Construction task cache management method, system and device

Abstract

The embodiment of the invention provides a construction task cache management method, system and device. The method comprises the steps of obtaining multi-dimensional feature factors of a plurality of construction tasks; determining a storage weight of each construction task based on the multi-dimensional feature factors; based on the storage weight of each construction task, caching the plurality of construction tasks to a multi-level storage space; wherein the multi-level storage space is used for storing construction tasks with different priorities; and based on the data access request, sequentially accessing according to the cache priority order of the storage space to obtain requested construction task data.

Description

Technical Field

[0001] This specification relates to the field of project management, and particularly to a construction task cache management method, system, and device. Background Art

[0002] With the continuous increase in the scale and complexity of construction projects, the efficiency and response speed of construction task management systems have become one of the key factors affecting project progress. In modern construction management processes, it has become standard practice to use computer systems to plan, schedule, and monitor construction tasks. However, these systems may encounter performance bottlenecks when processing large amounts of real-time data, especially in situations where rapid response to changes is required, and may have difficulty meeting the demands. Therefore, how to optimize system performance to improve construction task management efficiency has become a problem to be solved.

[0003] Therefore, it is necessary to provide a construction task cache management method, system, and device to achieve fast and efficient management of construction tasks. Summary of the Invention

[0004] One or more embodiments of this specification provide a construction task cache management method, the method including: obtaining multi-dimensional characteristic factors of a plurality of construction tasks; determining a storage weight of each of the construction tasks based on the multi-dimensional characteristic factors; caching the plurality of construction tasks into a multi-level storage space based on the storage weight sizes of each of the construction tasks; where the multi-level storage space is used to store the construction tasks with different priorities; and accessing in sequence according to the cache priority order of the storage spaces based on a data access request to obtain the requested construction task data.

[0005] One or more embodiments of this specification also provide a construction task cache management system, the system including: an obtaining module configured to obtain multi-dimensional characteristic factors of a plurality of construction tasks; a determining module configured to determine a storage weight of each of the construction tasks based on the multi-dimensional characteristic factors; a caching module configured to cache the plurality of construction tasks into a multi-level storage space based on the storage weight sizes of each of the construction tasks; where the multi-level storage space is used to store the construction tasks with different priorities; and a data access module configured to access in sequence according to the cache priority order of the storage spaces based on a data access request to obtain the requested construction task data.

[0006] One or more embodiments of this specification also provide a construction task cache management device, the device including at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least some of the computer instructions to implement the construction task cache management method as described in the above embodiments. Brief Description of the Drawings

[0007] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive. In these embodiments, the same reference numerals represent the same structures, where: Figure 1 is a schematic diagram of an application scenario of a construction task cache management system according to some embodiments of this specification; Figure 2 is an exemplary flowchart of a construction task cache management method according to some embodiments of this specification; Figure 3 is an exemplary flowchart of re - storing a construction task according to some embodiments of this specification; Figure 4 is an exemplary flowchart of determining a second storage weight according to some embodiments of this specification; Figure 5 is an exemplary flowchart of obtaining construction task data according to some embodiments of this specification; Figure 6 is an exemplary flowchart of determining whether to adjust the storage space of construction task data according to some embodiments of this specification; Figure 7 is an exemplary module diagram of a construction task cache management system according to some embodiments of this specification. Detailed implementation manners

[0008] To more clearly illustrate the technical solutions of the embodiments of this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the drawings represent the same structures or operations.

[0009] It should be understood that the "system", "device", "unit" and / or "module" used herein is a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0010] As shown in this specification and the claims, unless the context clearly indicates otherwise, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0011] Flowcharts are used in this specification to illustrate the operations performed by the systems according to the embodiments of this specification. It should be understood that the operations before or after may not be executed precisely in order. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0012] Figure 1 It is a schematic diagram of the application scenario of the construction task cache management system shown in some embodiments of this specification.

[0013] In some embodiments, as Figure 1 shown, the application scenario 100 of the construction task cache management system includes construction tasks 110, network 120, terminal 130, processor 140, storage device 150, etc.

[0014] The construction task 110 refers to the engineering tasks that require construction task cache management. For example, the construction task 110 can include various building construction tasks (such as residential buildings, commercial centers, school buildings), landscape projects, transportation hub projects (such as airports, railway stations, bus stations, ports, etc.), road and bridge projects, etc.

[0015] In some embodiments, the construction task 110 can include various materials and task lists related to the task. For example, design drawings, material lists, personnel lists, unit information (such as information related to the construction unit, construction company, supervision unit, design unit, and suppliers, etc.).

[0016] The network 120 can connect the components in the application scenario 100 of the construction task cache management system and / or connect other components outside the application scenario 100. In some embodiments, one or more components of the application scenario 100 of the construction task cache management system (such as the terminal 130, processor 140, and storage device 150, etc.) can be connected to each other and / or communicate through the network 120. For example, the terminal 130 can send the unit information of the construction project 110 to the processor 140, etc. through the network 120.

[0017] The terminal 130 can provide functional components related to user interaction and can implement user interaction functions (such as providing or presenting information and data to the user). The user can refer to the management personnel of the construction task 110. For example, the user can be a person in a unit related to the construction task (such as a team leader, a project manager, a process management personnel, etc.). Only as an example, the terminal 130 can be a mobile device, a tablet computer, a laptop computer, a desktop computer, etc., or any combination of one or more of other devices with input and / or output functions.

[0018] The processor 140 can process information and / or data related to the construction task cache management system to perform one or more functions described in this specification. In some embodiments, the processor 140 can obtain multi-dimensional feature factors of multiple construction tasks; based on the multi-dimensional feature factors, determine the storage weight of each construction task; based on the storage weight size of each construction task, cache multiple construction tasks to a multi-level storage space; wherein, the multi-level storage space is used to store construction tasks with different priorities; based on a data access request, access in sequence according to the cache priority order of the storage space to obtain the requested construction task data. For a detailed description of related content, reference can be made to the following text (such as Figure 2 , Figure 3 , etc.) for related descriptions.

[0019] In some embodiments, the processor 140 can include a central processing unit (CPU), a digital signal processor (DSP), a microcontroller unit (MCU), a computer, a user console, etc., or any combination thereof. In some embodiments, the processor 140 can include a single server or a server group. The server group can be centralized or distributed. In some embodiments, the processor 140 can be local or remote. In some embodiments, the processor 140 can be implemented on a cloud platform. Only as an example, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc., or any combination thereof.

[0020] The storage device 150 can store data, instructions, and / or any other information. In some embodiments, the storage device 150 can store data obtained from the terminal 130, the processor 140, etc., such as multi-dimensional feature factors of multiple construction tasks. In some embodiments, the storage device 150 can include a mass storage device, a removable storage device, a volatile read-write memory, a read-only memory (ROM), etc., or any combination thereof. In some embodiments, the storage device 150 can be executed on a cloud platform. In some embodiments, the storage device 150 can be connected to the network 120 to communicate with one or more other components of the application scenario 100 of the construction task cache management system (such as the terminal 130, the processor 140, etc.). In some embodiments, the storage device 150 can be a part of the processor 140.

[0021] It should be noted that the application scenario 100 of the construction task cache management system is provided for illustrative purposes only and is not intended to limit the scope of this specification. Those of ordinary skill in the art can make various changes and modifications according to the description of this specification. For example, the application scenario 100 of the construction task cache management system may also include a database, information sources, etc. Also, for example, the application scenario 100 of the construction task cache management system can be implemented on other devices to achieve similar or different functions. However, these changes and modifications will not depart from the scope of this specification.

[0022] Figure 2 is an exemplary flowchart of a construction task cache management method shown according to some embodiments of this specification. In some embodiments, the process 200 can be executed by a processing device (e.g., the processor 140). As Figure 2 shown, the process 200 includes the following steps.

[0023] Step 210, obtaining multi-dimensional feature factors of multiple construction tasks.

[0024] The multi-dimensional feature factors refer to a set of information / features that describe a construction task from multiple dimensions. In some embodiments, for each of the multiple construction tasks, the processor can separately obtain its corresponding multi-dimensional feature factors. For more descriptions of construction tasks, reference can be made to Figure 1 .

[0025] In some embodiments, the multi-dimensional feature factors include one or more different dimensional feature factors such as task status, construction period, worker activity, task area, task dependency, and user operation intention. It should be noted that the above descriptions of the different dimensional feature factors in the multi-dimensional feature factors are only examples, and the multi-dimensional feature factors may also include other dimensional feature factors, such as feature factors corresponding to dimensions such as material supply and safety risk. In some embodiments, the multi-dimensional feature factors can be represented in the form of a vector, and each element in the vector can represent a feature factor of one dimension in the multi-dimensional feature factors. For example, task status, construction period, worker activity, task area, task dependency, and user operation intention, etc.

[0026] The task status refers to the current state of a construction task. For example, the task status may include not started, in progress, paused, completed, overdue, etc. The task status can be used to identify tasks that need to be urgently processed or are about to be overdue (or already overdue).

[0027] The construction period refers to the time required to complete a construction task as expected. The construction period can be used to distinguish tasks that must be completed in the short term from long-term tasks.

[0028] Worker activity refers to the working state or enthusiasm of workers performing construction tasks. In some embodiments, worker activity can be determined based on factors such as the attendance rate of workers, the frequency of task completion, and the frequency of task acceptance. The workers described in the embodiments of this specification refer to workers performing construction tasks, and in some embodiments, they may also be referred to as construction workers.

[0029] The attendance rate includes the attendance rate of workers for construction tasks. For example, among all the workers for a construction task, the weekly attendance rate, monthly attendance rate, etc. of each worker.

[0030] The frequency of a worker completing tasks can be determined based on the ratio of the number of tasks completed by the worker to the number of tasks assigned. For example, if the number of construction tasks completed by a certain worker is a1 and the number of tasks assigned is b, then the frequency of this worker completing tasks is a1 / b.

[0031] The frequency of a worker accepting tasks can be determined based on the ratio of the number of tasks accepted by the worker to the number of tasks assigned. For example, if the number of construction tasks accepted by a certain worker is a2 and the number of tasks assigned is b, then the frequency of this worker accepting tasks is a2 / b.

[0032] In some embodiments, the higher the attendance rate, the frequency of task completion, and the frequency of task acceptance of workers, the higher the worker activity. In some embodiments, the processor 140 can also determine the worker activity based on the attendance rate, the frequency of task completion, and the frequency of task acceptance of workers by means of weighted summation. Among them, the weights corresponding to the attendance rate, the frequency of task completion, and the frequency of task acceptance can be preset by the user in advance.

[0033] The task area refers to the specific location where construction tasks are carried out. The task area can be divided based on the needs of construction task management. For example, the task area can be divided into different floors such as the 1st floor and the 2nd floor based on space; for another example, it can be divided into an electromechanical construction area, an exterior wall construction area, etc. based on the task type; for another example, the task area can be divided into foundation pit support operations on the B2 floor, construction of the electromechanical equipment room in area C, etc. by integrating multiple conditions. The above divisions of the task area are only examples and do not constitute limitations. Those skilled in the art can also divide the task area in other ways based on needs.

[0034] Task dependency refers to the dependency relationship between different construction tasks. The dependency relationships include a pre - post relationship, a parallel relationship, etc. For example, the foundation work and the construction of the main building structure have a pre - post relationship, and the masonry works carried out synchronously in each building have a parallel relationship, etc.

[0035] The user operation intention refers to the intention of the user to request access to construction task data. For example, the user operation intention may include task assignment (assigning workers to perform specific construction tasks), area inspection (inspecting the progress, quality, etc. of the construction area), task acceptance, etc.

[0036] In some embodiments, the processor may identify the user operation intention based on the user's click, query, and modification behaviors, combined with time and operation type. For example, when the user clicks on or queries a button related to area inspection, it can be determined that the user operation intention is area inspection; for another example, when the user may have a task assignment intention within a period of time after the morning meeting (such as 8:00 - 9:00 in the morning).

[0037] In some embodiments, the processor 140 may obtain multi - dimensional feature factors of multiple construction tasks based on the construction tasks. For example, the processor 140 may obtain a task list related to the construction tasks (such as a project list, construction drawings, personnel list, material list, etc.), and obtain the multi - dimensional feature factors by manual input or from a third - party information source (such as the Internet). In some embodiments, the processor 140 may also store the obtained multi - dimensional feature factors and the corresponding construction tasks in the storage device 150 in the form of a vector database.

[0038] Step 220, determine the storage weight of each construction task based on the multi - dimensional feature factors.

[0039] The storage weight refers to the weight corresponding to the importance or priority when storing the construction task. The larger the storage weight, the higher the importance or priority of the construction task.

[0040] In some embodiments, the storage weight can be preset. For example, the user can manually input the storage weight of each construction task. For another example, the storage weight can be obtained based on a preset weight calculation method.

[0041] Exemplarily, the processor 140 may determine the first weight of the feature factors of each dimension; based on the first weight, weight the multi - dimensional feature factors to determine the storage weight of each construction task.

[0042] The first weight refers to the weight of each dimension factor in the multi - dimensional feature factors. For example, for different dimension feature factors such as task status, construction period, worker activity, task area, task dependency, and user operation intention in the multi - dimensional feature factors, a first weight can be assigned respectively. The first weight can represent the importance or priority of each dimension feature factor. The larger the first weight, the higher the importance or priority of the feature factor. Among them, the first weight can be preset by the user. For example, if the task status and construction period are more important than other dimension feature factors, then the task status and construction period will be given a higher first weight.

[0043] In some embodiments, the processor may perform a weighted sum of the first weight and the feature factors corresponding to the respective dimensions in the multi-dimensional feature factor, and determine the result as the storage weight of the construction task. For example, for a multi-dimensional feature factor Z=(z1, z2, z3, …, z n ) of a certain construction task, the first weights corresponding to the feature factors of each preset dimension are α1, α2, α3, …, α n , respectively. Then, the storage weight W of this construction task is W = z1×α1 + z2×α2 + z3×α3 + … + z n ×α n . Further, the processor may determine the storage weight of each of the multiple construction tasks based on the above method.

[0044] In some embodiments of the present specification, determining the storage weight of each construction task through the first weight can ensure that construction tasks that are more important in terms of feature factors (such as construction period) in critical dimensions obtain higher storage weights, so as to be given priority in resource allocation and data access order, making it more in line with the user's needs and improving the efficiency and effect of construction task management.

[0045] Step 230, cache multiple construction tasks into a multi-level storage space based on the size of the storage weight of each construction task.

[0046] In some embodiments, the processor 140 may directly store multiple construction tasks in the storage device 150.

[0047] In some embodiments, the processor 140 may also store multiple construction tasks into a multi-level storage space according to the storage weight.

[0048] The multi-level storage space refers to different priority queues or database partitions. In some embodiments, the multi-level storage space is used to store construction tasks with different priorities. For example, a higher-level storage space is used to store construction tasks with larger storage weights (i.e., higher importance or priority). Among them, the levels of the multi-level storage space can be divided as needed. For example, it can be divided into a high-speed storage area, a medium-speed storage area, and a low-speed storage area, or it can be divided into other levels of storage spaces (such as a primary storage area, a secondary storage area, a tertiary storage area, a bottom-layer storage area, etc.). Among them, high speed, medium speed, and low speed refer to the storage and reading speeds of data in the storage space. In some embodiments, the multi-level storage space may be a part of the storage device 150.

[0049] In some embodiments of the present specification, caching construction tasks through the multi-level storage space can preferentially ensure the smooth storage and reading of tasks with higher importance or priority, and reduce the demand for storage resources.

[0050] Exemplarily, the processor 140 may divide the storage space into three levels: a high-speed storage area (for storing high-priority construction tasks with storage weights greater than a first threshold), a medium-speed storage area (for storing regular construction tasks with storage weights less than the first threshold and greater than a second threshold), and a low-speed storage area (for storing construction tasks with storage weights less than the second threshold, such as completed and accepted construction tasks). The processor 140 caches the construction task data corresponding to each construction task into the corresponding area in the multi-level storage space according to the storage weight of each construction task. Among them, the construction task data includes all data related to the construction task. For example, construction design drawings, personnel lists, material lists, and various other materials. Among them, the first threshold and the second threshold can be preset. For example, the first threshold can be 0.8, and the second threshold can be 0.5, etc.

[0051] In some embodiments, the high-speed storage area may be a first-level cache area, the medium-speed storage area may be a second-level cache area, and the low-speed storage area may be a disk or a database. Among them, the cache can adopt system local cache, third-party cache (such as Redis), etc.

[0052] In some embodiments, the processor 140 can also monitor the business events of the construction tasks, update the multi-dimensional feature factors and storage weights of the construction tasks when the business events change, and re-cache multiple construction tasks into the multi-level storage space based on the updated storage weights. For more descriptions of related embodiments, see Figure 3 。

[0053] Step 240: Based on the data access request, access sequentially according to the cache priority order of the storage space to obtain the requested construction task data.

[0054] The data access request refers to an instruction sent to the processor to access the data related to the construction task when the user or the system needs to call the relevant information of a specific construction task (such as viewing the details of the drawing, retrieving the progress report, or obtaining the acceptance record).

[0055] In some embodiments, the user can send a data access request to the processor 140 through the terminal device 130.

[0056] In some embodiments, the processor 140 can obtain the data access request sent by the user and directly obtain the construction task data corresponding to the data access request from the storage device 150. For example, when the user sends a request to access the data related to the personnel list of construction task A, the processor 140 can search for the data related to the personnel list of construction task A in the storage device 150 and send the obtained data to the user.

[0057] In some embodiments, the processor 140 may also access the storage space in sequence according to the cache priority order of the storage space based on the data access request to obtain the requested construction task data. For example, the processor 140 may first access the high-level storage space to determine whether there is construction task data corresponding to the data access request; if not, then access the low-level storage space until the construction task data corresponding to the data access request is obtained. For more descriptions of related embodiments, see Figure 5 。

[0058] The construction task cache management method shown in some embodiments of this specification determines the storage weights of each construction task by comprehensively analyzing the multi-dimensional characteristic factors of multiple construction tasks, and caches the construction tasks into the storage spaces with different priorities of the multi-level storage space according to the storage weights of each construction task. It can not only effectively distinguish the priorities of construction tasks, ensure that high-priority tasks are responded and processed faster, but also help improve the management efficiency and execution quality of construction projects, and promote the smooth progress of the project schedule.

[0059] In some embodiments, the processor 140 may also listen for business events related to construction tasks, and when the business events change, re-store the construction tasks into the multi-level storage space.

[0060] Figure 3 is an exemplary flowchart for re-storing construction tasks shown in some embodiments of this specification. In some embodiments, process 300 may be executed by a processing device (e.g., processor 140). As Figure 3 shown, process 300 includes the following steps.

[0061] Step 310, listen for business events related to construction tasks.

[0062] The business events related to construction tasks refer to the key operations or states that occur during the execution of construction tasks and have a direct impact on task execution. For example, business events include one or more of task status, construction period, workers, task area, and user operation intention.

[0063] In some embodiments, the processor 140 may listen for business events related to construction tasks in one or more of the following ways.

[0064] In some embodiments, the processor 140 may listen for business events related to construction tasks through operation logs. For example, the processor 140 may automatically record operations such as status modification and approval flow in the construction task operation logs to listen for business events related to construction tasks.

[0065] In some embodiments, the processor 140 may listen for business events related to construction tasks through Internet of Things devices. For example, the processor 140 may listen for business events related to construction tasks through various cameras, sensors, and other devices installed in the construction area via the Internet of Things.

[0066] In some embodiments, the processor 140 may also listen for business events related to construction tasks through manual input. For example, the processor 140 may listen for business events related to construction tasks through the input of personnel related to the execution of construction tasks, such as construction task managers, patrol personnel, etc.

[0067] Step 320, in response to listening for a business event change, update the multi-dimensional feature factors of multiple construction tasks.

[0068] Business event changes include construction progress changes, construction area changes, user intention changes, etc. The above description of business event changes is only an example. The processor 140 may listen for changes in key operations or states that have a direct impact on task execution as business event changes. For example, changes in construction workers, construction period changes, changes in construction resources (such as construction equipment like cranes, construction materials like concrete), etc.

[0069] In some embodiments, the processor 140 may update the multi-dimensional feature factors of multiple construction tasks based on the changed business event. For example, the processor 140 may replace the corresponding feature factors in the multi-dimensional feature factors with the changed business event to update the multi-dimensional feature factors of multiple construction tasks. Exemplarily, for construction task A, the processor 140 listens for business event changes including changes in construction workers and construction period. The processor 140 may replace the construction period and worker activity of the changed worker in the corresponding multi-dimensional feature factors of construction task A with the changed construction period and worker activity, and update the corresponding multi-dimensional feature factors of construction task A.

[0070] Step 330, based on the updated multi-dimensional feature factors, determine the second storage weight of each construction task.

[0071] The second storage weight refers to the storage weight corresponding to the construction task after a business event change. The second storage weight may correspond to the updated multi-dimensional feature factors.

[0072] In some embodiments, the processor 140 may determine the second weight corresponding to the feature factor of each dimension in the updated multi-dimensional feature factors, and determine the second storage weight corresponding to the construction task based on the second weight corresponding to the feature factor of each dimension. For example, the updated multi-dimensional feature factor Z’ of construction task A = (z’1, z’2, z’3, …, z’ n), the second weights corresponding to the characteristic factors of each preset dimension are α'1, α'2, α'3, …, α' n , then the second storage weight W' of this construction task = z'1×α'1 + z'2×α'2 + z'3×α'3 + … + z' n ×α' n . Further, the processor can determine the second storage weight of each of multiple construction tasks based on the above method.

[0073] In some embodiments, for each construction task, the processor 140 can also assign multiple initial weights to the characteristic factors of each dimension in the corresponding updated multi-dimensional characteristic factor, and determine the second storage weight based on the multiple initial weights. For more descriptions of related embodiments, see Figure 4 .

[0074] Step 340, based on the second storage weight, re-cache multiple construction tasks to the multi-level storage space.

[0075] In some embodiments, the processor 140 can re-cache multiple construction tasks (i.e., the construction tasks with changed business events) to the multi-level storage space based on the second storage weight, the first threshold, and the second threshold. For example. The processor 140 can store the construction tasks with a second storage weight greater than the first threshold in the high-speed storage area; store the construction tasks with a second storage weight less than the first threshold and greater than the second threshold in the medium-speed storage area; store the construction tasks with a second storage weight less than the second threshold in the low-speed storage area. For more descriptions of the first threshold, the second threshold, and the multi-level storage space, see Figure 2 .

[0076] In some embodiments of this specification, by dynamically optimizing the construction task cache distribution through business event listening, the cache policy is more in line with the actual construction scenario requirements, which helps to ensure that important or urgent tasks can quickly respond to query requests when needed.

[0077] Figure 4 is an exemplary flowchart of determining the second storage weight shown in some embodiments of this specification. In some embodiments, the process 400 can be executed by a processing device (for example, the processor 140). As Figure 4 shown, the process 400 includes the following steps.

[0078] Step 410, for each construction task, assign multiple initial weights to each of the corresponding updated multi-dimensional characteristic factors.

[0079] The initial weight refers to the weight assigned to the characteristic factor of each dimension in the updated multi-dimensional characteristic factor.

[0080] In some embodiments, the processor 140 may assign multiple initial weights to the feature factors of each dimension in the updated multi-dimensional feature factors corresponding to each construction task. Taking construction task A as an example, the updated multi-dimensional feature factors of construction task A are Z'=(z'1, z'2, …, z' n ), and the processor 140 may randomly assign multiple initial weights {a'1, b'1, c'1, …}, {a'2, b'2, c'2, …}, …, {a' n respectively and randomly to the feature factors z'1, z'2, …, z' n , b' n , c' n , …} of each dimension of the multi-dimensional feature factors Z', and obtain the weighted feature factor sets {z'1a'1, z'1b'1, z'1c'1, …}, {z'2a'2, z'2b'2, z'2c'2, …}, …, {z' n a' n , z' n b' n , z' n c' n , …} after the initial weights are assigned.

[0081] Step 420: Combine the updated multi-dimensional feature factors based on the assigned initial weights to obtain multiple weight assignment schemes corresponding to the construction task.

[0082] A weight assignment scheme refers to a scheme for assigning the initial weights of a construction task. Each weight assignment scheme represents a specific initial weight assignment method and determines the influence of each feature factor on the overall construction task priority and storage weight under this scheme.

[0083] In some embodiments, the processor 140 may combine the updated multi-dimensional feature factors based on the assigned initial weights to obtain multiple weight assignment schemes corresponding to the construction task. For example, for the weighted feature factor sets {z'1a'1, z'1b'1, z'1c'1, …}, {z'2a'2, z'2b'2, z'2c'2, …}, …, {z' n a' n , z' n b' n , z' n c' n , …} obtained in step 410, the processor 140 may extract one feature factor with an assigned initial weight from each weighted feature factor set and obtain multiple weight assignment schemes corresponding to the construction task through combination. For example, the obtained weight assignment schemes may include P1=(z'1a'1, z'2a'2, …, z' n a' n)、P2 = (z’1b’1, z’2a’2, …, z’ n a’ n ) etc. In some embodiments, the initial weights of at least one feature factor are different in any two of the multiple weight assignment schemes. For example, in weight assignment scheme P1 and weight assignment scheme P2, the initial weight of feature factor z’1 is different.

[0084] Step 430, screen the multiple weight assignment schemes to obtain a first set.

[0085] The first set refers to the set obtained by screening the multiple weight assignment schemes.

[0086] In some embodiments, the processor 140 can obtain the first set by screening the multiple weight assignment schemes in various ways.

[0087] In some embodiments, the processor 140 can preset the initial weight range corresponding to each feature factor, and eliminate the assignment schemes whose initial weights exceed the initial weight range. The remaining weight assignment schemes constitute the first set. For example, it can be preset that the initial weight range corresponding to the feature factor "construction period" is 0.4 - 0.6. Then, the processor 140 can eliminate the weight assignment schemes in which the initial weight corresponding to "construction period" is not within the range of 0.4 - 0.6. Similarly, further elimination and screening are performed based on the feature factors of other dimensions (such as worker activity, task status, etc.), and the finally remaining weight assignment schemes are determined as the first set.

[0088] In some embodiments, for the construction tasks corresponding to the updated multi-dimensional feature factors, the processor 140 can assign a reference storage weight to them. In some embodiments, the processor 140 can calculate the similarity between the initial storage weight of each weight assignment scheme and the reference storage weight, and eliminate the weight assignment schemes whose similarity is lower than the threshold. The remaining weight assignment schemes are determined as the first set. Among them, the similarity includes cosine similarity or Euclidean distance, etc.

[0089] In some embodiments, the reference storage weight can be determined based on expert experience. In some embodiments, the reference storage weight can also be the storage weight corresponding to the construction tasks before the update of the multi-dimensional feature factors.

[0090] The initial storage weight refers to the storage weight corresponding to the weight assignment scheme. The initial storage weight represents the priority of the construction task under the initial weights corresponding to the weight assignment scheme. In some embodiments, the initial storage weight can be determined based on the initial weights and the updated multi-dimensional feature factors. For example, weight assignment scheme P1 = (z’1a’1, z’2a’2, …, z’ n a’ nThe initial stored weight W p1 = z’1a’1 + z’2a’2 + … + z’ n a’ n .

[0091] In some embodiments of the present specification, obtaining the first set through a screening operation can filter out some abnormal or overly deviated weight allocation schemes, reduce the amount of data for subsequent processing, improve data processing efficiency, and save computing resources.

[0092] Step 440, perform a random adjustment operation on the initial weights corresponding to each weight allocation scheme in the first set to generate a second set.

[0093] The second set is a set composed of weight allocation schemes obtained by randomly adjusting the initial weights corresponding to each weight allocation scheme in the first set.

[0094] In some embodiments, for each weight allocation scheme in the first set, the processor 140 can randomly perturb each initial weight in the weight allocation scheme within a preset range (such as ±0.05, ±0.1, ±0.2, etc.) to generate a new weight allocation scheme, forming the second set. For example, for the weight allocation scheme P1 = (z’1a’1, z’2a’2, …, z’ n a’ n ), the processor 140 can randomly perturb each of the initial weights a’1, a’2, …, a’ n respectively within the range of ±0.05 to generate multiple new weight allocation schemes. In some embodiments, for the newly generated weight allocation schemes, the processor 140 can continue to randomly perturb each of the initial weights within the preset range to obtain a set composed of more and richer weight allocation schemes.

[0095] In some embodiments, the first set can be a part of the second set.

[0096] In some embodiments, by performing a random operation on the first set to generate the second set, more and richer weight allocation schemes can be obtained, providing more potential and better solutions for subsequent determination of the second stored weight, improving the accuracy of the subsequently determined second stored weight, and obtaining better storage and access effects for construction task data.

[0097] Step 450, using a machine learning model, based on the multi-dimensional feature factors before update, the multi-dimensional feature factors after update, and the total progress of the construction task, determine the target weight allocation scheme from the second set.

[0098] The machine learning model can include a neural network model, a deep neural network model, etc.

[0099] In some embodiments, the input of the machine learning model includes the multi-dimensional feature factors before the update, the multi-dimensional feature factors after the update, the total progress of the construction tasks, and the second set, and the output includes the target weight allocation scheme.

[0100] The target weight allocation scheme is a weight allocation scheme selected from the second set that can be used to determine the second storage weight.

[0101] The machine learning model can be obtained through training. The training samples include the sample multi-dimensional feature factors before the update, the sample multi-dimensional feature factors after the update, the total progress of the sample construction tasks, and the sample second set, and the labels include the target weight allocation scheme. In some embodiments, the training samples can be obtained from historical construction data, and the labels can be manually annotated.

[0102] In some embodiments of the present specification, by using the machine learning model, based on the multi-dimensional feature factors before the update, the multi-dimensional feature factors after the update, and the total progress of the construction tasks, the target weight allocation scheme is determined from the second set, which can efficiently and accurately determine the target weight allocation scheme, make it more in line with the true priority of the construction tasks after the business event change, and improve the accuracy and intelligence of the construction task storage management process.

[0103] Step 460, determine the second storage weight of the construction task based on the target weight allocation scheme.

[0104] In some embodiments, the processor 140 can perform a weighted sum of each feature factor in the target weight allocation scheme through its corresponding weight, and determine the result as the second storage weight. For example, for the construction task B after the business event change, the target weight allocation scheme P m =(z’1T1, z’2T2,..., z’ n T n ), then the processor 140 can determine the second storage weight W of the construction task B after the business event change B =z’1T1 + z’2T2 +... + z’ n T n .

[0105] In some embodiments, the processor 140 can also determine the vector distance between the multi-dimensional feature factors before the update and the multi-dimensional feature factors after the update. Among them, the vector distance can include cosine distance, Euclidean distance, Manhattan distance, Chebyshev distance, etc.

[0106] In some embodiments, the processor 140 can also determine the degree of change in the storage weight from a preset database based on the vector distance.

[0107] The degree of change in the stored weight refers to the magnitude of the change in the stored weight of the corresponding construction task before and after the update of the multi-dimensional feature factor. For example, the degree of change in the stored weight can be an increase of 0.5, a decrease of 0.4, etc.

[0108] The preset database stores a large number of corresponding relationships between the vector distances of the multi-dimensional feature factors before and after the update and the degree of change in the stored weight. The processor 140 can obtain the degree of change in the stored weight by querying the preset database based on the vector distance.

[0109] In some embodiments, the processor 140 can determine the second stored weight of the construction task from the second set based on the degree of change in the stored weight. For example, the processor 140 can also input the degree of change in the stored weight into the machine learning model to obtain the target weight allocation scheme, and determine the second stored weight based on the target weight allocation scheme by the method described in the above embodiments. Correspondingly, the training samples of the machine learning model can also include the sample degree of change in the stored weight.

[0110] In some embodiments of this specification, by determining the change in the stored weight through the vector distance of the multi-dimensional feature factors before and after the update and inputting it into the machine learning model, the impact of the change in the multi-dimensional feature factor on the stored weight can be clarified based on more abundant historical data, further improving the accuracy of the second stored weight.

[0111] In some embodiments of this specification, when it is monitored that the business event of the construction task changes, updating the multi-dimensional feature factor and the stored weight of the construction task accordingly ensures that the stored weight of the construction task can reflect its latest importance and urgency, facilitating the storage and query of construction task data, and having a good construction task data management effect.

[0112] Figure 5 It is an exemplary flowchart of obtaining construction task data shown in some embodiments of this specification. In some embodiments, the process 500 can be executed by a processing device (for example, the processor 140). As Figure 5 shown, the process 500 includes the following steps.

[0113] Step 510, based on the data access request, access the upper-level storage space.

[0114] The upper-level storage space refers to a storage space with a higher priority. That is, the upper-level storage space is a relative concept. Taking the multi-level storage space including a high-speed storage area, a medium-speed storage area, and a low-speed storage area as an example, the high-speed storage area is the upper-level storage space of the medium-speed storage area, and the medium-speed storage area is the upper-level storage space of the low-speed storage area.

[0115] In some embodiments, after receiving a data access request, the processor 140 may first access the requested construction task data in the storage space with the highest priority (i.e., the upper-level storage space). For example, the processor 140 may first access the requested construction task data in the high-speed storage area.

[0116] Step 520: Determine whether the construction task data is accessed in the upper-level storage space.

[0117] In some embodiments, if the processor 140 accesses the construction task data from the upper-level storage space, it returns the construction task data to the user. For example, the processor 140 may send the accessed construction task data to the terminal 130, and the user can obtain the requested construction task data through the terminal 130.

[0118] In some embodiments, if the processor 140 does not access the construction task data from the upper-level storage space, it executes step 530 to access the lower-level storage space.

[0119] Step 530: Access the lower-level storage space.

[0120] The lower-level storage space refers to other storage spaces except the storage space with the highest priority. That is, the lower-level storage space is a relative concept. Taking a multi-level storage space including a high-speed storage area, a medium-speed storage area, and a low-speed storage area as an example, the low-speed storage area is the lower-level storage space of the medium-speed storage area, and the medium-speed storage area is the lower-level storage space of the high-speed storage area.

[0121] In some embodiments, if the processor 140 does not access the requested construction task data in the upper-level storage space, it may access the requested construction task data in the lower-level storage space of the next level. For example, if the processor 140 does not access the requested construction task data in the high-speed storage area, it may access the requested construction task data in the lower-level storage space of the high-speed storage area, i.e., the medium-speed storage area.

[0122] Step 540: Determine whether the construction task data is accessed in the lower-level storage space.

[0123] In some embodiments, if the processor 140 accesses the construction task data from the lower-level storage space, it returns the construction task data to the user. For example, the processor 140 may send the accessed construction task data to the terminal 130, and the user can obtain the requested construction task data through the terminal 130.

[0124] In some embodiments, if the processor 140 does not access the construction task data from the lower-level storage space, it executes step 550 to access the even lower-level storage space.

[0125] Step 550: Access the lower-level storage space.

[0126] In some embodiments, if the processor 140 fails to access the requested construction task data in the lower-level storage space, it can access the requested construction task data in the even lower-level storage space. For example, if the processor 140 fails to access the requested construction task data in the medium-speed storage area, it can access the requested construction task data in the lower-level storage space of the medium-speed storage area, i.e., the low-speed storage area.

[0127] In some embodiments, if the processor 140 accesses the construction task data from the lower-level storage space, it returns the construction task data to the user. For example, the processor 140 can send the accessed construction task data to the terminal 130, and the user can obtain the requested construction task data through the terminal 130.

[0128] In some embodiments, if the processor 140 fails to access the construction task data from the lower-level storage space, it continues to access the even lower-level storage space until it accesses the construction task data or reaches the lowest-level storage space.

[0129] Step 560, access the lowest-level storage space.

[0130] The lowest-level storage space is the storage space with the lowest priority. For example, the lowest-level storage space can be a low-speed storage space, etc.

[0131] In some embodiments, if the processor 140 accesses the construction task data from the lowest-level storage space, it returns the construction task data to the user. For example, the processor 140 can send the accessed construction task data to the terminal 130, and the user can obtain the requested construction task data through the terminal 130.

[0132] In some embodiments, if the processor 140 fails to access the construction task data from the lowest-level storage space, it ends the query of the construction task data, returns an empty result to the user, indicating that there is no construction task data requested by the user, and ends the process. That is, when obtaining the construction task data requested by the user, the processor 140 can sequentially access each level of the multi-level storage space based on the cache priority order of the storage space until it accesses the construction task data requested by the user or reaches the lowest-level storage space.

[0133] In some embodiments of this specification, searching for construction task data level by level from the multi-level storage space ensures both efficiency and comprehensiveness in the data access process, enabling high-frequency or highly important construction task data to be quickly accessed, while also ensuring that even if the data storage location is relatively low, it can still be effectively retrieved.

[0134] In some embodiments, when construction task data is accessed from a lower-level storage space (i.e., a storage space that does not have the highest priority), the processor 140 may store the construction task data in the storage space at the next higher level than the lower-level storage space.

[0135] In some embodiments, the construction task data obtained by the user indicates that the construction data may be accessed more frequently, and the corresponding storage priority needs to be increased. The processor 140 may store the construction task data accessed from the lower-level storage space in the storage space at the next higher level than the lower-level storage space.

[0136] Exemplarily, if the construction task data is obtained from the low-speed storage area, the processor 140 may re-store the construction task data from the low-speed storage area to the medium-speed storage area; if the construction task data is obtained from the medium-speed storage area, the processor 140 may re-store the construction task data from the medium-speed storage area to the high-speed storage area.

[0137] In some embodiments, when construction task data is accessed from a lower-level storage space (i.e., a storage space that does not have the highest priority), the processor 140 may store the construction task data in the storage space at the next higher level than the lower-level storage space; based on the current progress of the construction task, determine the first storage weight of the construction task; determine whether the storage space at the next higher level matches the first storage weight; according to the determination result, determine whether to continue to adjust the storage space of the construction task data. For more descriptions of related embodiments, see Figure 6 。

[0138] Figure 6 is an exemplary flowchart for determining whether to adjust the storage space of construction task data according to some embodiments of this specification. In some embodiments, process 600 may be executed by a processing device (e.g., processor 140). As Figure 6 shown, process 600 includes the following steps.

[0139] Step 610, when construction task data is accessed from a lower-level storage space, store the construction task data in the storage space at the next higher level than the lower-level storage space.

[0140] The storage space at the next higher level refers to the previous storage space in the multi-level storage space whose priority is higher than the current storage space of the construction task. For more descriptions of the lower-level storage space, see Figure 5 ,such as step 530.

[0141] Taking a multi - level storage space including a high - speed storage area, a medium - speed storage area, and a low - speed storage area as an example, when construction task data is accessed from the medium - speed storage area, the processor 140 stores the construction task data in the high - speed storage area (i.e., the upper - level storage space of the medium - speed storage space); when construction task data is accessed from the low - speed storage area, the processor 140 stores the construction task data in the medium - speed storage area (i.e., the upper - level storage space of the low - speed storage space).

[0142] Step 620: Determine the first storage weight of the construction task based on the current progress of the construction task.

[0143] The first storage weight is the storage weight after change as the construction task is executed.

[0144] In some embodiments, after storing the construction task data in the upper - level storage space of the lower - level storage space, the processor 140 can determine the change situation of the corresponding multi - dimensional feature factors as the construction task is executed, and update the multi - dimensional feature factors based on a method similar to that in step 320. For example, the processor 140 can replace the corresponding feature factors in the multi - dimensional feature factors with the changed feature factors to update the multi - dimensional feature factors of the construction task. Exemplarily, for construction task A, the processor 140 determines the changes in its construction workers and construction period. The processor 140 can replace the construction period and the worker activity of the changed worker in the corresponding multi - dimensional feature factors of construction task A with the changed construction period and the worker activity, respectively, to update the multi - dimensional feature factors corresponding to construction task A. Among them, the change situation of the multi - dimensional feature factors can be determined based on various methods, such as through user input, Internet of Things devices, etc.

[0145] In some embodiments, the processor 140 can determine the third weight corresponding to each dimension of the feature factors in the updated multi - dimensional feature factors, and determine the first storage weight corresponding to the construction task based on the third weight corresponding to each dimension of the feature factors. For example, for the updated multi - dimensional feature factors X=(X1, X2, X3,..., X n ) of construction task C, the preset third weights corresponding to each dimension of the feature factors are β1, β2, β3,..., β n , then the first storage weight W C of this construction task C = X1×β1 + X2×β2 + X3×β3+...+X n ×β n . In some embodiments, the processor 140 can also determine the first storage weight in a method similar to that for determining the second storage weight.

[0146] Step 630: Determine whether the upper - level storage space matches the first storage weight.

[0147] In some embodiments, the processor 140 may determine whether the upper-level storage space matches the first storage weight based on a first threshold and a second threshold. For more descriptions of the first threshold and the second threshold, reference may be made to Figure 2 .

[0148] Taking the multi-level storage space including a high-speed storage area, a medium-speed storage area, and a low-speed storage area as an example, the construction task B was originally stored in the medium-speed storage area. After the processor 140 obtains the construction task data related to the construction task B, it stores it in the upper-level storage space (i.e., the high-speed storage area). Then, the processor 140 may determine the first storage weight of the construction task B based on the current progress of the construction task B, and determine whether the first storage weight of the construction task B matches the upper-level storage space (i.e., the high-speed storage area). For example, if the first storage weight of the construction task B is greater than the first threshold, the upper-level storage space matches the first storage weight; for another example, if the first storage weight of the construction task B is less than the first threshold, the upper-level storage space does not match the first storage weight.

[0149] Continuing with the foregoing example of the multi-level storage space, assuming that the construction task B was originally stored in the low-speed storage area, after the processor 140 obtains the construction task data related to the construction task B, it stores it in the upper-level storage space (i.e., the medium-speed storage area). Then, the processor 140 may determine whether the first storage weight of the construction task B matches the upper-level storage space (i.e., the medium-speed storage area) based on the current progress of the construction task B. For example, if the first storage weight of the construction task B is greater than the first threshold, the upper-level storage space does not match the first storage weight; if the first storage weight of the construction task B is less than the first threshold and greater than the second threshold, the upper-level storage space matches the first storage weight; if the first storage weight of the construction task B is less than the second threshold, the upper-level storage space does not match the first storage weight.

[0150] Step 640, according to the judgment result, determine whether to continue to adjust the storage space of the construction task data.

[0151] In some embodiments, when the judgment result is that the upper-level storage space matches the first storage weight, the processor 140 does not need to adjust the storage space of the construction task data. In some embodiments, when the judgment result is that the upper-level storage space does not match the first storage weight, the storage space of the construction task data is continuously adjusted until the storage space matches the first storage weight. For example, the processor 140 may continue to adjust the storage space of the construction task data based on the first storage weight by a method similar to step 230 until the storage space matches the first storage weight.

[0152] In some embodiments, the processor 140 may also determine the storage space for construction tasks based on the access frequency. For example, each time a construction task is accessed, the processor 140 may update the access frequency record of the task. If the access frequency exceeds a set threshold, the corresponding construction task is adjusted to a storage space with a higher priority.

[0153] In some embodiments, the processor 140 may also store inactive data in a storage space with a lower priority. For example, if a construction task has not been accessed for a long time (such as more than half a year, one year, etc.), the processor 140 may determine it as inactive data and adjust it to a storage space with a lower priority.

[0154] In some embodiments of this specification, by adjusting the storage space of construction tasks, the storage priority of the construction task data that is actually accessed can be improved for faster future access, and it is ensured that the construction task data can be reasonably distributed in the multi-level storage system according to its latest evaluated importance, thereby optimizing the overall data management and access efficiency.

[0155] It should be noted that the above descriptions of processes 200, 300, 400, 500, and 600 are only for illustration and explanation, and do not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to processes 200, 300, 400, 500, and 600 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0156] Figure 7 is an exemplary module diagram of a construction task cache management system according to some embodiments of this specification. As Figure 7 shown, the construction task cache management system 700 includes an acquisition module 710, a determination module 720, a cache module 730, and a data access module 740.

[0157] The acquisition module 710 is configured to acquire multi-dimensional feature factors of a plurality of construction tasks. In some embodiments, the multi-dimensional feature factors include one or more of task status, construction period, worker activity, task area, task dependency, and user operation intention.

[0158] The determination module 720 is configured to determine the storage weight of each of the construction tasks based on the multi-dimensional feature factors.

[0159] In some embodiments, the determination module 720 is further configured to determine a first weight for the feature factors of each dimension; based on the first weight, weight the multi-dimensional feature factors to determine the storage weight of each of the construction tasks.

[0160] In some embodiments, the determination module 720 is further configured to, for each of the construction tasks, assign multiple initial weights to each of the corresponding updated multi-dimensional feature factors; combine the updated multi-dimensional feature factors based on the assigned initial weights to obtain multiple weight assignment schemes corresponding to the construction task; wherein, any two weight assignment schemes among the multiple weight assignment schemes have different initial weights for at least one feature factor; screen the multiple weight assignment schemes to obtain a first set; perform a random adjustment operation on the initial weights corresponding to each weight assignment scheme in the first set to generate a second set; use a machine learning model to determine a target weight assignment scheme from the second set based on the multi-dimensional feature factors before update, the updated multi-dimensional feature factors, and the total progress of the construction task; and determine the second storage weight of the construction task based on the target weight assignment scheme.

[0161] In some embodiments, the determination module 720 is further configured to determine the vector distance between the multi-dimensional feature factors before update and the updated multi-dimensional feature factors; determine the degree of change in the storage weight from a preset database based on the vector distance; and determine the second storage weight of the construction task from the second set based on the degree of change in the storage weight.

[0162] The cache module 730 is configured to cache multiple construction tasks into a multi-level storage space based on the size of the storage weight of each construction task; wherein, the multi-level storage space is used to store the construction tasks with different priorities.

[0163] In some embodiments, the cache module 730 is further configured to determine whether to adjust the storage space of the construction task when the construction task data is accessed from the lower-level storage space.

[0164] In some embodiments, the cache module 730 is further configured to determine the first storage weight of the construction task based on the current progress of the construction task; determine whether the upper-level storage space matches the first storage weight; and determine whether to adjust the storage space of the construction task according to the determination result.

[0165] In some embodiments, the cache module 730 is further configured to monitor business events related to the construction task; update the multi-dimensional feature factors of the multiple construction tasks in response to monitoring a change in the business event; determine the second storage weight of each construction task based on the updated multi-dimensional feature factors; and re-cache the multiple construction tasks into the multi-level storage space based on the second storage weight.

[0166] The data access module 740 is configured to access in sequence according to the cache priority order of the storage space based on the data access request, so as to obtain the requested construction task data.

[0167] In some embodiments, the data access module 740 is further configured to access the upper-level storage space based on the data access request; if the construction task data is accessed from the upper-level storage space, the construction task data is returned; if the construction task data is not accessed from the upper-level storage space, the lower-level storage space is accessed until the construction task data is accessed.

[0168] It should be noted that the above description of the construction task cache management system 700 and its modules is only for convenience of description, and does not limit this specification to the scope of the examples given. In some embodiments, Figure 7 The acquisition module 710, the determination module 720, the cache module 730, and the data access module 740 disclosed in may be different modules in a system, or a module may implement the functions of two or more of the above modules. For example, each module may share a storage module, or each module may have its own storage module respectively. Such variations are all within the protection scope of this specification.

[0169] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification.

[0170] At the same time, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment.

[0171] It should be noted that in order to simplify the expression disclosed in this specification and thus help the understanding of one or more embodiments, in the previous description of the embodiments of this specification, sometimes multiple features are merged into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the object of this specification are more than those mentioned in the claims. In fact, the features of the embodiment are less than all the features of the single embodiment disclosed above.

Claims

1. A construction task cache management method, the method comprising: Obtaining multi-dimensional feature factors of multiple construction tasks; Determining a storage weight for each of the construction tasks based on the multi-dimensional feature factors; Caching the multiple construction tasks into a multi-level storage space based on the storage weight size of each construction task; wherein the multi-level storage space is used to store the construction tasks with different priorities; Based on a data access request, accessing in sequence according to the cache priority order of the storage spaces to obtain the requested construction task data.

2. The method according to claim 1, wherein the multi-dimensional feature factors include one or more of task status, construction period, worker activity, task area, task dependency, and user operation intention.

3. The method according to claim 1, wherein determining the storage weight for each construction task based on the multi-dimensional feature factors includes: Determining a first weight for the feature factors of each dimension; Based on the first weight, weighting the multi-dimensional feature factors to determine the storage weight for each construction task.

4. The method according to claim 1, wherein accessing in sequence according to the cache priority order of the storage spaces based on the data access request to obtain the requested construction task data includes: Based on the data access request, accessing the upper-level storage space; If the construction task data is accessed from the upper-level storage space, returning the construction task data; If the construction task data is not accessed from the upper-level storage space, accessing the lower-level storage space until the construction task data is accessed.

5. The method according to claim 4, the method further comprising: When the construction task data is accessed from the lower-level storage space, storing the construction task data into the upper-level storage space of the lower-level storage space; Determining a first storage weight for the construction task based on the current progress of the construction task; Judging whether the upper-level storage space matches the first storage weight; According to the judgment result, determining whether to continue to adjust the storage space of the construction task.

6. The method according to claim 1, the method further comprising: Listening for business events related to the construction task; In response to the monitored change of the business event, updating the multi-dimensional feature factors of the multiple construction tasks; Determining a second storage weight for each construction task based on the updated multi-dimensional feature factors; Based on the second storage weight, re-caching the multiple construction tasks into the multi-level storage space.

7. The method according to claim 6, wherein determining the second storage weight for each construction task based on the updated multi-dimensional feature factors includes: For each construction task, assigning multiple initial weights to each of the corresponding updated multi-dimensional feature factors; Based on the assigned initial weights, combining the updated multi-dimensional feature factors to obtain multiple weight assignment schemes corresponding to the construction task; wherein any two of the multiple weight assignment schemes have at least one initial weight of a feature factor different; Screen the multiple weight assignment schemes to obtain a first set; Perform a random adjustment operation on the initial weights corresponding to each weight assignment scheme in the first set to generate a second set; Using a machine learning model, based on the multi-dimensional feature factors before update, the multi-dimensional feature factors after update, and the total construction task progress, determine a target weight assignment scheme from the second set; Determine the second storage weight of the construction task based on the target weight assignment scheme.

8. The method according to claim 7, wherein the method further comprises: Determine the vector distance between the multi-dimensional feature factors before update and the multi-dimensional feature factors after update; Determine the degree of change in storage weight from a preset database based on the vector distance; Determine the second storage weight of the construction task from the second set based on the degree of change in storage weight.

9. A construction task cache management system, the system comprising: An acquisition module configured to acquire multi-dimensional feature factors of multiple construction tasks; A determination module configured to determine the storage weight of each construction task based on the multi-dimensional feature factors; A cache module configured to cache the multiple construction tasks into a multi-level storage space based on the storage weight of each construction task; wherein the multi-level storage space is used to store construction tasks with different priorities; A data access module configured to perform access in sequence according to the cache priority order of the storage space based on a data access request to obtain the requested construction task data.

10. A construction task cache management device, characterized in that, The device comprises: at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is used to execute at least part of the computer instructions to implement the construction task cache management method according to any one of claims 1 to 8.

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