Data sampling rate adjustment method based on equipment health degree score

Abstract

The invention relates to the technical field of industrial equipment maintenance and monitoring, and discloses a data sampling rate adjustment method based on equipment health degree scoring, and the method comprises the steps: collecting the operation index data of target equipment, and calculating the average value and gradient value of each operation index; standardizing the average value and the gradient value to obtain a standardized average value and a standardized gradient value; combining the standardized average value and gradient value, and calculating the health degree score of the target equipment; and adjusting the sampling rate of the target equipment according to the health degree score of the target equipment. The health degree score of the equipment is calculated and the health state of the equipment is reflected by selecting related parameters influencing the health degree of the equipment, calculating the average value and the gradient value of each operation index to serve as key parameters for standardization, performing standardization processing and then performing combination; therefore, the sampling rate is adjusted according to the change of the health state of the equipment, and the flexibility and efficiency of data acquisition are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment maintenance and monitoring, and in particular to a data sampling rate adjustment method based on equipment health scoring. Background Art

[0002] In modern industrial systems, Equipment Health Management (EHM) is a key technology for industrial production equipment, such as robots, machine tools, automated production equipment, and PLCs. Its purpose is to reduce equipment failure rates and maintenance costs by evaluating and predicting the health status of equipment.

[0003] In the existing technology, devices usually collect data through a predetermined fixed sampling frequency. However, the fixed sampling frequency strategy has the following main problems: (1) The limitation of fixed frequency: Regardless of the operating status and health of the device, the data collection frequency remains constant. This may result in the failure to obtain key data in a timely manner when the device is in poor health, and excessive redundant data is collected when the device is operating normally, increasing the resource burden; (2) The sampling rate cannot be adjusted in a targeted manner: When the health status of the device deteriorates, the fixed frequency collection cannot dynamically respond to changes in the device status and cannot provide sufficient real-time data to support device fault prediction and early warning.

[0004] In summary, the fixed sampling frequency strategy adopted by the existing technology is difficult to achieve a flexible response to changes in the health status of the equipment, and there are problems such as low sampling efficiency and low data value. Summary of the Invention

[0005] In view of this, the present invention provides a data sampling rate adjustment method based on device health score to solve the problem in the prior art that the sampling frequency is fixed and cannot dynamically respond to changes in device status.

[0006] In a first aspect, the present invention provides a method for adjusting a data sampling rate based on a device health score, the method comprising:

[0007] Collect operating indicator data of the target equipment and calculate the average and gradient values of each operating indicator, including temperature, mechanical vibration, operating system pressure, power system speed and current;

[0008] Normalize the average value and gradient value to obtain the standardized average value and gradient value;

[0009] Combine the normalized average and gradient values to calculate the health score of the target device;

[0010] Adjust the sampling rate of the target device based on the health score of the target device.

[0011] The present invention selects relevant parameters that affect the health of the equipment, calculates the average value and gradient value of each operating indicator as the key parameters for standardization, performs standardization processing, and then combines them to calculate the health score of the equipment to reflect the health status of the equipment. The sampling rate is adjusted according to the changes in the health status of the equipment, thereby improving the flexibility and efficiency of data collection.

[0012] In an optional embodiment, calculating the gradient value of the operating indicator includes:

[0013] Extract the target device's current operating index value, the previous operating index value, and the time interval between the current and previous moments;

[0014] The difference between the current operating index value of the target device and the operating index value at the previous moment is calculated, and the ratio of the difference to the time interval is determined as the gradient value of the operating index.

[0015] The present invention reflects the rate of change of each operating indicator and embodies the change of each operating indicator by calculating the gradient value of the operating indicator as a key parameter.

[0016] In an optional embodiment, the average value and the gradient value are normalized, including:

[0017] Extract the average value of the current operating indicator, the maximum value among the average values of the operating indicators in multiple time periods, and the minimum value among the average values of the operating indicators in multiple time periods, and calculate the standardized mean value of the operating indicator based on the average value, maximum value, and minimum value;

[0018] Extract the gradient value of the current operating indicator, the maximum value among the average values of the operating indicators in multiple time periods, and the minimum value among the average values of the operating indicators in multiple time periods, and calculate the standardized gradient value of the operating indicator based on the average value, maximum value, and minimum value.

[0019] The present invention standardizes the average value and gradient value of each operating indicator to ensure that the data of different operating indicators are converted to the same scale, so that the distribution of data is consistent, thereby more accurately reflecting the operating status of the equipment.

[0020] In an optional embodiment, the normalized mean and gradient values are combined, including:

[0021] Use the weight coefficient allocation strategy to allocate the weight coefficient corresponding to the standardized average value and the weight coefficient corresponding to the standardized gradient value;

[0022] A standardized combination is performed based on the standardized average value and the corresponding weight coefficient, the standardized gradient value and the corresponding weight coefficient to obtain a standardized combination result.

[0023] The present invention combines the standardized average value and gradient value, taking into account the advantages that the average value reflects the data center trend and the gradient value reflects the data change, and more comprehensively describes the characteristics of the data.

[0024] In an optional embodiment, calculating the health score of the target device includes:

[0025] Calculate the proportion, information entropy, and information entropy weight of each operating indicator in turn;

[0026] The calculated information entropy weight is normalized to obtain the health score weight of each operating indicator;

[0027] The health score of the target device is calculated based on the health score weights of each operating indicator and the standardized combined results.

[0028] The present invention combines the standardized average value and gradient value, taking into account the advantages that the average value reflects the data center trend and the gradient value reflects the data change, and more comprehensively describes the characteristics of the data.

[0029] In an optional implementation, adjusting the sampling rate of the target device according to the health score of the target device includes:

[0030] Get the maximum acquisition frequency, minimum acquisition frequency and adjustment coefficient of the target device;

[0031] The sampling rate of the target device is calculated based on the health score, maximum collection frequency, minimum collection frequency, and adjustment coefficient of the target device, and the sampling rate of the target device is adjusted.

[0032] The present invention adjusts the sampling rate according to the equipment health score to increase or decrease the acquisition frequency, thereby realizing intelligent monitoring equipment and facilitating equipment maintenance.

[0033] In an optional implementation, the sampling rate of the target device is calculated according to the following formula:

[0034]

[0035] Where f is the sampling rate, f max is the maximum acquisition frequency, f min is the minimum collection frequency, k is the adjustment coefficient, and H is the health score of the device.

[0036] The present invention improves the monitoring effect by adjusting the device sampling rate according to the device health score.

[0037] In a second aspect, the present invention provides a device for determining a sampling rate of a target device based on a health score of the target device, the device comprising:

[0038] The acquisition module is used to collect the operating indicator data of the target device and calculate the average value and gradient value of each operating indicator. The operating indicators include temperature, mechanical vibration, operating system pressure, power system speed and current;

[0039] A standardization processing module is used to standardize the average value and gradient value to obtain the standardized average value and gradient value;

[0040] A calculation module, used to combine the normalized average value and gradient value to calculate the health score of the target device;

[0041] The adjustment module is used to adjust the sampling rate of the target device according to the health score of the target device.

[0042] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, computer instructions stored in the memory, and the processor executing the computer instructions to execute the method for determining the sampling rate of a target device based on the health score of the target device according to the above-mentioned first aspect or any corresponding embodiment thereof.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for determining the sampling rate of a target device based on the health score of the target device according to the above-mentioned first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 is a flow chart of a method for determining a sampling rate of a target device based on a health score of the target device according to an embodiment of the present invention;

[0046] Figure 2 is a structural block diagram of an apparatus for determining a sampling rate of a target device according to a health score of the target device according to an embodiment of the present invention;

[0047] Figure 3 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0049] Currently, in the field of industrial equipment monitoring and maintenance, data collection often uses a fixed sampling rate strategy. This approach ignores changes in the equipment's operating status, especially fluctuations in its health. In this fixed sampling rate model, the sampling frequency remains constant regardless of the equipment's health, leading to the following shortcomings:

[0050] 1. Difficulty capturing early warning signs of equipment failure: When equipment health gradually deteriorates, a fixed sampling rate cannot provide sufficiently granular data to capture early signs of equipment failure. For example, when equipment temperature rises, vibration increases, or performance fluctuates slightly, a fixed sampling frequency may miss these important status changes, preventing timely warnings and increasing the risk of sudden equipment failure.

[0051] 2. Resource waste: For devices in good health, a fixed sampling rate results in unnecessary high-frequency data collection. Even when the device is operating stably and without any anomalies, the sampling system maintains a high-frequency data collection, resulting in the accumulation of invalid data, increasing the storage and transmission burden, and wasting computing resources and network bandwidth.

[0052] 3. Sampling frequency mismatches device health: Existing solutions lack a dynamic mechanism for adjusting device health, preventing real-time adjustments to sampling frequency based on the device's actual health. This lack of flexibility results in devices with poor health being under-monitored and devices in good health being over-collected.

[0053] 4. Failure to Provide Accurate Preventive Maintenance Support: Existing technologies make it difficult to accurately analyze and predict equipment health using data collected at a fixed frequency. Insufficient or excessive sampling frequency can prevent the monitoring system from providing warnings and adjustments when equipment enters a sub-healthy state, reducing the effectiveness of preventive maintenance.

[0054] According to an embodiment of the present invention, an embodiment of a method for adjusting a data sampling rate based on a device health score is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0055] In this embodiment, a data sampling rate adjustment method based on device health score is provided, which can be used in mobile terminals. Figure 1 FIG. 1 is a flow chart of a method for adjusting a data sampling rate based on a device health score according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0056] Step S101: collect operating index data of the target device and calculate the average value and gradient value of each operating index.

[0057] In an embodiment of the present invention, relevant operating indicators that evaluate or affect the health of the target equipment are selected. For industrial equipment, the operating indicators that cause equipment damage or degradation of health include temperature values, mechanical vibrations, operating system pressure, power system speed and current, etc. The temperature value, operating system pressure, power system speed and current of the target equipment over a period of time are collected, and the average value and gradient value of each operating indicator are calculated.

[0058] The average temperature value is T avg , the average value of mechanical vibration is V avg The average value of the operating system pressure is P avg The average speed of the power system is R avg The average value of the current is I avg .

[0059] Step S102 , normalizing the average value and the gradient value to obtain the normalized average value and gradient value.

[0060] In the embodiment of the present invention, the average value and gradient value of the operating index are used as key parameters for standardization. Therefore, the average value and gradient value of each operating index are standardized to obtain a standardized average value and a standardized gradient value.

[0061] Step S103 : combining the normalized average value and the gradient value to calculate the health score of the target device.

[0062] In an embodiment of the present invention, after the average value and the gradient value are independently normalized, two normalized results are obtained. The normalized average value and gradient value are combined through weight distribution, and the health score of the target device is comprehensively calculated to reflect the health of the target device. The higher the health score, the higher the device health, and vice versa.

[0063] Step S104: adjusting the sampling rate of the target device according to the health score of the target device.

[0064] In an embodiment of the present invention, the health status of the target device is determined based on the health score of the target device, the sampling rate of the target device is adjusted, and the data collection frequency is adjusted in real time. As a result, the collection frequency is reduced when the device is in good condition to save resources, and the collection frequency is increased when the device condition deteriorates to ensure timely monitoring and fault prediction.

[0065] The data sampling rate adjustment method based on the device health score provided in this embodiment selects relevant parameters that affect the device health, calculates the average value and gradient value of each operating indicator, uses them as key parameters for standardization, performs standardization processing, and then combines them to calculate the device health score to reflect the health status of the device. The sampling rate is adjusted according to changes in the health status of the device, thereby improving the flexibility and efficiency of data collection.

[0066] This embodiment provides a method for adjusting the data sampling rate based on the device health score. The process includes the following steps:

[0067] Step S201 : collecting the operating index data of the target device and calculating the average value and gradient value of each operating index.

[0068] Specifically, the calculation of the gradient value of the operating indicator in step S201 includes:

[0069] Step S2011: extract the current operating index value of the target device, the previous operating index value, and the time interval between the current and previous times.

[0070] Step S2012: Calculate the difference between the current operating index value of the target device and the operating index value at the previous moment, and determine the ratio of the difference to the time interval as the gradient value of the operating index.

[0071] In the embodiment of the present invention, taking temperature as an example, the current temperature value T is extracted. current , the temperature value T at the previous moment previous , the time interval between the current moment and the previous moment is Δt, and the temperature gradient is calculated according to the following formula:

[0072] ΔT=(T current -T previous ) / Δt

[0073] Similarly, the gradient values of mechanical vibration, operating system pressure, power system speed and current are calculated respectively. The gradient value of mechanical vibration is ΔV, the gradient value of operating system pressure is ΔP, the gradient value of power system speed is ΔR, and the gradient value of current is ΔI.

[0074] By calculating the gradient value of the operating indicator as the key parameter, the change rate of each operating indicator is reflected, and the change situation of each operating indicator is reflected.

[0075] Step S202 : normalize the average value and the gradient value to obtain the normalized average value and gradient value.

[0076] Specifically, the above step S202 includes:

[0077] Step S2021, extracting the average value of the current operating indicator, the maximum value among the average values of the operating indicators in multiple time periods, and the minimum value among the average values of the operating indicators in multiple time periods, and calculating the standardized mean value of the operating indicator based on the average value, the maximum value, and the minimum value.

[0078] Step S2022: extract the gradient value of the current operating indicator, the maximum value among the average values of the operating indicators in multiple time periods, and the minimum value among the average values of the operating indicators in multiple time periods, and calculate the standardized gradient value of the operating indicator based on the average value, maximum value, and minimum value.

[0079] In the embodiment of the present invention, the average value is standardized, and the average value of each indicator over multiple time periods is taken to calculate the maximum value and the minimum value respectively.

[0080] The standardized mean temperature is calculated according to the following formula:

[0081]

[0082] Among them, T norm represents the standardized mean temperature, T avg Indicates the current average temperature, T max Indicates the maximum value of the average temperature, T min Indicates the minimum value of the average temperature.

[0083] Similarly, the standardized mechanical vibration mean is calculated according to the following formula:

[0084]

[0085] Among them, V norm Represents the normalized vibration mean, V avg Indicates the current vibration average value, V max Indicates the maximum value of the average vibration value, V min Indicates the minimum value of the average vibration.

[0086] Similarly, the normalized operating system pressure is calculated according to the following formula:

[0087]

[0088] Among them, P norm represents the normalized mean pressure, P avg Indicates the current average pressure, Pmax Indicates the maximum value of the average pressure value, P min Indicates the minimum value of the mean pressure.

[0089] Similarly, the normalized powertrain speed is calculated according to the following formula:

[0090]

[0091] Among them, R norm represents the normalized mean speed, R avg Indicates the current average speed, R max Indicates the maximum value of the average speed, R min Indicates the minimum value of the average speed.

[0092] Similarly, the normalized current is calculated according to the following formula:

[0093]

[0094] Among them, I norm Represents the normalized current mean, I avg Indicates the current average value, I max Indicates the maximum value of the average current, I min Indicates the minimum value of the average current.

[0095] Similarly, the gradient value is standardized to obtain the standardized temperature gradient ΔT norm , Normalized vibration gradient ΔV norm , normalized pressure gradient ΔP norm , Normalized speed gradient ΔR norm , the normalized current gradient ΔI norm .

[0096] By standardizing the average value and gradient value of each operating indicator, we ensure that the data of different operating indicators are converted to the same scale, making the distribution of data consistent and reflecting the operating status of the equipment more accurately.

[0097] Step S203 : combining the normalized average value and the gradient value to calculate the health score of the target device.

[0098] Specifically, in step S203, combining the normalized average value and gradient value includes:

[0099] Step S2031 : using a weight coefficient allocation strategy to allocate a weight coefficient corresponding to the normalized average value and a weight coefficient corresponding to the normalized gradient value.

[0100] Step S2032 , performing standardized combination according to the standardized average value and the corresponding weight coefficient, the standardized gradient value and the corresponding weight coefficient, to obtain a standardized combination result.

[0101] In the embodiment of the present invention, after the average value and the gradient value are independently normalized, two normalization results are obtained, and the two normalization results are integrated through a weight coefficient allocation strategy.

[0102] Specifically, the weight coefficient a is introduced and the combined normalized value of each operating indicator is calculated according to the following formula:

[0103] Temperature: TT = a × T norm +(1-a)×ΔT norm (TT represents the combined normalized temperature);

[0104] Vibration: VV = a × Vnorm + (1-a) × ΔVnorm (VV represents the combined normalized vibration);

[0105] Pressure: PP = a × Pnorm + (1-a) × ΔPnorm (PP represents the combined normalized pressure);

[0106] Speed: RR = a × Rnorm + (1-a) × ΔRnorm (RR represents the speed after combined normalization);

[0107] Current: II = a×Inorm+(1-a)×ΔInorm (II represents the current after combined normalization).

[0108] By combining the standardized average value and gradient value, the average value reflects the center trend of the data, and the gradient value reflects the advantages of data changes, which can more comprehensively describe the characteristics of the data.

[0109] Specifically, calculating the health score of the target device in step S203 includes:

[0110] Step S2033, calculate the proportion, information entropy, and information entropy weight of each operating indicator in sequence.

[0111] In step S2034, the calculated information entropy weight is normalized to obtain the health score weight of each operating indicator.

[0112] Step S2035 , calculating the health score of the target device based on the health score weights of the various operating indicators and the standardized combination results.

[0113] In this embodiment of the present invention, the health weight of the temperature value is W tt , the health weight of mechanical vibration is W vv , the health weight of the operating system pressure is Wpp , the health weight of the power system speed is W rr , the health weight of the current is W ii .

[0114] The entropy weight method is used to determine the weight:

[0115] (1) Calculate the indicator weight: By collecting standardized combined data at different times, set as N, the proportion of the jth data of each indicator is p j for:

[0116] temperature: p tj is the proportion of the jth sample after temperature combination annotation;

[0117] vibration: p vj is the proportion of the jth sample after vibration combination annotation;

[0118] pressure: p pj is the proportion of the jth sample after pressure combination annotation;

[0119] Speed: p rj is the proportion of the jth sample after the speed combination is annotated;

[0120] Current: p ij is the proportion of the jth sample after current combination annotation.

[0121] (2) Calculate the information entropy of indicators

[0122] The information entropy of each operating indicator is calculated according to the following formula:

[0123] temperature: e t is the temperature index information entropy, In(N) is the total number of samples of the natural logarithm, ensuring that the entropy value is in the range of [0, 1], p ti is the proportion of the i-th sample after temperature combination annotation;

[0124] vibration: e v is the vibration index information entropy, p vi is the proportion of the i-th sample after vibration combination annotation;

[0125] pressure: e p is the information entropy of pressure index, p pi is the proportion of the i-th sample after pressure combination annotation;

[0126] Speed: er is the information entropy of the speed index, p ri is the proportion of the i-th sample after the speed combination is labeled;

[0127] Current: e i is the current index information entropy, p ii is the proportion of the i-th sample after current combination annotation.

[0128] The size of information entropy reflects the degree of dispersion of the operating indicator. The larger the entropy, the greater the uncertainty of the operating indicator.

[0129] (3) Calculate information entropy weight

[0130] The information entropy weight of each operating indicator is calculated according to the following formula

[0131] temperature: W t is the temperature information entropy weight;

[0132] vibration: W v is the vibration information entropy weight;

[0133] pressure: W p is the pressure information entropy weight;

[0134] Speed: W r is the speed information entropy weight;

[0135] Current: W i is the current information entropy weight.

[0136] Normalize the calculated information entropy weight to ensure that the sum of all indicator weights is 1, and calculate the temperature normalization weight W according to the following formula: tt :

[0137]

[0138] Similarly, calculate the vibration normalization weight W vv , pressure normalization weight W pp , speed normalization weight W rr , current normalization weight W ii .

[0139] Based on the calculated health score weight and the combined normalization results of each indicator, the device health score is calculated according to the following formula:

[0140] Score=(1-(W tt ×TT+W vv×VV+W pp ×PP+W rr ×RR+W ii ×ⅠⅠ))×100%

[0141] Among them, Score is the device health score, which ranges from 0 to 100. The score reflects the health of the device. The higher the score, the healthier the device, and vice versa.

[0142] By calculating the information entropy weight of each operating indicator, assigning a priority to each operating indicator, and calculating the device health score, it reflects the health status of the device, thereby providing a data basis for adjusting the sampling rate.

[0143] Step S204: adjusting the sampling rate of the target device according to the health score of the target device.

[0144] Specifically, the above step S204 includes:

[0145] Step S2041: Obtain the maximum acquisition frequency, minimum acquisition frequency, and adjustment coefficient of the target device.

[0146] Step S2042 : Calculate the sampling rate of the target device according to the health score of the target device, the maximum acquisition frequency, the minimum acquisition frequency, and the adjustment coefficient, and adjust the sampling rate of the target device.

[0147] In the embodiment of the present invention, the upper limit of the acquisition frequency is the maximum acquisition frequency, the lower limit of the acquisition frequency is the minimum acquisition frequency, and the adjustment coefficient is generally 1-3, which is determined according to the sensitivity of the device. The sampling rate of the device is calculated according to the following formula:

[0148]

[0149] Where f is the sampling rate, f max is the maximum acquisition frequency, f min is the minimum collection frequency, k is the adjustment coefficient, and H is the health score of the device, that is, Score.

[0150] As the device health score changes, the sampling rate also changes accordingly. When the device is in good condition, the sampling frequency is reduced to conserve resources, reduce the system resource storage, transmission, and computing burden, and effectively reduce the operating cost of device monitoring. When the device condition deteriorates, the sampling frequency is increased to ensure timely monitoring of the device and timely failure prediction, enabling efficient device monitoring and maintenance. Furthermore, dynamically adjusting the sampling rate ensures that sufficiently dense data is collected when the device condition deteriorates, ensuring that critical signals are captured in the event of an impending failure, significantly reducing the risk of device failure.

[0151] The data sampling rate adjustment method based on the device health score provided in this embodiment adjusts the sampling rate according to the device health score to increase or decrease the acquisition frequency, thereby realizing intelligent monitoring equipment, improving monitoring effects, and facilitating equipment maintenance.

[0152] The data sampling rate adjustment method based on device health score provided in the embodiment of the present invention has the following advantages:

[0153] (1) Adaptive adjustment of sampling rate: Automatically adjust the data collection frequency according to changes in the health status of the equipment, improving the flexibility and efficiency of data collection;

[0154] (2) Higher fault warning accuracy: By increasing the sampling rate when the health status deteriorates, it is possible to more accurately capture subtle changes before equipment failure, provide early warnings, and avoid the expansion of potential equipment failures;

[0155] (3) Efficient resource utilization: By reducing the sampling rate when the equipment is healthy, unnecessary data collection, transmission and processing are reduced, which greatly improves the efficiency of system resource utilization, extends the life of the equipment monitoring system, and reduces operating costs;

[0156] (4) Enhance the accuracy and real-time performance of equipment health monitoring: Through real-time assessment of equipment health status, the accuracy of equipment health management can be effectively improved, providing strong technical support for preventive maintenance and fault prediction of industrial equipment;

[0157] (5) Improve fault warning efficiency: Dynamic sampling rate adjustment can ensure that when the equipment status deteriorates, sufficiently dense data is collected, ensuring that key signals are captured in time when the equipment is about to fail, greatly reducing the risk of equipment failure;

[0158] (6) Reduce the operating cost of the monitoring system: When the equipment is healthy, it saves data acquisition resources, reduces the storage, transmission and computing burden of the system, and reduces energy consumption, thereby effectively reducing the overall operating cost of the equipment monitoring system.

[0159] This embodiment also provides a device for adjusting the data sampling rate based on the device health score. This device is used to implement the above-mentioned embodiments and preferred embodiments. Details that have already been described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0160] This embodiment provides a device for adjusting the data sampling rate based on the device health score. Figure 2 Shown, including:

[0161] The acquisition module 201 is used to collect operating index data of the target device and calculate the average value and gradient value of each operating index. The operating indexes include temperature, mechanical vibration, operating system pressure, power system speed and current.

[0162] The normalization processing module 202 is used to perform normalization processing on the average value and the gradient value to obtain the normalized average value and gradient value.

[0163] The calculation module 203 is configured to combine the normalized average value and the gradient value to calculate the health score of the target device.

[0164] The adjustment module 204 is configured to adjust the sampling rate of the target device according to the health score of the target device.

[0165] In some optional implementations, the acquisition module 201 includes:

[0166] The extraction unit is used to extract the operating index value of the target device at the current moment, the operating index value at the previous moment, and the time interval between the current moment and the previous moment.

[0167] The first calculation unit is configured to calculate a difference between an operating index value of the target device at a current moment and an operating index value at a previous moment, and determine a ratio of the difference to the time interval as a gradient value of the operating index.

[0168] In some optional implementations, the standardization processing module 202 includes:

[0169] The second calculation unit is used to extract the average value of the current operating indicator, the maximum value among the average values of the operating indicators in multiple time periods, and the minimum value among the average values of the operating indicators in multiple time periods, and calculate the standardized mean value of the operating indicator based on the average value, the maximum value, and the minimum value.

[0170] The third calculation unit is used to extract the gradient value of the current operating indicator, the maximum value among the average values of the operating indicators in multiple time periods, and the minimum value among the average values of the operating indicators in multiple time periods, and calculate the standardized gradient value of the operating indicator based on the average value, maximum value, and minimum value.

[0171] In some optional implementations, the calculation module 203 includes:

[0172] The weight allocation unit is used to allocate the weight coefficient corresponding to the normalized average value and the weight coefficient corresponding to the normalized gradient value by using a weight coefficient allocation strategy.

[0173] The combination unit is used to perform standardized combination according to the standardized average value and the corresponding weight coefficient, the standardized gradient value and the corresponding weight coefficient, to obtain a standardized combination result.

[0174] In some optional implementations, the calculation module 203 includes:

[0175] The fourth calculation unit is used to calculate the proportion, information entropy and information entropy weight of each operating indicator in sequence.

[0176] The normalization processing unit is used to normalize the calculated information entropy weight to obtain the health score weight of each operating indicator.

[0177] The fifth calculation unit is used to calculate the health score of the target device according to the health score weights of various operating indicators and the standardized combination results.

[0178] In some optional implementations, the adjustment module 204 includes:

[0179] The acquisition unit is used to obtain the maximum acquisition frequency, the minimum acquisition frequency and the adjustment coefficient of the target device.

[0180] The adjustment unit is used to calculate the sampling rate of the target device according to the health score of the target device, the maximum collection frequency, the minimum collection frequency and the adjustment coefficient, and adjust the sampling rate of the target device.

[0181] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0182] The data sampling rate adjustment device based on the device health score in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0183] The embodiment of the present invention also provides a computer device having the above Figure 2 The data sampling rate adjustment device based on the equipment health score is shown.

[0184] See also Figure 3 , Figure 3 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 3As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0185] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0186] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0187] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0188] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0189] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 3 The bus connection is taken as an example.

[0190] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, etc. The output device 40 can include a display device, etc.

[0191] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0192] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0193] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are intended to fall within the scope of this application.

Claims

1. A data sampling rate adjustment method based on device health score, characterized in that: The method comprises: Collecting operating indicator data of the target device and calculating the average value and gradient value of each operating indicator, wherein the operating indicators include temperature, mechanical vibration, operating system pressure, power system speed and current; Normalize the average value and gradient value to obtain the standardized average value and gradient value; Combine the normalized average and gradient values to calculate the health score of the target device; Adjust the sampling rate of the target device based on the health score of the target device.

2. The method according to claim 1, characterized in that Calculate the gradient value of the running indicator, including: Extract the target device's current operating index value, the previous operating index value, and the time interval between the current and previous moments; The difference between the current operating index value of the target device and the previous operating index value is calculated, and the ratio of the difference to the time interval is determined as the gradient value of the operating index.

3. The method according to claim 1, characterized in that The standardization of the average value and the gradient value includes: Extract the average value of the current operating indicator, the maximum value among the average values of the operating indicators in multiple time periods, and the minimum value among the average values of the operating indicators in multiple time periods, and calculate the standardized mean value of the operating indicator based on the average value, maximum value, and minimum value; Extract the gradient value of the current operating indicator, the maximum value among the average values of the operating indicators in multiple time periods, and the minimum value among the average values of the operating indicators in multiple time periods, and calculate the standardized gradient value of the operating indicator based on the average value, maximum value, and minimum value.

4. The method according to claim 1, wherein The combining of the normalized average value and gradient value includes: Use the weight coefficient allocation strategy to allocate the weight coefficient corresponding to the standardized average value and the weight coefficient corresponding to the standardized gradient value; A standardized combination is performed based on the standardized average value and the corresponding weight coefficient, the standardized gradient value and the corresponding weight coefficient to obtain a standardized combination result.

5. The method according to claim 4, characterized in that Calculating the health score of the target device includes: Calculate the proportion, information entropy, and information entropy weight of each operating indicator in turn; The calculated information entropy weight is normalized to obtain the health score weight of each operating indicator; The health score of the target device is calculated based on the health score weights of each operating indicator and the standardized combined results.

6. The method according to claim 1, characterized in that The step of adjusting the sampling rate of the target device according to the health score of the target device includes: Get the maximum acquisition frequency, minimum acquisition frequency and adjustment coefficient of the target device; The sampling rate of the target device is calculated based on the health score, maximum collection frequency, minimum collection frequency, and adjustment coefficient of the target device, and the sampling rate of the target device is adjusted.

7. The method according to claim 6, characterized in that Calculate the sampling rate of the target device according to the following formula: Where f is the sampling rate, f max is the maximum acquisition frequency, f min is the minimum collection frequency, k is the adjustment coefficient, and H is the health score of the device.

8. A data sampling rate adjustment device based on device health score, characterized in that: The device comprises: An acquisition module is used to collect operating indicator data of the target device and calculate the average value and gradient value of each operating indicator, wherein the operating indicators include temperature, mechanical vibration, operating system pressure, power system speed and current; A standardization processing module is used to standardize the average value and gradient value to obtain the standardized average value and gradient value; A calculation module, used to combine the normalized average value and gradient value to calculate the health score of the target device; The adjustment module is used to adjust the sampling rate of the target device according to the health score of the target device.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the data sampling rate adjustment method based on the device health score according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the data sampling rate adjustment method based on device health score according to any one of claims 1 to 7.

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