Data acquisition method, computer readable storage medium and electronic equipment

Abstract

The invention discloses a data acquisition method, a computer readable storage medium and electronic equipment. The method comprises the following steps: determining an initial polling interval of a target entity in a system; collecting actual operation data of each target entity according to the initial polling interval; calculating the change probability of the operation data of the target entity according to the actual operation data, and updating a polling interval according to the probability; and collecting actual operation data of the target entity according to the updated polling interval. According to the method, the polling interval is updated according to the change probability of the operation data of the target entity obtained by calculating the actual operation data of the target entity, and the actual operation data of the target entity is collected according to the updated polling interval, so that the transient characteristics of the target entity are effectively extracted and identified. And analysis and evaluation of the system are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and in particular to a data acquisition method, a computer-readable storage medium, and an electronic device. Background Art

[0002] In the development process of the DCS (Distributed Control System) system, projects like high-efficiency computer room require a lot of time to bind and verify data points on site during implementation. And as the complexity of the project gradually increases, the number of points also increases exponentially. The delay of data interaction in the early simulation and later implementation of the project gradually increases, and the resolution of the collection and setting of all parameters becomes lower, which makes it difficult to analyze and evaluate the system. Summary of the invention

[0003] The present invention aims to solve one of the technical problems in the related art at least to a certain extent. To this end, one object of the present invention is to propose a data acquisition method to achieve effective extraction and recognition of transient features of a target entity, which is conducive to analyzing and evaluating the system.

[0004] A second object of the present invention is to provide a computer-readable storage medium.

[0005] A third objective of the present invention is to provide an electronic device.

[0006] To achieve the above-mentioned purpose, the first aspect of the present invention proposes a data collection method, which includes: determining an initial polling interval of a target entity in a system; collecting actual operating data of each target entity according to the initial polling interval; calculating the probability of a change in the operating data of the target entity based on the actual operating data, and updating the polling interval based on the probability; and collecting the actual operating data of the target entity according to the updated polling interval.

[0007] According to the data collection method of an embodiment of the present invention, the polling interval is updated according to the probability of change of the operating data of the target entity calculated from the actual operating data of the target entity, and the actual operating data of the target entity is collected according to the updated polling interval, so as to realize effective extraction and identification of transient characteristics of the target entity, which is conducive to analysis and evaluation of the system.

[0008] In addition, the data collection method proposed in the above embodiment of the present invention may also have the following additional technical features:

[0009] According to one embodiment of the present invention, the calculation of the probability that the operating data of the target entity changes based on the actual operating data includes: obtaining the initial weight of the category to which the target entity corresponds to the actual operating data; calculating the probability of an association change between the target entity corresponding to the actual operating data and other entities; calculating the instantaneous change probability of the operating data of the target entity based on the actual operating data, the initial weight and the association change probability; and determining the probability that the operating data of the target entity changes based on the initial weight, the association change probability and the instantaneous change probability.

[0010] According to one embodiment of the present invention, the calculation of the probability of change in association between the target entity corresponding to the actual operation data and other entities includes: establishing a simulation model of the system; testing the simulation operation data of each target entity within a preset time under different bus load rates of the simulation model; and calculating the probability of change in association between each target entity and other entities based on the simulation operation data.

[0011] According to one embodiment of the present invention, the calculation of the instantaneous change probability of the operation data of the target entity based on the actual operation data, the initial weight and the associated change probability includes: determining the resolution of the actual operation data based on the initial weight and the associated change probability; quantizing the actual operation data based on the actual operation data and the resolution to obtain quantized data; calculating the instantaneous value of the current sampling point of the entity signal within the polling interval based on the resolution and the quantized data; and determining the instantaneous change probability of the actual operation data based on the instantaneous value of the current sampling point and the instantaneous value of the previous sampling point.

[0012] According to one embodiment of the present invention, determining the instantaneous change probability of the actual operation data includes: calculating the instantaneous change rate of the actual operation data according to the instantaneous value of the current sampling point and the instantaneous value of the previous sampling point; if the instantaneous change rate is greater than a first preset threshold, determining the instantaneous change probability to be 1; if the instantaneous change rate is greater than a second preset threshold and is less than or equal to the first preset threshold, determining the instantaneous change rate to be the instantaneous change probability; if the instantaneous change rate is less than or equal to the second preset threshold, determining the instantaneous change probability to be 0.

[0013] According to one embodiment of the present invention, the probability of a change in the operating data of the target entity being changed according to the initial weight, the associated change probability and the instantaneous change probability includes: if the instantaneous change probability is greater than a first preset threshold, taking the maximum value of a first product value and the instantaneous change probability as the probability of the change, wherein the first product value is the product of half of the sum of the associated change probability and the instantaneous change probability and the initial weight; if the instantaneous change probability is less than or equal to the first preset threshold, taking the maximum value of a second product value and the instantaneous change probability as the probability of the change, wherein the second product value is the product of the initial weight and the associated change probability.

[0014] According to one embodiment of the present invention, updating the polling interval according to the probability includes: determining the change direction and the deviation value according to the change probability of the current sampling point and the change probability of the previous sampling point; if the change direction is positive and the deviation value exceeds a second preset threshold, shortening the polling interval; if the change direction is negative and the deviation value exceeds the second preset threshold, increasing the polling interval.

[0015] According to one embodiment of the present invention, before collecting the actual operation data of the target entity according to the updated polling interval, the method also includes: obtaining the bus load rate of the system, and if the bus load rate is less than a preset load rate threshold, collecting the actual operation data of the target entity according to the updated polling interval.

[0016] To achieve the above objectives, a second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the data acquisition method as described above is implemented.

[0017] To achieve the above objectives, a third aspect of the present invention provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the data collection method as described above is implemented.

[0018] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of a data collection method according to an embodiment of the present invention;

[0020] Figure 2 is a flow chart of calculating the probability of change in operation data according to an embodiment of the present invention;

[0021] Figure 3is a flow chart of calculating the probability of association change according to an embodiment of the present invention;

[0022] Figure 4 is a flow chart of calculating the instantaneous change probability of actual operation data according to an embodiment of the present invention;

[0023] Figure 5 is a schematic diagram of adaptive updating of polling intervals according to a specific embodiment of the present invention;

[0024] Figure 6 It is a structural block diagram of a controller according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0026] The following is attached to the instruction manual Figure 1 -Attached Figure 6 And specific implementation methods are described in detail the data collection method, computer-readable storage medium, and electronic device of the embodiments of the present invention.

[0027] Figure 1 FIG. 1 is a flow chart of a data collection method according to an embodiment of the present invention. Figure 1 As shown, the data collection method may include:

[0028] S101, determining an initial polling interval of a target entity in the system;

[0029] S102, collecting actual operation data of each target entity according to the initial polling interval;

[0030] S103, calculating the probability of a change in the operation data of the target entity according to the actual operation data, and updating the polling interval according to the probability;

[0031] S104: Collect actual operation data of the target entity according to the updated polling interval.

[0032] With the increasing complexity of DCS system projects and limited network data transmission bandwidth, some detailed features reflecting the transient characteristics of the system are filtered out, and the characteristic parameters that are displayed cannot meet the requirements of feature recognition, and it is impossible to achieve effective analysis of the system. In order to achieve effective analysis of the system, effectively collect the operating data of the target entity in the system, and prevent the loss of a large number of transient characteristic details of the target entity in the system, the embodiment of the present invention collects the actual operating data of each target entity in the system, calculates the probability of change of the corresponding operating data of each target entity based on the actual operating data of each target entity, and updates the operating data collection polling interval of the corresponding target entity based on the probability of change of the corresponding operating data of each target entity.

[0033] Specifically, the target entities to be monitored in the system and the initial polling interval for collecting the actual operation data of each target entity are determined, wherein the initial polling interval can be determined based on the number of target entities to be monitored and the bus transmission bandwidth. The actual operation data of each target entity is collected according to the initial polling interval, and the probability of change in the operation data of each target entity is calculated based on the collected actual operation data of each target entity. The polling interval is updated according to the probability of change in the operation data of each target entity, the polling interval is shortened or increased, and the actual operation data of the target entity is collected according to the updated polling interval. According to the probability of change in the corresponding operation data of the target entity, the polling interval for collecting the operation data of the target entity is updated to collect the transient characteristics of the target entity, so as to realize the effective collection of the operation data of each target entity in the system.

[0034] In one embodiment of the present invention, when determining the target entity in the system, the entity category that needs to be monitored in the system may be determined, and the target entity in the system may be determined according to the entity category that needs to be monitored in the system.

[0035] As an example, the entities that need to be monitored in the high-efficiency computer room project system are roughly the following physical categories: temperature signals, flow signals, pressure signals, power signals, heat signals, frequency control and feedback signals of water pump butterfly valves, start-stop operation status signals, and switch in place status signals. Based on the entity categories established above, determine the target entity x that needs to be monitored in the system i It should be noted that there may be multiple signals of the same entity category. For example, in actual engineering, there are inlet and outlet pressures in the cooling circuit that need to be considered to prevent the main engine from being cut off from water. Although they are all pressure signals, there is one on the inlet and outlet sides.

[0036] In one embodiment of the present invention, when determining the initial polling interval, the initial polling interval can be determined according to the number of each target entity to be monitored and the bus transmission bandwidth. Exemplarily, the calculated bus bandwidth is a bytes per second. Combining with the number of target entities to be monitored, the appropriate initial polling interval T is calculated according to 80% of the bus load. begin .

[0037] Implementably, at the start stage of the system, the monitoring system runs according to the set initial polling interval T begin to obtain the actual operation data of each target entity. Entering the operation stage, according to the actual operation data of each target entity collected, calculate the probability that the actual operation data of each target entity changes, update the polling interval ΔT, and collect the actual operation data of the target entity according to the updated polling interval ΔT.

[0038] In one embodiment of the present invention, as Figure 2 shown, calculating the probability that the operation data of the target entity changes according to the actual operation data may include:

[0039] S201, obtaining the initial weight of the category to which the actual operation data corresponds to the target entity;

[0040] S202, calculating the associated change probability between the target entity corresponding to the actual operation data and other entities;

[0041] S203, calculating the instantaneous change probability of the operation data of the target entity according to the actual operation data, the initial weight and the associated change probability;

[0042] S204, determining the probability that the operation data of the target entity changes according to the initial weight, the associated change probability and the instantaneous change probability.

[0043] It should be noted that signals of different categories are different in physical manifestations. For example, when there is a disturbance in the system, the change speed of pressure is very fast, but due to the existence of heat capacity and pipe wall, the change speed of temperature is several orders of magnitude smaller than that of pressure.

[0044] To accurately calculate the probability that the operation data of the target entity changes, according to the categories of various target entities such as temperature, pressure, flow, and liquid in the established system, and establish an initial weight table under each category: ω(1), ω(2), ω(3), …, ω(n), where 0 < ω(m) < 1, 0 < m < n, where m represents the category and ω(m) represents the weight corresponding to category m.

[0045] In the embodiment of the present invention, when setting the initial weights of each category, it is determined according to the change characteristics of the actual operation data of the monitored entity.

[0046] Since the system is a whole, changes in the operating data of one target entity will also cause changes in the operating data of other target entities. In order to accurately calculate the probability of changes in the operating data of the target entity, the probability of changes in the operating data of each target entity and the associated change probability of the operating data of other entities is calculated. The instantaneous change probability of the target entity corresponding to the actual operating data is calculated based on the actual operating data, its corresponding initial weight and the associated change probability.

[0047] In order to accurately calculate the probability of a change in the operating data of the target entity, the embodiment of the present invention determines the probability of a change in the actual operating data of the target entity according to the initial weight, the associated change probability, and the instantaneous change probability.

[0048] In one embodiment of the present invention, Figure 3 As shown, the probability of association change between the target entity and other entities corresponding to the actual operation data is calculated, including:

[0049] S301, establishing a simulation model of the system;

[0050] S302, testing the simulation model under different bus load rates, and the simulation operation data of each target entity within a preset time;

[0051] S303: Calculate the probability of association change between each target entity and other entities according to the simulation operation data.

[0052] Specifically, a simulation model of the system is established. Since the model cannot obtain the actual characteristics of the target entity during operation in the loop stage, the safe approach is to obtain the simulation operation data of all target entities according to the same polling cycle, analyze the obtained simulation operation data, and then make adjustments based on whether the actual operation data is significantly close to the sampling frequency.

[0053] In order to improve the accuracy of the probability of association change between each target entity and other entities, in the model in-loop stage, different bus load rates are input to test and obtain the simulation running data x of each target entity within the preset time t under different bus load rates. i (t). It should be noted that the operation data of a single target entity is a time series data with the x-axis as time and the y-axis as sampling value. According to the simulation operation data x of each target entity within the preset time t under different bus load rates obtained by the test i (t), and calculate the probability P1(x1) of the correlation change between the operation data of each target entity under different bus load rates. i |x). Among them, the probability of association change between the target entity and other entities P1(x i |x) The maximum value of the range of change is xmax i, the minimum value is xmin i .

[0054] It should be noted that the operating data of the target entity in the embodiment of the present invention also includes some values ​​calculated according to the operating data of each target entity, for example, the air enthalpy value is calculated by dry-bulb and wet-bulb temperatures.

[0055] In one embodiment of the present invention, Figure 4 As shown, calculating the instantaneous change probability of the operation data of the target entity according to the actual operation data, the initial weight and the associated change probability may include:

[0056] S401, determining the resolution of the actual operation data according to the initial weight and the associated change probability.

[0057] Specifically, when determining the resolution of the actual operation data, that is, calculating the number of times the actual operation data of the target entity is sampled within the polling interval ΔT, the following expression for calculating the resolution of the actual operation data can be used for calculation. The expression for the resolution of the actual operation data is:

[0058]

[0059] Among them, k i Represents the actual running data x of target entity i i Resolution; abs() represents the absolute value function; xmax i Represents the actual running data x of target entity i i The maximum probability of association change between the running data of other entities; xmin i Represents the actual running data x of target entity i i The minimum probability of association change between the operating data of other entities; ω(m) represents the actual operating data x i The weight corresponding to the category to which the target entity i belongs, a represents the second preset threshold, and b represents the third preset threshold.

[0060] Implementable, obtain actual operation data x i The weight ω(m) of the category to which the target entity i belongs is determined according to the relationship between ω(m) and the second preset threshold a and the third preset threshold b to calculate the actual operation data x i The expression of the resolution is used to calculate the actual running data x i The resolution k i .

[0061] S402, quantizing the actual operation data according to the actual operation data and the resolution to obtain quantized data.

[0062] Specifically, according to the actual operation data x iand resolution k i The actual operation data sampled within the polling interval ΔT is quantized to obtain quantized data.

[0063]

[0064] in, represents the quantized data; k represents the resolution; x i Represents the original collected signal; Round() means returning the value rounded to the specified number of digits.

[0065] S403, calculating the instantaneous value of the current sampling point of the target entity within the polling interval according to the resolution and the quantized data.

[0066] Specifically, the following expression for calculating the instantaneous value of the current sampling point of the target entity can be used to calculate the instantaneous value of the current sampling point of the entity signal within the polling interval according to the resolution and quantized data.

[0067] In one embodiment of the present invention, the expression for calculating the instantaneous value of the current sampling point of the target entity is as follows:

[0068]

[0069] Among them, c i represents; ΔT represents the polling interval; k represents; It represents the sum of the first k quantized data in the polling interval; Z represents the bias coefficient to avoid negative values ​​in the data; To indicate; to indicate; to indicate.

[0070] S404: Determine the instantaneous change probability of the actual operation data according to the instantaneous value of the current sampling point and the instantaneous value of the previous sampling point.

[0071] Specifically, after the instantaneous value of the current sampling point is calculated by the above method, the instantaneous value of the previous sampling point is obtained, and the instantaneous change probability of the actual operation data is determined according to the instantaneous value of the current sampling point and the instantaneous value of the previous sampling point.

[0072] In one embodiment of the present invention, determining the instantaneous change probability of the actual operation data may include:

[0073] Calculate the instantaneous change rate of the actual operation data based on the instantaneous value of the current sampling point and the instantaneous value of the previous sampling point;

[0074] If the instantaneous change rate is greater than the first preset threshold, the instantaneous change probability is determined to be 1;

[0075] If the instantaneous change rate is greater than the second preset threshold value and less than or equal to the first preset threshold value, the instantaneous change rate is determined as the instantaneous change probability;

[0076] If the instantaneous change rate is less than or equal to the second preset threshold, the instantaneous change probability is determined to be 0.

[0077] In one embodiment of the present invention, the actual operation data x is calculated. i The instantaneous rate of change δ i The expression is as follows:

[0078]

[0079] Among them, δ i Indicates the instantaneous rate of change of actual operating data; c i Indicates the instantaneous value of the current sampling point; c i-1 Indicates the instantaneous value of the previous sampling point.

[0080] In one embodiment of the present invention, the actual operation data x is determined i The instantaneous change probability P2 i The expression is as follows:

[0081]

[0082] Among them, P2 i Indicates the instantaneous change probability of actual operating data; δ i Indicates the actual running data x i abs() represents the absolute value function, c represents the fourth preset threshold, and d represents the fifth preset threshold.

[0083] Specifically, the actual operation data x is calculated using the above method. i The instantaneous rate of change δ i The instantaneous rate of change of the actual operating data can be calculated by using the expression i The instantaneous rate of change δ i When the absolute value of is greater than the fourth preset threshold c, the actual operation data x i The instantaneous change probability P2 i Recorded as 1. If the actual running data x i The instantaneous rate of change δ i When the absolute value of is less than or equal to the fourth preset threshold value c and greater than the fifth preset threshold value d, the actual operation data x i The instantaneous change probability P2 i Recorded as the actual running data x i The instantaneous rate of change δ i If the actual running data x i The instantaneous rate of change δ i When the absolute value of is less than or equal to the fifth preset threshold value d, the actual operation data x i The instantaneous change probability P2 iRecorded as 0.

[0084] In the embodiment of the present invention, the fourth preset threshold c and the fifth preset threshold d are set according to actual needs.

[0085] In one embodiment of the present invention, determining the probability of a change in the operation data of the target entity according to the initial weight, the associated change probability, and the instantaneous change probability includes:

[0086] If the instantaneous change probability is greater than the first preset threshold, the maximum value of the first product value and the instantaneous change probability is taken as the change probability, wherein the first product value is the product of half of the sum of the associated change probability and the instantaneous change probability and the initial weight;

[0087] If the instantaneous change probability is less than or equal to the first preset threshold, the maximum value of the second product value and the instantaneous change probability is taken as the change probability, wherein the second product value is the product of the initial weight and the associated change probability.

[0088] In one embodiment of the present invention, the operation data x of the target entity is calculated. i The probability of a change is expressed as follows:

[0089]

[0090] Among them, P3 i Represents the running data x of target entity i i The probability of change; h represents the first preset threshold; w i Represents running data x i The initial weight corresponding to the category to which the target entity i belongs; P1 i represents the probability of association change; P2 i Represents the instantaneous change probability.

[0091] It is feasible to calculate the operation data x of the target entity by using the above method according to the relationship between the instantaneous change probability and the first preset threshold value. i An expression of the probability of change, which determines the probability of the target entity's operating data changing.

[0092] In one embodiment of the present invention, updating the polling interval according to the probability includes:

[0093] Determine the change direction and deviation value according to the change probability of the current sampling point and the change probability of the previous sampling point;

[0094] If the change direction is positive and the deviation value exceeds a second preset threshold, shortening the polling interval;

[0095] If the change direction is negative and the deviation value exceeds the second preset threshold, the polling interval is increased.

[0096] Specifically, the probability P3 that the corresponding operation data of each target entity undergoes significant changes in the transient state i (t), compare the probability of change P3 of the target entity's corresponding running data at the current sampling point i (t) and the probability of change of the previous sampling point P3 i (t-1) determines the change direction and deviation value of the target entity's corresponding running data. If the change direction is positive, that is, P3 i (t)>P3 i (t-1), and the deviation value exceeds the second preset threshold, the polling interval is shortened. If the change direction is negative, that is, P3 i (t)<P3 i (t-1), and the deviation value exceeds the second preset threshold, the polling interval is increased.

[0097] It should be noted that, in the embodiment of the present invention, when the polling interval is shortened or increased, a maximum depth setting is provided to prevent excessive collection of target entity operation data or too sparse collection of target entity operation data. Figure 5 The multi-layer adaptive change method shown is to perform adaptive changes with a depth of 3 at half or twice the reference interval (initial polling interval).

[0098] In one embodiment of the present invention, before collecting the actual operation data of the target entity according to the updated polling interval, the data collection method may further include:

[0099] The bus load rate of the system is obtained. If the bus load rate is less than the preset load rate threshold, the actual operation data of the target entity is collected according to the updated polling interval.

[0100] Specifically, when the bus load rate is below 90% and the polling interval is updated, the actual operation data of the target entity collected according to the updated polling interval is added to the corresponding polling queue, otherwise the polling interval is not updated.

[0101] In an embodiment of the present invention, in order to obtain the initial weight and associated change probability of the category to which the target entity belongs corresponding to the actual operation data, feature encoding can be performed on each target entity. Exemplarily, the feature encoding rule is as follows: signal GUID (unique id number in the project) + signal type. Signal GUID (unique id number in the project) + signal type + user attention. According to the signal GUID and signal type, the initial weight and associated change probability of the category to which the target entity belongs corresponding to the actual operation data are obtained from the preset database.

[0102] It should be noted that during the system trial operation or debugging phase, since some features in the actual project deployment cannot be fully identified, it is necessary to collect data according to the set polling combination polling cycle for subsequent optimization. In the subsequent stages, dynamic adjustments can be made based on the current collected signal information to gradually improve the overall signal transmission quality.

[0103] The data collection method of the embodiment of the present invention updates the polling interval according to the probability of change of the operating data of the target entity calculated from the actual operating data of the target entity, and collects the actual operating data of the target entity according to the updated polling interval, thereby effectively extracting and identifying the detailed features of the transient characteristics of the target entity, which is beneficial to the analysis and evaluation of the system.

[0104] The present invention provides a computer-readable storage medium.

[0105] In this embodiment, a computer program is stored on a computer-readable storage medium, and when the computer program is executed by a processor, the above-mentioned data collection method is implemented.

[0106] The invention provides an electronic device.

[0107] In this embodiment, the electronic device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the above-mentioned data collection method is implemented.

[0108] Figure 6 FIG. 1 is a block diagram of a controller according to an embodiment of the present invention. Figure 6 As shown, the controller 500 includes: a processor 501 and a memory 503. The processor 501 and the memory 503 are connected, such as through a bus 502. Optionally, the controller 500 may also include a transceiver 504. It should be noted that in actual applications, the transceiver 504 is not limited to one, and the structure of the controller 500 does not constitute a limitation on the embodiments of the present invention.

[0109] Processor 501 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the present invention. Processor 501 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0110] The bus 502 may include a path to transmit information between the above components. The bus 502 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 502 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0111] The memory 503 is used to store a computer program corresponding to the data acquisition method of the above embodiment of the present invention, and the computer program is controlled and executed by the processor 501. The processor 501 is used to execute the computer program stored in the memory 503 to implement the content shown in the above method embodiment. Among them, the controller 500 includes but is not limited to: mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), mobile terminals such as vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The controller 500 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0112] The computer-readable storage medium and electronic device of the embodiments of the present invention utilize the above data collection method to achieve effective extraction and recognition of transient features of the target entity, which is beneficial for analyzing and evaluating the system.

[0113] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0114] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0115] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0116] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “clockwise”, “counterclockwise”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0117] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0118] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0119] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0120] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A data collection method, characterized in that: The method comprises: Determine an initial polling interval for target entities in the system; Collecting actual operation data of each of the target entities according to the initial polling interval; Calculating the probability of a change in the operation data of the target entity according to the actual operation data, and updating the polling interval according to the probability; The actual operation data of the target entity is collected according to the updated polling interval.

2. The data collection method according to claim 1, characterized in that: The calculating, according to the actual operation data, the probability that the operation data of the target entity changes, includes: Obtaining an initial weight of the category to which the target entity corresponds to the actual operation data; Calculating the probability of association change between the target entity corresponding to the actual operation data and other entities; Calculating the instantaneous change probability of the operation data of the target entity according to the actual operation data, the initial weight and the associated change probability; The probability that the operating data of the target entity changes is determined according to the initial weight, the associated change probability, and the instantaneous change probability.

3. The data collection method according to claim 2, characterized in that: The calculating the probability of association change between the target entity corresponding to the actual operation data and other entities includes: Establishing a simulation model of the system; Testing the simulation model under different bus load rates, and the simulation operation data of each target entity within a preset time; The association change probability between each target entity and other entities is calculated based on the simulation operation data.

4. The data collection method according to claim 2, characterized in that: The calculating the instantaneous change probability of the operation data of the target entity according to the actual operation data, the initial weight and the associated change probability includes: Determining the resolution of the actual operation data according to the initial weight and the associated change probability; quantizing the actual operation data according to the actual operation data and the resolution to obtain quantized data; Calculating the instantaneous value of the current sampling point of the entity signal within the polling interval according to the resolution and the quantized data; The instantaneous change probability of the actual operation data is determined according to the instantaneous value of the current sampling point and the instantaneous value of the previous sampling point.

5. The data collection method according to claim 4, characterized in that: Determining the instantaneous change probability of the actual operation data includes: Calculating the instantaneous change rate of the actual operation data according to the instantaneous value of the current sampling point and the instantaneous value of the previous sampling point; If the instantaneous change rate is greater than a first preset threshold, determining that the instantaneous change probability is 1; If the instantaneous change rate is greater than the second preset threshold value and less than or equal to the first preset threshold value, determining the instantaneous change rate as the instantaneous change probability; If the instantaneous change rate is less than or equal to a second preset threshold, the instantaneous change probability is determined to be 0.

6. The data collection method according to claim 2, characterized in that: The determining the probability of a change in the operation data of the target entity according to the initial weight, the associated change probability, and the instantaneous change probability includes: If the instantaneous change probability is greater than a first preset threshold, taking the maximum value of the first product value and the instantaneous change probability as the change probability, wherein the first product value is the product of half of the sum of the associated change probability and the instantaneous change probability and the initial weight; If the instantaneous change probability is less than or equal to a first preset threshold, the maximum value of the second product value and the instantaneous change probability is taken as the change probability, wherein the second product value is the product of the initial weight and the associated change probability.

7. The data collection method according to claim 6, characterized in that: Updating the polling interval according to the probability includes: Determine a change direction and a deviation value according to the change probability of the current sampling point and the change probability of the previous sampling point; If the change direction is positive and the deviation value exceeds a second preset threshold, shortening the polling interval; If the change direction is negative and the deviation value exceeds a second preset threshold, the polling interval is increased.

8. The data collection method according to claim 1, characterized in that: Before collecting the actual operation data of the target entity according to the updated polling interval, the method further includes: The bus load rate of the system is obtained. If the bus load rate is less than a preset load rate threshold, the actual operation data of the target entity is collected according to the updated polling interval.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the data collection method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the computer program is executed by the processor, the data collection method according to any one of claims 1 to 7 is implemented.

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