operating status of the device based on the first target data includes: The confidence interval of a preset percentage of the first number of first target data corresponding to the target dimension is determined by Chebyshev's inequality; the target dimension is a preset number of division values ​​into which a day is divided according to a preset time interval, each division value is a dimension, and each dimension corresponds to an energy consumption data. The target upper limit value corresponding to the target dimension is determined based on the upper limit value of the confidence interval corresponding to the target dimension. Determine the target lower limit value corresponding to the target dimension based on the lower limit value of the confidence interval corresponding to the target dimension; The operating status of the equipment is diagnosed based on the energy consumption data of the diagnosis date and the comparison results of the target upper limit and target lower limit.

3. The method according to claim 2, characterized in that, The second quantity is 1; Accordingly, the operating status of the device is diagnosed based on the second target data, including: Determine the upper and lower limits of the target dimension based on the second target data corresponding to the target dimension. The operating status of the equipment is diagnosed based on the energy consumption data of the diagnosis date and the comparison results of the target upper limit and target lower limit.

4. The method according to claim 1, characterized in that, The historical date energy consumption data includes a preset number of energy consumption data points divided according to a preset time interval; Accordingly, based on the historical energy consumption data, energy consumption data difference parameters are determined for the historical dates, and based on the energy consumption data difference parameters, the operating mode of the equipment is determined, including: The total energy consumption value for a given historical date is obtained by adding up a predetermined number of energy consumption data points from the historical date energy consumption data. Based on the total energy consumption value, the standard deviation and mean of the energy consumption data for the historical dates are determined, and the ratio of the standard deviation to the mean is used as the energy consumption data difference parameter. If the energy consumption data difference parameter is greater than the preset difference value, the operating mode of the device is flexible mode; otherwise, it is rigid mode.

5. The method according to claim 1, characterized in that, Based on the characteristic data of the historical dates and the characteristic data of the diagnostic dates, candidate historical dates are determined from the historical dates, including: In the historical energy consumption data, candidate historical dates that are the same as the diagnosis date under preset conditions are identified; the preset conditions are dates whose temperature difference is within a preset range and whose public holiday category is the same as the diagnosis date.

6. A device for diagnosing equipment operating status, characterized in that, include: The data acquisition module is used to acquire historical energy consumption data of the device to be diagnosed; the historical energy consumption data includes at least historical date energy consumption data and characteristic data of that historical date; the characteristic data is weather and public holiday categories; A candidate historical date determination module is used to determine candidate historical dates from the historical dates based on the feature data of the historical dates and the feature data of the diagnostic dates; The diagnostic module is used to diagnose the operating status of the device based on the historical energy consumption data and the number of candidate historical dates; The diagnostic module includes an operating mode determination unit, a first diagnostic unit, a second diagnostic unit, a third diagnostic unit, and a fourth diagnostic unit. The operating mode determination unit is used to determine the energy consumption data difference parameter in the historical date based on the historical energy consumption data, and to determine the operating mode of the device based on the energy consumption data difference parameter. The first diagnostic unit is configured to determine the operating status of the device based on a pre-trained diagnostic model and the diagnostic energy consumption data of the diagnostic dates if the device's operating mode is elastic mode and the number of historical dates meets a first preset condition; the diagnostic model is trained based on the historical energy consumption data; the first preset condition is that the historical energy consumption time is greater than or equal to a first preset time and the historical energy consumption days are greater than or equal to a first preset number of days. The second diagnostic unit is used to determine the operating status of the device based on a pre-trained diagnostic model and the diagnostic energy consumption data of the diagnostic dates if the device's operating mode is rigid and the number of historical dates meets a second preset condition; the second preset condition is that the historical energy consumption time is greater than or equal to a second preset time and the number of historical energy consumption days is greater than or equal to a second preset number of days. The third diagnostic unit is used to determine a first number of first target historical dates from the candidate historical dates if the number of historical dates does not meet the first or second preset condition, but the number of candidate historical dates meets the third preset condition, and to diagnose the operating status of the device based on the first target data; the first target data is the historical date energy consumption data corresponding to the first target historical date; the third preset condition is that the number of historical energy consumption days is greater than or equal to the third preset number of days. The fourth diagnostic unit is used to determine a second number of second target historical dates from the candidate historical dates if the number of historical dates does not meet the first preset condition or the second preset condition, and the number of candidate historical dates does not meet the third preset condition, and to diagnose the operating status of the device based on the second target data; the second target data is the historical date energy consumption data corresponding to the second target historical date; the first number is greater than the second number.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the device operation status diagnosis method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the device operation status diagnosis method according to any one of claims 1-5.

Images