A method for automatically generating a log

Abstract

Embodiments of the present specification provide a method for automatically generating a log. The method comprises receiving a log generation request; obtaining a matching pool, the matching pool comprising a plurality of cache pools at different levels, each cache pool comprising a plurality of elements and a comprehensive score of each element, the comprehensive score of each element being determined by a usage frequency of the element and a system complexity of the element; matching a target log element from the matching pool based on intelligent matching logic; and automatically populating the target log element to generate the log.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of construction data processing, and in particular, to a method for automatically generating a log. BACKGROUND

[0002] The log can achieve real-time, true, accurate and detailed recording of the entire construction project, and is an important basis for project management, quality traceability, government spot checks and other aspects, which has extremely important significance for the work promotion, management, supervision, risk control and other aspects of the construction industry. The log contains a large amount of element information, such as construction site and number of attendants, and the traditional manual recording method is low in efficiency and may have subjective errors.

[0003] Therefore, it is desirable to provide a method for automatically generating a log to intelligently, automatically, accurately and dynamically generate a log. SUMMARY

[0004] One or more embodiments of the present specification provide a method for automatically generating a log. The method comprises: receiving a log generation request; obtaining a matching pool, the matching pool comprising a plurality of cache pools, each cache pool comprising a plurality of elements and a comprehensive score of each element, the comprehensive score of each element being determined by a usage frequency of the element and a system complexity of the element; determining a matching element of a target log element from the matching pool based on intelligent matching logic; and automatically filling the target log element to generate a log.

[0005] In some embodiments, the method further comprises generating the matching pool, the generating the matching pool comprising: determining the usage frequency of each element; determining the system complexity of each element; determining the comprehensive score of each element based on the usage frequency of each element and the system complexity of each element, the comprehensive score being a weighted sum of the usage frequency and the system complexity; and determining a grading result of each element based on the comprehensive score of each element, the matching pool being generated based on the grading result.

[0006] In some embodiments, the determining the usage frequency of each element comprises: determining a usage frequency of each element within a certain period; determining a total usage frequency of all elements within the certain period; and determining the usage frequency of each element based on the usage frequency of each element and the total usage frequency.

[0007] In some embodiments, the determining the system complexity of each of the elements comprises: determining a system access number of each of the elements; determining an average response time of each of the elements; and determining the system complexity of each of the elements based on the system access number of each of the elements and the average response time of each of the elements.

[0008] In some embodiments, the system complexity is a weighted sum of the system access number and the average response time.

[0009] In some embodiments, the intelligent matching logic comprises direct matching logic, recursive matching logic and approximate matching logic, and the multi-level cache pool comprises a first-level cache pool, a second-level cache pool and a third-level cache pool divided based on a comprehensive score of an element: the direct matching logic is to directly match an element in the first-level cache pool with the target log element to determine the matching element of the target log element; the recursive matching logic is to recursively search in the second-level cache pool if the matching element of the target log element does not exist in the first-level cache pool, until the matching element of the target log element is determined; and the approximate matching logic is to determine the matching element of the target log element in the third-level cache pool through an approximate matching algorithm if the matching element of the target log element does not exist in the first-level cache pool and the second-level cache pool.

[0010] In some embodiments, the approximate matching algorithm comprises a fuzzy matching algorithm and / or a minimum edit distance algorithm.

[0011] In some embodiments, the multi-level cache pool at least comprises the first-level cache pool divided based on the comprehensive score of the element, and the automatically filling the target log element to generate a log comprises: if the target log element can be matched in the first-level cache pool, directly filling the matching element into the target log element to generate the log; and if the target log element cannot be matched in the first-level cache pool, determining the matching element through the recursive matching logic or the approximate matching logic, and filling the matching element into the target log element to generate the log.

[0012] In some embodiments, the method further comprises dynamically adjusting the matching pool, and the dynamically adjusting the matching pool comprises: updating the usage frequency of each of the elements; updating the system complexity of each of the elements; and dynamically adjusting the hierarchical results of the elements based on the updated usage frequency and the updated system complexity to dynamically adjust the matching pool.

[0013] In some embodiments, the method further comprises: determining whether the log generation request meets a target condition, the target condition comprising at least a post permission condition and a project status condition; and in response to the log generation request meeting the target condition, obtaining the matching pool. BRIEF DESCRIPTION OF DRAWINGS

[0014] The present specification will be further illustrated in the way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same numbers represent the same structures, wherein:

[0015] Figure 1 is a schematic diagram of an exemplary application scenario of the method for automatically generating a log according to some embodiments of the present specification;

[0016] Figure 2 is a flowchart of an exemplary method for automatically generating a log according to some embodiments of the present specification;

[0017] Figure 3 is a schematic diagram of an exemplary matching pool according to some embodiments of the present specification;

[0018] Figure 4 is a schematic diagram of an exemplary intelligent matching logic according to some embodiments of the present specification;

[0019] Figure 5 is a flowchart of an exemplary method for dynamically adjusting a matching pool according to some embodiments of the present specification. DETAILED DESCRIPTION

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, the present specification can be applied to other similar scenarios without creative labor. Unless it is obvious from the language environment or otherwise stated, the same reference numbers in the drawings represent the same structures or operations.

[0021] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0022] As used in the description and the claims herein, the meaning of "a," "an," and "the" includes singular and plural referents unless the context clearly dictates otherwise. Generally, the term "comprises" or "comprising" as used herein is intended to indicate that the method includes the recited steps and / or elements but not excluding elements or steps not specifically recited. The term "consisting of" is intended to exclude any element or step not specified. The term "consisting essentially of" is intended to exclude any element or step not specified except for an impurity of natural occurrence.

[0023] Flow diagrams are used in this specification to illustrate the operation of systems according to embodiments of the specification. It will be understood that the operations as set forth in the flow diagrams do not necessarily have to be executed in the precise order reported in the flow diagrams. Rather, various operations can be handled in different order or simultaneously.

[0024] Figure 1 is a schematic diagram of an exemplary application scenario of the method of automatically generating a log according to some embodiments of the specification. As shown in Figure 1 The application scenario 100 of the method of automatically generating a log (referred to as "application scenario 100" for short) can include a storage device 110, a processing device 120, a terminal 130, and a network 140.

[0025] The storage device 110 can store data or information. In some embodiments, the storage device 110 can store log-related data and / or information, such as elements, comprehensive scores of each element, matching pools, etc. In some embodiments, the storage device 110 can store data and / or information processed by the processing device 120, such as target log elements, matching elements, etc. The storage device 110 can include one or more storage components, each of which can be a separate device or part of other devices. The storage device can be local or implemented through the cloud.

[0026] The processing device 120 can process data and / or information obtained from other devices or system components, perform the method of automatically generating a log as shown in some embodiments of the present specification based on the data, information and / or processing results, and complete one or more functions described in some embodiments of the present specification. For example, the processing device 120 can obtain pre-stored data and / or information, such as elements, comprehensive scores of each element, a matching pool, etc., from the storage device 110 for performing the method of automatically generating a log as shown in some embodiments of the present specification. Illustratively, the processing device 120 can obtain a matching pool and determine matching elements of a target log element from the matching pool based on intelligent matching logic. Further, the processing device 120 can automatically fill in the target log element to generate a log. For another example, the processing device 120 can obtain user instructions from the terminal 130 for performing the method of automatically generating a log as shown in some embodiments of the present specification. Illustratively, the processing device 120 can receive a log generation request initiated by a user through the terminal 130.

[0027] In some embodiments, the processing device 120 can include one or more sub-processing devices (e.g., single-core processing devices or multi-core multi-core processing devices). By way of example only, the processing device 120 can include a central processing unit (CPU), a graphics processing unit (GPU), a reduced instruction set computer (RISC), a microprocessor, etc., or any combination thereof.

[0028] The terminal 130 can interact with a user. The user can issue operation instructions to the processing device 120 through the terminal 130 to make the processing device 120 complete a designated operation, such as generating a log, etc. In some embodiments, the terminal 130 can receive the generated log from the processing device 120, and the user can perform project management, quality traceability, etc. of a construction project according to the log. In some embodiments, the terminal 130 can be one or any combination of a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, etc., other devices with input and / or output functions.

[0029] The network 140 can connect components of the system and / or connect the system with external resource parts. The network 140 enables communication between components and between the system and other parts outside the system, and facilitates exchange of data and / or information. In some embodiments, one or more components in the application scenario 100 (e.g., the storage device 110, the processing device 120, the terminal 130) can send data and / or information to other components through the network 140. In some embodiments, the network 140 can be any one or more of a wired network or a wireless network.

[0030] It should be noted that the foregoing description is only for the purpose of illustration, and is not intended to limit the scope of the present description. Various changes and modifications can be made to the present description under the guidance of one of ordinary skill in the art, with the contents of the present description serving as guidance. The features, structures, methods and other characteristics of the exemplary embodiments described in the present description can be combined in various manners that are not explicitly described in the present description. For example, the processing device 120 can be based on a cloud computing platform, such as a public cloud, a private cloud, a community cloud, and a hybrid cloud, etc. However, these changes and modifications do not depart from the scope of the present description.

[0031] Figure 2 is a flowchart of an exemplary method of automatically generating a log according to some embodiments of the present description. In some embodiments, the method of automatically generating a log can be implemented by a system of automatically generating a log (referred to as "the system" for short, which can include all devices in the application scenario 100). As shown in Figure 2 , the flow 200 includes the following steps. In some embodiments, the flow 200 can be executed by the processing device 120.

[0032] Step 210, receiving a log generation request.

[0033] The log refers to a file that records data and information related to a construction project. It can be understood that construction practitioners (such as construction personnel, safety personnel, supervision personnel, etc.) need to record various logs of a construction project in a timely, true, accurate and detailed manner as a record and evidence of the construction process. In some embodiments, the log can include three types of logs: construction log, safety log and supervision log. The construction log refers to a file that records relevant construction activities (such as construction organization and management, specific construction technology, etc.) and changes in site conditions of all construction stages of a construction project, which is usually recorded by construction personnel. The safety log refers to a file that records important production activities and important technical activities during the entire construction process of a construction project, which is usually recorded by safety personnel. The supervision log refers to a file that records relevant supervision activities of a construction project, which is usually recorded by supervision personnel.

[0034] The log generation request refers to the request information for generating a log input by a user. For example, the log generation request of a construction log, etc. In some embodiments, the log generation request can contain project information and log type. The project information refers to the relevant information of a construction project. For example, project code (i.e. project ID), project name, user code (i.e. user ID), etc.

[0035] In some embodiments, the processing device 120 can receive the log generation request by receiving a user instruction. The user can be a terminal operator (i.e. the aforementioned construction practitioner). For example, the processing device 120 can receive the log generation request of a safety log sent by a safety personnel through the terminal 130, etc.

[0036] In some embodiments, the processing device 120 can determine whether the log generation request meets the target condition.

[0037] The target condition refers to a condition that needs to be met. In some embodiments, the target condition can at least include a post permission condition and a project state condition. The post permission condition refers to a relevant condition of the post permission that the user initiating the instruction needs to have. For example, the post permission condition can be that the user initiating the log generation request needs to correspond to the post permission of the log type one by one (i.e., the construction log corresponds to the construction clerk, the safety log corresponds to the safety clerk, and the supervision log corresponds to the supervisor). The project state condition refers to a condition for confirming whether the construction project needs to generate a log based on the project state (the specific implementation state of the project at the current time, such as the start state, the construction state, the stop state, the acceptance state, and the completion state). For example, the project state condition can be that when the project state is the start state, the construction state, or the acceptance state, it is confirmed that the construction project needs to generate a log; when the project state is the stop state or the completion state, it is confirmed that the construction project does not need to generate a log.

[0038] In some embodiments, the processing device 120 can compare the log generation request with the target condition one by one based on the log generation request to determine whether the log generation request meets the target condition.

[0039] For example, the processing device 120 can identify the post permission of the user according to the user code in the log generation request, compare the post permission with the post permission condition in the target condition, and determine whether the log generation request meets the post permission condition in the target condition. For example, the processing device 120 receives a log generation request of a construction log type, and if the post permission of the user is identified as a construction clerk according to the user code, it is determined that the log generation request meets the post permission condition in the target condition; if the post permission of the user is identified as a supervisor according to the user code, it is determined that the log generation request does not meet the post permission condition in the target condition.

[0040] For example, the processing device 120 can identify the project status of the current project according to the project code in the log generation request, compare the project status with the project status condition in the target condition, and determine whether the log generation request meets the project status condition in the target condition. For example, the processing device 120 receives a log generation request with a log type of construction log, and identifies the project status of the current project as the construction status according to the project code. The processing device 120 determines that the log generation request meets the project status condition in the target condition. If the processing device 120 identifies the project status of the current project as the stop status according to the project code, the processing device 120 determines that the log generation request does not meet the project status condition in the target condition. In some embodiments, the processing device 120 can monitor the project status change every certain period, compare the changed project status with the project status condition, and determine whether the log generation request meets the project status condition in the target condition, thereby determining the execution strategy of the log generation request. The certain period can be set based on experience or demand. For example, the processing device 120 can monitor the project status change every day. If the project status changes from the construction status to the completion status, the processing device 120 determines that the log generation request does not meet the project status condition in the target condition, and suspends the log generation. If the project status changes from the stop status to the construction status, the processing device 120 determines that the log generation request meets the project status condition in the target condition, and reactivates the log generation function. It can be understood that the periodic monitoring of the project status change and the determination of the execution strategy can flexibly realize the dynamic allocation and use of computing resources.

[0041] Further, the processing device 120 can obtain a matching pool in response to the log generation request meeting the target condition. For more information about the matching pool, see step 210 and the related description.

[0042] In some embodiments of the present disclosure, by determining whether the log generation request meets the target condition and obtaining the matching pool in response to the log generation request meeting the target condition, the log generation request can be conditionally screened before the matching pool is obtained, the loading of data that does not meet the condition is reduced, the processing of unnecessary data is avoided, the response speed of the system is optimized, and the matching accuracy of log elements and the system resource utilization rate are improved.

[0043] At step 220, a matching pool is obtained.

[0044] The matching pool refers to a collection for storing elements. An element refers to the smallest unit of a log. For example, an element can be a field including a key and a value, where the key is derived from a template definition, such as the number of attendance, construction time, etc., and the value is derived from specific data, such as 100 people, 60 days, etc.

[0045] In some embodiments, the matching pool can comprise a multi-level cache pool. The cache pool refers to a memory area storing elements. The multi-level cache pool is used to store elements of different levels, i.e., each level of the cache pool is used to store elements of the same level. More about the multi-level cache pool, elements of different levels can be found in Figure 3 , Figure 4 and the related description.

[0046] In some embodiments, each level of the cache pool can comprise a plurality of elements and a comprehensive score of each element. The comprehensive score refers to data information indicating the importance of the element in the log. The higher the comprehensive score, the higher the value and the importance of the element in the log. In some embodiments, the comprehensive score of each element can be determined by the usage frequency of the element and the system complexity of the element. The usage frequency refers to data information indicating the frequency of the element being used (or appearing in the log) in a unit time period. The higher the usage frequency, the more frequently the element is used (or appears) in a unit time period. The system complexity refers to data information indicating the complexity of the system computing resources required to request the element. The higher the system complexity, the more complex the system computing resources required to request the element. For example, the processing device 120 can determine the usage frequency of each element and the system complexity of each element by a preset algorithm, a machine learning model, etc., and analyze and process the usage frequency of each element and the system complexity of each element, to determine the comprehensive score of each element. The specific preset algorithm, machine learning model can be set based on experience or demand. More about the usage frequency, system complexity, comprehensive score can be found in Figure 3 and the related description.

[0047] In some embodiments, the processing device 120 can obtain the matching pool based on preset information. For example, the processing device 120 can directly obtain the matching pool preset by the user through the storage device 110, the terminal 130, etc.

[0048] In some embodiments, before obtaining the matching pool, the processing device 120 can generate the matching pool. For example, the processing device can determine the grading result of each element based on the comprehensive score of each element, and generate the matching pool based on the grading result. More about generating the matching pool based on the grading result can be found in Figure 3 and the related description.

[0049] Step 230, determining the matching element of the target log element from the matching pool based on the intelligent matching logic.

[0050] The target log element refers to an element that needs to be filled in when generating a log. For example, the target log elements of a construction log can be construction site, construction personnel, construction time, construction task, etc.; the target log elements of a safety log can be special inspection items, abnormal accidents, number of safety personnel, etc.; the target log elements of a supervision log can be supervision personnel, quality inspection records, construction material yield, etc. In some embodiments, the processing device 120 can obtain the target log elements based on a log generation request (e.g., user input information).

[0051] The intelligent matching logic refers to a logical rule for intelligently matching elements for a log. For example, the intelligent matching logic can determine the matching elements corresponding to the target log elements from the matching pool based on the target log elements and the comprehensive score, usage frequency, system complexity, etc. of the elements. The matching element refers to an element corresponding to the target log element. For example, the matching element is an element having the same or similar characteristics as the target log element. For example, the matching element can be an element having the same or similar keywords as the target log element. The similarity of the keywords refers to calculating the similarity or minimum edit distance of two keywords using an algorithm, and determining whether they are similar by comparing with a certain threshold. The intelligent matching logic can be preset based on experience or demand.

[0052] In some embodiments, the intelligent matching logic can include one or a combination of direct matching logic, recursive matching logic, and approximate matching logic. More details about the above matching logic can be found in Figure 4 and related descriptions thereof.

[0053] In some embodiments, the processing device 120 can traverse all elements in the matching pool, and based on the intelligent matching logic, filter out the matching elements of the target log elements for the data in the different-level cache pools. For example, the data in the different-level cache pools have different comprehensive scores, usage frequencies, and system complexities. Therefore, different intelligent matching logics can be used to determine the target log elements. For example, for data in the cache pool with a high comprehensive score (e.g., high usage frequency, high system complexity), the direct matching logic can be used to quickly determine the matching elements of the target log elements. For the target log elements that cannot find data in the cache pool with a high comprehensive score (e.g., high usage frequency, high system complexity), the recursive matching logic can be used to recursively search the next-level cache pool to ensure the completeness and real-time performance of the matching results. For data in the cache pool with a low comprehensive score (e.g., low usage frequency, low system complexity), the approximate matching logic can be used to reduce the time for determining the target log elements while ensuring the completeness of the matching results, thereby improving the response speed. More details about matching the target log elements from the matching pool based on the intelligent matching logic can be found in Figure 4 and related descriptions thereof.

[0054] Step 240, automatically filling the target log element to generate the log.

[0055] In some embodiments, the processing device 120 can fill the values of the aforementioned matched elements into the blank values of the target log element directly, to achieve the automatic filling of the target log element, to generate the log.

[0056] In some embodiments, the multi-level cache pool can at least include a first-level cache pool divided based on the comprehensive scores of the elements. The first-level cache pool can refer to a set of elements with higher comprehensive scores. In some embodiments, the processing device 120 can traverse the comprehensive scores of all the elements, filter and divide the elements with the comprehensive scores greater than a first comprehensive score into the first-level cache pool. The first comprehensive score refers to a score benchmark value for dividing the first-level cache pool, which can be preset based on experience or demand. The multi-level cache pool can be divided into multiple levels (e.g., two levels, three levels, four levels, etc.) based on the above method, and it should be understood that the number of levels of the cache pool is only an example. More about the multi-level cache pool can be found in Figure 3 and the related description thereof.

[0057] In some embodiments, if the target log element can be matched in the first-level cache pool, the processing device 120 can fill the matched elements into the target log element to generate the log. For example, if the processing device 120 can determine the matched elements of the target log element in the first-level cache pool based on the intelligent matching logic, it is determined that the target log element can be matched in the first-level cache pool, and the processing device 120 can fill the values of the matched elements into the blank values of the target log element to generate the log.

[0058] In some embodiments, if the target log element cannot be matched in the first-level cache pool, the processing device 120 can determine the matched elements through the recursive matching logic or the approximate matching logic, and fill the matched elements into the target log element to generate the log. For example, if the processing device 120 cannot determine the matched elements of the target log element in the first-level cache pool based on the intelligent matching logic, it is determined that the target log element cannot be matched in the first-level cache pool, and the processing device 120 can traverse the elements outside the first-level cache pool in the matching pool through the recursive matching logic or the approximate matching logic, filter out the matched elements of the target log element, and fill the values of the matched elements into the blank values of the target log element to generate the log. More about the recursive matching logic and the approximate matching logic can be found in Figure 4 and the related description thereof.

[0059] For example, the generation of the construction log can include: for target log elements that can be matched in the first buffer pool, such as construction site, construction personnel, construction time, and the like, the processing device 120 can determine matching elements of the target log elements in the first buffer pool, fill the values of the matching elements into the blank values of the target log elements, to generate the construction log; for target log elements that cannot be matched in the first buffer pool, such as the number of attendance, the number of material consumption, and the like, the processing device 120 can determine matching elements of the target log elements through recursive matching logic or approximate matching logic, fill the values of the matching elements into the blank values of the target log elements, to generate the construction log.

[0060] For example, the generation of the safety log can include: for target log elements that can be matched in the first buffer pool, such as project progress, number of safety personnel, and the like, the processing device 120 can determine matching elements of the target log elements in the first buffer pool, fill the values of the matching elements into the blank values of the target log elements, to generate the safety log; for target log elements that cannot be matched in the first buffer pool, such as safety hazards, abnormal accidents, and the like, the processing device 120 can determine matching elements of the target log elements through recursive matching logic or approximate matching logic, fill the values of the matching elements into the blank values of the target log elements, to generate the safety log.

[0061] For example, the generation of the supervision log can include: for target log elements that can be matched in the first buffer pool, such as quality inspection records, supervising engineers, and the like, the processing device 120 can determine matching elements of the target log elements in the first buffer pool, fill the values of the matching elements into the blank values of the target log elements, to generate the supervision log; for target log elements that cannot be matched in the first buffer pool, such as material yield, supervision communication records, and the like, the processing device 120 can determine matching elements of the target log elements through recursive matching logic or approximate matching logic, fill the values of the matching elements into the blank values of the target log elements, to generate the supervision log.

[0062] In some embodiments of the present specification, by adopting different intelligent matching logics to determine matching elements and automatically fill target log elements, log content can be automatically and intelligently filled based on different scenarios, while ensuring the standardization of log recording and avoiding errors and errors in manual recording. At the same time, different intelligent matching logics not only ensure the completeness and real-time of the matching results, but also improve the response speed and accuracy.

[0063] In some embodiments, the processing device 120 can dynamically adjust the matching pool. For example, the processing device 120 can update the usage frequency and system complexity of each element, and dynamically adjust the hierarchical results of the elements based on the updated usage frequency and the updated system complexity. More details about dynamically adjusting the matching pool can be found in Figure 5 and related descriptions thereof.

[0064] In some embodiments of the present specification, by receiving a log generation request, a matching pool is obtained, which includes a multi-level cache pool. Each level of the cache pool includes multiple elements and a comprehensive score for each element. The comprehensive score of each element is determined by the frequency of use of the element and the system complexity of the element. The various elements required for log generation can be divided into a multi-level matching pool, and the allocation of each element in the matching pool can be scientifically and reasonably achieved. The frequency of use and system complexity parameters are introduced to make the allocation logic more intelligent and efficient, and to achieve adaptive optimization of different elements in the log generation process; based on the intelligent matching logic, the target log elements are matched from the matching pool, and the target log elements are automatically filled in to generate the log, which can avoid resource waste and repeated calculations, ensure that the required elements can be matched quickly and efficiently when the log is generated, improve the response speed and data accuracy of the automatically generated log, and thereby improve the quality and efficiency of construction project management, and enhance log traceability and standardization.

[0065] Figure 3 is a schematic diagram of an exemplary generation of a matching pool according to some embodiments of this specification.

[0066] like Figure 3 As shown, the processing device 120 can determine each element (i.e. Figure 3 The usage frequency 320 of element 1 311, element 2 312, ... element n 313 in the example. For example, the processing device 120 can analyze and count the historical usage times of each element based on the historical data of each element stored in the storage device 110, and directly use the usage frequency 320 as the usage frequency. The historical usage times refer to the number of times a certain element is used in the log in the historical time. For more information about usage frequency, please refer to Figure 2 and its related descriptions.

[0067] In some embodiments, processing device 120 may determine a usage frequency 322 of each element within a certain period 321; determine a total usage frequency 323 of all elements 310 within the certain period 321; and determine a usage frequency 320 of each element based on each element's usage frequency 322 and the total usage frequency 323. Certain period 321 may be set based on experience or demand.

[0068] The usage frequency 322 refers to the number of times each element is used. In some embodiments, the processing device 120 may sum the number of times each element is used within a certain period 321 to determine the usage frequency 322. For example, the processing device 120 may calculate the usage frequency 322 of each element within a certain period 321 according to formula (1).

[0069]

[0070] Among them, f irepresents the frequency of use of element i in period T, t represents the use time of element i, and u i,t represents the number of uses of element i at use time t.

[0071] The total use frequency 323 refers to the total number of uses of all elements. In some embodiments, the processing device 120 can sum the use frequency 322 of each element in a certain period 321 to determine the total use frequency 323. For example, the processing device 120 can calculate the total use frequency 323 of all elements in a certain period 321 according to formula (2).

[0072]

[0073] where F represents the total use frequency of N elements in period T, and N represents the total number of elements.

[0074] In some embodiments, the processing device 120 can calculate the use frequency 320 of each element based on the use frequency 322 of each element and the total use frequency 323 of all elements by a preset algorithm. For example, the processing device 120 can determine the use frequency 320 of each element according to formula (3).

[0075]

[0076] where R i represents the use frequency of element i.

[0077] In some embodiments, the processing device 120 can grade each element based on the use frequency 320 of each element to obtain a frequency grading result. The frequency grading result refers to the result of grading elements based on the high and low of the use frequency 320. The frequency grading result can include elements of different use frequency levels. In some embodiments, the frequency grading result can include high-frequency elements, medium-frequency elements, and low-frequency elements. The high-frequency element refers to an element that appears frequently in the log. For example, the high-frequency element can be the construction site, the construction personnel, the construction time, etc. It can be understood that the high-frequency element will appear frequently in all log types, and can be directly obtained from the system, which is a necessary core data item in the log generation process. The medium-frequency element refers to an element that appears frequently in the log. For example, the medium-frequency element can be the material consumption, the construction equipment, etc. It can be understood that the medium-frequency element appears frequently in a specific construction stage or a specific log type, but is not required to be recorded in every log. The low-frequency element refers to an element that appears less frequently in the log. For example, the low-frequency element can be a special inspection item, an abnormal accident, an attendance number, etc. It can be understood that the low-frequency element only appears in specific situations or specific log types.

[0078] In some embodiments, the processing device 120 may classify each element based on the first usage frequency and the second usage frequency to obtain a frequency classification result. The first usage frequency refers to the usage frequency reference value for distinguishing high-frequency elements from medium-frequency elements, and the second usage frequency refers to the usage frequency reference value for distinguishing medium-frequency elements from low-frequency elements. The first usage frequency and the second usage frequency may be preset based on experience or demand. In some embodiments, the first usage frequency may be the third quantile of the usage frequencies of all elements 310, and the second usage frequency may be the first quantile of the usage frequencies 320 of all elements 310. Exemplarily, the processing device 120 may classify factors whose usage frequencies 320 are greater than the first usage frequency as high-frequency elements, factors whose usage frequencies 320 are not greater than the first usage frequency and not less than the second usage frequency as medium-frequency elements, and factors whose usage frequencies 320 are less than the second usage frequency as low-frequency elements.

[0079] In some embodiments of this specification, by determining the usage frequency of each element within a certain period, the total usage frequency of all elements within a certain period is determined. Based on the usage frequency of each element and the total usage frequency, the usage frequency of each element is determined. This can fully consider the impact of the two factors of usage frequency and total usage frequency on the usage frequency, determine a scientific and accurate usage frequency, and then obtain a scientific frequency grading result.

[0080] Furthermore, the processing device 120 may determine that each element (i.e. Figure 3 For example, the processing device 120 may process the historical data of each element stored in the storage device 110 based on a preset algorithm, machine learning model, etc. to determine the system complexity 330 of each element. The preset algorithm and machine learning model may be preset based on experience or requirements. For more information about system complexity, please refer to Figure 2 and its related descriptions.

[0081] In some embodiments, the processing device 120 may determine the number of system accesses 331 for each element, determine the average response time for each element, and determine the system complexity 330 of each element based on the number of system accesses 331 and the average response time.

[0082] System access count 331 refers to the frequency of system access requests for an element within a certain period 321. It is understood that the more frequently an element is accessed by the system, the more widely it is used and should be matched first. In some embodiments, processing device 120 can directly obtain system access count 331 for each element through the system.

[0083] The average response time 332 refers to the average time consumption for an element to respond to a system access request. It can be understood that when the response time of an element is too long, the data of the element needs to be constructed in advance to avoid excessive time consumption caused by frequent requests. In some embodiments, the processing device 120 can directly obtain the average response time 332 of each element through the system.

[0084] In some embodiments, the processing device 120 can determine the system complexity 330 of each element based on the system access quantity 331 and the average response time 332 of each element through a preset algorithm, a machine learning model, etc. The preset algorithm and the machine learning model can be preset based on experience or demand.

[0085] In some embodiments, the system complexity 330 can be a weighted sum of the system access quantity 331 and the average response time 332. The more the system access quantity 331 and the longer the average response time 332 of an element, the higher the system complexity 330 of the element. For example, the processing device 120 can determine the system complexity 330 of each element according to formula (4).

[0086] S i = a x N i + b x T i (4),

[0087] wherein, S i represents the system complexity of the element i, N i represents the system access quantity of the element i, T i represents the average response time of the element i, a represents a weight parameter of the system access quantity N i , used to adjust the influence degree of the system access quantity N i on the system complexity S i , b represents a weight parameter of the average response time T i , used to adjust the influence degree of the average response time T i on the system complexity S i , and the weight parameter a and the weight parameter b can be set based on experience or demand.

[0088] In some embodiments, the processing device 120 can rank each element based on the system complexity 330 of each element to obtain a complexity ranking result. The complexity ranking result refers to the result of ranking each element based on the system complexity 330. The complexity ranking result can include elements of different system complexity levels. In some embodiments, the complexity ranking result can include high complexity elements, medium complexity elements, and low complexity elements. The high complexity element refers to an element with a high system complexity 330 in the log. For example, the high complexity element can be a safety hazard record, a construction material yield, etc. It can be understood that the high complexity element requires more system computing resources. The medium complexity element refers to an element with a medium system complexity 330 in the log. For example, the medium complexity element can be a construction division situation, a construction equipment parameter, etc. It can be understood that the medium complexity element requires moderate system computing resources. The low complexity element refers to an element with a low system complexity 330 in the log. For example, the low complexity element can be a construction unit, a construction site, etc. It can be understood that the low complexity element requires less system computing resources.

[0089] In some embodiments, the processing device 120 can rank each element based on the first system complexity and the second system complexity to obtain a complexity ranking result. The first system complexity refers to a system complexity benchmark value that distinguishes high complexity elements from medium complexity elements, and the second system complexity refers to a system complexity benchmark value that distinguishes medium complexity elements from low complexity elements. The first system complexity and the second system complexity can be preset based on experience or demand. In some embodiments, the first system complexity can be the third quantile of the system complexity of all elements 310, and the second system complexity can be the first quantile of the system complexity of all elements 310. For example, the processing device 120 can divide elements with a system complexity 330 greater than the first system complexity into high complexity elements, divide elements with a system complexity 330 not greater than the first system complexity and not less than the second system complexity into medium complexity elements, and divide elements with a system complexity 330 less than the second system complexity into low complexity elements.

[0090] In some embodiments of the present specification, by determining the system access quantity of each element, determining the average response time of each element, and determining the system complexity of each element based on the system access quantity and the average response time, the influence of the system access quantity and the average response time on the system complexity can be fully considered, a scientific and accurate system complexity can be determined, and a scientific complexity ranking result can be obtained. By setting the system complexity as the weighted sum of the system access quantity and the average response time, the influence degree of the aforementioned two factors on the system complexity can be adjusted by adjusting the weight parameters of the system access quantity and the average response time, so as to dynamically adjust the system complexity based on the actual situation.

[0091] In some embodiments, the processing device 120 can determine a comprehensive score 340 of each element based on the usage frequency 320 and the system complexity 330. In some embodiments, the comprehensive score 340 can be a weighted sum of the usage frequency 320 and the system complexity 330. The higher the usage frequency 320 and the system complexity 330 of an element, the higher the comprehensive score of the element. For example, the processing device 120 can determine a calculation rule of the comprehensive score 340 based on formula (5), and implement the calculation of the comprehensive score 340 of each element through a mathematical model.

[0092] C i = γ × R + δ × S i (5), i

[0093] wherein C i denotes the comprehensive score of the element i, γ denotes a weight parameter of the usage frequency R i , used to adjust the influence degree of the usage frequency R i on the comprehensive score C i , δ denotes a weight parameter of the system complexity S i , used to adjust the influence degree of the system complexity S i on the comprehensive score C i , and the weight parameters γ and β can be set based on experience or requirements.

[0094] Finally, the processing device 120 can determine a grading result 350 of each element based on the comprehensive score 340 of each element, and generate a matching pool 360 based on the grading result 350.

[0095] The grading result 350 refers to the result of grading the elements based on the high and low of the comprehensive score 340. The grading result 350 can include elements of different comprehensive score levels. In some embodiments, the grading result 350 can include first-level elements, second-level elements, and third-level elements. The first-level elements refer to elements with high priority in the log. For example, high-frequency elements, high-complexity elements. The second-level elements refer to elements with moderate priority in the log. For example, medium-frequency elements, medium-complexity elements. The third-level elements refer to elements with low priority in the log. For example, low-frequency elements, low-complexity elements.

[0096] ​In some embodiments, the processing device 120 can grade each element based on the first comprehensive score, the second comprehensive score, and obtain a grading result 350. The first comprehensive score refers to a comprehensive score benchmark value for distinguishing between first-level elements and second-level elements, and the second comprehensive score refers to a comprehensive score benchmark value for distinguishing between second-level elements and third-level elements. The first comprehensive score and the second comprehensive score can be preset based on experience or requirements. In some embodiments, the first comprehensive score can be the third quantile of the comprehensive scores 340 of all elements 310, and the second comprehensive score can be the first quantile of the comprehensive scores 340 of all elements 310. For example, the processing device 120 can divide the elements with a comprehensive score 340 greater than the first comprehensive score into first-level elements, divide the elements with a comprehensive score 340 not greater than the first comprehensive score and not less than the second comprehensive score into second-level elements, and divide the elements with a comprehensive score 340 less than the second comprehensive score into third-level elements.

[0097] In some embodiments, the processing device 120 can generate a matching pool 360 based on the grading result 350. For example, the processing device 120 can determine a set of first-level elements as a first-level cache pool, determine a set of second-level elements as a second-level cache pool, and determine a set of third-level elements as a third-level cache pool, and the first-level cache pool, the second-level cache pool, and the third-level cache pool (i.e., a multi-level cache pool) together constitute the matching pool 360. For more information about the cache pool and the matching pool, see Figure 2 and related descriptions thereof.

[0098] In some embodiments of the present specification, by determining the usage frequency and system complexity of each element, and then determining the comprehensive score of each element, which is the weighted sum of the usage frequency and the system complexity, determining the grading result of each element based on the comprehensive score of each element, and generating the matching pool based on the grading result, the accurate usage frequency and system complexity can be determined, and the usage frequency and system complexity of the element are further calculated jointly by using the mathematical model, and the element is automatically assigned to the appropriate matching pool, so as to improve the accuracy of generating the log.

[0099] Figure 4 is a schematic diagram of an exemplary intelligent matching logic according to some embodiments of the present specification.

[0100] In some embodiments, the multi-level cache pool 420 can include a first-level cache pool 421, a second-level cache pool 422, and a third-level cache pool 423, which are divided based on the comprehensive score of the element. For more information about the first-level cache pool, the second-level cache pool, and the third-level cache pool, see Figure 3 and related descriptions thereof.

[0101] In some embodiments, the intelligent matching logic 410 can include a direct matching logic 411, a recursive matching logic 412, and an approximate matching logic 413.

[0102] The direct matching logic 411 refers to the logic of directly matching, which is used for matching the first-level elements in the first-level cache pool with the target log elements. In some embodiments, the direct matching logic 411 can be used to directly match the elements 421-1 in the first-level cache pool 421 with the target log elements 430 to determine the matching elements 440 of the target log elements 430. For example, the processing device 120 can traverse all the first-level elements in the first-level cache pool and screen out the first-level elements with the same keywords as those of the target log elements as the matching elements 440. It can be understood that, for high-frequency and high-complexity elements in the first-level cache pool 421, the direct matching method can fill the log in a very low delay time.

[0103] The recursive matching logic 412 refers to the logic of recursively searching the multi-level cache pool by level to match, which is used for matching the second-level elements in the second-level cache pool with the target log elements. In some embodiments, the recursive matching logic 412 can be used to recursively search the second-level cache pool 422 until the matching elements 440 of the target log elements 430 are determined, if the matching elements 440 of the target log elements 430 do not exist in the first-level cache pool 421. For example, the processing device 120 can traverse all the first-level elements in the first-level cache pool, and when the first-level elements with the same keywords as those of the target log elements do not exist in the first-level cache pool, the processing device 120 can traverse all the second-level elements in the second-level cache pool and screen out the second-level elements with the same keywords as those of the target log elements as the matching elements 440 in the second-level cache pool 422. If the second-level elements with the same keywords as those of the target log elements do not exist in the second-level cache pool 422, the processing device 120 can further traverse all the third-level elements in the third-level cache pool and screen out the third-level elements with the same keywords as those of the target log elements as the matching elements 440 in the third-level cache pool. It can be understood that, if the matching elements 440 of the target log elements 430 cannot be found in the first-level cache pool 421, the system automatically recursively searches the next-level cache pool, which can ensure the completeness and real-time performance of the matching results.

[0104] Approximate match refers to a matching manner in which the result contains similar but not exactly the same content as the to-be-matched item in the matching process. The approximate match logic 413 is the logic corresponding to the approximate match, used for matching the tertiary elements in the tertiary buffer pool with the target daily element. The approximate match algorithm 450 refers to the algorithm corresponding to the approximate match. In some embodiments, the approximate match logic 413 can determine the matching element 440 of the target log element 430 in the tertiary matching pool through the approximate match algorithm 450, if there is no matching element 440 of the target log element 430 in the primary cache pool 421 and the secondary cache pool 422. For example, the processing device 120 can traverse all the primary elements in the primary buffer pool, the secondary elements in the secondary buffer pool, and when there is no primary element or secondary element in the primary buffer pool or the secondary buffer pool whose keyword is consistent with the keyword of the target daily element, traverse all the tertiary elements in the tertiary buffer pool, and determine the matching element 440 of the target log element 430 in the tertiary buffer pool through the approximate match algorithm 450. It can be understood that in the matching process of low-priority elements, the system uses the approximate match algorithm 450 to generate similar elements, which can improve the response speed.

[0105] In some embodiments, the approximate match algorithm 450 can include a fuzzy match algorithm 451 and / or a minimum edit distance algorithm 452.

[0106] The fuzzy matching algorithm 451 refers to an algorithm for matching based on reducing the similarity requirement of the matching. In some embodiments, the processing device 120 can calculate the similarity between the tertiary elements in the tertiary buffer pool and the target log element 430, and determine the tertiary element with the highest similarity among the tertiary elements meeting the similarity condition as the matching element 440 of the target log element 430. The similarity refers to a parameter representing the similarity degree of the keywords between the elements. The greater the similarity, the greater the similarity degree between the elements. The similarity condition refers to a related condition of the similarity, which can be set based on experience or demand. For example, the similarity condition can be that the similarity between the elements is greater than a preset similarity threshold. In some embodiments, the processing device 120 can determine the ratio of the number of matching characters and the total number of characters between the target log element 430 and the tertiary element as the similarity between the target log element 430 and the tertiary element. The number of matching characters refers to the number of characters of the keywords of the target log element 430 consistent with the keywords of the tertiary element. The total number of characters refers to the total number of characters in the keywords of the target log element 430 and the keywords of the tertiary element. For example, the keywords of the target log element are “check temperature”, the keywords of the tertiary element 1 are “detect temperature”, and the keywords of the tertiary element 2 are “temperature”. The matching characters between the target log element and the tertiary element 1 are “check” and “temperature”, the number of matching characters is 4 (the keywords of the target log element are 100% consistent with themselves, i.e., the number of matching characters of “check temperature” and “check temperature” is 4) + 3 (the keywords of the target log element are consistent with three characters of the keywords of the tertiary element 1, i.e., the number of matching characters of “check temperature” and “detect temperature” is 3) = 7, the total number of characters is 4 + 4 = 8, and the similarity is the number of matching characters / the total number of characters = 7 / 8 = 0.875. The matching characters between the target log element and the tertiary element 2 are “temperature”, the number of matching characters is 4 (the keywords of the target log element are 100% consistent with themselves, i.e., the number of matching characters of “check temperature” and “check temperature” is 4) + 2 (the keywords of the target log element are consistent with two characters of the keywords of the tertiary element 2, i.e., the number of matching characters of “check temperature” and “temperature” is 2) = 6, the total number of characters is 4 + 4 = 8, and the similarity is the number of matching characters / the total number of characters = 6 / 8 = 0.75. If the similarity condition is that the similarity between the elements is greater than a preset similarity threshold 0.8, the tertiary element 1 with the highest similarity among the tertiary elements meeting the similarity condition is determined as the matching element of the target log element.

[0107] The minimum edit distance algorithm 452 refers to an algorithm for matching based on minimum edit distance of keyword conversion of elements. The edit distance refers to the number of minimum single-character edit operations (such as insertion, deletion, and replacement) required to convert one string into another string. The smaller the edit distance, the more similar the two strings are. The minimum edit distance refers to the minimum edit distance. In some embodiments, the processing device 120 can calculate the edit distance of the keyword conversion of the tertiary elements in the tertiary buffer pool to the keyword of the target log element 430, and determine the tertiary element with the minimum edit distance among the tertiary elements meeting the edit distance condition as the matching element 440 of the target log element 430. The edit distance condition refers to a related condition of the edit distance, which can be set based on experience or demand. For example, the edit distance condition can be that the edit distance between the elements is less than a preset edit distance threshold. In some embodiments, the processing device 120 can sum the operation numbers of the keyword conversion of the tertiary elements to the keyword of the target log element 430 to determine the edit distance of the tertiary element and the target log element 430. For example, the processing device 120 can determine the edit distance of the tertiary element and the target log element 430 according to formula (6).

[0108]

[0109] wherein L i represents the edit distance of the element i and the target log element, x i represents the operation number of the conversion of the element i to the target log element, and M represents the total number of tertiary elements.

[0110] For example, the keyword of the target log element is “high-strength steel bar”, the keyword of the tertiary element 1 is “high-strength steel bar”, and the keyword of the tertiary element 2 is “ribbed steel bar”. The operation number of the conversion of the tertiary element 1 to the target log element is 1 (deleting “degree”), and the edit distance is 1. The operation number of the conversion of the tertiary element 2 to the target log element is 3 (replacing “high-strength” with “ribbed” and adding “degree”), and the edit distance is 3. If the edit distance condition is that the edit distance between the elements is less than a preset edit distance threshold of 2, the tertiary element 1 with the minimum edit distance among the tertiary elements meeting the edit distance condition is determined as the matching element of the target log element.

[0111] In some embodiments, the fuzzy matching algorithm 451 and the minimum edit distance algorithm 452 can be used separately or jointly. For example, among multiple tertiary elements, the processing device 120 can simultaneously calculate the weighted sum of the similarity and the edit distance of each tertiary element and the target log element 430, and determine the tertiary element with the highest weighted sum exceeding a preset weighted sum threshold as the matching element 440 of the target log element 430. The weight of the similarity and the weight of the edit distance can be set based on experience or demand.

[0112] In some embodiments of the present disclosure, by using two approximate matching algorithms (fuzzy matching algorithm and / or minimum edit distance algorithm), the fault tolerance and intelligent processing capability of the system for data can be improved, approximate matching can be implemented for low-priority elements, and the time and system computing resources consumed by accurate matching can be saved.

[0113] In some embodiments of the present disclosure, by setting intelligent matching logic including direct matching logic, recursive matching logic and approximate matching logic, the corresponding matching strategy can be intelligently adjusted for different application scenarios, the system response speed and accuracy can be improved, the integrity and real-time performance of the matching result can be ensured, and the utilization efficiency of computing resources can be improved.

[0114] Figure 5 is a flowchart of an exemplary method of dynamically adjusting the matching pool according to some embodiments of the present disclosure. As shown in Figure 5 , the flow 500 includes the following steps. In some embodiments, the flow 500 can be executed by the processing device 120.

[0115] Step 510, update the usage frequency of each element.

[0116] In some embodiments, the processing device 120 can recalculate the usage frequency of each element after every update cycle. The update cycle refers to the period of time that needs to be interval when updating the data, which can be set based on experience or demand. For specific content of usage frequency and its determination, please refer to Figure 2 , Figure 3 and related descriptions.

[0117] Step 520, update the system complexity of each element.

[0118] In some embodiments, the processing device 120 can recalculate the system complexity of each element after every update cycle. For specific content of system complexity and its determination, please refer to Figure 2 , Figure 3 and related descriptions.

[0119] Step 530, based on the updated usage frequency and the updated system complexity, dynamically adjust the classification result of the element to dynamically adjust the matching pool.

[0120] In some embodiments, the processing device 120 can recalculate the updated comprehensive score of each element based on the updated usage frequency and the updated system complexity, and reclassify based on the updated comprehensive score to determine a new classification result, so as to dynamically adjust the matching pool. For specific content of comprehensive score and its calculation, classification result determination, please refer to Figure 2 , Figure 3 and related descriptions.

[0121] In some embodiments, for elements with too long average response time, the processing device 120 can construct the related data of the elements in advance to avoid system blocking caused by repeated access.

[0122] In some embodiments of the present specification, by updating the usage frequency of each element, updating the system complexity of each element, and dynamically adjusting the ranking results of the elements based on the updated usage frequency and the updated system complexity, the matching pool can be dynamically adjusted, the matching pool can be periodically updated and adjusted, the related data of the elements can be updated in time and the ranking results can be determined in real time, and the flexibility and efficiency of the log generation system can be ensured.

[0123] The above has described the basic concept, and it is obvious that the above detailed disclosure is only used as an example and does not limit the present specification. Although it is not explicitly stated here, those skilled in the art can make various modifications, improvements and corrections to the present specification. Such modifications, improvements and corrections are suggested in the present specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0124] At the same time, the present specification uses specific words to describe the embodiments of the present specification. As "one embodiment", "an embodiment", and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the present specification. Therefore, it should be emphasized and noted that the "an embodiment" or "one embodiment" or "one alternative embodiment" mentioned in different places in the present specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present specification can be properly combined.

[0125] In addition, unless the claim explicitly states, the order of the processing elements and sequences described in the present specification, the use of numerals and letters, or the use of other names, is not intended to limit the order of the processes and methods of the present specification. Although some currently considered useful embodiments of the invention are discussed in the above disclosure through various examples, it should be understood that such details are only for the purpose of illustration, and the additional claims are not limited to the disclosed embodiments, on the contrary, the claims are intended to cover all modifications and equivalent combinations that meet the spirit and scope of the embodiments of the present specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on existing servers or mobile devices.

[0126] For simplicity and to facilitate understanding of one or more embodiments, a description of an embodiment sometimes refers to a plurality of features in a single embodiment, drawing, or description of an embodiment. However, this method of disclosure is not to be interpreted as meaning that the claimed embodiment requires more features than are explicitly recited in the claims. In fact, claims that do not specifically claim a combination of features are intended to cover the various possible combinations of features as would be understood by a person of ordinary skill in the art.

[0127] Some embodiments use numerical values to describe components, quantities of attributes. It should be understood that such numerical values used in the description of embodiments are, in some examples, modified by the adjectives "about," "approximately," or "substantially." Unless otherwise stated, "about," "approximately," or "substantially" indicate that the described numerical value allows for a variation of ±20%. Accordingly, numerical values used in the specification and claims of some embodiments are approximations that can vary depending on the desired characteristics of the individual embodiments. In some embodiments, the numerical values used in the specification and claims are approximations that can vary depending on the desired characteristics of the individual embodiments. In some embodiments, numerical values should be considered in the context of the description of the embodiment and, when appropriate, the context of a claim. Although the numerical ranges and parameters setting forth the broadest scope of the embodiments described in some embodiments of the specification are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values set forth in the specific examples are provided to be as precise as practicable.

[0128] Each patent, patent application, patent publication, and other material, such as articles, books, specifications, publications, documents, and the like, referenced herein are hereby incorporated by reference in their entirety for the teachings relevant to the sentence and / or paragraph in which such reference is made. Discrepancies between applications history documents and the present specification, other than limitations on the scope of the claims, are excepted. It is intended to have the description, definitions, and / or terminology used in the attached materials incorporated by reference in their entirety for the teachings relevant to the sentence and / or paragraph in which such reference is made.

[0129] Finally, it should be understood that the embodiments described herein are intended to be illustrative only and that the scope of the present specification is rather intended to be indicated by the appended claims.

Claims

1. A method for automatically generating a log, characterized in that: The method comprises: Receive log generation request; Obtain a matching pool, wherein the matching pool includes a multi-level cache pool, each level of the cache pool includes multiple elements and a comprehensive score of each element, the comprehensive score of each element is determined by the frequency of use of the element and the system complexity of the element, the system complexity represents the complexity of the system computing resources required to request the element, and determining the system complexity of each element includes: Determine the number of system visits for each of said elements; determining the average response time for each of said elements; determining the system complexity of each of the elements based on the number of system accesses of each of the elements and the average response time of each of the elements; Based on intelligent matching logic, matching elements of the target log elements are determined from the matching pool. The intelligent matching logic includes direct matching logic, recursive matching logic, and approximate matching logic. The multi-level cache pool includes a first-level cache pool, a second-level cache pool, and a third-level cache pool divided based on the comprehensive scoring of the elements: The direct matching logic is to directly match the elements in the first-level cache pool with the target log elements to determine the matching elements of the target log elements; The recursive matching logic is that if the matching element of the target log element does not exist in the first-level cache pool, the second-level cache pool is recursively searched until the matching element of the target log element is determined; The approximate matching logic is to determine the matching element of the target log element in the third-level cache pool by using an approximate matching algorithm if the matching element of the target log element does not exist in the first-level cache pool and the second-level cache pool; and The target log element is automatically filled in to generate a log, and the log is a log recording construction project related information.

2. The method according to claim 1, characterized in that The method further includes generating the matching pool, wherein generating the matching pool includes: determining said frequency of use of each said element; determining the system complexity of each of the elements; Determining the comprehensive score of each of the elements based on the usage frequency of each of the elements and the system complexity of each of the elements, the comprehensive score being a weighted sum of the usage frequency and the system complexity; and Based on the comprehensive score of each of the elements, a ranking result of each of the elements is determined, and the matching pool is generated based on the ranking result.

3. The method according to claim 2, characterized in that Determining the usage frequency of each element includes: Determine the frequency of use of each of the elements within a certain period; Determine the total frequency of use of all elements during the specified period; and The usage frequency of each of the elements is determined based on the usage frequency of each of the elements and the total usage frequency.

4. The method according to claim 1, wherein The system complexity is a weighted sum of the number of system accesses and the average response time.

5. The method according to claim 1, wherein The approximate matching algorithm includes a fuzzy matching algorithm and / or a minimum edit distance algorithm.

6. The method according to any one of claims 1 to 5, characterized in that The multi-level cache pool includes at least the first-level cache pool divided based on the comprehensive score of the elements, and the automatically filling the target log element to generate a log includes: If the target log element can be matched in the first-level cache pool, directly fill the matching element into the target log element to generate the log; and If the target log element cannot be matched in the first-level cache pool, the matching element is determined by the recursive matching logic or the approximate matching logic, and the matching element is filled into the target log element to generate the log.

7. The method according to any one of claims 1 to 5, characterized in that The method further includes dynamically adjusting the matching pool, wherein the dynamically adjusting the matching pool includes: updating the usage frequency of each of the elements; Updating the system complexity of each of the elements; Based on the updated usage frequency and the updated system complexity, the grading results of the elements are dynamically adjusted to dynamically adjust the matching pool.

8. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Determining whether the log generation request meets target conditions, where the target conditions include at least a position authority condition and a project status condition; In response to the log generation request satisfying the target condition, the matching pool is obtained.

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