Monitoring data processing method and device, computer device and storage medium

Abstract

The present disclosure relates to a kind of monitoring data processing method, device, computer equipment, storage medium and computer program product.The method comprises: determining the target partition identification corresponding to the target data to be acquired;According to the target partition identification, the target data partition, data monitoring point and target algorithm matched with the target data to be acquired are determined, wherein the data monitoring point includes the monitoring device information arranged on the equipment to be monitored;From the data slot in the target data partition, the monitoring data corresponding to the data monitoring point is acquired;The monitoring data corresponding to the data monitoring point is processed using the target algorithm, and the target data is obtained.The method can improve the data processing efficiency and be suitable for more application scenarios.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of device operation and maintenance, and particularly relates to a monitoring data processing method and device, computer equipment and a storage medium. BACKGROUND

[0002] In enterprise asset monitoring and operation, a large number of sensing monitoring devices or services need to be deployed and installed to obtain real-time states of enterprise assets. In general, in order to more clearly reflect the real-time state of the assets, real-time monitoring data reported by the monitoring devices need to be combined with device installation relationships, location relationships, etc. to calculate the actual required data, such as converting the temperature of an optical fiber into the temperature of a cable, converting the temperature of a temperature sensor into the temperature of a room, etc.

[0003] However, for different devices and different scenarios, there are great differences in data reporting frequency, data type, calculation relationship, calculation method, etc. The calculation of the real-time acquired monitoring data is difficult, and it is difficult to guarantee the timeliness of the acquired data and the calculated data. SUMMARY

[0004] Therefore, it is necessary to provide a monitoring data processing method and device, computer equipment, storage medium and computer program product which can improve data processing efficiency and are suitable for more application scenarios.

[0005] In a first aspect, an embodiment of the present disclosure provides a monitoring data processing method. The method comprises:

[0006] determining a target partition identifier corresponding to target data to be acquired;

[0007] determining a target data partition, a data monitoring point and a target algorithm matched with the target data to be acquired according to the target partition identifier, wherein the data monitoring point comprises monitoring device information arranged on a device to be monitored;

[0008] acquiring monitoring data corresponding to the data monitoring point from a data slot in the target data partition;

[0009] processing the monitoring data corresponding to the data monitoring point by using the target algorithm to obtain target data.

[0010] In one embodiment, the storage mode of the monitoring data into the data slot comprises:

[0011] acquiring monitoring data to be stored corresponding to a data monitoring point;

[0012] determining a data partition corresponding to a partition identifier matched with the data monitoring point;

[0013] Determine the data slot in the data partition that matches the data monitoring point;

[0014] The monitoring data to be stored is stored in the data slot.

[0015] In one embodiment, the stored data in the data slot includes monitoring data and the acquisition time corresponding to the monitoring data. The step of obtaining the monitoring data corresponding to the data monitoring point from the data slot in the target data partition includes:

[0016] Based on the slot identifier corresponding to the target data partition, the data slot in the target data partition is determined, and the data slot corresponds to the data monitoring point.

[0017] If all data slots in the target data partition contain monitoring data and the data collection time is within a preset time period, the monitoring data corresponding to the data monitoring point is obtained from the data slots in the target data partition.

[0018] In one embodiment, the stored data in the data slot includes monitoring data and the acquisition time corresponding to the monitoring data. The target data partition includes multiple data slots. The step of obtaining the monitoring data corresponding to the data monitoring point from the data slots in the target data partition includes:

[0019] When monitoring data is stored in multiple data slots in the target data partition and the acquisition time of the monitoring data is within a preset time period, multiple calibration slots are determined that correspond one-to-one with the multiple data slots. The data slots and the calibration slots are used to store candidate monitoring data at different acquisition times of the same data monitoring point. The acquisition time of the candidate monitoring data in the calibration slot is earlier than the acquisition time of the candidate monitoring data in the data slot.

[0020] When candidate monitoring data is stored in all the multiple data slots and the multiple calibration slots, the candidate monitoring data whose acquisition time meets the preset conditions is determined as the monitoring data corresponding to the data monitoring point.

[0021] In one embodiment, the step of determining the candidate monitoring data that meets the preset conditions at the collection time is the monitoring data corresponding to the data monitoring point, including:

[0022] The calibration duration is determined based on the data collection time interval of the data monitoring points;

[0023] The standard acquisition time is determined from the acquisition time of the candidate monitoring data based on the calibration duration and the acquisition time of the candidate monitoring data;

[0024] The target time period is determined based on the calibration duration and the standard acquisition time.

[0025] The candidate monitoring data collected during the target time period are determined as the monitoring data corresponding to the data monitoring point.

[0026] In one embodiment, before processing the monitoring data corresponding to the data monitoring point using the target algorithm, the method further includes:

[0027] Delete the monitoring data corresponding to the data monitoring point in the data slot.

[0028] Secondly, embodiments of this disclosure also provide a monitoring data processing apparatus. The apparatus includes:

[0029] The first determining module is used to determine the target partition identifier corresponding to the target data to be acquired.

[0030] The second determining module is used to determine the target data partition, data monitoring point and target algorithm that match the target data to be acquired based on the target partition identifier, wherein the data monitoring point includes monitoring device information set on the device to be monitored;

[0031] The acquisition module is used to acquire monitoring data corresponding to the data monitoring point from the data slot in the target data partition;

[0032] The processing module is used to process the monitoring data corresponding to the data monitoring points using the target algorithm to obtain the target data.

[0033] In one embodiment, the storage module for storing monitoring data in data slots includes:

[0034] The first acquisition submodule is used to acquire the monitoring data to be stored corresponding to the data monitoring points;

[0035] The first determining submodule is used to determine the data partition corresponding to the partition identifier that matches the data monitoring point;

[0036] The second determining submodule is used to determine the data slot in the data partition that matches the data monitoring point;

[0037] The storage module is used to store the monitoring data to be stored into the data slot.

[0038] In one embodiment, the data stored in the data slot includes monitoring data and the acquisition time corresponding to the monitoring data. The acquisition module includes:

[0039] The third determining submodule is used to determine the data slot in the target data partition according to the slot identifier corresponding to the target data partition, wherein the data slot corresponds to the data monitoring point.

[0040] The second acquisition submodule is used to acquire the monitoring data corresponding to the data monitoring point from the data slots in the target data partition, provided that all data slots in the target data partition store monitoring data and the data collection time is within a preset time period.

[0041] In one embodiment, the stored data in the data slot includes monitoring data and the corresponding acquisition time of the monitoring data. The target data partition includes multiple data slots. The acquisition module includes:

[0042] The fourth determination submodule is used to determine multiple calibration slots that correspond one-to-one with the multiple data slots when monitoring data is stored in multiple data slots in the target data partition and the acquisition time of the monitoring data is within a preset time period. The data slots and the calibration slots are used to store candidate monitoring data at different acquisition times of the same data monitoring point. The acquisition time of the candidate monitoring data in the calibration slot is earlier than the acquisition time of the candidate monitoring data in the data slot.

[0043] The fifth determination submodule is used to determine the candidate monitoring data whose acquisition time meets the preset conditions as the monitoring data corresponding to the data monitoring point when the candidate monitoring data is stored in the multiple data slots and the multiple calibration slots.

[0044] In one embodiment, the fifth determining submodule includes:

[0045] The first determining unit is used to determine the calibration duration based on the data acquisition time interval of the data monitoring points;

[0046] The second determining unit is used to determine the standard acquisition time from the acquisition time of the candidate monitoring data based on the calibration duration and the acquisition time of the candidate monitoring data;

[0047] The third determining unit is used to determine the target time period based on the calibration duration and the standard acquisition time.

[0048] The fourth determining unit is used to determine the candidate monitoring data collected during the target time period as the monitoring data corresponding to the data monitoring point.

[0049] In one embodiment, the processing module further includes, prior to:

[0050] The deletion module is used to delete the monitoring data corresponding to the data monitoring point in the data slot.

[0051] Thirdly, embodiments of this disclosure also provide a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the embodiments of this disclosure.

[0052] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the method described in any one of the embodiments of this disclosure.

[0053] Fifthly, embodiments of this disclosure also provide a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the embodiments of this disclosure.

[0054] In this embodiment, a monitoring device is installed on the device to be monitored. The monitoring device monitors the status of the device and determines the target partition identifier corresponding to the target data to be acquired during status monitoring. Based on the target partition identifier, the target data partition, data monitoring point, and target algorithm matching the target data to be acquired can be determined. Thus, the corresponding monitoring data is acquired based on the target data partition and data monitoring point, and the acquired monitoring data is processed using the target algorithm to obtain the target data. This achieves the calculation of the target data to be acquired based on the monitoring data corresponding to the device to be monitored. In this embodiment, by setting data slots for the monitoring data and setting the target partition identifier, the target data is obtained. By obtaining the correlation between target data and data partitions, monitoring data can be directly retrieved from the corresponding data slots based on the target data to be acquired. This eliminates the need for separate settings based on different scenarios, data types, and monitoring cycles, reducing the computational difficulty of obtaining target data from real-time acquired monitoring data. Since the process in this embodiment involves acquiring data and storing it in data slots, it can be directly called during calculation. Even under complex conditions such as different application scenarios and different devices, it can efficiently and quickly process and calculate monitoring data, ensuring the real-time and accuracy of target data acquisition. This enables real-time monitoring of device status, facilitates timely detection of anomalies, and is applicable to more application scenarios. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating a monitoring data processing method in one embodiment;

[0056] Figure 2 This is a schematic diagram illustrating the storage method of storing monitoring data in data slots in one embodiment;

[0057] Figure 3This is a schematic diagram of the structure of a monitoring data processing system in one embodiment;

[0058] Figure 4 This is a flowchart illustrating a monitoring data processing method in one embodiment;

[0059] Figure 5 This is a flowchart illustrating the storage method of monitoring data in one embodiment;

[0060] Figure 6 This is a schematic diagram of the time-series distribution of data in one embodiment;

[0061] Figure 7 This is a structural block diagram of a monitoring data processing device in one embodiment;

[0062] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of this disclosure and are not intended to limit the embodiments of this disclosure.

[0064] In enterprise asset monitoring and maintenance, a large number of sensor monitoring devices or services need to be deployed and installed to obtain the real-time status of enterprise assets. Typically, to more clearly reflect the real-time status of assets, it is necessary to calculate the required data by combining the real-time monitoring data reported by the monitoring devices with factors such as device installation relationships and location relationships. For example, converting fiber optic temperature to cable temperature or temperature sensor temperature to room temperature. Typically, one or more monitoring devices may be installed on the device to be monitored. When monitoring the device status in real time, it is necessary to acquire the monitoring data collected by the monitoring devices and perform calculations according to the corresponding methods to obtain the required data, thereby enabling real-time monitoring of the device status.

[0065] Based on practical technical needs similar to those described above, embodiments of this disclosure provide a monitoring data processing method, apparatus, computer equipment, storage medium, and computer program product.

[0066] In one embodiment, such as Figure 1 As shown, a monitoring data processing method is provided, the method comprising:

[0067] Step S110: Determine the target partition identifier corresponding to the target data to be acquired;

[0068] In this embodiment, a target partition identifier is determined based on the target data to be acquired. The target data includes data obtained directly or indirectly from monitoring data, which includes data collected by a monitoring device on the device to be monitored. In one example, the monitoring data is data that can be directly collected by the monitoring device, and the target data to be acquired is data that reflects the state of the device to be monitored. In some scenarios, target data reflecting the state of the device to be monitored can be obtained by calculating the corresponding monitoring data. When monitoring the state of the device to be monitored, it may be necessary to combine data from multiple dimensions for state judgment, or it may be necessary to monitor the state of the device from multiple dimensions. Therefore, in some examples, multiple data to be acquired need to be obtained directly or indirectly from the monitoring data, and the state of the device to be monitored is then integrated based on these multiple data. In this embodiment, the data to be acquired corresponds to a partition identifier. The target partition identifier can be determined based on the target data to be acquired. Typically, there is a one-to-one correspondence between the data to be acquired and the partition identifier. In one possible implementation, the association between the data to be acquired and the partition identifier is determined in advance based on the actual application scenario. In one example, the association between the data to be acquired and the partition identifier can be modified and adjusted according to the actual application scenario.

[0069] Step S120: Determine the target data partition, data monitoring point and target algorithm that match the target data to be acquired according to the target partition identifier, wherein the data monitoring point includes monitoring device information set on the device to be monitored;

[0070] In this embodiment, after determining the target partition identifier, the target data partition, data monitoring point, and target algorithm that match the target data to be acquired are determined based on the target partition identifier. Different partition identifiers correspond to different target data partitions, monitoring points, and algorithms. Typically, a data partition includes data slots for storing monitoring data. In one example, the data to be acquired is obtained directly or indirectly from monitoring data. Based on the relationship between the data to be acquired and the monitoring data, the data monitoring point and algorithm corresponding to the data to be acquired can be determined. Using the algorithm, the monitoring data corresponding to the corresponding data monitoring point can be calculated to obtain the data to be acquired. In this embodiment, the data to be acquired corresponds to a partition identifier. Based on the above-mentioned association between the data to be acquired and the corresponding data monitoring point and algorithm, an association between the partition identifier, data monitoring point, and target algorithm can be established. Typically, data corresponds to a data storage area. In this embodiment, the data to be acquired corresponds to a data partition, and the monitoring data corresponding to the data to be acquired is stored in this data partition. There is a correspondence between the data to be acquired and the data partition, and the data to be acquired corresponds to a partition identifier; therefore, there is a correspondence between the partition identifier and the data partition. In this embodiment, the data monitoring point includes monitoring device information set on the device to be monitored. When monitoring the status of the device, monitoring data can be obtained by setting a monitoring device on the device. The monitoring device can be configured according to the different devices and monitoring data in the actual application scenario. For example, when the device to be monitored is an optical fiber, the monitoring device may include, but is not limited to, temperature sensors, pressure sensors, etc. Each monitoring device on the device to be monitored has corresponding monitoring device information, which may include, but is not limited to, the location and type of the monitoring device. The monitoring device information can be represented by a monitoring device identifier, monitoring device name, etc. In one example, the monitoring device information can correspond to a unique monitoring device. In one possible implementation, this can be achieved by setting an identifier; the data monitoring point is the unique identifier corresponding to the monitoring device. Specifically, a unique identifier is set for each monitoring device, and this unique identifier can identify the unique corresponding monitoring device. In one example, one data monitoring point can correspond to multiple partition identifiers.

[0071] Step S130: Obtain the monitoring data corresponding to the data monitoring point from the data slot in the target data partition;

[0072] In this embodiment of the disclosure, after determining the target data partition and data monitoring points, monitoring data corresponding to the data monitoring points is obtained from the data slots in the target data partition. Specifically, data slots are matched with data monitoring points, with one data slot corresponding to one data monitoring point, and a data partition may include one or more data slots. Based on the determined target data partition, monitoring data can be obtained from the data slots in the target data partition, and the monitoring data in the data slots corresponds to the data monitoring points. In one example, the monitoring data in the data slots is obtained and uploaded in real time by the monitoring device on the device to be monitored. In one example, when the target data partition includes multiple data slots, it is necessary to obtain the corresponding monitoring data from each of the multiple data slots.

[0073] Step S140: The target algorithm is used to process the monitoring data corresponding to the data monitoring point to obtain the target data.

[0074] In this embodiment, a target algorithm is used to process the monitoring data corresponding to the data monitoring points to obtain target data. The status of the device to be monitored can be determined using the target data. In one example, the monitoring data can be processed using a preset computing unit. When multiple algorithms are involved in the actual application scenario, multiple corresponding computing units are set according to the calculation principles of different algorithms. During the calculation, the monitoring data is uploaded to the corresponding computing unit to complete the calculation and obtain the target data. In this embodiment, the target computing unit can be determined according to the target algorithm. The acquired monitoring data is uploaded to the target computing unit, and after the target computing unit calculates and processes the data, the target data is obtained.

[0075] In this embodiment, a monitoring device is installed on the device to be monitored. The monitoring device monitors the status of the device and determines the target partition identifier corresponding to the target data to be acquired during status monitoring. Based on the target partition identifier, the target data partition, data monitoring point, and target algorithm matching the target data to be acquired can be determined. Thus, the corresponding monitoring data is acquired based on the target data partition and data monitoring point, and the acquired monitoring data is processed using the target algorithm to obtain the target data. This achieves the calculation of the target data to be acquired based on the monitoring data corresponding to the device to be monitored. In this embodiment, by setting data slots for the monitoring data and setting the target partition identifier, the target data is obtained. By obtaining the correlation between target data and data partitions, monitoring data can be directly retrieved from the corresponding data slots based on the target data to be acquired. This eliminates the need for separate settings based on different scenarios, data types, and monitoring cycles, reducing the computational difficulty of obtaining target data from real-time acquired monitoring data. Since the process in this embodiment involves acquiring data and storing it in data slots, it can be directly called during calculation. Even under complex conditions such as different application scenarios and different devices, it can efficiently and quickly process and calculate monitoring data, ensuring the real-time and accuracy of target data acquisition. This enables real-time monitoring of device status, facilitates timely detection of anomalies, and is applicable to more application scenarios.

[0076] In one example, a point number can be used as a unique identifier for the data, distinguishing and differentiating it. The point number includes a globally unique identifier composed of the identifier of the device to be monitored and the identifier of a specific monitoring device on that device. For instance, if the data to be acquired corresponds to a target point number, when storing the monitoring data to be stored in a data slot, the data to be stored can be the monitoring value corresponding to the data monitoring point (which can be represented by the source point number `origin`). After format conversion, the monitoring data to be stored is stored in the data slot, resulting in the monitoring data. The structure of the monitoring data in the data slot can include the source point number of the monitoring data to be stored, the target point number (dest) corresponding to the data to be acquired that needs to participate in the calculation, the monitoring value (mVal), and the data generation time or acquisition time (eventTime). In one example, independent data partitions can be created using the target point number as the key. Each data partition can have a corresponding partition identifier, which can be obtained directly or indirectly from the target point number.

[0077] In one possible implementation, a triplet can be used to store the association between the data to be acquired, the corresponding data monitoring points, and the algorithm. The triplet is an abstract structure used to describe the aggregation calculation, and it includes the identifier of the data to be acquired (e.g., target point number, partition identifier, etc.), the data monitoring point corresponding to the data to be acquired (e.g., source point number), and the algorithm. In one example, the triplet could be...<dest,originDict,algorithm> Here, originDict represents the dictionary of source points that need to participate in the calculation, with origin as the key, position number and other parameters as the value, and the length is not less than 1, and algorithm is the corresponding algorithm.

[0078] In one embodiment, such as Figure 2 As shown, the storage methods for storing monitoring data in data slots include:

[0079] Step S210: Obtain the monitoring data to be stored corresponding to the data monitoring points;

[0080] Step S220: Determine the data partition corresponding to the partition identifier that matches the data monitoring point;

[0081] Step S230: Determine the data slot in the data partition that matches the data monitoring point;

[0082] Step S240: Store the monitoring data to be stored into the data slot.

[0083] In this embodiment of the disclosure, monitoring data is stored according to its partition identifier. Specifically, the monitoring data to be stored corresponding to the monitoring point is acquired. In one example, the monitoring data to be stored can be collected by the monitoring device corresponding to the monitoring point. A data partition corresponding to the partition identifier matching the monitoring point is determined. The association between the monitoring point and the partition identifier can be set according to the actual application scenario. Typically, one data partition corresponds to one piece of data to be acquired. Based on the association between the data to be acquired and the monitoring data, the association between the partition identifier and the monitoring data can be determined, thereby establishing the association between the monitoring point and the partition identifier. One monitoring point can be matched with multiple partition identifiers. After determining the data partition, the data slot matching the monitoring point can be determined from the corresponding data partition. The data slot corresponds to the monitoring point. The monitoring data to be stored is then stored in the corresponding data slot, thus completing the acquisition and storage of the monitoring data. In one example, when collecting monitoring data through a monitoring device, the monitoring device can be a periodic monitoring device. Correspondingly, when storing data, it can also be periodic storage. Each time a new period of monitoring data is acquired, it is stored in the corresponding data slot, replacing the original monitoring data in the data slot.

[0084] In this embodiment of the disclosure, during the acquisition and storage of monitoring data, the monitoring data can be stored in the corresponding data slot of the corresponding data partition based on the association between the data monitoring point and the partition identifier. This enables data diversion processing during the storage process. In subsequent processes, the monitoring data can be directly retrieved from the corresponding data slot for calculation and processing. There is no need to make additional settings for the storage and retrieval of monitoring data according to different scenarios, data types, and monitoring cycles. This reduces the difficulty of calculating the real-time acquired monitoring data. Calculation and analysis can be achieved directly by calling the data, enabling efficient and rapid processing of monitoring data. This ensures the real-time and accuracy of target data acquisition, realizes real-time monitoring of equipment status, and facilitates timely detection of anomalies.

[0085] Figure 3 This is a schematic diagram illustrating the structure of a monitoring data processing system according to an exemplary embodiment. Figure 4 This is a flowchart illustrating a monitoring data processing method according to an exemplary embodiment, with reference to... Figure 3As shown, the monitoring device performs real-time monitoring of the equipment to be monitored, collects monitoring data, and sends the monitoring data to be stored to the distribution operator through a forwarding service. The monitoring device corresponds to data monitoring points, which can be represented by source point numbers. In one example, the forwarding service may include, but is not limited to, IoT management services. In this embodiment, the data to be acquired can be calculated and analyzed based on different monitoring data. The data to be acquired corresponds to a target point number, and a partition identifier matching the target point number can be set. The corresponding data partitions are obtained using the partition identifier. In one example, the partition identifier can be obtained by hashing the target point number, reducing the collision rate of the partition identifier. For example, the collision rate can be reduced by using double hashing. First, a hash operation is performed on the target point number to obtain the first operation result, and then a hash operation is performed on the first operation result to obtain the second operation result. The second operation result is used as the partition identifier. The first operation result can be calculated using MurmurHash to obtain the second operation result. The data splitting operator splits data based on the target point number and forwards it to the downstream correlation calculation operator. Specifically, the splitting operator determines the corresponding target point number based on the source point number of the monitoring data, splits the monitoring data using the target point number, and stores the monitoring data in the data slot of the corresponding target point number's data partition. The correlation calculation operator in the data partition obtains all the monitoring data that needs to participate in the calculation based on the target point number and performs the calculation using the corresponding algorithm to obtain the corresponding data. In one example, the algorithm in the algorithm module can be called by the correlation calculation operator to perform the calculation. The calculated data is sent to the storage and forwarding module, which processes the calculated data according to the actual application scenario. For example, the storage and forwarding module can store the data in a preset database and send it to the downstream service for use. In this embodiment, the target data is obtained after calculation. The target data can be used to judge and monitor the status of the device to be monitored, which facilitates timely detection of anomalies. Here, the operator is a calculation link used to execute a certain logic. This embodiment mainly involves the splitting operator and the correlation calculation operator.

[0086] Figure 5 This is a flowchart illustrating a method for storing monitoring data according to an exemplary embodiment. (Refer to...) Figure 5As shown, during the data splitting process, since each target point number corresponds to an independent data partition, one monitoring data point may correspond to one or more target point numbers. When partitioning and storing the monitoring data, a list of associated target point numbers is determined based on the source point number of the monitoring data to be stored. This list can be obtained from a preset cache area. Based on the number of target point numbers in the target point number list, the monitoring data to be stored is cloned to obtain a number of monitoring data points with the same number of target point numbers. The target point numbers are then set into the corresponding fields of the monitoring data to be stored for differentiation. Based on the target point numbers in the cloned and set data, the corresponding partition is determined, and the data is sent to the corresponding data partition for storage. The partition can be determined based on the hash value of the target point number. A secondary hash value calculation is performed on the target point number to obtain the partition number, thus identifying the corresponding data partition.

[0087] In one embodiment, the stored data in the data slot includes monitoring data and the acquisition time corresponding to the monitoring data. The step of obtaining the monitoring data corresponding to the data monitoring point from the data slot in the target data partition includes:

[0088] Based on the slot identifier corresponding to the target data partition, the data slot in the target data partition is determined, and the data slot corresponds to the data monitoring point.

[0089] If all data slots in the target data partition contain monitoring data and the data collection time is within a preset time period, the monitoring data corresponding to the data monitoring point is obtained from the data slots in the target data partition.

[0090] In this embodiment, monitoring data within a preset time period needs to be obtained from data slots. Specifically, data slots within the target data partition are determined based on the slot identifier corresponding to the target data partition, where each data slot corresponds to a data monitoring point. If monitoring data is stored in all data slots within the target data partition and the data collection time falls within the preset time period, the monitoring data corresponding to the data monitoring point is obtained from the data slots within the target data partition. The preset time period can be determined in advance based on the actual application scenario. Different data to be acquired can correspond to the same or different preset time periods. When the data collection time falls within the preset time period, the accuracy of the target data calculated from the monitoring data at this time is considered to be high, and it can be used to determine the status of the monitored equipment. In one example, the monitoring data is collected by a monitoring device. The device collects the data in real time and uploads it to the corresponding data slot. When retrieving monitoring data from the target data partition, some data slots in the target data partition may not contain any monitoring data. In this case, the status of the data slots in the target data partition can be determined. If all data slots contain monitoring data and the data collection time is within a preset time period, the monitoring data corresponding to the monitoring point is retrieved from the corresponding data slot. In another example, if the data collection time of the monitoring data in the data slot is not within the preset time period, the accuracy of the target data obtained from the monitoring data processing at this time can be considered low. In this case, subsequent processing is set to send a timeout message, and no further calculation of the monitoring data is performed.

[0091] In this embodiment, the status of the data slot and the data acquisition time are judged when acquiring monitoring data. This ensures the integrity and accuracy of the acquired monitoring data, thereby guaranteeing the accuracy of the target data after processing, improving the accuracy of monitoring the equipment under monitoring, and facilitating timely detection of anomalies. By acquiring monitoring data through this embodiment, no additional settings are required based on different application scenarios and monitoring cycles. The accuracy and validity of the acquired monitoring data are guaranteed directly by judging the slot status and data acquisition time, reducing the difficulty and complexity of acquiring and calculating monitoring data, and improving the efficiency of data acquisition and calculation.

[0092] In one embodiment, the stored data in the data slot includes monitoring data and the acquisition time corresponding to the monitoring data. The target data partition includes multiple data slots. Obtaining the monitoring data corresponding to the data monitoring point from the data slots in the target data partition includes:

[0093] When monitoring data is stored in multiple data slots in the target data partition and the acquisition time of the monitoring data is within a preset time period, multiple calibration slots are determined that correspond one-to-one with the multiple data slots. The data slots and the calibration slots are used to store candidate monitoring data at different acquisition times of the same data monitoring point. The acquisition time of the candidate monitoring data in the calibration slot is earlier than the acquisition time of the candidate monitoring data in the data slot.

[0094] When candidate monitoring data is stored in all the multiple data slots and the multiple calibration slots, the candidate monitoring data whose acquisition time meets the preset conditions is determined as the monitoring data corresponding to the data monitoring point.

[0095] In this embodiment, time correction can also be performed on the monitoring data in the data slots according to actual application needs. Specifically, when acquiring monitoring data, if multiple data slots in the target data partition store monitoring data and the acquisition time of the monitoring data is within a preset time period, multiple calibration slots corresponding one-to-one with the multiple data slots are determined. In one possible implementation, whether time correction is required can be preset. When multiple data slots in the target data partition store monitoring data and the acquisition time of the monitoring data is within a preset time period, it is determined whether a time correction process is preset. If time correction is required, multiple calibration slots corresponding one-to-one with the multiple data slots are determined. The data slots and calibration slots correspond one-to-one. The data slots and their corresponding calibration slots are used to store candidate monitoring data at different acquisition times for the same data monitoring point. The acquisition time of the candidate monitoring data in the calibration slot is earlier than the acquisition time of the candidate monitoring data in the data slot. In one example, two sets of candidate monitoring data are needed for time correction; therefore, calibration slots and data slots are set to store these two sets of candidate monitoring data. In this embodiment, the monitoring data collected by the monitoring device is collected, uploaded, and stored in the corresponding slots. As the monitoring data is uploaded and stored, the slots corresponding to the data partitions are filled. During time correction, the calibration slot is used to store candidate monitoring data when the slot was previously filled, and the data slot is used to store candidate monitoring data when the slot is currently filled. In one example, monitoring data is collected and stored in the data slots. When the data slots in the data partition are filled, the corresponding monitoring data is moved to the corresponding calibration slot, and the system continues to wait for the data slots to fill, thus obtaining two sets of candidate monitoring data. When both the data slots and calibration slots contain candidate monitoring data, time correction can be performed. Specifically, each candidate monitoring data has a corresponding collection time when it is collected and stored in the slot. Based on the collection time, candidate monitoring data whose collection time meets preset conditions is determined from the candidate monitoring data as the monitoring data of the data monitoring point, which can be used for subsequent analysis and processing to obtain the target data. The preset conditions can be determined based on the actual application scenario. For example, the preset conditions can be set as a set of candidate monitoring data with the closest acquisition time, or a set of candidate monitoring data with a shorter time difference between the acquisition time and the preset time. In this embodiment, the data slots and calibration slots correspond one-to-one. When determining the monitoring data for subsequent processing calculations from the candidate monitoring data, each set of data slots and calibration slots needs to determine one set of monitoring data for subsequent processing calculations, thereby ensuring that the monitoring data and the data monitoring points correspond.

[0096] In this embodiment, candidate monitoring data is stored in calibration slots and data slots, and the data whose acquisition time meets the conditions is determined as the monitoring data corresponding to the data monitoring point. This ensures that the acquisition time of the monitoring data used for subsequent analysis and calculation meets the requirements, improves the accuracy of the calculated target data, avoids the problem of large data errors caused by acquisition time not meeting the requirements, and thus ensures timely detection of problems such as abnormalities of the monitored equipment. This method is applicable to more application scenarios.

[0097] In one embodiment, the determination of candidate monitoring data whose collection time meets preset conditions is the monitoring data corresponding to the data monitoring point, including:

[0098] The calibration duration is determined based on the data collection time interval of the data monitoring points;

[0099] The standard acquisition time is determined from the acquisition time of the candidate monitoring data based on the calibration duration and the acquisition time of the candidate monitoring data;

[0100] The target time period is determined based on the calibration duration and the standard acquisition time.

[0101] The candidate monitoring data collected during the target time period are determined as the monitoring data corresponding to the data monitoring point.

[0102] In this embodiment, when screening and determining candidate monitoring data, the calibration duration is determined based on the data acquisition time interval of the data monitoring points. Typically, the monitoring data corresponding to a data monitoring point can be collected periodically according to a preset data acquisition time interval. The calibration duration is usually shorter than the data acquisition time interval. Multiple data monitoring points can correspond to different data acquisition time intervals. Since the calibration duration is determined based on the data acquisition time interval in this embodiment, the time difference between the data acquisition time intervals of multiple data monitoring points is within a preset range. This preset range can be a small range set according to the actual application scenario to ensure that the monitoring data obtained based on the calibration duration can accurately calculate the target data. In one example, the calibration duration can be determined based on the shortest data acquisition time interval corresponding to multiple data monitoring points, and the calibration duration is shorter than the shortest data acquisition time interval. In another example, the calibration duration can be determined as 0.5 times the corresponding data acquisition time interval (such as the shortest data acquisition time interval). A standard acquisition time is determined based on the calibration duration and the acquisition time of the candidate monitoring data. The standard acquisition time is determined from the acquisition time of the candidate monitoring data. In one example, the maximum acquisition time corresponding to the candidate monitoring data can be used as the initial acquisition time. The time difference between the initial acquisition time and the acquisition time of the candidate monitoring data is determined, and combined with the calibration duration, the standard acquisition time is obtained. The target time period is determined based on the calibration duration and the standard acquisition time. In one example, the time period corresponding to the standard acquisition time ± the calibration duration can be determined as the target time period. Candidate monitoring data whose acquisition time falls within the target time period are identified as the monitoring data corresponding to the data monitoring points. In one example, after determining the monitoring data corresponding to the data monitoring points, it can be sent to the corresponding algorithm module for calculation, and the monitoring data corresponding to the aforementioned data monitoring points in the data slots and all candidate monitoring data in the calibration slots are deleted. The remaining candidate monitoring data in the data slots is transferred to the corresponding calibration slots. In one example, if time correction needs to continue, the process continues until the candidate monitoring data fills the slots, and the above correction process is repeated; if time correction does not need to continue, there is no need to transfer the remaining candidate monitoring data in the data slots. The monitoring data is directly stored in the data slots and subsequent calculation and analysis are performed. In one example, whether to continue performing time correction can be determined based on the actual application scenario. For example, it can be determined based on the instructions issued by the operation and maintenance personnel, the collection time of the monitoring data corresponding to the determined data monitoring point, or it can be set in advance according to the actual application scenario, etc.

[0103] This embodiment of the disclosure determines the standard acquisition time based on the calibration duration and acquisition time, obtains the target time period, and determines the monitoring data corresponding to the data monitoring point. It can efficiently and accurately determine the required monitoring data from the candidate monitoring data, further optimizes the process of time correction of monitoring data, ensures the accuracy and effectiveness of the target data calculated from the monitoring data, is applicable to more application scenarios, facilitates timely detection of anomalies in the monitored equipment, ensures the normal operation of the monitored equipment, and reduces the workload of maintenance personnel.

[0104] Time correction can solve the problem of large calculation errors that may occur when multiple data slots have the same or similar data collection and upload frequencies, and data from an incorrect time is selected for a particular slot. In one possible implementation, monitoring devices A, B, and C report data a1, b1, and c1 at 09:00:00, 09:05:00, and 09:05:01, respectively. Then, A reports data a2 again at 09:05:02. If time synchronization correction is not performed, a1, b1, and c1 will be selected to calculate the target data m. In this case, the calculated result has a large error, and m cannot accurately represent the real-time status. The reasonable combination should be a2, b1, and c1. In this embodiment, when performing time correction, sl represents the slots (i.e., the corresponding monitoring data in the data slots) when all data slots were filled last time, and sl_time i This indicates the time of data collection for each monitoring data point; sc represents the slots when all data slots are filled, sc_time i This indicates the acquisition time for each monitoring data point; n represents the number of data slots; p i This represents the time difference between the two most recent data points corresponding to the same data slot, where p i =sc_time i -sl_time i , 1≤i≤n, p max =max(p i ), 1≤max≤n; t=c×p max t represents the time tolerance value, i.e., the calibration duration, and c is the tolerance coefficient. c can be set according to the actual application scenario. Generally, 0 < c < 1. In one example, c can be set to 0.5. Based on the above, when performing time correction, it is necessary to select a set of standard values ​​(i.e., monitoring data corresponding to the data monitoring points) that are closest in time to the n*2 elements (i.e., candidate monitoring data) from sc and sl for calculation. The specific steps are as follows: Step 1: Set the reference value r = sc_time max Calculate d_sc_time k =|r-sc_time k|, where 1 ≤ k ≤ n and k ≠ max, if and r > sc_time k , then r = sl_time max ; Step 2: Calculate d_sc_time k and d_sl_time k respectively. Select sc j from sc, where j ∈ k and d_sc_time j < t. Select sl j from sl, where j ∈ k and d_sl_time j < t. Combine with the reference value r (standard acquisition time) to form a standard group and send it to the algorithm module; Step 3: Clear the elements of the standard group and the remaining elements in sl, and move the remaining elements in sc to sl. Wait for new elements to arrive. When sl is full, if continuous correction is required, execute Step 1. If it is satisfied, send it to the algorithm module for calculation. If it is not satisfied, wait until sc is full and repeat the above steps. In a possible implementation, it can be configured in specific usage according to the actual application scenario whether to perform time correction and whether to correct only once or continuously.

[0105] To further clarify the process of time correction, taking the above time correction method applied to a specific application scenario as an example, as shown in Table 1, assume there are four data slots. During the time correction process, the elements of the last two times corresponding to these four slots need to be obtained, as shown in Table 1.

[0106] Table 1

[0107]

[0108]

[0109] According to Table 1, the temporal distribution of the data in the corresponding data slots is as Figure 6 shown. The light-colored points are the previous group (sl), and the dark-colored points are the current group (sc). It can be seen that actually the elements in slots 1 and 2 of sl and the elements in slots 3 and 4 of sc should belong to the data of the same cycle. That is, the specific processing flow is as follows: In this example, the time tolerance value t is 17s (tolerance coefficient is 0.5). Step 1: Set the reference value r = sc_time max . In this example, max = 1, that is, r = 10:01:00. Calculate d_sc_time k = |r - sc_time k |, where 1 ≤ k ≤ n and k ≠ max. In this example, they are 2s, 29s, and 28s respectively. Among them, d_sc_time3 and d_sc_time4 are 29s and 28s respectively, both exceeding the time tolerance value t. Therefore, r is set to sl_timemax , that is, 10:00:26. Step 2: Calculate d_sc_time k and d_sl_time k , select sc from sc j , where j ∈ k and d_sc_time j < t, in this example, sc3 and sc4 (10:00:31, 10:00:32) are selected, and sl is selected from sl j , where j ∈ k and d_sl_time j < t, in this example, sl2 (10:00:26) is selected, and sl1 (10:00:26) of the reference value r is combined to form a standard group and sent to the algorithm module. Step 3: Clear the elements of the standard group and the remaining elements in sl, move the remaining elements in sc to sl, and wait for new elements to arrive. After clearing, it is shown in Table 2 as follows.

[0110] Table 2

[0111] 1st bit 2nd bit 3rd bit 4th bit Current group (sc) Previous group (sl) 10:01:00 10:00:58

[0112] When sl is full, if continuous correction is required, execute Step 1. If it is satisfied, send it to the algorithm module for calculation. If it is not satisfied, wait until sc is full and repeat the above steps (here, the last sentence of Step 3 in the交底书 can be replaced with "and repeat the above process after sl and sc are full"). In this example, after sl is full, it is shown in Table 3 as follows.

[0113] Table 3

[0114] 1st bit 2nd bit 3rd bit 4th bit Current group (sc) Previous group (sl) 10:01:00 10:00:58 10:01:02 10:01:03 [[ID=**29**]] [[ID=**30**]]

[0115] In one embodiment, before using the target algorithm to process the monitoring data corresponding to the data monitoring point, it further includes:

[0116] Delete the monitoring data corresponding to the data monitoring point in the data slot.

[0117] In the embodiment of the present disclosure, after obtaining the monitoring data corresponding to the data monitoring point, the monitoring data corresponding to the data monitoring point in the data slot is deleted to wait for the subsequent monitoring data to be stored. Among them, in this embodiment, the role of the monitoring data is to judge the state of the device to be monitored, so as to detect abnormalities in time. After obtaining the corresponding monitoring data, subsequent calculations can be performed to obtain the target data. At this time, the data in the data slot is correspondingly deleted, so that when the subsequent monitoring data is uploaded, it can be timely stored in the corresponding slot. In one example, one data slot corresponds to the monitoring data of one acquisition moment. When the current monitoring data in the data slot is called, it can be directly deleted and stored in the data slot when the monitoring data of the next acquisition moment is uploaded. Note: The text seems to be incomplete or contains some unclear terms like "交底书". I've tried my best to translate it as accurately as possible based on the given rules. If there are any specific clarifications needed for better translation, please let me know. Also, the tags and

[0115] seem to be left as they are in the original text and might be some kind of formatting or specific identifiers not fully explained in the context. I've just translated them as-is.

[0118] In this embodiment, after the monitoring data in the data slot is retrieved, the data in the data slot is deleted in a timely manner, thereby ensuring that subsequent monitoring data can be stored in the corresponding data slot in a timely manner. When the device to be monitored is monitored in real time, the timeliness and reliability of the monitoring data stored in the data slot are guaranteed, which facilitates the timely detection of equipment abnormalities and other problems, and is applicable to more application scenarios.

[0119] In one embodiment, after monitoring data is stored in the corresponding data partition by a splitting operator, it can be used for calculation to obtain the corresponding data. In this embodiment, the monitoring data monitorData that enters its respective partition after splitting can be used to calculate the corresponding target data through a slot allocation algorithm. The specific steps are as follows: Step 1: Construct a triplet, using the target point number in monitorData as the key to read the source point number dictionary originDict, association algorithm identifier, and parameters required for association calculation from the cache; Step 2: Determine if the source point number list is empty, then no further steps are needed; Step 3: Obtain the slot list slots of the current partition from the cache, specifically an array; Step 4: Obtain the position pos corresponding to origin in monitorData and other parameters from originDict, where pos is the index of mVal in monitorData in slots. In one example, the position pos can be configured in the user page, i.e., slots. pos=mVal, In one example, the target point number data consisting of device "cable segment 100000" and "A phase cable temperature" is calculated from two source point numbers. The sequence number will be stored in originDict as the index of the source point number in slots; Step 5: Determine whether the data in the slot of the partition is the first arriving element in the current partition slot. If yes, generate a timer. The timeout period can be set on the user page according to the actual application scenario; Step 6: Determine whether all slots in slots are filled. If yes, proceed to step 7; otherwise, proceed to step 8; Step 7: Determine whether time correction is needed. If yes, proceed to step 9; otherwise, proceed to step 10 and calculate directly; Step 8: Determine whether timeout has occurred. If yes, proceed to step 11; otherwise, end, continue waiting, and then start from step 1 again; Step 9: Perform time correction on all slots; Step 10: Send the corrected data in slots to the algorithm module for business calculation, and send the calculation result to the storage and forwarding module; Step 11: Clear the data in all slots, delete the timer, and continue waiting for data. Normally, data arrives one record at a time. Each time data arrives, if it's not full, a timeout is checked; if it's timed out, it's discarded. If it's full, a time correction is performed; otherwise, calculation proceeds directly. This embodiment sets up a separate calculation partition for the target point number and uses data slots to determine the calculation trigger time, improving the timeliness of data calculation. Furthermore, separate computing services and resource allocation can be deployed for algorithms with high computational pressure, ensuring scalability and flexibility, improving data processing efficiency, and making it suitable for more application scenarios.

[0120] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the accompanying drawings may include multiple steps or stages, which are not necessarily completed at the same time, but may be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0121] Based on the same inventive concept, this disclosure also provides a monitoring data processing apparatus for implementing the monitoring data processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more monitoring data processing apparatus embodiments provided below can be found in the limitations of the monitoring data processing method described above, and will not be repeated here.

[0122] In one embodiment, such as Figure 7 As shown, a monitoring data processing device 700 is provided. The device includes:

[0123] The first determining module 710 is used to determine the target partition identifier corresponding to the target data to be acquired.

[0124] The second determining module 720 is used to determine the target data partition, data monitoring point and target algorithm that match the target data to be acquired based on the target partition identifier, wherein the data monitoring point includes monitoring device information set on the device to be monitored;

[0125] The acquisition module 730 is used to acquire monitoring data corresponding to the data monitoring point from the data slot in the target data partition;

[0126] The processing module 740 is used to process the monitoring data corresponding to the data monitoring point using the target algorithm to obtain the target data.

[0127] In one embodiment, the storage module for storing monitoring data in data slots includes:

[0128] The first acquisition submodule is used to acquire the monitoring data to be stored corresponding to the data monitoring points;

[0129] The first determining submodule is used to determine the data partition corresponding to the partition identifier that matches the data monitoring point;

[0130] The second determining submodule is used to determine the data slot in the data partition that matches the data monitoring point;

[0131] The storage module is used to store the monitoring data to be stored into the data slot.

[0132] In one embodiment, the stored data in the data slot includes monitoring data and the corresponding acquisition time of the monitoring data. The acquisition module includes:

[0133] The third determining submodule is used to determine the data slot in the target data partition according to the slot identifier corresponding to the target data partition, wherein the data slot corresponds to the data monitoring point.

[0134] The second acquisition submodule is used to acquire the monitoring data corresponding to the data monitoring point from the data slots in the target data partition, provided that all data slots in the target data partition store monitoring data and the data collection time is within a preset time period.

[0135] In one embodiment, the stored data in the data slot includes monitoring data and the corresponding acquisition time of the monitoring data. The target data partition includes multiple data slots. The acquisition module includes:

[0136] The fourth determination submodule is used to determine multiple calibration slots that correspond one-to-one with the multiple data slots when monitoring data is stored in multiple data slots in the target data partition and the acquisition time of the monitoring data is within a preset time period. The data slots and the calibration slots are used to store candidate monitoring data at different acquisition times of the same data monitoring point. The acquisition time of the candidate monitoring data in the calibration slot is earlier than the acquisition time of the candidate monitoring data in the data slot.

[0137] The fifth determination submodule is used to determine the candidate monitoring data whose acquisition time meets the preset conditions as the monitoring data corresponding to the data monitoring point when the candidate monitoring data is stored in the multiple data slots and the multiple calibration slots.

[0138] In one embodiment, the fifth determining submodule includes:

[0139] The first determining unit is used to determine the calibration duration based on the data acquisition time interval of the data monitoring points;

[0140] The second determining unit is used to determine the standard acquisition time from the acquisition time of the candidate monitoring data based on the calibration duration and the acquisition time of the candidate monitoring data;

[0141] The third determining unit is used to determine the target time period based on the calibration duration and the standard acquisition time.

[0142] The fourth determining unit is used to determine the candidate monitoring data collected during the target time period as the monitoring data corresponding to the data monitoring point.

[0143] In one embodiment, the processing module further includes, prior to:

[0144] The deletion module is used to delete the monitoring data corresponding to the data monitoring point in the data slot.

[0145] Each module in the aforementioned monitoring data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0146] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores monitoring data and other data involved in the methods described in this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a monitoring data processing method.

[0147] Those skilled in the art will understand that Figure 8 The structures shown are merely block diagrams of some structures related to the embodiments of this disclosure and do not constitute a limitation on the computer devices on which the embodiments of this disclosure are applied. Specific computer devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0148] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0149] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0150] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0151] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0152] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this disclosure may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this disclosure may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0153] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0154] The above-described embodiments are merely illustrative of several implementation methods of the present disclosure, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the patent for the embodiments of the present disclosure. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of the present disclosure, and these all fall within the protection scope of the embodiments of the present disclosure. Therefore, the protection scope of the embodiments of the present disclosure should be determined by the appended claims.

Claims

1. A monitoring data processing method, characterized in that, The method includes: Determine the target partition identifier corresponding to the target data to be acquired; The target data partition, data monitoring point, and target algorithm that match the target data to be acquired are determined based on the target partition identifier, wherein the data monitoring point includes monitoring device information set on the device to be monitored; Obtain the monitoring data corresponding to the data monitoring point from the data slot in the target data partition; The data stored in the data slots includes monitoring data and the corresponding acquisition time. The target data partition includes multiple data slots. Obtaining the monitoring data corresponding to the data monitoring point from the data slots in the target data partition includes: when monitoring data is stored in multiple data slots in the target data partition and the acquisition time of the monitoring data is within a preset time period, determining multiple calibration slots that correspond one-to-one with the multiple data slots. The data slots and the calibration slots are used to store candidate monitoring data for different acquisition times of the same data monitoring point. The acquisition time of the candidate monitoring data in the calibration slot is earlier than the acquisition time of the candidate monitoring data in the data slot. When candidate monitoring data is stored in both the multiple data slots and the multiple calibration slots, the candidate monitoring data whose acquisition time meets the preset conditions is determined as the monitoring data corresponding to the data monitoring point. The target algorithm is used to process the monitoring data corresponding to the data monitoring points to obtain the target data.

2. The method according to claim 1, characterized in that, The storage methods for monitoring data in data slots include: Obtain the monitoring data to be stored corresponding to the data monitoring points; Determine the data partition corresponding to the partition identifier that matches the data monitoring point; Determine the data slot in the data partition that matches the data monitoring point; The monitoring data to be stored is stored in the data slot.

3. The method according to claim 1, characterized in that, The stored data in the data slot includes monitoring data and the corresponding acquisition time. The step of obtaining the monitoring data corresponding to the data monitoring point from the data slot in the target data partition includes: Based on the slot identifier corresponding to the target data partition, the data slot in the target data partition is determined, and the data slot corresponds to the data monitoring point. If all data slots in the target data partition contain monitoring data and the data collection time is within a preset time period, the monitoring data corresponding to the data monitoring point is obtained from the data slots in the target data partition.

4. The method according to claim 1, characterized in that, The candidate monitoring data that meets the preset conditions at the time of collection are the monitoring data corresponding to the data monitoring points, including: The calibration duration is determined based on the data collection time interval of the data monitoring points; The standard acquisition time is determined from the acquisition time of the candidate monitoring data based on the calibration duration and the acquisition time of the candidate monitoring data; The target time period is determined based on the calibration duration and the standard acquisition time. The candidate monitoring data collected during the target time period are determined as the monitoring data corresponding to the data monitoring point.

5. The method according to claim 1, characterized in that, Before processing the monitoring data corresponding to the data monitoring points using the target algorithm, the process further includes: Delete the monitoring data corresponding to the data monitoring point in the data slot.

6. A monitoring data processing device, characterized in that, The device includes: The first determining module is used to determine the target partition identifier corresponding to the target data to be acquired. The second determining module is used to determine the target data partition, data monitoring point and target algorithm that match the target data to be acquired based on the target partition identifier, wherein the data monitoring point includes monitoring device information set on the device to be monitored; An acquisition module is used to acquire monitoring data corresponding to the data monitoring point from the data slots in the target data partition. The stored data in the data slots includes monitoring data and the acquisition time corresponding to the monitoring data. The target data partition includes multiple data slots. The acquisition module includes: a fourth determination submodule, used to determine multiple calibration slots that correspond one-to-one with the multiple data slots when monitoring data is stored in multiple data slots in the target data partition and the acquisition time of the monitoring data is within a preset time period. The data slots and the calibration slots are used to store candidate monitoring data for different acquisition times of the same data monitoring point. The acquisition time of the candidate monitoring data in the calibration slot is earlier than the acquisition time of the candidate monitoring data in the data slot; a fifth determination submodule, used to determine the candidate monitoring data whose acquisition time meets the preset conditions as the monitoring data corresponding to the data monitoring point when candidate monitoring data is stored in both the multiple data slots and the multiple calibration slots. The processing module is used to process the monitoring data corresponding to the data monitoring points using the target algorithm to obtain the target data.

7. The apparatus according to claim 6, characterized in that, The storage module for storing monitoring data into data slots includes: The first acquisition submodule is used to acquire the monitoring data to be stored corresponding to the data monitoring points; The first determining submodule is used to determine the data partition corresponding to the partition identifier that matches the data monitoring point; The second determining submodule is used to determine the data slot in the data partition that matches the data monitoring point; The storage module is used to store the monitoring data to be stored into the data slot.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the monitoring data processing method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the monitoring data processing method according to any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the monitoring data processing method according to any one of claims 1 to 5.

Images