Data processing method and system

Abstract

The present disclosure provides a data processing method and system, belonging to the field of data processing technology. The method comprises: determining a change in the target device's operating environment based on the target device's operating environment data; determining a change in the target device's operating data based on the target device's operating test data; determining a first period based on the target device's operating environment change and the target device's operating data change; updating an evaluation model based on the first period, and evaluating the target device's operating status based on the updated evaluation model. The data processing method and system provided by the present disclosure enable accurate evaluation of the device's operating status.

Description

Technical Field

[0001] The present disclosure belongs to the field of data processing technology, and more specifically, relates to a data processing method and system. Background Art

[0002] With the continuous advancement of science and technology, various equipment are widely used in different fields, such as infrastructure, industrial manufacturing, and energy production. Testing equipment performance is therefore crucial. During the equipment trial operation, acceptance, and official use stages, it is necessary to measure the equipment's operating parameters under different conditions, such as flow rate, speed, temperature, and pressure, to determine whether it can meet the design performance indicators. For example, for a water conservancy pump station unit, during performance testing, its flow rate, head, and efficiency during operation can be monitored to determine whether it can meet the design performance indicator requirements. For another example, for a water disinfection device, by monitoring parameters such as flow rate, pressure, and dosage, the stability and accuracy of the equipment under different operating conditions can be confirmed.

[0003] By training a condition assessment model, we can automatically predict the health status of a device based on its operating data, thereby determining whether it can meet the required performance indicators. However, because device operating data is constantly changing, an assessment model with fixed parameters may not be able to adapt to new data, making it impossible to accurately assess the operating status. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a data processing method and system to achieve accurate evaluation of the operating status of equipment.

[0005] A first aspect of the present disclosure provides a data processing method, including:

[0006] determining an amount of change in the operating environment of the target device based on the operating environment data of the target device;

[0007] determining an amount of change in operating data of the target device based on operating test data of the target device;

[0008] determining a first period based on a change in the operating environment of the target device and a change in the operating data of the target device;

[0009] The evaluation model is updated based on the first cycle, and the operating status of the target device is evaluated based on the updated evaluation model.

[0010] A second aspect of the present disclosure provides a data processing system, including:

[0011] A first calculation module, configured to determine a change in the operating environment of the target device based on the operating environment data of the target device;

[0012] a second calculation module, configured to determine a change in the operating data of the target device based on the operating test data of the target device;

[0013] a third calculation module, determining a first period based on a change in the operating environment of the target device and a change in the operating data of the target device;

[0014] The model updating module is used to update the evaluation model based on the first cycle and evaluate the operating status of the target device based on the updated evaluation model.

[0015] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned data processing method when executing the computer program.

[0016] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned data processing method are implemented.

[0017] The data processing method and system provided by the embodiments of the present disclosure have the following beneficial effects:

[0018] The embodiment of the present disclosure takes into account that changes in the target device's operating test data come not only from changes in its own performance, but also from changes in the operating environment. Therefore, the update cycle (i.e., the first cycle) of the evaluation model is determined based on the operating environment and the amount of change in the operating data of the target device. When the amount of change in the operating environment and the operating data of the target device is small, there is no need to frequently update the parameters of the evaluation model, and a larger update cycle can be set. Conversely, when the amount of change in the operating environment and the operating data of the target device is large, the parameters of the evaluation model need to be updated in a timely manner, and a smaller update cycle can be set.

[0019] The embodiment of the present disclosure determines the update cycle of the evaluation model based on the operating environment of the target device and the change in the operating data. On the basis of ensuring the accuracy of the evaluation model, the number of model updates is reduced as much as possible, thereby reducing the amount of calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1A flowchart of a data processing method provided in one embodiment of the present disclosure;

[0022] Figure 2 A structural block diagram of a data processing system provided in one embodiment of the present disclosure;

[0023] Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present disclosure with unnecessary detail.

[0025] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0026] Please refer to Figure 1 , Figure 1 A flowchart of a data processing method provided in one embodiment of the present disclosure is provided, the method comprising:

[0027] S101: Determine an operating environment change amount of a target device based on operating environment data of the target device.

[0028] In this embodiment, the target equipment may include various equipment in the fields of infrastructure, industrial manufacturing, energy production, etc., such as pump groups in water pumping stations, water treatment equipment in water pumping stations (including disinfection equipment, mixing equipment), working robots, photovoltaic equipment, wind power generation equipment, etc.

[0029] The target device's operating environment data is the device's environmental data, including external environmental factors such as temperature, humidity, and electromagnetic interference. Changes in this data can affect the target device's operating status. By collecting statistics on the operating environment data over a set period of time, the amount of change in the operating environment data can be determined.

[0030] Taking the target device as a pump unit in a water pump station as an example, the operating environment data of the target device can include air temperature, water level, and water quality. Among them, the temperature difference between different seasons will affect the operating temperature of the equipment in the pump station. For example, during high temperatures in summer, the pump unit and motor may face the risk of overheating, while during low temperatures in winter, some components may experience performance degradation or damage due to the low temperature. For water pump stations that rely on natural water sources, changes in the water level of the water source will also affect the operating status of the pump unit. For example, in the rainy season, the water level may rise, and in the dry season, the water level may drop, which will affect the suction conditions and working efficiency of the pump. In addition, in the rainy season, the water may contain more sediment and impurities, which will affect the operation of the pump unit.

[0031] For example, if the target device is a water disinfection device, the operating environment data for the target device may include water temperature, water hardness, pH value, and impurity content (including suspended solids and organic matter). For example, different disinfection methods have different adaptability ranges for water temperatures. For example, ultraviolet disinfection is most effective within a certain water temperature range, while excessively high or low water temperatures may reduce its efficiency. For another example, water with high hardness may form scale on the device surface, affecting its heat and mass transfer performance.

[0032] S102: Determine the change in the operating data of the target device based on the operating test data of the target device.

[0033] In this embodiment, the operation test data of the target device is the performance test data of the target device itself, including power, rotation speed, vibration, noise, etc. By collecting statistics of the operation test data within a set time, the change amount of the operation data can be obtained.

[0034] Taking the target equipment as a water pump station pump group as an example, the operating test data of the target equipment may include performance parameters such as flow rate, efficiency, and head, temperature data such as pump body temperature and bearing temperature, frequency data such as vibration amplitude and vibration frequency, electrical parameters such as current and voltage, and noise data such as noise level and noise frequency.

[0035] Taking the target device as a water disinfection device as an example, the operation test data of the target device may include disinfection dosage, ultraviolet intensity, energy consumption, etc.

[0036] S103: Determine a first period based on the change amount of the operating environment of the target device and the change amount of the operating data of the target device.

[0037] In this embodiment, the update cycle (i.e., the first cycle) of the evaluation model can be determined based on the changes in the target device's operating environment and operating data, thereby enabling dynamic adjustment of the update cycle. When the changes in the target device's operating environment and operating data are small, frequent updates of the evaluation model parameters are unnecessary, and a longer update cycle can be set. Conversely, when the changes in the target device's operating environment and operating data are significant, timely updates of the evaluation model parameters are required, and a shorter update cycle can be set.

[0038] S104: updating the evaluation model based on the first cycle, and evaluating the operating status of the target device based on the updated evaluation model.

[0039] In this embodiment, when the first cycle arrives, the evaluation model can be retrained based on the most recent historical running test data, thereby updating the evaluation model parameters to adapt to the changes in the new running state.

[0040] Specifically, the evaluation model can be derived from a machine learning or deep learning model. By retraining the evaluation model by inputting recent historical test data and corresponding health status data (e.g., healthy, warning, or dangerous), an updated model can be obtained. Before inputting the recent historical test data into the evaluation model, feature extraction can be performed on the historical test data. This can include calculating statistical features (including mean, variance, and standard deviation) or extracting specific characteristic parameters such as vibration peaks and frequency components. Feature extraction can remove irrelevant or unimportant features, reduce computational complexity, and improve model execution speed.

[0041] From the above, it can be concluded that the embodiment of the present disclosure takes into account that the changes in the target device's operating test data come not only from changes in its own performance, but also from changes in the operating environment. Therefore, the update cycle (i.e., the first cycle) of the evaluation model is determined based on the operating environment and the change in the operating data of the target device. When the changes in the operating environment and the operating data of the target device are small, there is no need to frequently update the parameters of the evaluation model, and a larger update cycle can be set. Conversely, when the changes in the operating environment and the operating data of the target device are large, the parameters of the evaluation model need to be updated in a timely manner, and a smaller update cycle can be set.

[0042] The embodiment of the present disclosure determines the update cycle of the evaluation model based on the operating environment of the target device and the change in the operating data. On the basis of ensuring the accuracy of the evaluation model, the number of model updates is reduced as much as possible, thereby reducing the amount of calculation.

[0043] In one embodiment of the present disclosure, determining the first period based on the change in the operating environment of the target device and the change in the operating data of the target device includes:

[0044] A first proportional parameter is determined based on the amount of change in the operating environment.

[0045] A second proportional parameter is determined based on the amount of change in the operating data.

[0046] The set reference period is adjusted based on the first proportional parameter and the second proportional parameter to obtain a first period.

[0047] In this embodiment, a specific implementation method for determining the first period based on the change in the operating environment of the target device and the change in the operating data of the target device is provided. First, the change in the operating environment of the target device can be quantitatively evaluated in a variety of ways, such as calculating statistical indicators such as the standard deviation, variance or change rate of the operating environment data over a period of time. These indicators can reflect the degree of fluctuation and stability of the operating environment data. Then, a first mapping relationship between the change in the operating environment and the first proportional parameter is established. The larger the change in the operating environment, the smaller the value of the first proportional parameter. Based on the above first mapping relationship and the change in the operating environment, the first proportional parameter corresponding to the change in the operating environment can be obtained.

[0048] Similarly, the change in the target device's operating data can be quantitatively assessed in a variety of ways, such as by calculating statistical indicators such as the standard deviation, variance, or rate of change of the operating test data over a period of time. These indicators can reflect the degree of fluctuation and stability of the operating test data. A second mapping relationship is then established between the change in the operating data and a second proportional parameter. The greater the change in the operating data, the smaller the value of the second proportional parameter. Based on this second mapping relationship and the change in the operating data, the second proportional parameter corresponding to the change in the operating data can be obtained.

[0049] Based on the first and second proportional parameters, a weighted sum of the first and second proportional parameters can be performed to obtain a fourth proportional parameter, and then the fourth proportional parameter can be multiplied by a set reference period to obtain the first period. The set reference period is a preset constant, and those skilled in the art can set a specific value of the reference period based on actual needs, which is not limited here.

[0050] It can be concluded from the above that this embodiment quantifies the changes in the operating environment and the operating data, and implements dynamic adjustment of the first cycle based on the quantification results, which is conducive to improving the accuracy and timeliness of the target device operating status assessment.

[0051] In one embodiment of the present disclosure, the operating environment change amount includes change rates of multiple operating environment parameters, and determining the first proportional parameter based on the operating environment change amount includes:

[0052] Determine the proportion of the operating environment parameters whose corresponding change rates exceed a first threshold among the multiple operating environment parameters to obtain a first proportion.

[0053] A first proportion parameter is determined based on the first proportion; the first proportion and the first proportion parameter are negatively correlated.

[0054] In this embodiment, a specific implementation method for determining a first proportional parameter based on the amount of change in the operating environment is provided. First, the change rates of multiple operating environment parameters (such as temperature, humidity, electromagnetic interference, etc.) can be calculated separately, and the proportion of operating environment parameters whose corresponding change rates exceed a first threshold value among the multiple operating environment parameters can be counted to obtain a first proportion. Then, the first proportional parameter is determined based on the size of the first proportion, wherein a larger first proportion indicates a larger amount of change in the operating environment parameter and a smaller first proportional parameter. The change rate of any operating environment parameter is used to indicate the degree of change between the measured value of the operating environment parameter and the reference value. The calculation process can include: first calculating the difference between the measured value of any operating environment parameter and the corresponding reference value, and then calculating the ratio of the difference to the reference value to obtain the change rate of any operating environment parameter.

[0055] Taking the pump units in a water pumping station as an example, the operating environmental parameters of the pump units include air temperature, water level, and water quality. The air temperature change rate indicates the degree of change in the ambient air temperature. Its calculation process may include calculating a first difference between the measured air temperature value (which may be an average value over a period of time) and a reference air temperature value, then calculating a first ratio between the first difference and the reference air temperature value, and determining this first ratio as the air temperature change rate. The water level change rate indicates the degree of change in the water level. Its calculation process may include calculating a second difference between the measured water level value (which may be an average value over a period of time) and the reference water level value, then calculating a second ratio between the second difference and the reference water level value, and determining this second ratio as the water level change rate. The water quality change rate indicates the degree of change in water quality. Its calculation process may include calculating a third difference between the measured water quality parameter value (specifically, turbidity) and the reference water quality value, then calculating a third ratio between the third difference and the reference water quality value, and determining this third ratio as the water quality change rate. Among them, the air temperature reference value, water level reference value and water quality reference value are all preset constants. Under normal circumstances, the pump group operates in the operating environment determined by the above reference values.

[0056] Based on this, we can calculate the number of data points exceeding the first threshold for the temperature change rate, water level change rate, and water quality change rate. Then, we can calculate the ratio of the number of data points exceeding the first threshold to the total number of data points to obtain the first percentage. For example, if the temperature change rate, water level change rate, and water quality change rate are all greater than the first threshold, the first percentage is 2 / 3. A larger first percentage indicates a greater degree of environmental change.

[0057] On the basis of obtaining the first proportion, the first proportion parameter can be determined based on the negative correlation between the first proportion and the first proportion parameter. For example, the first proportion parameter can be calculated using the following formula:

[0058] ;in, represents the first scale parameter, Indicates the first proportion.

[0059] Taking the target equipment as water disinfection equipment as an example, the operating environment parameters of the water disinfection equipment include water temperature, water hardness, pH value, impurity content in water, etc. The same method can be used to calculate the water temperature change rate, water hardness change rate, pH value change rate and impurity content change rate in water (including suspended matter change rate and organic matter change rate) respectively, and then count the number of data that exceed the first threshold among the five data of water temperature change rate, water hardness change rate, pH value change rate, suspended matter change rate and organic matter change rate, and then obtain the first proportion, and then calculate the first proportion parameter based on the first proportion.

[0060] It can be concluded from the above that this embodiment quantifies the first proportional parameter based on comprehensive consideration of operating environment changes in multiple dimensions, which can more accurately capture the potential impact of environmental changes on the operating status of the device.

[0061] In one embodiment of the present disclosure, the operating data variation includes a change rate of a plurality of operating parameters, and determining the second proportional parameter based on the operating data variation includes:

[0062] Determine the proportion of the operating parameters whose corresponding change rates exceed the second threshold among the multiple operating parameters to obtain a second proportion.

[0063] A second proportion parameter is determined based on the second proportion; the second proportion and the second proportion parameter are negatively correlated.

[0064] In this embodiment, a specific implementation method for determining a second proportional parameter based on the change in operating data is provided. First, the rates of change of multiple operating parameters (such as power, speed, vibration, and noise) are calculated separately. The percentage of operating parameters whose corresponding rates of change exceed a second threshold is then counted to obtain a second percentage. The second proportional parameter is then determined based on the magnitude of the second percentage. A larger second percentage indicates a greater change in the operating parameter and a smaller second proportional parameter. The rate of change of any operating parameter indicates the degree of variation between the measured value of that operating parameter and a reference value. The calculation process may include first calculating the difference between the measured value of any operating parameter and its corresponding reference value, and then calculating the ratio of this difference to the reference value to obtain the rate of change of any operating parameter.

[0065] Taking the pump units in a water pumping station as an example, the operating parameters of the pump units include performance parameters, temperature, vibration, electrical parameters, and noise. The performance parameter change rate indicates the degree of change in a performance parameter. Performance parameter change rates can include flow rate change rate, efficiency change rate, and head change rate. For example, the flow rate change rate calculation process may include calculating a fourth difference between a measured flow rate (which can be an average flow rate over a period of time) and a reference flow rate, then calculating a fourth ratio between the fourth difference and the reference flow rate, and determining this fourth ratio as the flow rate change rate. The temperature change rate indicates the degree of temperature change, and its calculation process may include calculating a fifth difference between a measured temperature value (which can be an average temperature value over a period of time) and a reference temperature value, then calculating a fifth ratio between the fifth difference and the reference temperature value, and determining this fifth ratio as the temperature change rate. The vibration change rate indicates the degree of vibration change, and its calculation process may include calculating a sixth difference between a measured vibration amplitude value (which can be an average vibration amplitude value over a period of time) and a reference vibration value, then calculating a sixth ratio between the sixth difference and the reference vibration value, and determining this sixth ratio as the vibration change rate. The electrical parameter change rate indicates the degree of change in an electrical parameter. The electrical parameter change rate can include the current parameter change rate and the voltage parameter change rate. For example, the current parameter change rate can be calculated by calculating the seventh difference between the measured current value (which can be the average current value over a period of time) and the current reference value, then calculating the seventh ratio of the seventh difference to the current reference value, and determining the seventh ratio as the current change rate. The noise change rate indicates the degree of change in noise. The noise change rate can be calculated by calculating the eighth difference between the measured noise amplitude value (which can be the average noise amplitude value over a period of time) and the noise reference value, then calculating the eighth ratio of the eighth difference to the noise reference value, and determining the eighth ratio as the noise change rate. The flow reference value, temperature reference value, vibration amplitude reference value, current reference value, and noise reference value are all preset constants that can be obtained by averaging the operating test data of the pump unit during normal operation.

[0066] Based on this, we can calculate the number of data points exceeding the second threshold across the eight data points: performance parameter change rate (including flow rate change rate, efficiency change rate, and head change rate), temperature change rate, vibration change rate, electrical parameter change rate (including current change rate and voltage change rate), and noise change rate. We then calculate the ratio of the number of data points exceeding the second threshold to the total number of data points to obtain the second percentage. For example, if both the current change rate and the voltage change rate exceed the second threshold, the second percentage is 2 / 8. A larger first percentage indicates a greater change in the operating data.

[0067] On the basis of obtaining the second proportion, the second proportion parameter can be determined based on the negative correlation between the second proportion and the second proportion parameter. For example, the second proportion parameter can be calculated using the following formula:

[0068] ;in, represents the second scale parameter, Indicates the second proportion.

[0069] Taking a water disinfection device as an example, the operating parameters of the water disinfection device include disinfection dose, UV intensity, energy consumption, etc. The same method can be used to calculate the disinfection dose change rate, UV intensity change rate, and energy consumption change rate, respectively. Then, the number of data points exceeding the second threshold value among the disinfection dose change rate, UV intensity change rate, and energy consumption change rate is counted to obtain the second proportion, and then the second proportional parameter is calculated based on the second proportion. The first threshold value and the second threshold value are both preset constants, and those skilled in the art can flexibly set them according to actual needs.

[0070] It can be concluded from the above that this embodiment quantifies the second proportional parameter based on comprehensive consideration of the operating data changes in multiple dimensions, which can more accurately capture the impact of the operating data on the operating status of the device.

[0071] In one embodiment of the present disclosure, the data processing method further includes:

[0072] In response to the first time interval being less than the first duration, the first period is adjusted based on a set third proportional parameter; the first time interval is the time interval between the current time point and the time point when the fault occurs.

[0073] In this embodiment, considering that the operating state of the target device may be unstable after each fault occurs and the fault may further deteriorate, it is necessary to shorten the update cycle of the evaluation model to accurately evaluate the operating state of the target device.

[0074] Specifically, a first time interval between the fault occurrence time and the current time point can be calculated. If the first time interval is shorter (less than the first duration), the first period is multiplied by a set third proportional parameter (e.g., 0.8) to further reduce the first period. The first duration is a preset constant, and those skilled in the art can set the specific value of the first duration based on actual needs.

[0075] From the above, it can be concluded that this embodiment shortens the update cycle of the evaluation model within the first period after the fault occurs, which is conducive to timely detection of whether the fault is effectively controlled or whether new fault signs appear.

[0076] In one embodiment of the present disclosure, the evaluation model is a support vector machine model, and updating the evaluation model based on the first cycle includes:

[0077] The update time of the evaluation model is determined based on the first cycle.

[0078] The evaluation model is updated at update time.

[0079] The update process includes:

[0080] A first evaluation value is determined based on the operating environment data of the target device; the first evaluation value is used to indicate the degree of degradation of the operating environment data.

[0081] A second evaluation value is determined based on the operational test data of the target device; the second evaluation value is used to indicate a degree of degradation of the operational test data.

[0082] In response to the first evaluation value being greater than the third threshold, or the second evaluation value being greater than the fourth threshold, the gamma parameter of the support vector machine model is increased by the first step length to obtain the first gamma parameter, the first gamma parameter is determined as the initial parameter of the support vector machine model, and the support vector machine model is retrained.

[0083] In this embodiment, a support vector machine model is used to evaluate the state of the target device. It classifies or regresses the data by finding an optimal hyperplane. In the state evaluation of the target device, different operating test data can be classified as different categories to achieve regression prediction.

[0084] For example, the first evaluation value can be calculated by comparing the difference between the target device's operating environment data and a reference value of the operating environment data, thereby quantifying the degree of degradation of the operating environment data. Specifically, the difference between the measured value of each operating environment parameter and the corresponding reference value can be calculated to obtain the degradation degree value corresponding to each operating environment parameter. The first evaluation value can then be obtained by taking a weighted average of the degradation degree values ​​corresponding to each operating environment parameter.

[0085] Taking the target equipment as a water pump station pump group as an example, the deterioration degree of the air temperature can be calculated first. The specific process may include: when the measured air temperature value is greater than the upper limit of the air temperature, the deterioration degree of the air temperature can be quantified by calculating the difference between the measured air temperature value and the upper limit of the air temperature; when the measured air temperature value is less than the lower limit of the air temperature, the deterioration degree of the air temperature can be quantified by calculating the difference between the measured air temperature value and the lower limit of the air temperature. Specifically, the deterioration degree of the air temperature can be calculated using the following formula:

[0086]

[0087] in, Indicates the degree of deterioration of the temperature. represents the measured value of temperature, Indicates the upper limit of temperature. Indicates the lower limit of temperature.

[0088] The same method can be used to calculate the deterioration degree values ​​of other operating environment parameters (such as water level and water quality). The first evaluation value can be obtained by weighted summing the deterioration degree values ​​of each operating environment parameter.

[0089] Taking the target equipment as water disinfection equipment as an example, the deterioration degree values ​​of water temperature, pH value, suspended matter and organic matter can be calculated respectively, and then the deterioration degree values ​​of the above operating environment parameters corresponding to the water disinfection equipment are weighted and summed to obtain the first evaluation value of the water disinfection equipment.

[0090] Similarly, the second evaluation value can be calculated by comparing the difference between the target device's operational test data and a reference value for the operational test data, thereby quantifying the degree of degradation of the operational test data. Specifically, the difference between the data of each operational parameter and its corresponding reference value can be calculated to obtain the degree of degradation value corresponding to each operational parameter. The second evaluation value can then be obtained by taking a weighted average of the degree of degradation values ​​corresponding to each operational parameter.

[0091] Taking the target device as a water pump station pump group as an example, the flow rate deterioration value can be calculated first. The specific process may include: when the measured flow rate value is greater than the flow rate upper limit value, the flow rate deterioration value can be quantified by calculating the difference between the measured flow rate value and the flow rate upper limit value; when the measured flow rate value is less than the flow rate lower limit value, the flow rate deterioration value can be quantified by calculating the difference between the measured flow rate value and the flow rate lower limit value. Specifically, the flow rate deterioration value can be calculated using the following formula:

[0092]

[0093] in, Indicates the deterioration degree of flow. represents the measured value of temperature, Indicates the upper limit of temperature. Indicates the lower limit of temperature.

[0094] The same method can be used to calculate the deterioration degree values ​​of other operating test data (such as efficiency, head, and electrical parameters). The second evaluation value can be obtained by weighted summing the deterioration degree values ​​of each operating test data.

[0095] Taking the target equipment as water disinfection equipment as an example, the deterioration degree value of the disinfection dose, the deterioration degree value of the ultraviolet intensity, and the deterioration degree value of the energy consumption can be calculated respectively, and then the deterioration degree values ​​of the above operating parameters corresponding to the water disinfection equipment can be weighted and summed to obtain the second evaluation value of the water disinfection equipment.

[0096] If the first evaluation value is greater than the third threshold, or the second evaluation value is greater than the fourth threshold, this indicates that the target device's operating environment data or operational test data has deteriorated significantly, increasing the probability of a target device failure. In this case, the first step length (i.e., an adjustment amount) can be increased based on the original gamma parameter value to increase the gamma parameter. This increased first gamma parameter is used as the initial parameter for the support vector machine model, and the support vector machine model is retrained to focus more on local data points, making it easier to identify localized failures. Alternatively, multiple gamma parameters can be preset. When the first evaluation value is greater than the third threshold, or the second evaluation value is greater than the fourth threshold, a larger gamma parameter is selected for retraining the support vector machine model. The third and fourth thresholds are both preset constants and can be set by those skilled in the art as needed.

[0097] From the above, it can be concluded that this embodiment, based on the preliminary analysis of the operating environment data and operating test data of the target device, can obtain the degree of degradation of the operating environment data or operating test data, and then set the corresponding gamma parameter for retraining the support vector machine model, which is conducive to improving the training accuracy of the model.

[0098] In one embodiment of the present disclosure, the data processing method further includes:

[0099] In the process of updating the evaluation model, if the number of fault samples is less than the set number, the penalty parameter C in the support vector machine model is increased.

[0100] In this embodiment, considering that when the number of fault samples is too small (less than a set number), the model may not fully learn the diversity and complexity of fault modes, resulting in insufficient fault detection capabilities. To address this issue, a penalty parameter C can be increased to make the model pay more attention to fault samples and improve its ability to identify fault states. For example, a second step size (i.e., an adjustment amount) can be added to the original penalty parameter C to increase the penalty parameter C.

[0101] From the above, it can be concluded that in this embodiment, when the number of fault samples is too small, by increasing the penalty for errors, the model will work harder to fit the features of the fault samples, thereby compensating for the problem of insufficient number of fault samples to a certain extent.

[0102] In one embodiment of the present disclosure, updating the evaluation model based on the first cycle includes:

[0103] In response to the first period being greater than a second time period, the evaluation model is updated based on operational test data within the second time period before the update time.

[0104] In this embodiment, if the first period is longer (greater than the second time period), it indicates that the operating environment data or operating test data of the target device is relatively constant. At this time, there is no need for too much operating test data to update the evaluation model. Only the most recent part of the data (that is, the operating test data within the second time period before the update time) is selected for updating the evaluation model, so that the model can better reflect the current status and future trends of the device, while reducing the amount of calculation.

[0105] Corresponding to the data processing method of the above embodiment, Figure 2 This is a structural block diagram of a data processing system provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The data processing system 20 includes: a first calculation module 21, a second calculation module 22, a third calculation module 23 and a model updating module 24.

[0106] The first calculation module 21 is configured to determine a change in the operating environment of the target device based on the operating environment data of the target device.

[0107] The second calculation module 22 is configured to determine a change in the operating data of the target device based on the operating test data of the target device.

[0108] The third calculation module 23 determines a first period based on the change amount of the operating environment of the target device and the change amount of the operating data of the target device.

[0109] The model updating module 24 is configured to update the evaluation model based on the first cycle, and evaluate the operating status of the target device based on the updated evaluation model.

[0110] In one embodiment of the present disclosure, the third calculation module 23 is specifically configured to:

[0111] A first proportional parameter is determined based on the amount of change in the operating environment.

[0112] A second proportional parameter is determined based on the amount of change in the operating data.

[0113] The set reference period is adjusted based on the first proportional parameter and the second proportional parameter to obtain a first period.

[0114] In one embodiment of the present disclosure, the operating environment change includes the change rates of multiple operating environment parameters, and the third calculation module 23 is further configured to:

[0115] Determine the proportion of the operating environment parameters whose corresponding change rates exceed a first threshold among the multiple operating environment parameters to obtain a first proportion.

[0116] A first proportion parameter is determined based on the first proportion; the first proportion and the first proportion parameter are negatively correlated.

[0117] In one embodiment of the present disclosure, the operating data variation includes the change rates of multiple operating parameters, and the third calculation module 23 is further configured to:

[0118] Determine the proportion of the operating parameters whose corresponding change rates exceed the second threshold among the multiple operating parameters to obtain a second proportion.

[0119] A second proportion parameter is determined based on the second proportion; the second proportion and the second proportion parameter are negatively correlated.

[0120] In one embodiment of the present disclosure, the third calculation module 23 is specifically configured to:

[0121] In response to the first time interval being less than the first duration, the first period is adjusted based on a set third proportional parameter; the first time interval is the time interval between the current time point and the time point when the fault occurs.

[0122] In one embodiment of the present disclosure, the model updating module 24 is specifically configured to:

[0123] The update time of the evaluation model is determined based on the first cycle.

[0124] The evaluation model is updated at update time.

[0125] The update process includes:

[0126] A first evaluation value is determined based on the operating environment data of the target device; the first evaluation value is used to indicate the degree of degradation of the operating environment data.

[0127] A second evaluation value is determined based on the operational test data of the target device; the second evaluation value is used to indicate a degree of degradation of the operational test data.

[0128] In response to the first evaluation value being greater than the third threshold, or the second evaluation value being greater than the fourth threshold, the gamma parameter of the support vector machine model is increased by the first step length to obtain the first gamma parameter, the first gamma parameter is determined as the initial parameter of the support vector machine model, and the support vector machine model is retrained.

[0129] In one embodiment of the present disclosure, the model updating module 24 is further configured to:

[0130] In response to the first period being greater than a second time period, the evaluation model is updated based on operational test data within the second time period before the update time.

[0131] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules / units in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 24 are shown.

[0132] It should be understood that in the embodiments of the present disclosure, the processor 301 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0133] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0134] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store device type information.

[0135] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the data processing method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0136] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0137] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0138] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0139] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0141] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present disclosure.

[0142] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0143] The above are only specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or replacements within the technical scope disclosed in this disclosure, and such modifications or replacements should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A data processing method, characterized in that: include: determining an amount of change in the operating environment of the target device based on the operating environment data of the target device; determining an amount of change in operating data of the target device based on operating test data of the target device; determining a first proportional parameter based on the operating environment change; determining a second proportional parameter based on the change in the operating data; Adjusting a set reference period based on the first proportional parameter and the second proportional parameter to obtain a first period; updating the evaluation model based on the first cycle, and evaluating the operating status of the target device based on the updated evaluation model; The operating environment change amount includes change rates of multiple operating environment parameters, and determining the first proportional parameter based on the operating environment change amount includes: Determine, among the plurality of operating environment parameters, a proportion of operating environment parameters whose corresponding change rates exceed a first threshold, to obtain a first proportion; determining the first proportion parameter based on the first proportion; the first proportion and the first proportion parameter are negatively correlated; The operating data variation includes a change rate of a plurality of operating parameters, and determining the second proportional parameter based on the operating data variation includes: Determining, among the plurality of operating parameters, a proportion of operating parameters whose corresponding change rates exceed a second threshold, to obtain a second proportion; determining the second proportion parameter based on the second proportion; the second proportion and the second proportion parameter are negatively correlated; Adjusting a set reference period based on the first proportion parameter and the second proportion parameter to obtain a first period includes: The first proportional parameter and the second proportional parameter are weightedly summed to obtain a fourth proportional parameter, and then the fourth proportional parameter is multiplied by a set reference period to obtain the first period.

2. The data processing method according to claim 1, wherein: Also includes: In response to the first time interval being less than the first duration, adjusting the first period based on a set third proportional parameter; The first time interval is the time interval between the current time point and the time point when the fault occurs.

3. The data processing method according to claim 1, wherein: The evaluation model is a support vector machine model, and updating the evaluation model based on the first cycle includes: determining an update time of the evaluation model based on the first period; The evaluation model is updated at the update time. The specific update process includes: determining a first evaluation value based on the operating environment data of the target device; the first evaluation value is used to indicate the degree of deterioration of the operating environment data; determining a second evaluation value based on the operation test data of the target device; wherein the second evaluation value is used to indicate the degree of degradation of the operation test data; In response to the first evaluation value being greater than the third threshold, or the second evaluation value being greater than the fourth threshold, the gamma parameter of the support vector machine model is increased by the first step length to obtain a first gamma parameter, the first gamma parameter is determined as the initial parameter of the support vector machine model, and the support vector machine model is retrained.

4. The data processing method according to claim 3, wherein: The updating of the evaluation model based on the first cycle includes: In response to the first period being greater than a second time period, the evaluation model is updated based on operational test data within the second time period before the update time.

5. A data processing system, characterized in that: include: A first calculation module, configured to determine a change in the operating environment of the target device based on the operating environment data of the target device; a second calculation module, configured to determine a change in the operating data of the target device based on the operating test data of the target device; A third calculation module is used to determine a first proportional parameter based on the change in the operating environment; determining a second proportional parameter based on the amount of change in the operating data; Adjusting the set reference period based on the first proportional parameter and the second proportional parameter to obtain a first period; A model updating module, configured to update the evaluation model based on the first cycle, and evaluate the operating status of the target device based on the updated evaluation model; The operating environment change includes the change rate of multiple operating environment parameters. The third calculation module is further configured to: Determine, among the plurality of operating environment parameters, a proportion of operating environment parameters whose corresponding change rates exceed a first threshold, to obtain a first proportion; Determining a first proportion parameter based on the first proportion; the first proportion and the first proportion parameter are negatively correlated; The operating data variation includes the change rates of multiple operating parameters. The third calculation module is further configured to: Determining, among the plurality of operating parameters, a proportion of operating parameters whose corresponding change rates exceed a second threshold, to obtain a second proportion; determining a second proportion parameter based on the second proportion; the second proportion and the second proportion parameter are negatively correlated; The third calculation module is further configured to: The first proportional parameter and the second proportional parameter are weightedly summed to obtain a fourth proportional parameter, and then the fourth proportional parameter is multiplied by a set reference period to obtain the first period.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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