Data acquisition method and device, medium and product

Abstract

The invention relates to a data acquisition method and device, a medium and a product. The method comprises the following steps: acquiring a first observation parameter of each type of monitoring data of a monitoring device for a preset time monitoring window; wherein the first observation parameter comprises a first window observation value of each type of monitoring data and a first observation fluctuation threshold value of the first window observation value; determining a first prediction parameter of each type of monitoring data for the first time monitoring window; the first prediction parameter at least comprises a prediction value, an upper limit of the prediction value and a lower limit of the prediction value; based on the first observation parameter and the first prediction parameter, determining whether the first time monitoring window satisfies an acquisition condition; and under the condition of determining that the acquisition condition is satisfied, performing data acquisition on the monitoring equipment to obtain a target observation value. According to the method, a large amount of redundant data in the data acquisition process can be avoided, the data storage and calculation cost of hardware is reduced, and the data acquisition efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of dynamic environment monitoring of mobile communication rooms, and in particular to a data acquisition method, equipment, medium and product. Background Art

[0002] The dynamic environment monitoring system is an operation and maintenance center (OMC) that remotely monitors the operation of the computer room, base station communication power supply, air-conditioning system equipment and the comprehensive environment. It is also a basic information collection platform for the construction of professional operation and maintenance management support capabilities for power operation and maintenance. The dynamic environment monitoring system consists of a monitoring center (SC), a field supervision unit (FSU) and monitoring equipment.

[0003] In the related art, the FSU collects performance data of different monitoring devices and needs to return the full performance data to the SC each time. However, the SC does not classify and collect the returned full performance data according to the monitoring scenario requirements, which will generate a large amount of redundant data and lead to low transmission efficiency. In the related art, the SC is connected to multiple FSUs at the same time. The SC needs to perform full comparison and processing on the performance data returned by each FSU. Too much data to be compared and processed will lead to excessively high storage and computing costs, and the data processing will take a long time, which cannot meet the requirements for real-time monitoring of monitoring equipment. Summary of the invention

[0004] The present disclosure provides a data collection method, device, medium and product.

[0005] According to a first aspect of the present disclosure, a data collection method is provided, the method comprising:

[0006] Obtaining first observation parameters of each type of monitoring data of the monitoring device for a preset time monitoring window; wherein the first observation parameters include: first window observation values ​​of each type of monitoring data and first observation fluctuation thresholds of the first window observation values;

[0007] Determine a first prediction parameter of each type of monitoring data for a first time monitoring window; the first prediction parameter includes at least: a prediction value, an upper limit of the prediction value, and a lower limit of the prediction value;

[0008] Determining whether the first time monitoring window meets a collection condition based on the first observation parameter and the first prediction parameter;

[0009] When it is determined that the collection conditions are met, data is collected from the monitoring device to obtain target observation values.

[0010] Further, the determining of the first prediction parameter of each type of monitoring data for the first time monitoring window includes:

[0011] The observation values ​​of the first historical window in the historical database are processed by a deep learning prediction model to obtain first prediction parameters for each type of monitoring data for the first time monitoring window.

[0012] Further, the determining whether the first time monitoring window meets the collection condition based on the first observation parameter and the first prediction parameter includes:

[0013] Determining that the first window observation value and the first prediction parameter satisfy a first preset condition, or determining that the first window observation value and the first prediction parameter satisfy a second preset condition, determining that the first time monitoring window satisfies the acquisition condition;

[0014] Among them, the first preset condition is that the absolute value of the difference between the window observation value and the predicted value is greater than the observation fluctuation threshold, and the second preset condition is that the window observation value is greater than the upper limit of the predicted value, or the window observation value is less than the lower limit of the predicted value.

[0015] Furthermore, when it is determined that the collection condition is met, data is collected on the monitoring device to obtain the target observation value, and the method further includes:

[0016] When it is determined that the second preset condition is satisfied between the first window observation value and the first prediction parameter, the second observation fluctuation threshold under the second time monitoring window is determined based on the target observation value, the first observation fluctuation threshold, and the first window observation value, wherein the second time monitoring window is the next time monitoring window after the first time monitoring window.

[0017] Further, the determining a second observation fluctuation threshold under a second time monitoring window based on the target observation value, the first observation fluctuation threshold, and the first window observation value includes:

[0018] Calculating the absolute value of the difference between the target observation value and the window observation value;

[0019] determining a minimum value between the absolute value and the first observed fluctuation threshold;

[0020] The minimum value is determined as the second fluctuation threshold.

[0021] Furthermore, the method further comprises:

[0022] When it is determined that the observation parameter and the first prediction parameter do not satisfy the first preset condition and the second preset condition, it is determined that the first time monitoring window does not satisfy the acquisition condition, and based on the second observation parameter and the first prediction parameter of the second time monitoring window, it is determined whether the second time monitoring window satisfies the acquisition condition; wherein the second time monitoring window is the next time monitoring window after the first time monitoring window.

[0023] Furthermore, when it is determined that the collection condition is met, data is collected on the monitoring device to obtain the target observation value, and the method further includes:

[0024] Extracting a second historical window observation value from the historical database according to a preset period; wherein the second historical window observation value is a window observation value obtained by collecting data from the monitoring device in a time monitoring window before the second time monitoring window, and the second time monitoring window is the next time monitoring window after the first time monitoring window;

[0025] Sending the second historical window observation value to the deep learning prediction model to determine the second prediction parameter of each type of monitoring data for the second time monitoring window;

[0026] Based on the second observation parameter and the second prediction parameter of the second time monitoring window, it is determined whether the second time monitoring window meets the acquisition condition.

[0027] According to a second aspect of the present disclosure, a data acquisition device is provided, the device comprising:

[0028] An acquisition module, configured to acquire first observation parameters of each type of monitoring data of a monitoring device for a preset time monitoring window; wherein the first observation parameters include: first window observation values ​​of each type of monitoring data and first observation fluctuation thresholds of the window observation values;

[0029] A first determination module is used to determine a first prediction parameter of each type of monitoring data for a first time monitoring window; the first prediction parameter includes at least: a prediction value, an upper limit of the prediction value, and a lower limit of the prediction value;

[0030] A second determination module, configured to determine whether the first time monitoring window satisfies a collection condition based on the first observation parameter and the first prediction parameter;

[0031] The data acquisition module is used to collect data from the monitoring device to obtain target observation values ​​when it is determined that the acquisition conditions are met.

[0032] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0033] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above method of the present disclosure is implemented.

[0034] According to a fifth aspect of the present disclosure, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, the method of the present disclosure is implemented.

[0035] A data collection method, device, medium and product provided by the embodiments of the present disclosure, in the embodiments of the present application, first, the first observation parameters of each type of monitoring data of the monitoring device for a preset time monitoring window are obtained; wherein the first observation parameters include: the first window observation value of each type of monitoring data and the first observation fluctuation threshold of the window observation value; then, the first prediction parameter of each type of monitoring data for the first time monitoring window is determined; the first prediction parameter includes at least: the prediction value, the upper limit of the prediction value, and the lower limit of the prediction value; then, based on the first observation parameter and the first prediction parameter, it is determined whether the first time monitoring window meets the collection conditions; finally, when it is determined that the collection conditions are met, data is collected from the monitoring device to obtain the target observation value.

[0036] Through the above processing method, it is possible to collect data from the monitoring equipment when it is determined that the collection conditions are met, thereby avoiding the generation of a large amount of redundant data and improving the efficiency of data collection. At the same time, it is possible to reduce the number of data collection requests and response time, as well as reduce the hardware storage and computing costs of data, further improving the efficiency of data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other purposes, features and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0038] Figure 1 A flow chart of a data collection method provided for an exemplary embodiment of the present disclosure;

[0039] Figure 2 A schematic diagram of the structure of a dynamic environment monitoring system provided by an exemplary embodiment of the present disclosure;

[0040] Figure 3 A schematic diagram of a server workflow provided for an exemplary embodiment of the present disclosure;

[0041] Figure 4 A schematic diagram of a server workflow provided for another exemplary embodiment of the present disclosure;

[0042] Figure 5 A schematic diagram of a server workflow provided for another exemplary embodiment of the present disclosure;

[0043] Figure 6 A flow chart of a data collection method provided for another exemplary embodiment of the present disclosure;

[0044] Figure 7 A schematic diagram of a server workflow provided for another exemplary embodiment of the present disclosure;

[0045] Figure 8 A flow chart of a data collection method provided for another exemplary embodiment of the present disclosure;

[0046] Fig. 9 A schematic diagram of a server workflow provided for another exemplary embodiment of the present disclosure;

[0047] Fig.10 A schematic diagram of a server workflow provided for another exemplary embodiment of the present disclosure;

[0048] Fig.11 A schematic block diagram of functional modules of a data acquisition device provided by an exemplary embodiment of the present disclosure;

[0049] Fig.12 A structural block diagram of an electronic device provided for an exemplary embodiment of the present disclosure;

[0050] Fig.13 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure;

[0051] Fig.14 A structural block diagram of a computer program product provided for an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0052] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0053] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0054] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different modules or units, and are not used to limit the order or interdependence of the functions performed by these modules or units.

[0055] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0056] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0057] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0058] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0059] As an optional but non-limiting implementation, in response to receiving an active request from the user, the method of sending a prompt message to the user may be, for example, a pop-up window, in which the prompt message may be presented in text. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0060] In one embodiment, Figure 1 As shown, a data collection method is provided, comprising the following steps:

[0061] Step 101: Obtain a first observation parameter of each type of monitoring data of a monitoring device for a preset time monitoring window.

[0062] Here, when it is necessary to collect data for the monitoring device, the first observation parameters of each type of monitoring data of the monitoring device for a preset time monitoring window (Time observation window, TOW) can be obtained through the server, wherein the first observation parameters include: the first window observation values ​​(Window observations, WO) of each type of monitoring data and the first observation fluctuation threshold (Fluctuation Threshold, FT) of the first window observation values.

[0063] In a possible embodiment, Figure 2 As shown, Figure 2 The structural diagram of the dynamic environment monitoring system is shown exemplarily, the server is set in the monitoring center, and the monitoring center interacts with multiple dynamic environment monitoring units 1, dynamic environment monitoring unit 2, and dynamic environment monitoring unit N through the traditional B interface; multiple dynamic environment monitoring units detect various types of monitoring data of monitoring devices 1-N in real time through the A interface. Then, after the dynamic environment monitoring unit sends the monitoring data to the monitoring center, the server can obtain the first observation parameters of various types of monitoring data of the monitoring device for the preset time monitoring window from the monitoring center, wherein the first observation parameters include: the first window observation value of each type of monitoring data and the first observation fluctuation threshold of the first window observation value. The first window observation value is the data value obtained by monitoring the monitoring data within the preset time monitoring window, and the first observation fluctuation threshold is the incremental data collection trigger threshold. When the first window observation value meets the first observation fluctuation threshold, the server triggers the dynamic environment monitoring unit to collect data from the monitoring device, and the time monitoring window is the observation period of the monitoring data.

[0064] It should be noted that the monitoring data includes state quantities and analog quantities. Since different types of monitoring data have different change frequencies and data fluctuation amplitudes, it is possible to obtain the first window observation value and the first observation fluctuation threshold of each type of monitoring data of the monitoring device for a preset time monitoring window according to business needs, thereby obtaining the first observation parameter.

[0065] In the disclosed embodiment, the preset time monitoring window can be the most recent time monitoring window, or the time monitoring window before the current moment. Here, the preset time monitoring window is not specifically limited, and is subject to implementation. Exemplarily, the first window observation value can be the data value in the most recent time monitoring window, or the data value in other time monitoring windows. The first observation fluctuation threshold can be set according to the data value in the most recent time monitoring window. The setting method of the first window observation value and the first observation fluctuation threshold is not limited here.

[0066] Step 102: determine a first prediction parameter for each type of monitoring data for a first time monitoring window.

[0067] Here, after obtaining the first observation parameters of each type of monitoring data of the monitoring device for a preset time monitoring window, the server can determine the first prediction parameters of each type of monitoring data for the first time monitoring window, wherein the first prediction parameters include at least: a predicted value (Predicted Value, PV), an upper limit of the predicted value (The Upper limit of the Predicted Value, PV_U), and a lower limit of the predicted value (The Lower limit of the Predicted Value, PV_L).

[0068] In a possible embodiment, determining a first prediction parameter of each type of monitoring data for a first time monitoring window includes the following steps:

[0069] The observation values ​​of the first historical window in the historical database are processed through a deep learning prediction model to obtain the first prediction parameters of each type of monitoring data for the first time monitoring window.

[0070] In an embodiment of the present disclosure, after the server obtains the first observation parameters of each type of monitoring data of the monitoring device for a preset time monitoring window, the server can process the observation values ​​of the first historical window in the historical database through a pre-trained deep learning prediction model to obtain the first prediction parameters of each type of monitoring data for the first time monitoring window, wherein the first prediction parameters include at least: a predicted value, an upper limit of the predicted value, and a lower limit of the predicted value.

[0071] Exemplarily, a neural network time series prediction model (NeuralProphet) can be used to model, train and predict the historical window observations of one year to obtain the first prediction parameters of each type of monitoring data for the first time monitoring window. It should be noted that other deep learning prediction models can also be used to obtain the first prediction parameters, and the method for obtaining the first prediction parameters is not limited here, and is subject to what can be implemented.

[0072] like Figure 3 As shown, Figure 3 The schematic diagram of the workflow of the server is shown as an example. After the server determines the first observation parameter WO and the first observation fluctuation threshold FT of each type of monitoring data for the first time monitoring window TOW, the first time monitoring window is opened by multiple threads. After the first time monitoring window ends, the first prediction parameter of each type of monitoring data for the first time monitoring window can be determined by the deep learning prediction model. If the first time monitoring window has not ended, the server continues to detect each type of monitoring data in the first time monitoring window.

[0073] Step 103: Determine whether the first time monitoring window meets the acquisition condition based on the first observation parameter and the first prediction parameter.

[0074] Here, after the server determines the first prediction parameter for each type of monitoring data for the first time monitoring window, it can determine whether the first time monitoring window meets the collection condition based on the first observation parameter and the first prediction parameter.

[0075] In a possible implementation, determining whether the first time monitoring window meets the acquisition condition based on the first observation parameter and the first prediction parameter includes the following steps:

[0076] Determining that the first window observation value and the first prediction parameter satisfy the first preset condition, or determining that the first window observation value and the first prediction parameter satisfy the second preset condition, determining that the first time monitoring window satisfies the acquisition condition;

[0077] When it is determined that the observation parameter and the first prediction parameter do not meet the first preset condition and the second preset condition, it is determined that the first time monitoring window does not meet the acquisition condition, and based on the second observation parameter and the first prediction parameter of the second time monitoring window, it is determined whether the second time monitoring window meets the acquisition condition.

[0078] Specifically, after the server determines the first prediction parameter of each type of monitoring data for the first time monitoring window, the server determines whether the first time monitoring window meets the collection condition based on the first observation parameter and the first prediction parameter, wherein the first preset condition is that the absolute value of the difference between the window observation value and the prediction value is greater than the observation fluctuation threshold, that is, |WO-PV|>FT, and the second preset condition is that the window observation value is greater than the upper limit of the prediction value, or the window observation value is less than the lower limit of the prediction value, that is, WO>PV_U or WO<PV_L.

[0079] For example, Figure 4 As shown, Figure 4Another workflow diagram of the server is shown as an example. After the server determines the first observation parameter WO and the first observation fluctuation threshold FT of each type of monitoring data for the first time monitoring window, the first time monitoring window is opened by multiple threads. When the first time monitoring window ends, the first prediction parameter of each type of monitoring data for the first time monitoring window is determined, and then based on the first observation parameter and the first prediction parameter, it is determined whether the first time monitoring window meets the collection condition. There are three scenarios here. Scenario one corresponds to the first preset condition, and scenario two corresponds to the second preset condition. When it is determined that the first window observation value meets the first prediction parameter, or when it is determined that the first window observation value meets the first prediction parameter, that is, when the first scenario or the second scenario is met, it is determined that the first time monitoring window meets the collection condition.

[0080] In another possible embodiment, when it is determined that the observation parameters and the first prediction parameters do not satisfy the first preset condition and the second preset condition, that is, when the first scenario and the second scenario are not satisfied, it is determined to be the third scenario, then it is determined that the first time monitoring window does not satisfy the acquisition condition, and based on the second observation parameters and the first prediction parameters of the second time monitoring window, it is determined whether the second time monitoring window satisfies the acquisition condition, wherein the second time monitoring window is the next time monitoring window after the first time monitoring window.

[0081] Step 104, when it is determined that the collection conditions are met, data is collected from the monitoring equipment to obtain target observation values.

[0082] Here, based on the first observation parameter and the first prediction parameter, the server collects data from the monitoring device and obtains the target observation value when it is determined that the collection condition is met.

[0083] In a possible embodiment, Figure 5 As shown, Figure 5 Another workflow diagram of the server is shown exemplarily. The server determines whether the first time monitoring window meets the collection conditions based on the first observation parameter and the first prediction parameter. When it is determined that the collection conditions are met, that is, when scenario one or scenario two is met, the server triggers the dynamic environment monitoring unit to collect data from the monitoring device to obtain the target observation value.

[0084] A data collection method, device, medium and product provided by the embodiments of the present disclosure, in the embodiments of the present application, first, the first observation parameters of each type of monitoring data of the monitoring device for a preset time monitoring window are obtained; wherein the first observation parameters include: the first window observation value of each type of monitoring data and the first observation fluctuation threshold of the first window observation value; then, the first prediction parameter of each type of monitoring data for the first time monitoring window is determined; the first prediction parameter includes at least: the prediction value, the upper limit of the prediction value, and the lower limit of the prediction value; then, based on the first observation parameter and the first prediction parameter, it is determined whether the first time monitoring window meets the collection conditions; finally, when it is determined that the collection conditions are met, data is collected from the monitoring device to obtain the target observation value.

[0085] Through the above processing method, it is possible to collect data from the monitoring equipment when it is determined that the collection conditions are met, thereby avoiding the generation of a large amount of redundant data and improving the efficiency of data collection. At the same time, it is possible to reduce the number of data collection requests and response time, as well as reduce the hardware storage and computing costs of data, further improving the efficiency of data collection.

[0086] In one embodiment, Figure 6 As shown, in step 104, after determining that the collection conditions are met, data is collected from the monitoring device to obtain the target observation value, a data collection method is also provided, including the following steps:

[0087] Step 601, when it is determined that the first window observation value and the first prediction parameter meet the second preset condition, determine the second observation fluctuation threshold under the second time monitoring window based on the target observation value, the first observation fluctuation threshold, and the first window observation value.

[0088] In a possible embodiment, determining a second observation fluctuation threshold value under a second time monitoring window based on a target observation value, a first observation fluctuation threshold value, and a first window observation value includes the following steps:

[0089] Calculate the absolute value of the difference between the target observation and the window observation;

[0090] determining the minimum of the absolute value and the first observed fluctuation threshold;

[0091] The minimum value is determined as the second fluctuation threshold.

[0092] Specifically, when it is determined that the second preset condition is satisfied between the first window observation value and the first prediction parameter, the server can determine the second observation fluctuation threshold under the second time monitoring window based on the target observation value, the first observation fluctuation threshold, and the first window observation value, wherein the second time monitoring window is the next time monitoring window after the first time monitoring window.

[0093] For example, Figure 7 As shown, Figure 7 Another workflow diagram of the server is shown as an example. When the server determines that the second preset condition is satisfied between the first window observation value and the first prediction parameter, that is, scenario 2 is satisfied, the server calculates the absolute value of the difference between the target observation value and the window observation value, and after calculating the absolute value of the difference between the target observation value and the window observation value, determines the minimum value between the absolute value and the first observation fluctuation threshold, and determines the minimum value as the second fluctuation threshold. For example, when the absolute value of the difference between the target observation value and the window observation value is 20, and the first observation fluctuation threshold is 30, the second fluctuation threshold can be determined to be 20 by the formula: second fluctuation threshold = min (FT, |AC-WO|), where AC is the target observation value.

[0094] In this embodiment, when determining that the second preset condition is satisfied between the first window observation value and the first prediction parameter, the server determines the second observation fluctuation threshold value under the second time monitoring window based on the target observation value, the first observation fluctuation threshold value, and the first window observation value. It can be seen from the above description that the server dynamically updates the observation fluctuation threshold value under the second time monitoring window through the target observation value, the first observation fluctuation threshold value, and the first window observation value, continuously optimizes the observation parameters, and the server triggers the collection of various types of data through the updated observation parameters, so that the collected monitoring data is more in line with the collection requirements, avoiding a large amount of redundant data, and improving the efficiency and reliability of the data collection method.

[0095] In one embodiment, Figure 8 As shown, in step 104, after determining that the collection conditions are met, data is collected from the monitoring device to obtain the target observation value, a data collection method is also provided, including the following steps:

[0096] Step 801: extracting second historical window observation values ​​from a historical database according to a preset period.

[0097] Here, the server collects data from the monitoring device, and after obtaining the target observation value, extracts the second historical window observation value from the historical database according to a preset period, wherein the second historical window observation value is the window observation value obtained by collecting data from the monitoring device in the time monitoring window before the second time monitoring window, and the second time monitoring window is the next time monitoring window after the first time monitoring window.

[0098] In a possible embodiment, Fig. 9 As shown, Fig. 9Another workflow diagram of the server is shown as an example. After the server collects data from the monitoring device and obtains the target observation value, the server caches the target observation value in the cache database and periodically synchronizes the window observation value in the database to the historical database. For example, the user can customize and synchronize the window observation value in the database to the historical database every week, and the server extracts the second historical window observation value from the historical database according to the preset period, wherein the second historical window observation value is the window observation value obtained by collecting data from the monitoring device in the time monitoring window before the second time monitoring window.

[0099] Step 802: Send the observation values ​​of the second historical window to the deep learning prediction model to determine the second prediction parameters of each type of monitoring data for the second time monitoring window.

[0100] After the server extracts the second historical window observation values ​​from the historical database according to a preset period, the second historical window observation values ​​can be sent to the deep learning prediction model to determine the second prediction parameters of each type of monitoring data for the second time monitoring window.

[0101] In a possible embodiment, Fig. 9 As shown, after the server extracts the second historical window observation value from the historical database according to the preset period, the server sends the second historical window observation value to the deep learning prediction model for incremental learning to determine the second prediction parameter of each type of monitoring data for the second time monitoring window. Exemplarily, a neural network time series can be used for incremental learning to determine the second prediction parameter of each type of monitoring data for the second time monitoring window. It should be noted that other deep learning prediction models can also be used to determine the second prediction parameter. The method for obtaining the second prediction parameter is not limited here, and it is subject to what can be implemented.

[0102] Step 803: Determine whether the second time monitoring window meets the acquisition condition based on the second observation parameter and the second prediction parameter of the second time monitoring window.

[0103] The server sends the observation values ​​of the second historical window to the deep learning prediction model, and after determining the second prediction parameters of each type of monitoring data for the second time monitoring window, determines whether the second time monitoring window meets the collection conditions based on the second observation parameters and second prediction parameters of the second time monitoring window.

[0104] In a possible embodiment, Fig.10 As shown, Fig.10Another workflow diagram of the server is shown as an example. After the server determines the second prediction parameters of each type of monitoring data for the second time monitoring window, it determines whether the second time monitoring window meets the collection conditions based on the second observation parameters and the second prediction parameters of the second time monitoring window. The specific steps for determining whether the second time monitoring window meets the collection conditions can be found in step 103 to determine whether the first time monitoring window meets the collection conditions, which will not be repeated here. After determining whether the second time monitoring window meets the collection conditions, if the second time monitoring window meets the collection conditions, data is collected from the monitoring device to obtain the target observation value. If the second time monitoring window does not meet the collection conditions, it is determined whether the next time monitoring window of the second time monitoring window meets the collection conditions.

[0105] In this embodiment, first, the server extracts the second historical window observation value from the historical database according to a preset period, and then the server sends the second historical window observation value to the deep learning prediction model to determine the second prediction parameter of each type of monitoring data for the second time monitoring window. Finally, the server determines whether the second time monitoring window meets the collection condition based on the second observation parameter and the second prediction parameter of the second time monitoring window. It can be seen from the above description that the second prediction parameter is determined by the server through the deep learning prediction model. The server performs real-time dynamic analysis on the prediction parameter and triggers the collection of various types of data, and continuously optimizes the prediction parameter by post-analysis and incremental learning of the collected monitoring data, thereby improving the data collection performance. The server collects data according to the optimized prediction parameter, which improves the value density of the collected monitoring data and further improves the reliability of the data collection method.

[0106] In the case of dividing each functional module according to each function, an embodiment of the present disclosure provides a data acquisition device, which may be a server or a chip applied to a server. Fig.11 The following is a schematic block diagram of the functional modules of a data acquisition device provided by an exemplary embodiment of the present disclosure. Fig.11 As shown, the data acquisition device comprises:

[0107] An acquisition module 1101 is used to acquire first observation parameters of each type of monitoring data of a monitoring device for a preset time monitoring window; wherein the first observation parameters include: first window observation values ​​of each type of monitoring data and first observation fluctuation threshold values ​​of the first window observation values;

[0108] A first determination module 1102 is used to determine a first prediction parameter of each type of monitoring data for a first time monitoring window; the first prediction parameter includes at least: a prediction value, an upper limit of the prediction value, and a lower limit of the prediction value;

[0109] A second determination module 1103, configured to determine whether the first time monitoring window meets a collection condition based on the first observation parameter and the first prediction parameter;

[0110] The data collection module 1104 is used to collect data from the monitoring device to obtain target observation values ​​when it is determined that the collection conditions are met.

[0111] In one embodiment, the first determining module 1102 includes:

[0112] An acquisition unit is used to process the observation values ​​of the first historical window in the historical database through a deep learning prediction model to obtain the first prediction parameters of each type of monitoring data for the first time monitoring window.

[0113] In one embodiment, the second determining module 1103 includes:

[0114] A first determining unit, configured to determine that the first time monitoring window satisfies the acquisition condition when it is determined that the first window observation value and the first prediction parameter satisfy a first preset condition, or when it is determined that the first window observation value and the first prediction parameter satisfy a second preset condition;

[0115] Among them, the first preset condition is that the absolute value of the difference between the window observation value and the predicted value is greater than the observation fluctuation threshold, and the second preset condition is that the window observation value is greater than the upper limit of the predicted value, or the window observation value is less than the lower limit of the predicted value.

[0116] In one embodiment, the apparatus further comprises:

[0117] A third determination module is used to determine a second observation fluctuation threshold under a second time monitoring window based on the target observation value, the first observation fluctuation threshold, and the first window observation value when it is determined that the second preset condition is satisfied between the first window observation value and the first prediction parameter, wherein the second time monitoring window is the next time monitoring window after the first time monitoring window.

[0118] In one embodiment, the third determination module includes:

[0119] A calculation unit, used to calculate the absolute value of the difference between the target observation value and the window observation value;

[0120] a second determining unit, configured to determine a minimum value between the absolute value and the first observed fluctuation threshold;

[0121] The third determining unit is configured to determine the minimum value as a second fluctuation threshold.

[0122] In one embodiment, the apparatus further comprises:

[0123] The fourth determination module is used to determine that the first time monitoring window does not meet the acquisition condition when it is determined that the observation parameter and the first prediction parameter do not meet the first preset condition and the second preset condition, and determine whether the second time monitoring window meets the acquisition condition based on the second observation parameter and the first prediction parameter of the second time monitoring window; wherein the second time monitoring window is the next time monitoring window after the first time monitoring window.

[0124] In one embodiment, the apparatus further comprises:

[0125] An extraction module is used to extract a second historical window observation value from a historical database according to a preset period; wherein the second historical window observation value is a window observation value obtained by collecting data from the monitoring device in a time monitoring window before a second time monitoring window, and the second time monitoring window is a next time monitoring window after the first time monitoring window;

[0126] a fifth determination module, configured to send the second historical window observation value to the deep learning prediction model to determine a second prediction parameter of each type of monitoring data for the second time monitoring window;

[0127] A sixth determination module is used to determine whether the second time monitoring window meets the acquisition condition based on the second observation parameter and the second prediction parameter of the second time monitoring window.

[0128] An embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory for storing instructions executable by the at least one processor; wherein the at least one processor is configured to execute the instructions to implement the above method disclosed in the embodiment of the present disclosure.

[0129] Fig.12 The structure diagram of an electronic device provided by an exemplary embodiment of the present disclosure is shown in FIG. Fig.12 As shown, the electronic device 1200 includes at least one processor 1201 and a memory 1202 coupled to the processor 1201 , and the processor 1201 can execute corresponding steps in the above method disclosed in the embodiment of the present disclosure.

[0130] The processor 1201 may also be referred to as a central processing unit (CPU), which may be an integrated circuit chip having signal processing capabilities. Each step in the method disclosed in the embodiment of the present disclosure may be completed by an integrated logic circuit of hardware in the processor 1201 or by instructions in the form of software. The processor 1201 may be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present disclosure may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a memory 1202, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, or other mature storage media in the art. The processor 1201 reads the information in the memory 1202 and completes the steps of the method in combination with its hardware.

[0131] In addition, when various operations / processes according to the present disclosure are implemented by software and / or firmware, they can be transmitted from a storage medium or a network to a computer system having a dedicated hardware structure, such as Fig.13 The computer system 1300 shown is installed with the programs constituting the software. When the various programs are installed, the computer system can perform various functions, including the functions described above. Fig.13 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure.

[0132] Computer system 1300 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0133] like Fig.13As shown, the computer system 1300 includes a computing unit 1301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1302 or a computer program loaded from a storage unit 1308 to a random access memory (RAM) 1303. In the RAM 1303, various programs and data required for the operation of the computer system 1300 can also be stored. The computing unit 1301, the ROM 1302, and the RAM 1303 are connected to each other via a bus 1304. An input / output (I / O) interface 1305 is also connected to the bus 1304.

[0134] A plurality of components in the computer system 1300 are connected to the I / O interface 1305, including: an input unit 1306, an output unit 1307, a storage unit 1308, and a communication unit 1309. The input unit 1306 may be any type of device capable of inputting information to the computer system 1300, and the input unit 1306 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 1307 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1308 may include, but is not limited to, a disk, an optical disk. The communication unit 1309 allows the computer system 1300 to exchange information / data with other devices over a network such as the Internet, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0135] The computing unit 1301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1301 performs the various methods and processes described above. For example, in some embodiments, the above methods disclosed in the embodiments of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1200 via the ROM 1302 and / or the communication unit 1309. In some embodiments, the computing unit 1301 may be configured to perform the above methods disclosed in the embodiments of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0136] The embodiment of the present disclosure further provides a computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above method disclosed in the embodiment of the present disclosure.

[0137] The computer-readable storage medium in the disclosed embodiments may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specifically, the computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0138] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0139] Fig.14 A computer program product is provided for an exemplary embodiment of the present disclosure. The computer program product 1400 includes a computer program 1401. When the computer program 1401 is executed by a processor, the method disclosed in the embodiment of the present disclosure is implemented.

[0140] In embodiments of the present disclosure, computer program codes for performing the operations of the present disclosure may be written in one or more programming languages ​​or combinations thereof, including but not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0141] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0142] The modules, components or units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the names of the modules, components or units do not, in some cases, limit the modules, components or units themselves.

[0143] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0144] The above descriptions are only some embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, a technical solution formed by replacing the above features with the technical features with similar functions disclosed in the present disclosure (but not limited to).

[0145] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A data collection method, characterized in that: The method comprises: Obtaining first observation parameters of each type of monitoring data of the monitoring device for a preset time monitoring window; wherein the first observation parameters include: first window observation values ​​of each type of monitoring data and first observation fluctuation thresholds of the first window observation values; Determine a first prediction parameter of each type of monitoring data for a first time monitoring window; the first prediction parameter includes at least: a prediction value, an upper limit of the prediction value, and a lower limit of the prediction value; Determining whether the first time monitoring window meets a collection condition based on the first observation parameter and the first prediction parameter; When it is determined that the collection conditions are met, data is collected from the monitoring device to obtain target observation values.

2. The method according to claim 1, characterized in that The determining of the first prediction parameter of each type of monitoring data for the first time monitoring window includes: The observation values ​​of the first historical window in the historical database are processed by a deep learning prediction model to obtain first prediction parameters for each type of monitoring data for the first time monitoring window.

3. The method according to claim 1, characterized in that The determining, based on the first observation parameter and the first prediction parameter, whether the first time monitoring window meets the acquisition condition includes: Determining that the first time monitoring window satisfies the acquisition condition when it is determined that the first window observation value and the first prediction parameter satisfy a first preset condition, or when it is determined that the first window observation value and the first prediction parameter satisfy a second preset condition; Among them, the first preset condition is that the absolute value of the difference between the window observation value and the predicted value is greater than the observation fluctuation threshold, and the second preset condition is that the window observation value is greater than the upper limit of the predicted value, or the window observation value is less than the lower limit of the predicted value.

4. The method according to claim 3, characterized in that When it is determined that the acquisition condition is met, data is collected from the monitoring device to obtain the target observation value, and the method further includes: When it is determined that the second preset condition is satisfied between the first window observation value and the first prediction parameter, the second observation fluctuation threshold under the second time monitoring window is determined based on the target observation value, the first observation fluctuation threshold, and the first window observation value, wherein the second time monitoring window is the next time monitoring window after the first time monitoring window.

5. The method according to claim 4, characterized in that The determining a second observation fluctuation threshold value under a second time monitoring window based on the target observation value, the first observation fluctuation threshold value, and the first window observation value includes: Calculating the absolute value of the difference between the target observation value and the window observation value; determining a minimum value between the absolute value and the first observed fluctuation threshold; The minimum value is determined as the second fluctuation threshold.

6. The method according to claim 3, characterized in that The method further comprises: When it is determined that the observation parameter and the first prediction parameter do not satisfy the first preset condition and the second preset condition, it is determined that the first time monitoring window does not satisfy the acquisition condition, and based on the second observation parameter and the first prediction parameter of the second time monitoring window, it is determined whether the second time monitoring window satisfies the acquisition condition; wherein the second time monitoring window is the next time monitoring window after the first time monitoring window.

7. The method according to claim 1, characterized in that When it is determined that the acquisition condition is met, data is collected from the monitoring device to obtain the target observation value, and the method further includes: Extracting a second historical window observation value from the historical database according to a preset period; wherein the second historical window observation value is a window observation value obtained by collecting data from the monitoring device in a time monitoring window before the second time monitoring window, and the second time monitoring window is the next time monitoring window after the first time monitoring window; Sending the second historical window observation value to the deep learning prediction model to determine the second prediction parameter of each type of monitoring data for the second time monitoring window; Based on the second observation parameter and the second prediction parameter of the second time monitoring window, it is determined whether the second time monitoring window meets the acquisition condition.

8. An electronic device, characterized in that: include: at least one processor; a memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method as claimed in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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