Machine account data management method and device, electronic equipment and readable storage medium
The method optimizes ledger data management by determining data volume and complexity, allocating resources accordingly, and storing data in differentiated databases, addressing inefficiencies and inaccuracies in traditional methods to enhance production efficiency and equipment reliability.
Patent Information
- Application Number
- CN202411193691.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-28
- Publication Date
- 2024-12-13
AI Technical Summary
Traditional ledger data management methods for inspection equipment are inefficient, prone to inaccuracies, and struggle with large-scale data processing, leading to difficulties in timely tracking and analysis, which affects production efficiency and equipment reliability.
A method and device for managing ledger data that involves determining data volume and characteristic information, allocating resources based on data complexity, and storing data in differentiated databases to optimize processing efficiency and accuracy.
Enhances data processing efficiency, reduces errors, and optimizes resource utilization, thereby improving production efficiency and equipment reliability by ensuring timely and accurate data management.
Smart Images
Figure CN119127495A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure belongs to the field of data management technology, and more specifically, to ledger data management methods and devices, electronic devices, and readable storage media. Background Art
[0002] In many fields today, the ledger data management of inspection equipment is crucial. As the scale of enterprises and organizations continues to expand, the number and types of inspection equipment are also increasing, and the traditional ledger data management method has gradually exposed some problems. For example, in some enterprises, the management of ledger data still relies on manual records and paper documents, which is not only inefficient, but also prone to problems such as inaccurate data and loss. In addition, due to the lack of effective data management methods, it is difficult for enterprises to track and analyze the use and maintenance records of inspection equipment in a timely manner, and they cannot promptly discover possible problems with the equipment, which affects the normal operation and production efficiency of the equipment.
[0003] In addition, as the amount of data continues to increase, traditional data management methods are difficult to cope with the processing and analysis of large-scale data, and cannot meet the needs of enterprises for rapid data retrieval, statistics and analysis. Therefore, in order to improve the efficiency and accuracy of inspection equipment ledger data management and meet the needs of enterprises for equipment management, a more advanced and intelligent ledger data management method is needed. Summary of the invention
[0004] The purpose of the present disclosure is to provide a ledger data management method and device, an electronic device, and a readable storage medium to improve the efficiency and accuracy of inspection equipment ledger data management.
[0005] A first aspect of the embodiments of the present disclosure provides a method for managing ledger data, including: In response to receiving the inventory data of the inspection device, the data volume and characteristic information of the inventory data are determined.
[0006] Determine the target thread from multiple threads based on the data volume, and calculate the data complexity of the ledger data based on the feature information.
[0007] Allocate resources to the target thread based on data complexity to manage ledger data.
[0008] A second aspect of the embodiments of the present disclosure provides a ledger data management device, including: The first calculation module is used to determine the data volume and characteristic information of the ledger data in response to receiving the ledger data of the inspection device.
[0009] The second calculation module is used to determine the target thread from multiple threads based on the data volume, and calculate the data complexity of the ledger data based on the characteristic information.
[0010] The data management module is used to allocate resources to the target thread based on data complexity to manage the ledger data.
[0011] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the above-mentioned ledger data management method when executing the computer program.
[0012] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned ledger data management method are implemented.
[0013] The beneficial effects of the ledger data management method and device, electronic device, and readable storage medium provided by the embodiments of the present disclosure are: by automatically responding to and receiving ledger data, real-time data collection and integration are achieved, data processing efficiency is greatly improved, and time delays and error risks caused by manual operations are effectively avoided. Secondly, intelligent selection and allocation of processing resources based on data volume and feature information not only ensures the efficiency of data processing, but also optimizes resource utilization and reduces operating costs. The close combination of data complexity assessment and resource allocation enables high-complexity data to obtain sufficient processing resources and ensures the accuracy and timeliness of data processing. This series of optimization measures jointly promotes the intelligent upgrade of inspection equipment ledger data management, improves the efficiency and accuracy of inspection equipment ledger data management, and thus significantly improves the production efficiency and equipment reliability of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0015] Figure 1 A flowchart of a ledger data management method provided in one embodiment of the present disclosure; Figure 2 A structural block diagram of a ledger data management device provided in one embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION
[0016] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.
[0017] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.
[0018] Please refer to Figure 1 , Figure 1 A flowchart of a ledger data management method provided in one embodiment of the present disclosure, the method may include S101 to S103.
[0019] S101: In response to receiving the inventory data of the inspection device, determine the data volume and characteristic information of the inventory data.
[0020] In this embodiment, the ledger data of the inspection equipment can be divided into multiple data item categories, including equipment number, equipment name, quantity, specification model, measurement range, grade / precision, factory number, manufacturer, storage location, custodian, purchase date, purchase price and status. When the ledger data of the inspection equipment is received, the data volume and characteristic information of the ledger data are first determined. The size of the data volume can reflect the scale of the data. The characteristic information can include field characteristics and relationship characteristics of the ledger data, for example, characteristic information such as the type, attributes and relevance of the ledger data, and these characteristic information can be used to calculate the complexity of the ledger data.
[0021] For example, a large amount of ledger data is received. First, determine the amount of data in this part, such as the number of rows recorded, the number of devices, etc. For unordered ledger data, the number of characters can be recorded to calculate the amount of data. At the same time, analyze the characteristic information of the ledger data, such as the diversity and relevance of attributes such as the model, inspection date, measurement range, grade / precision, etc. of the equipment.
[0022] S102: Determine a target thread from multiple threads based on the data volume, and calculate data complexity of the ledger data based on the feature information.
[0023] In this embodiment, determining a target thread from multiple threads based on the amount of data includes: Determine the number of threads required based on the amount of data.
[0024] A target thread is determined from among the plurality of threads based on the number of threads.
[0025] In this embodiment, the number of threads required is determined based on the amount of ledger data because the amount of data directly affects the efficiency and resource requirements of data processing. Larger amounts of data require more threads to process in parallel to increase processing speed. Target threads are selected from multiple threads based on the determined number of threads, and these target threads will be responsible for processing the corresponding data.
[0026] Exemplarily, the corresponding number of threads is determined according to the specific value or range of the data volume through a preset algorithm or rule. For example, it can be set that when the data volume is less than a certain threshold, the required number of threads is a small fixed value. When the data volume is within a certain range, the required number of threads is a medium value. When the data volume exceeds a certain large threshold, the required number of threads is a large value. After determining the number of threads, a corresponding number of threads are selected from the available multiple threads as target threads.
[0027] For example, the preset rule is that when the amount of data is less than 100 records, the number of threads required is 1. When the amount of data is between 100 and 500 records, the number of threads required is 2. When the amount of data is greater than 500 records, the number of threads required is 3. The received ledger data has 300 records, so according to the rules, the number of threads required is determined to be 2. Then, 2 threads are selected from multiple threads as target threads to process the data. This ensures that when processing ledger data, thread resources can be fully utilized to improve data processing efficiency. At the same time, according to the difference in the amount of ledger data in different parts, the system can flexibly adjust the number of threads to adapt to different processing needs.
[0028] S103: Allocate resources to the target thread based on the data complexity to manage the ledger data.
[0029] In this embodiment, according to the calculated data complexity of the ledger data, resources are reasonably allocated to the previously determined target thread, so as to effectively manage the ledger data. When the data complexity is higher, more resources are required to ensure accurate processing and efficient management of the data.
[0030] Exemplarily, the complexity level is evaluated according to the specific value of the data complexity, such as high, medium, low, etc. According to the complexity level, corresponding resources are allocated to the target thread, such as CPU time, memory space, etc. For data with high complexity, more resources will be allocated to ensure that the thread can process the data quickly and accurately. For data with low complexity, fewer resources will be allocated to avoid wasting resources.
[0031] In this embodiment, the ledger data is managed, including: Determine the data cleaning rules and standardization rules corresponding to the ledger data.
[0032] In the target thread, the ledger data is processed based on data cleaning rules and standardization rules to obtain standard ledger data, and the standard ledger data is managed.
[0033] In this embodiment, by determining the data cleaning rules and standardization rules of the ledger data, the original ledger data is processed to remove invalid or erroneous data, and the data is standardized to obtain standard ledger data for subsequent management and analysis.
[0034] Exemplarily, data cleaning rules may include removing duplicate data, processing missing values, filtering abnormal data, etc. Standardization rules may include unified data format, unit conversion, coding unification, etc. Then, in the target thread, the corresponding algorithms or tools are used to process the ledger data according to these rules. During the processing, the data is checked line by line, and the cleaning and standardization operations are performed according to the rules. Finally, the processed standard ledger data is stored and managed for subsequent query, analysis, and use.
[0035] For example, a data record in a standard format may be "BR002-033, floor thickness gauge, 1 piece, DJLC-A, 50-260mm (error ±1mm), 261-350mm (error ±2mm), 50-350mm, 79305, Factory A, No. C, B Street, Wang Qiang, 39807, 20000, out of service". The data corresponds to "equipment number, equipment name, quantity, specification model, measurement range, grade / accuracy, factory number, manufacturer, storage location, custodian, purchase date, purchase price, status" respectively. From the above, it can be concluded that this embodiment realizes the real-time collection and integration of data by automatically responding to and receiving ledger data, greatly improving the efficiency of data processing and effectively avoiding the time delays and error risks caused by manual operations. Secondly, the intelligent selection and allocation of processing resources based on the amount of data and characteristic information not only ensures the efficiency of data processing, but also optimizes resource utilization and reduces operating costs. The close combination of data complexity assessment and resource allocation enables high-complexity data to obtain sufficient processing resources and ensure the accuracy and timeliness of data processing. This series of optimization measures jointly promotes the intelligent upgrade of inspection equipment ledger data management, improves the efficiency and accuracy of inspection equipment ledger data management, and thus significantly improves the production efficiency and equipment reliability of the enterprise.
[0036] In one embodiment of the present disclosure, determining the required number of threads based on the data volume includes: If the data amount is less than the first data amount, the required number of threads is determined to be the first number.
[0037] If the data amount is greater than or equal to the first data amount and less than or equal to the second data amount, the required number of threads is determined to be the second number.
[0038] If the data amount is greater than the second data amount, the required number of threads is determined to be a third number.
[0039] Among them, the first data amount is smaller than the second data amount, the first quantity is smaller than the second quantity, and the second quantity is smaller than the third quantity.
[0040] In this embodiment, the first data volume and the second data volume can be determined based on the number of ledgers processed in the first historical time period, that is, the first data volume and the second data volume are determined based on the number of ledgers processed in the first historical time period. The first time period is a preset time period, such as a week, a month, a quarter, a year, etc. Therefore, the values of the first data volume and the second data volume are not fixed, but change dynamically.
[0041] In this embodiment, the data volume is divided into three intervals, each corresponding to a different number of threads. When the data volume is small, fewer threads are used to avoid wasting resources. When the data volume is moderate, a medium number of threads are used. When the data volume is large, more threads are used to improve processing efficiency.
[0042] Exemplarily, it can be implemented by programming, and the first data volume, the second data volume and the corresponding first quantity, the second quantity and the third quantity are set in the program. When the ledger data is received, the data volume is calculated and compared with the set threshold to determine the interval to which it belongs, thereby determining the required number of threads.
[0043] Exemplarily, in the ledger data processing within a historical month (first duration), it is found that when the data volume is less than 500, 2 threads can efficiently process it. When the data volume is between 500 and 1000, 5 threads are more appropriate. When the data volume exceeds 1000, 10 threads are required. Then, the first data volume can be set to 500 and the second data volume can be set to 1000. When processing new ledger data, if the data volume is 800, which is greater than the first data volume (500) and less than the second data volume (1000), the number of threads required is determined to be the second number (5). If the data volume is 1200, which is greater than the second data volume (1000), the number of threads required is determined to be the third number (10). In this way, threads can be reasonably allocated based on historical experience to improve processing efficiency.
[0044] This solution significantly improves processing efficiency by dynamically adjusting the number of threads to match the amount of data. Based on historical data processing experience, the amount of data is divided into different intervals, and different numbers of threads are configured accordingly, effectively avoiding resource waste and processing bottlenecks. In addition, automatic judgment and adjustment are achieved through programming, which further simplifies the operation process and reduces the error rate of human intervention. Overall, this embodiment improves the flexibility and response speed of the system while ensuring processing efficiency.
[0045] This embodiment can reasonably allocate thread resources according to the actual data volume, thereby improving the efficiency of data processing.
[0046] In one embodiment of the present disclosure, after obtaining the standard ledger data, the method further includes: Determine the equipment type corresponding to the standard ledger data.
[0047] Based on the equipment type corresponding to the standard ledger data, the standard ledger data is stored in different ledger databases, wherein different ledger databases have different storage parameters.
[0048] In this embodiment, different ledger databases have different storage parameters to meet the storage and management requirements of data of different device types. Storage parameters may include storage capacity, response speed, data format, access rights, etc.
[0049] For example, a correspondence between device type and ledger database can be set. After obtaining standard ledger data, the device type information is determined based on the information in the data. Then, based on the preset correspondence, the standard ledger data is stored in the corresponding ledger database. At the same time, different storage parameters are set for each ledger database, such as storage capacity, response speed, data format, and access rights.
[0050] In this embodiment, the device types include first-category devices and second-category devices, the different ledger databases include a first ledger database and a second ledger database, the storage capacity of the first ledger database is greater than the storage capacity of the second ledger database, and the response speed of the first ledger database is less than the response speed of the second ledger database.
[0051] Based on the equipment type corresponding to the standard ledger data, the standard ledger data is stored in different ledger databases, including: The standard ledger data of the equipment type belonging to the first category of equipment is stored in the first ledger database.
[0052] The standard ledger data of the equipment type belonging to the second category of equipment is stored in the second ledger database.
[0053] In this embodiment, the first type of equipment may include equipment types with less demand and less access, and the second type of equipment may include equipment types with more demand and more access. For example, small detection instruments (such as thermometers, pressure gauges, etc.) can be used as the second type of equipment. The second type of equipment has a higher purchase frequency and a higher frequency of use. The first type of equipment may include equipment types with a lower frequency of use and a lower frequency of data update.
[0054] Exemplarily, according to the equipment, application scenarios or functional attributes used for inspection, the inspection equipment can be divided into types such as pile foundation, structural part, energy saving, building materials, pipes, wires, waterproofing, lightning protection, intelligence, fire protection, etc., and the corresponding database table is recommended for each type to store the corresponding type of equipment. For example, building materials can include relevant ledger data of inspection equipment such as measuring cylinders, glass thermometers, pipe room dehumidifiers, waterproof room dehumidifiers, proportioning rooms, cement rooms, dial indicators and electronic balances. Various types of inspection equipment can be divided into first-class equipment or second-class equipment according to the frequency of access to different database tables in historical ledger data management.
[0055] For example, the data of the flame detection function tester is stored in the fire protection database table. In the past six months, the purchase volume and purchase frequency of this equipment were high, and the frequency of updating the relevant data was high. Therefore, the flame detection function tester is included in the second category of equipment. When there is new ledger data of the flame detection function tester, the ledger data can be stored in the second ledger database. The second ledger database has a smaller storage capacity, but a faster response speed. When the staff needs to query or update relevant data, they can get a faster response.
[0056] Through this embodiment, standard ledger data is classified and stored according to device type, and different ledger databases have differentiated storage parameters, which can more effectively meet the storage and management needs of different types of device data and balance storage cost and access efficiency. This method improves the flexibility and pertinence of ledger data management, and helps to improve the overall data management efficiency and user experience.
[0057] In one embodiment of the present disclosure, the data complexity of the ledger data is calculated based on the feature information, including: Determine field characteristics and relationship characteristics based on the characteristic information of ledger data.
[0058] Calculate the data complexity of ledger data based on field features and relationship features.
[0059] In this embodiment, the field features and relationship features are input into the first formula to calculate the data complexity of the ledger data: The first formula is:
[0060]
[0061] in, Represents the weight corresponding to the i-th field feature, represents the i-th field feature, n represents the number of field features, Indicates the weight corresponding to the a-th standard field feature, represents the ath standard field feature, a represents the number of standard field features, represents the weight corresponding to the jth relationship feature, represents the jth relationship feature, and m represents the number of relationship features.
[0062] In this embodiment, relationship features may include one-to-one relationships, one-to-many relationships, and many-to-many relationships. For example, there may be a one-to-one relationship between two data items in the ledger. For example, in the inspection equipment ledger, each inspection equipment has a unique corresponding factory number, and one factory number corresponds to only one device. A one-to-many relationship indicates that one data item can be associated with multiple other data items, and those multiple data items can only be associated with this one data item. For example, in the department and employee ledger, a department can have multiple employees, but an employee can only belong to one department. A many-to-many relationship indicates that multiple data items can be associated with multiple other data items, and this association is mutual. For example, in the supplier and commodity ledger, a supplier can provide multiple commodities, and a commodity can also be provided by multiple suppliers.
[0063] For example, taking the inspection equipment ledger as an example, the ledger contains field features such as equipment name, model, production date, inspection date, and a one-to-many relationship feature between equipment and accessories.
[0064] First, determine the weights of each field feature. At the same time, determine the weights of the standard field features.
[0065] For the relationship features, the weight of the one-to-many relationship between the device and the accessories is determined. These weights and features are substituted into the first formula for calculation to obtain the value of data complexity.
[0066] For example, if the calculation results show that the data complexity is high, it means that the ledger data is relatively complex and requires more resources to process and manage. In this way, the data complexity of the ledger data can be accurately calculated, providing a basis for subsequent resource allocation and management.
[0067] Allocate resources to the target thread based on complexity, including: The complexity is input into the second formula to determine the resources of the target thread.
[0068] The second formula includes:
[0069] Among them, Z represents the computing resources of the target thread, represents the standard complexity, C represents the number of threads in the target thread, Indicates the total number of threads, Indicates the total computing resources.
[0070] The calculation process of standard complexity includes: The complexity is adjusted to the standard complexity based on the normalization formula. The normalization formula is: =
[0071] in, represents the minimum value of complexity, Indicates the maximum value of complexity.
[0072] In one embodiment of the present disclosure, allocating resources to a target thread based on complexity includes: If the data complexity is less than the first complexity, the first computing resource is allocated to the target thread.
[0073] If the data complexity is greater than or equal to the first complexity and less than or equal to the second complexity, the second computing resource is allocated to the target thread.
[0074] If the data complexity is greater than the second complexity, the third computing resource is allocated to the target thread.
[0075] Among them, the first complexity is smaller than the second complexity, the first computing resources are smaller than the second computing resources, and the second computing resources are smaller than the third computing resources.
[0076] For example, in a data processing task, multiple target threads need to process different data. For the data processed by thread P, after evaluation, its data complexity is less than the first complexity. According to the setting, the first computing resource, such as less CPU time and memory space, is allocated to thread P. This is suitable for situations where the data is relatively simple and the processing requirements are low.
[0077] For data processed by thread Q, if the data complexity is greater than the first complexity and less than the second complexity, second computing resources, such as appropriate CPU time and memory space, are allocated to thread Q to meet the data processing requirements.
[0078] For the data processed by thread L, the data complexity is greater than the second complexity. At this time, the third computing resource, that is, more CPU time and memory space, is allocated to thread L to ensure that the complex data can be processed efficiently. In this way, computing resources can be reasonably allocated according to the complexity of the data, improving the efficiency of data processing and the rationality of resource utilization.
[0079] Corresponding to the ledger data management method in the above embodiment, Figure 2 This is a structural block diagram of a ledger data management device provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 , the ledger data management device 20 includes: The first calculation module 21 is used to determine the data volume and characteristic information of the ledger data in response to receiving the ledger data of the inspection device.
[0080] The second calculation module 22 is used to determine the target thread from multiple threads based on the data volume, and calculate the data complexity of the ledger data based on the feature information.
[0081] The data management module 23 is used to allocate resources to the target thread based on the data complexity to manage the ledger data.
[0082] In one embodiment of the present disclosure, the second calculation module 22 is specifically configured to determine the required number of threads to be the first number if the data amount is less than the first data amount.
[0083] If the data amount is greater than or equal to the first data amount and less than or equal to the second data amount, the required number of threads is determined to be the second number.
[0084] If the data amount is greater than the second data amount, the required number of threads is determined to be a third number.
[0085] Among them, the first data amount is smaller than the second data amount, the first quantity is smaller than the second quantity, and the second quantity is smaller than the third quantity.
[0086] In one embodiment of the present disclosure, the data management module 23 is specifically used to determine data cleaning rules and standardization rules corresponding to the ledger data.
[0087] In the target thread, the ledger data is processed based on data cleaning rules and standardization rules to obtain standard ledger data, and the standard ledger data is managed.
[0088] In one embodiment of the present disclosure, the ledger data management device 20, after obtaining the standard ledger data, further includes: The third calculation module is used to determine the equipment type corresponding to the standard ledger data.
[0089] Based on the equipment type corresponding to the standard ledger data, the standard ledger data is stored in different ledger databases, wherein different ledger databases have different storage parameters.
[0090] In one embodiment of the present disclosure, the device type includes a first type of device and a second type of device, the different ledger databases include a first ledger database and a second ledger database, the storage capacity of the first ledger database is greater than the storage capacity of the second ledger database, and the response speed of the first ledger database is less than the response speed of the second ledger database. The third calculation module is specifically used to store the standard ledger data of the device type belonging to the first type of device into the first ledger database.
[0091] The standard ledger data of the equipment type belonging to the second category of equipment is stored in the second ledger database.
[0092] In one embodiment of the present disclosure, the second calculation module 22 is specifically used to determine field features and relationship features based on feature information of the ledger data.
[0093] Calculate the data complexity of ledger data based on field features and relationship features.
[0094] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.
[0095] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.
[0096] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.
[0097] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.
[0098] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the ledger data management method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device 300 described in the embodiments of the present disclosure, which will not be repeated here.
[0099] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.
[0100] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
[0101] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.
[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0103] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.
[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.
[0105] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0106] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.
Claims
1. A ledger data management method, characterized in that: include: In response to receiving the ledger data of the inspection device, determining the data volume and characteristic information of the ledger data; Determining a target thread from a plurality of threads based on the data volume, and calculating data complexity of the ledger data based on the characteristic information; Resources are allocated to the target thread based on the data complexity to manage the ledger data.
2. The ledger data management method according to claim 1, characterized in that: The determining a target thread from a plurality of threads based on the data amount comprises: Determining the required number of threads based on the amount of data; A target thread is determined from the plurality of threads based on the number of threads.
3. The ledger data management method according to claim 2, characterized in that: The step of determining the required number of threads based on the data volume includes: If the data amount is less than the first data amount, determining the required number of threads to be the first number; If the data amount is greater than or equal to the first data amount and less than or equal to the second data amount, determining the required number of threads to be the second number; If the data amount is greater than the second data amount, determining the required number of threads to be a third number; Among them, the first data amount is smaller than the second data amount, the first number is smaller than the second number, and the second number is smaller than the third number.
4. The ledger data management method according to claim 1, characterized in that: The managing of the ledger data includes: Determine data cleaning rules and standardization rules corresponding to the ledger data; In the target thread, the ledger data is processed based on the data cleaning rule and the standardization rule to obtain standard ledger data, and the standard ledger data is managed.
5. The ledger data management method according to claim 4, characterized in that: After obtaining the standard ledger data, the following steps are also included: Determine the equipment type corresponding to the standard ledger data; The standard ledger data is stored in different ledger databases based on the equipment type corresponding to the standard ledger data; wherein different ledger databases have different storage parameters.
6. The ledger data management method according to claim 5, characterized in that: The device types include first-type devices and second-type devices, the different ledger databases include a first ledger database and a second ledger database, the storage capacity of the first ledger database is greater than the storage capacity of the second ledger database, and the response speed of the first ledger database is less than the response speed of the second ledger database; The storing the standard ledger data in different ledger databases based on the device type corresponding to the standard ledger data includes: The standard ledger data of the equipment type belonging to the first type of equipment is stored in the first ledger database; The standard ledger data of the equipment type belonging to the second category of equipment is stored in the second ledger database.
7. The ledger data management method according to claim 1, characterized in that: The calculating the data complexity of the ledger data based on the characteristic information includes: Determine field characteristics and relationship characteristics based on the characteristic information of the ledger data; The data complexity of the ledger data is calculated based on the field characteristics and the relationship characteristics.
8. A ledger data management device, characterized in that: include: A first calculation module, configured to determine the data volume and characteristic information of the ledger data in response to receiving the ledger data of the inspection device; A second calculation module, used to determine a target thread from multiple threads based on the data volume, and calculate the data complexity of the ledger data based on the characteristic information; A data management module is used to allocate resources to the target thread based on the data complexity so as to manage the ledger data.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.