Data processing method and related equipment

Abstract

The invention provides a data processing method and related equipment. The method comprises the following steps: determining the importance of electric power measurement data and a corresponding importance coefficient based on a preset parameter; determining an update value parameter based on the importance, the importance coefficient and an update data volume; determining a synchronization decision value based on the updated value parameter and a current resource parameter; judging whether the synchronization decision value is greater than or equal to a synchronization threshold value; responding to the condition that the synchronization decision value is greater than or equal to the synchronization threshold value, and obtaining a synchronization cost performance for a preset synchronization mode based on the updated value parameter and the cost parameter; and determining a target synchronization mode in the preset synchronization modes based on the synchronization mode cost performance so as to synchronize the electric power measurement data.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technologies, and in particular, to a data processing method and related devices. Background Art

[0002] With the continuous deepening of the construction of the new power system, the number of power terminals has been increasing continuously, and power services have become more diversified, resulting in a continuous generation of a large amount of metering data in the power system. However, the large amount of power metering data has the characteristics of large volume, strong suddenness, and high value density, which brings huge challenges to the storage and synchronization of power metering data. Although there are currently some incremental data synchronization technologies, there are still the following two disadvantages: First, the impact of data value and system resource consumption on triggering incremental data synchronization is not fully considered, and the integration of data value and system resource consumption is ignored, resulting in excessive pressure on the system during data synchronization and low efficiency of power metering data synchronization. Second, the data synchronization method is not selected by taking into account the cost performance of the whole link, resulting in excessive consumption of system resources during data synchronization and low effectiveness of power metering data synchronization. Summary of the Invention

[0003] The present disclosure provides a data processing method, device, equipment, storage medium, and program product to solve the technical problem of relatively low efficiency and effectiveness of data synchronization to a certain extent.

[0004] In a first aspect of the present disclosure, a data processing method is provided, including:

[0005] Determining the importance degree of power metering data and the corresponding importance degree coefficient based on preset parameters;

[0006] Determining an update value parameter based on the importance degree, the importance degree coefficient, and the updated data volume;

[0007] Determining a synchronization decision value based on the update value parameter and the current resource parameter;

[0008] Judging whether the synchronization decision value is greater than or equal to a synchronization threshold;

[0009] In response to the synchronization decision value being greater than or equal to the synchronization threshold, obtaining the synchronization cost performance for a preset synchronization method based on the update value parameter and the cost parameter;

[0010] Determining a target synchronization method in the preset synchronization methods based on the synchronization method cost performance to synchronize the power metering data.

[0011] In some embodiments, the preset parameters include a necessity parameter, an influence degree parameter, and an affected degree parameter;

[0012] Wherein, the necessity parameter includes: L m is the necessary series for the m-th type of power metering data, is the maximum necessary series in the power metering data;

[0013] The influence degree parameter includes: is the quantity of the m-th type of power metering data affecting other types of power metering data, is the minimum influence quantity of the power metering data, is the maximum influence quantity of the power metering data;

[0014] The influenced degree parameter includes: is the quantity of other types of power metering data that affect the m-th type of power metering data, is the minimum influenced quantity of the power metering data, is the maximum influenced quantity of the power metering data.

[0015] In some embodiments, determining the importance degree of the power metering data and the corresponding importance degree coefficient based on preset parameters includes:

[0016] μ is the importance degree weight factor.

[0017] In some embodiments, determining the update value parameter based on the importance degree, the importance degree coefficient, and the update data volume of the update data includes:

[0018] η m is the importance degree coefficient of the m-th type of power metering data, γ m is the importance degree of the m-th type of power metering data, A m (t) is the data volume newly added or modified to the m-th type of power metering data since the last synchronization.

[0019] In some embodiments, the current resource parameter includes: computing resource parameter, memory resource parameter, and bandwidth resource parameter;

[0020] The synchronization decision value includes: θ m (t) is the update data value parameter at time t, U(t) is the usage rate of the central processing unit at time t, M(t) is the memory occupancy size of the system at time t, M s is the total system memory, B(t) is the bandwidth occupied by the system at time t, B s is the total bandwidth of the system.

[0021] In some embodiments, the cost parameter includes: historical cost, synchronization method cost, data recovery cost, and time interval cost;

[0022] Obtaining the synchronization cost performance for a preset synchronization method based on the updated value parameter and the cost parameter, including:

[0023]

[0024] where ξ1, ξ2, ξ3, and ξ4 are the cost weighting factors of the historical cost, synchronization method cost, data recovery cost, and time interval cost respectively, T is the time interval of regular full synchronization, n is the number of full synchronizations that have been performed, φ m (t) is the historical cost function of data synchronization, is the bandwidth resource required for synchronizing the m-th type of measurement data using the preset synchronization method i, is the memory resource required for synchronizing the m-th type of measurement data using the preset synchronization method i, is the memory resource required for data recovery of the m-th type of measurement data using the preset synchronization method i, is the average utilization rate of the central processing unit when synchronizing the m-th type of measurement data using the preset synchronization method i, is the average utilization rate of the central processing unit when performing data recovery on the m-th type of measurement data using the preset synchronization method i, C i,m (t) is the average cost when synchronizing using the preset synchronization method i before time t.

[0025] In some embodiments, determining the target synchronization method in the preset synchronization methods based on the synchronization method cost performance includes:

[0026] Selecting the preset synchronization method corresponding to the maximum value in the synchronization method cost performance as the target synchronization method.

[0027] In a second aspect of the present disclosure, a data processing device is provided, including:

[0028] An importance module for determining the importance of power measurement data and the corresponding importance coefficient based on preset parameters;

[0029] An update value module for determining an updated value parameter based on the importance, the importance coefficient, and the amount of updated data;

[0030] A synchronization decision module for determining a synchronization decision value based on the updated value parameter and the current resource parameter; and for determining whether the synchronization decision value is greater than or equal to a synchronization threshold;

[0031] A cost performance module for, in response to the synchronization decision value being greater than or equal to the synchronization threshold, obtaining the synchronization cost performance for a preset synchronization method based on the updated value parameter and the cost parameter;

[0032] A synchronization selection module, configured to determine a target synchronization method from the preset synchronization methods based on the cost performance of the synchronization method, so as to synchronize the power metering data.

[0033] In some embodiments, the preset parameters include a necessity parameter, an influence degree parameter, and an influenced degree parameter;

[0034] Among them, the necessity parameter includes: L m is the necessary level of the m-th type of power metering data, is the maximum necessary level in the power metering data;

[0035] The influence degree parameter includes: is the number of other types of power metering data affected by the m-th type of power metering data, is the minimum influence number of the power metering data, is the maximum influence number of the power metering data;

[0036] The influenced degree parameter includes: is the number of other types of power metering data that affect the m-th type of power metering data, is the minimum influenced number of the power metering data, is the maximum influenced number of the power metering data.

[0037] In some embodiments, the importance module is configured to determine the importance of the power metering data and the corresponding importance coefficient based on the preset parameters, including:

[0038] μ is an importance weight factor.

[0039] In some embodiments, the update value module is configured to determine an update value parameter based on the importance, the importance coefficient, and the update data volume of the update data, including:

[0040] η m is the importance coefficient of the m-th type of power metering data, γ m is the importance of the m-th type of power metering data, A m (t) is the data volume updated for the m-th type of power metering data since the last synchronization.

[0041] In some embodiments, the current resource parameters include: computing resource parameters, memory resource parameters, and bandwidth resource parameters;

[0042] The synchronization decision value includes: θm (t) is the updated data value parameter at time t, U(t) is the utilization rate of the central processing unit at time t, M(t) is the memory occupancy size of the system at time t, and M s is the total memory of the system, B(t) is the bandwidth occupied by the system at time t, and B s is the total bandwidth of the system.

[0043] In some embodiments, the cost parameters include: historical cost, synchronization method cost, data recovery cost, and time interval cost;

[0044] The cost performance module is used to obtain the synchronization cost performance for a preset synchronization method based on the updated value parameter and the cost parameter, including:

[0045]

[0046] Among them, ξ1, ξ2, ξ3, and ξ4 are the cost weighting factors of historical cost, synchronization method cost, data recovery cost, and time interval cost respectively, T is the time interval of periodic full synchronization, n is the number of full synchronizations that have been performed, and φ m (t) is the historical cost function of data synchronization, is the bandwidth resource required for the m-th measurement data to be synchronized using the preset synchronization method i, is the memory resource required for the m-th measurement data to be synchronized using the preset synchronization method i, is the memory resource required for the m-th measurement data to be data recovered using the preset synchronization method i, is the average utilization rate of the central processing unit when the m-th measurement data is synchronized using the preset synchronization method i, is the average utilization rate of the central processing unit when the m-th measurement data is data recovered using the preset synchronization method i, and C i,m (t) is the average cost when synchronizing using the preset synchronization method i before time t.

[0047] In some embodiments, the synchronization selection module is used to determine the target synchronization method among the preset synchronization methods based on the synchronization method cost performance, including:

[0048] Select the preset synchronization method corresponding to the maximum value in the synchronization method cost performance as the target synchronization method.

[0049] In the third aspect of the present disclosure, an electronic device is provided, which is characterized in that it includes one or more processors, a memory; and one or more programs, wherein the one or more programs are stored in the memory and are executed by the one or more processors, and the programs include instructions for executing the method according to the first aspect or the second aspect.

[0050] In a fourth aspect of the present disclosure, there is provided a non-volatile computer-readable storage medium including a computer program, which, when executed by one or more processors, causes the processors to execute the method described in the first aspect or the second aspect.

[0051] In a fifth aspect of the present disclosure, there is provided a computer program product including computer program instructions, which, when running on a computer, cause the computer to execute the method described in the first aspect.

[0052] As can be seen from the above, for a data processing method and related devices provided by the present disclosure, the importance degree and its importance degree coefficient of each power metering data are determined according to preset parameters, and then the update value parameter of the updated data is obtained based on this, and the current resources are combined to determine the synchronization decision value for judging whether to synchronize; if the synchronization decision value is greater than or equal to the synchronization threshold, synchronization is triggered, the synchronization cost performance of different synchronization methods is determined based on the update value parameter, and the target synchronization method with the highest cost performance is determined from them to synchronize the power metering data. Based on the characteristics of large volume and high value density of power metering data, the metering data can be classified and graded according to the importance degree, fully considering the influence of data value and system resource consumption on the triggering of incremental data synchronization, a synchronization triggering mechanism for updated data is proposed, and the method with the highest cost performance is selected to synchronize incremental data, effectively improving the effectiveness and efficiency of incremental data synchronization. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings in the following description are only the embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0054] Figure 1 It is a schematic diagram of the data processing architecture of the embodiments of the present disclosure.

[0055] Figure 2 It is a schematic diagram of the hardware structure of an exemplary electronic device according to the embodiments of the present disclosure.

[0056] Figure 3 It is a schematic flowchart of the data processing method according to the embodiments of the present disclosure.

[0057] Figure 4 It is an example of the data processing method according to the embodiments of the present disclosure.

[0058] Figure 5 It is a schematic diagram of the data processing device according to the embodiments of the present disclosure.

[0059] Figure 6 Schematic diagram of the data processing system according to the embodiments of the present disclosure. Detailed implementation manners

[0060] To make the objectives, technical solutions and advantages of the present disclosure more clear and understandable, the present disclosure will be further described in detail below with reference to specific embodiments and the accompanying drawings.

[0061] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be the ordinary meanings understood by those of ordinary skill in the field to which the present disclosure belongs. The terms "first", "second" and similar terms used in the embodiments of the present disclosure do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left" and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0062] It can be understood that, before using the technical solutions disclosed in the embodiments of the present disclosure, the types, usage scopes, 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.

[0063] For example, when receiving the user's active request, a prompt message is sent to the user to clearly prompt the user that the operation requested by the user 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, an application program, a server or a storage medium that executes the operations of the technical solutions of the present disclosure according to the prompt message.

[0064] As an optional but non-limiting implementation manner, the manner of sending a prompt message to the user in response to receiving the user's active request may be, for example, in the form of a pop-up window, and the prompt message may be presented in text in the pop-up window. In addition, the pop-up window may also carry a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0065] It can be understood that the above process of notifying and obtaining the user's authorization is only illustrative and does not limit the implementation manners of the present disclosure, and other manners that meet relevant laws and regulations can also be applied to the implementation manners of the present disclosure.

[0066] Figure 1The figure shows a schematic diagram of the data processing architecture according to an embodiment of the present disclosure. Refer to Figure 1 , the data processing architecture 100 may include a server 110, a terminal 120, and a network 130 providing a communication link. The server 110 and the terminal 120 may be connected through the wired or wireless network 130. Among them, the server 110 may be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, security services, and CDN.

[0067] The terminal 120 may be implemented by hardware or software. For example, when the terminal 120 is implemented by hardware, it may be various electronic devices with a display screen and supporting page display, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, and desktop computers, etc. When the terminal 120 device is implemented by software, it may be installed in the above-listed electronic devices; it may be implemented as multiple software or software modules (such as software or software modules for providing distributed services), or may be implemented as a single software or software module, which is not specifically limited herein.

[0068] It should be noted that the data processing method provided by the embodiments of the present application may be executed by the terminal 120 or by the server 110. It should be understood that Figure 1 the numbers of the terminals, networks, and servers in

[0069] Figure 2 The figure shows a schematic diagram of the hardware structure of an exemplary electronic device 200 according to an embodiment of the present disclosure. As Figure 2 shown, the electronic device 200 may include: a processor 202, a memory 204, a network module 206, a peripheral interface 208, and a bus 210. Among them, the processor 202, the memory 204, the network module 206, and the peripheral interface 208 are communicatively connected to each other inside the electronic device 200 through the bus 210.

[0070] The processor 202 can be a Central Processing Unit (CPU), a data processor, a Neural Network Processor (NPU), a Microcontroller Unit (MCU), a programmable logic device, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits. The processor 202 can be used to execute functions related to the technologies described in this disclosure. In some embodiments, the processor 202 may further include multiple processors integrated as a single logic component. For example, as Figure 2 shown, the processor 202 may include multiple processors 202a, 202b, and 202c.

[0071] The memory 204 can be configured to store data (e.g., instructions, computer code, etc.). As Figure 2 shown, the data stored in the memory 204 may include program instructions (e.g., program instructions for implementing the data processing method of the embodiments of this disclosure) and the data to be processed (e.g., the memory may store configuration files of other modules, etc.). The processor 202 can also access the program instructions and data stored in the memory 204, and execute the program instructions to operate on the data to be processed. The memory 204 may include a volatile storage device or a non-volatile storage device. In some embodiments, the memory 204 may include a Random Access Memory (RAM), a Read Only Memory (ROM), an optical disc, a magnetic disk, a hard disk, a Solid State Drive (SSD), a flash memory, a memory stick, etc.

[0072] The network module 206 can be configured to provide communication with other external devices to the electronic device 200 via a network. The network can be any wired or wireless network capable of transmitting and receiving data. For example, the network can be a wired network, a local wireless network (e.g., Bluetooth, WiFi, Near Field Communication (NFC), etc.), a cellular network, the Internet, or a combination of the above. It can be understood that the type of the network is not limited to the above specific examples. In some embodiments, the network module 306 may include any combination of any number of Network Interface Controllers (NICs), radio frequency modules, transceivers, modems, routers, gateways, adapters, cellular network chips, etc.

[0073] The peripheral interface 208 can be configured to connect the electronic device 200 to one or more peripheral devices to achieve information input and output. For example, the peripheral devices may include input devices such as a keyboard, a mouse, a touchpad, a touch screen, a microphone, various sensors, etc. and output devices such as a display, a speaker, a vibrator, an indicator light, etc.

[0074] The bus 210 can be configured to transfer information between various components of the electronic device 200 (such as the processor 202, the memory 204, the network module 206, and the peripheral interface 208), such as internal buses (e.g., the processor-memory bus), external buses (USB ports, PCI-E buses), etc.

[0075] It should be noted that although the architecture of the above-mentioned electronic device 200 only shows the processor 202, the memory 204, the network module 206, the peripheral interface 208, and the bus 210, in the specific implementation process, the architecture of the electronic device 200 may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the architecture of the above-mentioned electronic device 200 may also only include the components necessary to implement the solution of the embodiments of the present disclosure, and does not necessarily include all the components shown in the figure.

[0076] With the continuous deepening of the construction of the new power system, the number of power terminals is increasing continuously, and power services are becoming more diversified, resulting in a continuous generation of a large amount of metering data in the power system. The intelligent monitoring terminal uploads the collected power metering data to the target memory for storage at regular time intervals, so that the processor can process and analyze these metering data to provide support for the efficient operation of various services in the power system. However, the large amount of power metering data has the characteristics of large volume, strong suddenness, and high value density, which brings great challenges to the storage and synchronization of power metering data. Therefore, there is an urgent need for an incremental data synchronization method for mass storage. Based on the characteristics of large volume and high value density of power metering data, the metering data is classified and graded, an incremental data synchronization trigger mechanism is designed, and the most cost-effective method is selected for incremental data synchronization.

[0077] Currently, there have been some studies on incremental data synchronization. For example, according to the number of changed resources of each type of resource and the total amount of resources of each type of resource at the last synchronization, calculate the recent change ratio of resources of each type; and according to the start time of the last synchronization task, the resource change coefficient of each type of resource, and the recent change ratio of resources, calculate the start time of pulling data for each type of resource; finally, obtain the incremental data of each type of resource from the start time of pulling data to the current time in sequence according to the resource dependency relationship, and synchronize the incremental data of each type of resource. Or, synchronize based on the timed data increment extracted by the timestamp increment, capture the changed data in the business system at a certain frequency; query the total number of data records, analyze the size of the incremental interval data volume, and calculate the available memory of the system; finally, estimate the expected memory used during synchronization optimization and reasonably allocate the data synchronization memory.

[0078] However, the existing incremental data synchronization methods still have the following two disadvantages: First, they do not fully consider the impact of data value and system resource consumption on triggering incremental data synchronization, and ignore the integration of data value and system resource consumption, resulting in excessive pressure on the system during data synchronization and low efficiency of power metering data synchronization. Second, they do not consider the cost performance of the entire link when selecting a data synchronization method, resulting in excessive occupation of system resources during data synchronization and low effectiveness of power metering data synchronization. Therefore, how to improve the efficiency and effectiveness of data synchronization has become a technical problem that needs to be solved urgently.

[0079] In view of this, the embodiments of the present disclosure provide a data processing method, device, equipment, storage medium and program product. For preset parameters, determine the importance and its importance coefficient of each power metering data, and then obtain the update value parameter of the update data accordingly. Combine the current resources to determine the synchronization decision value for judging whether to synchronize; if the synchronization decision value is greater than or equal to the synchronization threshold, trigger synchronization, determine the synchronization cost performance of different synchronization methods based on the update value parameter, and determine the target synchronization method with the highest cost performance from them to synchronize the power metering data. Based on the characteristics of large volume and high value density of power metering data, the metering data can be classified and graded according to the degree of importance, fully considering the impact of data value and system resource consumption on the trigger of incremental data synchronization, proposing a synchronization trigger mechanism for update data, and selecting the method with the highest cost performance to perform incremental data synchronization, effectively improving the effectiveness and efficiency of incremental data synchronization.

[0080] See Figure 3 , Figure 3 shows a schematic flowchart of the data processing method according to the embodiments of the present disclosure. The data processing method according to the embodiments of the present disclosure can be deployed on the client or the server side to synchronize the collected power metering data. Figure 3 In, the data processing method 300 may further include the following steps.

[0081] Step S310, determine the importance and the corresponding importance coefficient of the power metering data based on the preset parameters.

[0082] Among them, the power metering data may refer to the data related to power supply and power consumption in the power system, which is used to monitor and evaluate the operation status, load condition, and power consumption situation of the power system, etc. For example, the power metering data may include voltage, current, phase angle, etc. The importance of each power metering data to the business operation of the power system is different, and the value of the generated update data is different.

[0083] In some embodiments, the preset parameters include a necessity parameter, an influence degree parameter, and an influenced degree parameter;

[0084] Among them, the necessity parameter includes: L m is the necessary level of the m-th type of power metering data, and is the maximum necessary level in the power metering data;

[0085] The influence degree parameter includes: is the quantity of the m-th type of power metering data affecting other types of power metering data, is the minimum influence quantity of the power metering data, and is the maximum influence quantity of the power metering data;

[0086] The influenced degree parameter includes: is the quantity of other types of power metering data that affect the m-th type of power metering data, is the minimum influenced quantity of the power metering data, and is the maximum influenced quantity of the power metering data.

[0087] Specifically, there are K types of power metering data that need to be synchronized. The power metering data set Ω = {X1, X2,..., X m ,..., X M} can be defined. In order to accurately determine the importance of the power metering data, the factors affecting the importance can be divided into three: necessity parameter, influence degree parameter, and influenced degree parameter.

[0088] The necessity parameter α m is the necessary degree of the power metering data X m for completing the power system service function. The power metering data that ultimately determines the system function can be regarded as the most necessary data, that is, the first necessary data, and the corresponding necessary level L m = 1; The power metering data that directly affects the first necessary data can be used as the second necessary data, and the corresponding necessary level L m = 2; The power metering data that directly affects the second necessary data can be used as the third necessary data, and the corresponding necessary level L m = 3; And so on, the necessity of all types of power metering data can be determined. The necessity of the m-th type of power metering data can be expressed as:

[0089]

[0090] where L m is the necessary level of the m-th type of power metering data, and is the maximum necessary level among the K types of power metering data.

[0091] The influence degree parameter β m is the power metering data Xm The degree of influence on other types of data. The more other types of power metering data it affects, the greater the degree of influence. The degree of influence of the m-th type of power metering data can be expressed as:

[0092]

[0093] Among them, is the quantity of the m-th type of power metering data that affects other types of power metering data, is the minimum influence quantity of K types of power metering data, is the maximum influence quantity of K types of power metering data.

[0094] The influenced degree parameter χ m is the degree to which the power metering data X m is influenced by other types of power metering data. The more other types of power metering data that affect X m , the greater the influenced degree of X m . The influenced degree of the m-th type of power metering data can be expressed as:

[0095]

[0096] Among them, is the quantity of other types of power metering data that can affect the m-th type of power metering data, is the minimum influenced quantity of K types of power metering data, is the maximum influenced quantity of K types of power metering data.

[0097] Based on the necessity parameter, influence degree parameter, and influenced degree parameter, calculate the importance degree γ m of the power metering data X m , which can be expressed as:

[0098] Among them, μ is the importance degree weight factor, which is used to adjust the influence degree of the three factors of necessity parameter, influence degree parameter, and influenced degree parameter on calculating the importance degree. The greater the importance degree γ m , the greater the importance of the m-th type of power metering data X m for the power system business function.

[0099] Accordingly, the importance degree set {γ1, γ2,..., γ m ,..., γ M}. Then, according to the importance levels, rearrange the importance set from large to small and divide it into three intervals: high importance interval, medium importance interval, and low importance interval. All power metering data can be classified into three categories according to the interval to which their importance belongs: high importance metering data, medium importance metering data, and low importance metering data. For example, divide the power metering data into three categories based on the number K of types of power metering data, and the difference in the quantity between high importance metering data, medium importance metering data, and low importance metering data is less than 2. Or, divide the power metering data into three categories based on an importance threshold. Among them, power metering data with an importance greater than the first importance threshold can be used as high importance metering data, power metering data with an importance less than or equal to the first importance threshold and greater than the second importance threshold can be used as medium importance metering data, and power metering data with an importance less than the second importance threshold can be used as low importance metering data.

[0100] Step S320, determine an update value parameter based on the importance, the importance coefficient, and the update data volume.

[0101] Among them, the update data can refer to newly added or modified power metering data. When synchronizing the update data, the system will occupy certain computing resources, memory resources, and bandwidth resources, and the synchronization efficiency of the update data is also closely related to the consumption of these system resources.

[0102] Specifically, based on the importance and classification of each type of power metering data, the update value parameter θ m of the m-th type of metering data X m (t) can be expressed as:

[0103]

[0104] Among them, η m is the importance grading coefficient of the m-th type of power metering data. The importance grading coefficients of power metering data in the same importance category are the same. The grading coefficient of high importance metering data is greater than that of medium importance metering data, and the grading coefficient of low importance metering data is the smallest. A m (t) is the data volume updated (such as newly added or modified) for the m-th type of power metering data since the last synchronization.

[0105] Step S330, determine a synchronization decision value based on the update value parameter and the current resource parameter.

[0106] Among them, whether to trigger the data synchronization of power metering data X m can be determined by the update value parameter θ m of power metering data X m(t) is jointly determined by the current system resources. The system resources include the computing resources, memory resources, and bandwidth resources of the system at time t. Then, the power metering data X m The synchronization decision value δ m (t) can be expressed as:

[0107]

[0108] where U(t) is the utilization rate of the central processing unit at time t, M(t) is the memory occupancy size of the system at time t, M s is the total system memory, B(t) is the bandwidth occupied by the system at time t, and B s is the total bandwidth of the system.

[0109] Step S340, determine whether the synchronization decision value is greater than or equal to the synchronization threshold.

[0110] where, if the synchronization decision value δ m (t) is greater than the synchronization threshold then trigger the update data synchronization of the power metering data X m The greater the update value parameter of the power metering data X m , the less computing resources, memory resources, and bandwidth resources are occupied by the current system, and the easier it is to trigger the synchronization of the update data.

[0111] It can be seen that the method of the embodiment of the present disclosure proposes an update data synchronization trigger mechanism that takes into account the update value parameter and system resource consumption, calculates the importance of each power metering data, classifies the power metering data according to the importance; and realizes the determination of the trigger of the incremental data synchronization by comprehensively considering the incremental data value of each metering data and the resource consumption situation of the current system. It effectively overcomes the problem that the prior art does not fully consider the impact of data value and system resource consumption on triggering incremental data synchronization, ignores the integration of data value and system resource consumption, resulting in excessive pressure on the system during data synchronization and low efficiency of power metering data synchronization. By calculating the importance of each power metering data, classifying the power metering data according to the importance, and comprehensively considering the update value parameter of each metering data and the resource consumption situation of the current system to realize the determination of the trigger of the update data synchronization, it reduces the pressure on the system caused by the synchronization of massive data, effectively improves the efficiency of power metering data synchronization, and improves the stability of the system.

[0112] Step S350, in response to the synchronization decision value being greater than or equal to the synchronization threshold, obtain the synchronization cost performance for the preset synchronization method based on the update value parameter and the cost parameter.

[0113] Among them, the preset synchronization methods may include full synchronization, incremental synchronization, and differential synchronization, and the costs consumed by each synchronization method are different. The cost parameters mainly include historical cost, synchronization method cost, data recovery cost, and time interval cost.

[0114] Specifically, based on the incremental data value and data synchronization cost, measure data X m The performance-price ratio of data synchronization using method i can be expressed as:

[0115]

[0116] Among them, ξ1, ξ2, ξ3, and ξ4 are the cost weighting factors of historical cost, synchronization method cost, data recovery cost, and time interval cost respectively, T is the time interval of regular full synchronization, n is the number of full synchronizations that have been performed, and φ m (t) is the historical cost function of data synchronization, is the bandwidth resource required for the mth measurement data to be synchronized using method i, is the memory resource required for the mth measurement data to be synchronized using method i, is the memory resource required for the mth measurement data to perform data recovery using method i, is the average utilization rate of the central processing unit when the mth measurement data is synchronized using method i, is the average utilization rate of the central processing unit when the mth measurement data performs data recovery using method i, C i,m (t) is the average cost when synchronizing using method i before time t.

[0117] It can be seen that when the updated value parameter θ m (t) is the same, the synchronization method with the lower data synchronization cost has a higher performance-price ratio. The historical cost includes the total data synchronization cost carried out between the current time t and the last full synchronization; the synchronization method cost mainly includes the computing resources, memory resources, and bandwidth resources required by each synchronization method; the data recovery cost includes the computing resources and memory resources required for data recovery using each synchronization method; the time interval cost is related to the position of the current time t, and the longer the time interval until the next regular full synchronization, the lower the time interval cost.

[0118] Step S360, determine the target synchronization method in the preset synchronization methods based on the performance-price ratio of the synchronization method to synchronize the power measurement data.

[0119] In some embodiments, determining the target synchronization method in the preset synchronization methods based on the performance-price ratio of the synchronization method includes:

[0120] Select the preset synchronization method corresponding to the maximum value in the cost performance of the synchronization methods as the target synchronization method.

[0121] Specifically, by comparing the cost performance of incremental data synchronization, differential data synchronization, and full - volume data synchronization, the incremental data synchronization method is selected, and the method with the highest cost performance is selected as the target synchronization method for data synchronization.

[0122] As an alternative embodiment, refer to Figure 4 , Figure 4 which shows an example of the data processing method according to an embodiment of the present disclosure. Figure 4 In it, in step S410, power measurement data can be collected. For example, power measurement data can be collected based on data collection terminals such as voltage sensors and current sensors. In step S420, the types and mutual relationships of the collected power measurement data can be determined (for example, some power measurement data affect each other), and the necessity, influence degree, and affected degree of various types of power measurement data can be calculated. In step S430, the importance degree γ m of all power measurement data can be calculated, and based on this, all power measurement data can be divided into three categories, namely high - importance measurement data, medium - importance measurement data, and low - importance measurement data. In step S440, the m - th type of measurement data X m that has not yet been judged for the trigger of updated data synchronization can be selected. In step S450, based on data importance, importance grading coefficient, data volume, system memory, and bandwidth conditions, etc., the update value parameter θ m of the m - th type of power measurement data X m (t) and the synchronization decision value δ m (t) can be calculated. In step S460, based on the synchronization decision value δ m (t), it can be judged whether the current synchronization trigger condition for updated data is reached. For example, if the synchronization decision value δ m of the power measurement data X m (t) is greater than the synchronization threshold , then the data synchronization of the power measurement data X m is triggered; otherwise, it jumps to step 440. In step S470, based on the update value parameter θ m (t), the cost performance of various data synchronization methods is calculated by combining historical cost, synchronization method cost, data recovery cost, and time - interval cost. In step S480, the cost performance of incremental data synchronization, differential data synchronization, and full - volume data synchronization is compared, and the synchronization method with the highest cost performance is selected to perform data synchronization on the power measurement data X m . In step S490, if all types of power measurement data have been judged for the trigger of updated data synchronization, the process ends; otherwise, it jumps to step 440.

[0123] It can be seen that the method according to the embodiments of the present disclosure proposes an updated data synchronization selection considering the cost performance of the entire link, extracts the updated value parameters calculated from the updated data, and calculates the cost performance of the data synchronization method by combining the historical cost, the cost of the synchronization method, the data recovery cost, and the time interval cost, so as to obtain the cost performance of the full synchronization, incremental synchronization, and differential synchronization; and accordingly selects the synchronization method with the highest cost performance for data synchronization. This effectively overcomes the problem in the prior art that the data synchronization method is selected without considering the cost performance of the entire link, resulting in excessive occupation of system resources during data synchronization and low effectiveness of power metering data synchronization. Based on the incremental data value of each power metering data, the cost performance of the data synchronization method is calculated by combining the historical cost, the cost of the synchronization method, the data recovery cost, and the time interval cost, so as to obtain the cost performance of the full synchronization, incremental synchronization, and differential synchronization, and select the synchronization method with the highest cost performance for data synchronization, avoiding excessive occupation of system resources, effectively improving the effectiveness of power metering data synchronization, and improving the availability of the system.

[0124] It should be noted that the method of the embodiments of the present disclosure can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In such a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiments of the present disclosure, and these multiple devices will interact with each other to complete the described method.

[0125] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the above embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the specific order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0126] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides a data processing device, see Figure 5 , the data processing device includes:

[0127] An importance module, configured to determine the importance of power metering data and the corresponding importance coefficient based on preset parameters;

[0128] An updated value module, configured to determine an updated value parameter based on the importance, the importance coefficient, and the updated data volume;

[0129] A synchronization decision module, configured to determine a synchronization decision value based on the updated value parameter and the current resource parameter; and determine whether the synchronization decision value is greater than or equal to a synchronization threshold;

[0130] A cost performance module, configured to, in response to the synchronization decision value being greater than or equal to the synchronization threshold, obtain a synchronization cost performance for a preset synchronization method based on the updated value parameter and the cost parameter;

[0131] A synchronization selection module, configured to determine a target synchronization method in the preset synchronization methods based on the synchronization method cost performance, so as to synchronize the power metering data.

[0132] In some embodiments, the preset parameters include a necessity parameter, an influence degree parameter, and an influenced degree parameter;

[0133] Wherein, the necessity parameter includes: L m is the necessary level of the m-th type of power metering data, is the maximum necessary level in the power metering data;

[0134] The influence degree parameter includes: is the number of other types of power metering data affected by the m-th type of power metering data, is the minimum influence number of the power metering data, is the maximum influence number of the power metering data;

[0135] The influenced degree parameter includes: is the number of other types of power metering data that affect the m-th type of power metering data, is the minimum influenced number of the power metering data, is the maximum influenced number of the power metering data.

[0136] In some embodiments, the importance module is configured to determine the importance of the power metering data and the corresponding importance coefficient based on the preset parameters, including:

[0137] μ is an importance weight factor.

[0138] In some embodiments, the updated value module is configured to determine an updated value parameter based on the importance, the importance coefficient, and the updated data volume of the updated data, including:

[0139] η m is the importance coefficient of the m-th type of power metering data, γ m is the importance of the m-th type of power metering data, Am The data volume updated for the m-th type of power metering data since the last synchronization is (t).

[0140] In some embodiments, the current resource parameters include: computing resource parameters, memory resource parameters, and bandwidth resource parameters;

[0141] The synchronization decision value includes: θ m The updated data value parameter at time t is (t), the utilization rate of the central processing unit at time t is U(t), the memory occupancy of the system at time t is M(t), and M s is the total system memory, the bandwidth occupied by the system at time t is B(t), and B s is the total bandwidth of the system.

[0142] In some embodiments, the cost parameters include: historical cost, synchronization method cost, data recovery cost, and time interval cost;

[0143] The performance-to-cost ratio module is used to obtain the synchronization performance-to-cost ratio for a preset synchronization method based on the updated value parameter and the cost parameter, including:

[0144]

[0145] Among them, ξ1, ξ2, ξ3, and ξ4 are the cost weighting factors of historical cost, synchronization method cost, data recovery cost, and time interval cost respectively, T is the time interval of regular full-volume synchronization, n is the number of full-volume synchronizations that have been performed, and φ m (t) is the historical cost function of data synchronization, is the bandwidth resource required for synchronizing the m-th type of metering data using the preset synchronization method i, is the memory resource required for synchronizing the m-th type of metering data using the preset synchronization method i, is the memory resource required for data recovery of the m-th type of metering data using the preset synchronization method i, is the average utilization rate of the central processing unit when synchronizing the m-th type of metering data using the preset synchronization method i, is the average utilization rate of the central processing unit when performing data recovery on the m-th type of metering data using the preset synchronization method i, and C i,m (t) is the average cost when synchronizing using the preset synchronization method i before time t.

[0146] In some embodiments, the synchronization selection module is used to determine the target synchronization method among the preset synchronization methods based on the synchronization method performance-to-cost ratio, including:

[0147] Select the preset synchronization method corresponding to the maximum value in the synchronization method performance-to-cost ratio as the target synchronization method.

[0148] Specifically, referring to Figure 6 , Figure 6 which shows a schematic diagram of a data synchronization system according to an embodiment of the present disclosure. Figure 6 In , the data synchronization system for synchronizing updated data mainly includes a power metering data acquisition module, an importance calculation and classification module, an incremental data synchronization trigger module, and a data synchronization method selection module. Among them, the power metering data acquisition module can collect power metering data generated by various types of metering devices in the power system and transmit the data to the importance calculation and classification module. The importance calculation and classification module may include Figure 5 the importance module in , which is used to calculate the importance of various types of power metering data based on the proposed power metering data importance calculation method, classify all power metering data according to the importance level, and select a power metering data that has not been judged for incremental data synchronization trigger and transmit it to the incremental data synchronization trigger module. The updated data synchronization trigger module may include Figure 5 the update value module and the synchronization decision module in , which are used to calculate the update value parameter and synchronization decision value of the power metering data based on data importance, importance classification coefficient, data volume, system memory, and bandwidth conditions, etc., compare the synchronization decision value with the synchronization threshold, determine whether to trigger incremental data synchronization, and transmit the trigger determination result to the importance calculation and classification module and the data synchronization method selection module. The data synchronization method selection module may include Figure 5 the cost performance module and the synchronization selection module in , which are used to calculate the cost performance of the data synchronization method based on the incremental data value, combined with historical cost, synchronization method cost, data recovery cost, and time interval cost, and select the synchronization method with the highest cost performance for power metering data synchronization.

[0149] Specifically, first, each module can be initialized; the power metering data acquisition module acquires power metering data generated by various types of metering devices in the power system and transmits the data to the importance calculation and classification module; the importance calculation and classification module calculates the importance of various power metering data and classifies all power metering data into three categories based on this, namely high-importance metering data, medium-importance metering data, and low-importance metering data; the importance calculation and classification module selects a piece of power metering data that has not been judged for incremental data synchronization trigger and transmits it to the incremental data synchronization trigger module; the incremental data synchronization trigger module calculates the incremental data value and synchronization decision value of the power metering data based on data importance, importance grading coefficient, data volume, system memory, and bandwidth conditions, etc., and judges whether the current trigger condition for incremental data synchronization is reached based on the synchronization decision value. If the synchronization decision value of the power metering data is greater than the synchronization threshold, incremental data synchronization is triggered; otherwise, incremental data synchronization is not triggered; the incremental data synchronization trigger module transmits the trigger determination result to the importance calculation and classification module and the data synchronization method selection module. If synchronization is not triggered, go to the previous step "the importance calculation and classification module selects a piece of power metering data that has not been judged for incremental data synchronization trigger and transmits it to the incremental data synchronization trigger module"; the data synchronization method selection module calculates the cost performance of three synchronization methods, namely full synchronization, incremental synchronization, and differential synchronization, and selects the method with the highest cost performance to perform data synchronization on the power metering data.

[0150] It can be seen that according to the data processing method, device, and system of the embodiments of the present disclosure, it is possible to effectively reduce the pressure on the system caused by massive data synchronization, improve the efficiency of power metering data synchronization, and improve the stability of the system. In addition, it effectively avoids occupying too much system resources, improves the effectiveness of power metering data synchronization, and improves the usability of the system.

[0151] For the convenience of description, when describing the above device, various modules are described separately according to their functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0152] The device of the above embodiment is used to implement the corresponding data processing method in any of the previous embodiments and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0153] Based on the same technical concept, corresponding to the method of any of the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium, and the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the data processing method described in any of the above embodiments.

[0154] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0155] [[ID=e3]]The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the data processing method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0156] Those of ordinary skill in the art should understand that: the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples; under the concept of the present disclosure, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present disclosure as described above, and they are not provided in detail for the sake of brevity.

[0157] In addition, for simplicity of explanation and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. In addition, the device may be shown in block diagram form to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (i.e., these details should be completely within the understanding of those skilled in the art). In the case where specific details (such as circuits) are set forth to describe the exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0158] Although the present disclosure has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0159] Embodiments of the present disclosure are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the embodiments of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A data processing method, comprising: Determining the importance of power metering data and the corresponding importance coefficient based on preset parameters; Determining an update value parameter based on the importance, the importance coefficient, and the amount of updated data; Determining a synchronization decision value based on the update value parameter and the current resource parameter; Judging whether the synchronization decision value is greater than or equal to a synchronization threshold; In response to the synchronization decision value being greater than or equal to the synchronization threshold, obtaining a synchronization cost performance for a preset synchronization method based on the update value parameter and the cost parameter; Determining a target synchronization method in the preset synchronization method based on the synchronization method cost performance to synchronize the power metering data.

2. The method according to claim 1, wherein, The preset parameters include a necessity parameter, an influence parameter, and an affected parameter; Among them, the necessary parameters include: L m is the necessary order of the m-th type of power metering data, is the maximum necessary order in the power metering data; The influence degree parameter includes: The quantity of the m-th type of power metering data affecting other types of power metering data, The minimum influence quantity of power metering data, The maximum influence quantity of power metering data; The influence degree parameter includes: The quantity of other types of power metering data that affect the m-th type of power metering data, The minimum affected quantity of power metering data, The maximum affected quantity of power metering data.

3. The method according to claim 2, wherein Determining the importance of power metering data and the corresponding importance coefficient based on preset parameters, comprising: μ is the importance weight factor.

4. The method according to claim 1, wherein Determining an update value parameter based on the importance, the importance coefficient, and the amount of updated data of the updated data, comprising: η m is the importance coefficient of the m-th type of power metering data, and γ m is the importance of the m-th type of power metering data, and A m (t) is the amount of data newly added or modified for the m-th type of power metering data since the last synchronization.

5. The method according to claim 1, wherein The current resource parameter includes: a computing resource parameter, a memory resource parameter, and a bandwidth resource parameter; The synchronous decision value includes: θ m (t) is the updated data value parameter at time t, U(t) is the usage rate of the central processing unit at time t, M(t) is the memory occupancy of the system at time t, M s is the total system memory, B(t) is the bandwidth occupied by the system at time t, B s is the total bandwidth of the system.

6. The method according to claim 1, wherein, The cost parameter includes: a historical cost, a synchronization method cost, a data recovery cost, and a time interval cost; Obtaining a synchronization cost performance for a preset synchronization method based on the update value parameter and the cost parameter, comprising: Among them, ξ1, ξ2, ξ3, and ξ4 are the cost weighting factors of historical cost, synchronous mode cost, data recovery cost, and time interval cost respectively, T is the time interval of regular full synchronization, n is the number of full synchronizations that have been performed, and φ m (t) is the historical cost function of data synchronization, is the bandwidth resource required for the m-th measurement data to be synchronized using the preset synchronization mode i, is the memory resource required for the m-th measurement data to be synchronized using the preset synchronization mode i, is the memory resource required for the m-th measurement data to perform data recovery using the preset synchronization mode i, is the average utilization rate of the central processing unit when the m-th measurement data is synchronized using the preset synchronization mode i, is the average utilization rate of the central processing unit when the m-th measurement data performs data recovery using the preset synchronization mode i, C i,m (t) is the average cost when synchronizing using the preset synchronization mode i before time t.

7. The method according to claim 1, wherein Determining a target synchronization method in the preset synchronization method based on the synchronization method cost performance, comprising: Selecting the preset synchronization method corresponding to the maximum value in the synchronization method cost performance as the target synchronization method.

8. A data processing device, comprising: An importance module for determining the importance of power metering data and the corresponding importance coefficient based on preset parameters; An update value module for determining an update value parameter based on the importance, the importance coefficient, and the amount of updated data; A synchronization decision module for determining a synchronization decision value based on the update value parameter and the current resource parameter; And judging whether the synchronization decision value is greater than or equal to a synchronization threshold; A cost performance module for, in response to the synchronization decision value being greater than or equal to the synchronization threshold, obtaining a synchronization cost performance for a preset synchronization method based on the update value parameter and the cost parameter; A synchronization selection module for determining a target synchronization method in the preset synchronization method based on the synchronization method cost performance to synchronize the power metering data.

9. The apparatus according to claim 8, wherein The preset parameters include a necessity parameter, an influence parameter, and an affected parameter; Among them, the necessity parameters include: L m is the necessary series number of the m-th kind of power metering data, is the maximum necessary series number in the power metering data; The influence degree parameter includes: The number of the m-th type of power metering data affecting other types of power metering data, The minimum influence number of power metering data, The maximum influence number of power metering data; The influence degree parameter includes: The quantity of other types of power metering data that affect the m-th type of power metering data, The minimum affected quantity of power metering data, The maximum affected quantity of power metering data.

10. The device according to claim 9, wherein the importance module is used to determine the importance of power metering data and the corresponding importance coefficient based on preset parameters, comprising: μ is the importance weight factor.

11. The apparatus according to claim 8, wherein, The update value module is used to determine an update value parameter based on the importance, the importance coefficient, and the amount of updated data of the updated data, comprising: η m is the importance coefficient of the m-th kind of power metering data, and γ m is the importance of the m-th kind of power metering data, and A m (t) is the amount of data updated for the m-th kind of power metering data since the last synchronization.

12. The apparatus according to claim 8, wherein, The current resource parameter includes: a computing resource parameter, a memory resource parameter, and a bandwidth resource parameter; The synchronization decision value includes: θ m (t) is the updated data value parameter at time t, U(t) is the utilization rate of the central processing unit at time t, M(t) is the memory occupancy of the system at time t, M s is the total system memory, B(t) is the bandwidth occupied by the system at time t, B s is the total bandwidth of the system.

13. The apparatus according to claim 8, wherein The cost parameter includes: a historical cost, a synchronization method cost, a data recovery cost, and a time interval cost; The cost performance module is used to obtain a synchronization cost performance for a preset synchronization method based on the update value parameter and the cost parameter, comprising: Among them, ξ1, ξ2, ξ3, and ξ4 are the cost weighting factors of historical cost, synchronous mode cost, data recovery cost, and time interval cost respectively, T is the time interval of regular full synchronization, n is the number of full synchronizations that have been performed, and φ m (t) is the historical cost function of data synchronization, is the bandwidth resource required for synchronizing the m-th measurement data using the preset synchronization mode i, is the memory resource required for synchronizing the m-th measurement data using the preset synchronization mode i, is the memory resource required for data recovery of the m-th measurement data using the preset synchronization mode i, is the average utilization rate of the central processing unit when synchronizing the m-th measurement data using the preset synchronization mode i, is the average utilization rate of the central processing unit when performing data recovery on the m-th measurement data using the preset synchronization mode i, C i,m (t) is the average cost when synchronizing using the preset synchronization mode i before time t.

14. The apparatus according to claim 8, wherein The synchronization selection module is used to determine the target synchronization method in the preset synchronization methods based on the performance-price ratio of the synchronization methods, and includes: Select the preset synchronization method corresponding to the maximum value in the performance-price ratio of the synchronization methods as the target synchronization method.

15. An electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

16. A non-transitory computer-readable storage medium, which stores computer instructions for causing a computer to execute the method according to any one of claims 1 to 7.

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