Big data model-based non-abandoned project management system and method

Abstract

The invention discloses a big data model-based non-missing item management system and method, and relates to the technical field of non-missing item management, a quality value of each initial scheduling scheme is calculated through a quality function, and a final scheduling scheme is obtained after a part of the initial scheduling scheme is reserved according to the quality value and randomized. And establishing a scheme set for all the final scheduling schemes, repeatedly carrying out quality value calculation, scheme retention and randomization operation steps on the final scheduling schemes in the scheme set for iteration, and outputting all the scheme sets when a convergence condition is met. And selecting the final scheduling scheme with the maximum quality value in all the scheme sets to manage non-abandoned items in the region. The management system can automatically adapt to changes of environments and requirements, the flexibility and responsiveness of a scheduling scheme are improved, and then the management efficiency of regional non-abandoned projects is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intangible cultural heritage project management, and particularly relates to a management system and method for intangible cultural heritage projects based on a big data model. Background Art

[0002] The intangible cultural heritage project management system is a comprehensive management platform designed to effectively protect, inherit, and carry forward intangible cultural heritage (referred to as "intangible cultural heritage" for short). The core purpose of this system is to use modern information technology means to help government agencies, cultural departments, scientific research institutions, and all sectors of society better manage intangible cultural heritage resources, promote the sustainable development of intangible cultural heritage projects, and ensure the effective inheritance and promotion of intangible cultural heritage in modern society.

[0003] The existing technologies have the following deficiencies:

[0004] 1. In the existing technologies, the resource allocation and scheduling of intangible cultural heritage projects usually rely on simple rules or fixed empirical rules. Although this method can operate in some cases, when faced with multiple intangible cultural heritage projects, complex priority rankings, and the allocation of multiple resources, it fails to dynamically adjust resource allocation and activity arrangements, and it is often difficult to achieve the optimal or near-optimal state.

[0005] 2. When the existing systems face complex and changeable project requirements, they often adopt preset scheduling strategies and cannot be adjusted in real time. When performing personalized scheduling for different types of intangible cultural heritage projects in a region, it is often difficult to accurately allocate resources according to the actual needs of different projects, resulting in resource waste and conflicts.

[0006] Based on this, the present invention proposes a management system and method for intangible cultural heritage projects based on a big data model, which can automatically adapt to changes in the environment and requirements, improve the flexibility and responsiveness of the scheduling scheme, and thus improve the management efficiency of regional intangible cultural heritage projects. Summary of the Invention

[0007] The purpose of the present invention is to provide a management system and method for intangible cultural heritage projects based on a big data model to solve the deficiencies in the background art.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A management method for intangible cultural heritage projects based on a big data model, the management method comprising the following steps:

[0009] The management system obtains information on intangible cultural heritage projects in the region through the API interface of the regional platform, and after obtaining the historical data of each intangible cultural heritage project through the big database, generates a priority index for each intangible cultural heritage project based on the big data model;

[0010] Generate the initial scheduling plan by combining the priority index and regional resource information, and use the Python tool to spread several alternative scheduling plans based on the initial scheduling plan. Mark both the initial scheduling plan and several alternative scheduling plans as the initial scheduling plans;

[0011] Calculate the quality value of each initial scheduling plan through the quality function, and retain some initial scheduling plans according to the quality value. After performing randomization operations, obtain the final scheduling plan. Establish a plan set for all final scheduling plans, and repeat the steps of calculating the quality value, retaining the plan, and performing randomization operations on the final scheduling plans in the plan set for iteration. When the convergence condition is met, output all plan sets, and select the final scheduling plan with the largest quality value in all plan sets to manage the intangible cultural heritage projects in the region.

[0012] In a preferred embodiment, after obtaining the historical data of each intangible cultural heritage project through the large database, generate a priority index for each intangible cultural heritage project based on the big data model, including the following steps:

[0013] Obtain the historical data of each intangible cultural heritage project through the large database. The historical data includes the pedestrian flow index, project success rate, and deadline urgency index;

[0014] Substitute the pedestrian flow index, project success rate, deadline urgency index, and all intangible cultural heritage projects into the big data model. After the big data model calculates and obtains the relative proximity of each intangible cultural heritage project, output the priority index of each intangible cultural heritage project according to the relative proximity.

[0015] In a preferred embodiment, substitute the pedestrian flow index, project success rate, deadline urgency index, and all intangible cultural heritage projects into the big data model. Assume that there are m intangible cultural heritage projects in the region, and construct a decision matrix X based on the intangible cultural heritage projects and historical data as follows: In the formula, x ij represents the value of the i-th intangible cultural heritage project on the j-th historical data. Standardize the data in the decision matrix into dimensionless data. The standardization formula is: In the formula, y ij represents the standardized value of the i-th intangible cultural heritage project on the j-th historical data, x ij represents the value of the i-th intangible cultural heritage project on the j-th historical data, and m is the number of intangible cultural heritage projects; calculate the positive solution and negative solution, and calculate the distance between each intangible cultural heritage project and the positive solution and negative solution. Calculate the relative proximity based on the distance between the intangible cultural heritage project and the positive solution and negative solution, and calculate the priority index of each intangible cultural heritage project based on the relative proximity of the intangible cultural heritage project. The expression is: In the formula, SR iis the priority index of the i-th intangible cultural heritage project, and m is the number of intangible cultural heritage projects.

[0016] In a preferred embodiment, the positive solution and the negative solution are calculated, and the expression is:

[0017] In the formula, A + is the positive solution, A - is the negative solution, max(*) represents taking the maximum value, min(*) represents taking the minimum value, and y i1 represents the pedestrian flow index of the i-th intangible cultural heritage project, and y i2 represents the project success rate of the i-th intangible cultural heritage project, and y i3 represents the deadline urgency index of the i-th intangible cultural heritage project.

[0018] In a preferred embodiment, the distances of each intangible cultural heritage project from the positive solution and the negative solution are calculated, and the expression is:

[0019] In the formula, represents the distance of the i-th intangible cultural heritage project from the positive solution, represents the distance of the i-th intangible cultural heritage project from the negative solution, and y ij represents the standardized value of the i-th intangible cultural heritage project on the j-th historical data, represents the positive solution value of the j-th historical data, represents the negative solution value of the j-th historical data;

[0020] The relative closeness is calculated based on the distances of the intangible cultural heritage project from the positive solution and the negative solution, and the expression is:

[0021] In the formula, C i is the relative closeness of the i-th intangible cultural heritage project.

[0022] In a preferred embodiment, an initial scheduling plan is generated by combining the priority index and the regional resource information, including the following steps:

[0023] The content of the initial scheduling plan includes fund allocation, management personnel allocation, and the time priority of activity arrangement;

[0024] Mark the amount of funds in the regional resources as A and the number of management personnel as B. Then, the initial allocated funds and the initial allocated number of management personnel generated by each intangible cultural heritage project according to the priority index are respectively: In the formula, a i is the initial allocated funds of the i-th intangible cultural heritage project, is the initial allocated number of management personnel of the i-th intangible cultural heritage project, and SR i is the priority index of the i-th intangible cultural heritage project;

[0025] After sorting all the intangible cultural heritage items in descending order according to the priority index, a project ranking list is generated, and a schedule for all intangible cultural heritage items is generated in ascending order according to the project ranking list.

[0026] In a preferred embodiment, the quality value of each initial scheduling scheme is calculated through a quality function, including the following steps:

[0027] Obtain the allocation conflict rate and constraint violation rate of each initial scheduling scheme, and substitute the allocation conflict rate and constraint violation rate into the quality function to calculate and obtain the quality value. The expression is: In the formula, zl is the quality value, θ is the allocation conflict rate, is the constraint violation rate, α and β are the proportionality coefficients of the allocation conflict rate and the constraint violation rate respectively, and both α and β are greater than 0.

[0028] In a preferred embodiment, a partial initial scheduling scheme is retained according to the quality value and randomized to obtain a final scheduling scheme, including the following steps:

[0029] Sort all the initial scheduling schemes in descending order according to the quality value to generate a scheme list, select and retain G initial scheduling schemes in ascending order according to the scheme list, where G is a quantity threshold, and after randomly perturbing the capital allocation amount and the management personnel component of all the retained initial scheduling schemes, obtain the final scheduling scheme, and establish a scheme set for all the final scheduling schemes;

[0030] Repeat the steps of calculating the quality value, retaining the scheme, and randomizing the operation for the final scheduling scheme in the scheme set for iteration. When the number of iterations is equal to the number threshold or there is a final scheduling scheme with a quality value greater than or equal to the quality threshold in any scheme set, it is determined that the convergence condition is met, output all the scheme sets, and select the final scheduling scheme with the largest quality value in all the scheme sets to manage the intangible cultural heritage items in the region.

[0031] In a preferred embodiment, the acquisition logic of the pedestrian flow index is: after obtaining the pedestrian flow at multiple historical time points of the intangible cultural heritage project, calculate the pedestrian flow mean value and the pedestrian flow standard deviation based on the pedestrian flow at multiple time points, and calculate and obtain the pedestrian flow index according to the pedestrian flow mean value and the pedestrian flow standard deviation. The expression is: In the formula, y 1 is the pedestrian flow index, μ is the pedestrian flow mean value, and σ is the pedestrian flow standard deviation;

[0032] The calculation logic of the project success rate is: obtain the total number of historical handling times and the total number of successful times of the intangible cultural heritage project, and obtain the project success rate by dividing the total number of successful times by the total number of handling times;

[0033] The calculation logic of the deadline urgency index is as follows: obtain the deadline of the intangible cultural heritage project, calculate the remaining time by subtracting the current time from the deadline, obtain the number of unfinished tasks for handling the intangible cultural heritage project, normalize the remaining time and the number of unfinished tasks so that the value ranges of the remaining time and the number of unfinished tasks are mapped to between [0, 1], obtain the normalized value of the remaining time and the normalized value of the number of unfinished tasks, and sum the normalized value of the remaining time and the normalized value of the number of unfinished tasks to obtain the deadline urgency index.

[0034] An intangible cultural heritage project management system based on a big data model, comprising an initial plan generation module, a processing module, and a management module:

[0035] The initial plan generation module: obtains the information of intangible cultural heritage projects in the region through the API interface of the regional platform, obtains the historical data of each intangible cultural heritage project through the big database, generates a priority index for each intangible cultural heritage project based on the big data model, combines the priority index with the regional resource information to generate an initial scheduling plan, and uses the python tool to spread a number of alternative scheduling plans based on the initial scheduling plan, and marks both the initial scheduling plan and the number of alternative scheduling plans as the initial scheduling plan;

[0036] The processing module: calculates the quality value of each initial scheduling plan through a quality function, retains some initial scheduling plans according to the quality value and performs a randomization operation to obtain a final scheduling plan, and establishes a plan set for all the final scheduling plans;

[0037] The management module: repeats the steps of calculating the quality value, plan retention, and randomization operation for the final scheduling plans in the plan set for iteration. When the convergence condition is met, all the plan sets are output, and the final scheduling plan with the largest quality value in all the plan sets is selected to manage the intangible cultural heritage projects in the region.

[0038] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0039] The present invention calculates the quality value of each initial scheduling plan through a quality function, retains some initial scheduling plans according to the quality value and performs a randomization operation to obtain a final scheduling plan, establishes a plan set for all the final scheduling plans, repeats the steps of calculating the quality value, plan retention, and randomization operation for the final scheduling plans in the plan set for iteration. When the convergence condition is met, all the plan sets are output, and the final scheduling plan with the largest quality value in all the plan sets is selected to manage the intangible cultural heritage projects in the region. This management system can automatically adapt to changes in the environment and requirements, improve the flexibility and responsiveness of the scheduling plan, and thus improve the management efficiency of the intangible cultural heritage projects in the region.

[0040] The present invention obtains the information of intangible cultural heritage projects in the region through the API interface of the regional platform. The information of intangible cultural heritage projects includes the number of intangible cultural heritage projects and the content of intangible cultural heritage projects. After obtaining the historical data of each intangible cultural heritage project through the big database, a priority index is generated for each intangible cultural heritage project based on the big data model. An initial scheduling plan is generated by combining the priority index with the regional resource information, and a number of alternative scheduling plans are spread based on the initial scheduling plan using Python tools. Both the initial scheduling plan and the number of alternative scheduling plans are marked as the initial scheduling plan. The initial scheduling plan is generated based on the priority value and the regional resource information, avoiding the deficiencies brought by static rules and being beneficial to improving the resource allocation efficiency of intangible cultural heritage projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0042] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0044] Embodiment 1: Please refer to Figure 1 As shown, a management method for intangible cultural heritage projects based on a big data model in this embodiment includes the following steps:

[0045] The management system obtains the information of intangible cultural heritage projects in the region through the API interface of the regional platform. The information of intangible cultural heritage projects includes the number of intangible cultural heritage projects and the content of intangible cultural heritage projects. After obtaining the historical data of each intangible cultural heritage project through the large database, a priority index is generated for each intangible cultural heritage project based on the big data model. Combining the priority index with the regional resource information, a primary scheduling plan is generated, and a number of alternative scheduling plans are spread based on the primary scheduling plan using the Python tool. Both the primary scheduling plan and the number of alternative scheduling plans are marked as the initial scheduling plan. The content of the initial scheduling plan includes fund allocation, management personnel allocation, and the time priority of activity arrangement. The quality value of each initial scheduling plan is calculated through the quality function, and some initial scheduling plans are retained based on the quality value and randomized to obtain the final scheduling plan. All the final scheduling plans are established into a plan set, and the steps of calculating the quality value, retaining the plan, and randomizing the final scheduling plan in the plan set are repeated for iteration. When the convergence condition is met, all the plan sets are output, and the final scheduling plan with the largest quality value in all the plan sets is selected to manage the intangible cultural heritage projects in the region.

[0046] This application calculates the quality value of each initial scheduling plan through the quality function, and some initial scheduling plans are retained based on the quality value and randomized to obtain the final scheduling plan. All the final scheduling plans are established into a plan set, and the steps of calculating the quality value, retaining the plan, and randomizing the final scheduling plan in the plan set are repeated for iteration. When the convergence condition is met, all the plan sets are output, and the final scheduling plan with the largest quality value in all the plan sets is selected to manage the intangible cultural heritage projects in the region. This management system can automatically adapt to the changes in the environment and requirements, improve the flexibility and responsiveness of the scheduling plan, and thus improve the management efficiency of the intangible cultural heritage projects in the region.

[0047] This application obtains the information of intangible cultural heritage projects in the region through the API interface of the regional platform. The information of intangible cultural heritage projects includes the number of intangible cultural heritage projects and the content of intangible cultural heritage projects. After obtaining the historical data of each intangible cultural heritage project through the large database, a priority index is generated for each intangible cultural heritage project based on the big data model. Combining the priority index with the regional resource information, a primary scheduling plan is generated, and a number of alternative scheduling plans are spread based on the primary scheduling plan using the Python tool. Both the primary scheduling plan and the number of alternative scheduling plans are marked as the initial scheduling plan. The primary scheduling plan is generated based on the priority value and the regional resource information, avoiding the deficiencies brought by static rules and being beneficial to improving the resource allocation efficiency of intangible cultural heritage projects.

[0048] Example 2: The management system obtains the information of intangible cultural heritage projects in the region through the API interface of the regional platform. The information of intangible cultural heritage projects includes the number of intangible cultural heritage projects and the content of intangible cultural heritage projects, and includes the following steps:

[0049] The management system first needs to establish an API interface connection with the regional platform. The regional platform provides an interface for obtaining information on intangible cultural heritage projects within the region. This step usually involves identity authentication, API key management, and the establishment of network requests. Identity verification is carried out through methods such as API keys and OAuth to ensure that the system has the right to access the data of the regional platform. An API request is sent to the regional platform using the HTTP or HTTPS protocol to obtain information on intangible cultural heritage projects.

[0050] The management system obtains detailed information on all intangible cultural heritage projects in the region through API requests, including the number of projects, project content, project types, etc. The data obtained usually includes the names, historical backgrounds, number of participants, resource requirements, etc. of intangible cultural heritage projects. A request is sent and the required data types are specified, such as the number, type, description, etc. of intangible cultural heritage projects. Intangible cultural heritage project information is extracted from the data returned by the regional platform.

[0051] The obtained intangible cultural heritage project information needs to be parsed into structured data and stored in the database or memory of the management system for subsequent processing. At this stage, the system needs to store data according to fields such as the project ID, name, type, etc. Data cleaning (such as format conversion, removal of duplicates) may also be carried out. The data in JSON, XML, or other formats returned by the API is parsed into the structured data required by the management system, and the parsed data is stored in a database (such as MySQL, MongoDB, PostgreSQL) or memory (such as a caching system).

[0052] After obtaining the historical data of each intangible cultural heritage project through the large database, a priority index is generated for each intangible cultural heritage project based on the big data model, including the following steps:

[0053] Obtain the historical data of each intangible cultural heritage project through the large database. The historical data includes the pedestrian flow index, project success rate, and deadline urgency index;

[0054] Substitute the pedestrian flow index, project success rate, deadline urgency index, and all intangible cultural heritage projects into the big data model. After the big data model calculates the relative proximity of each intangible cultural heritage project, the priority index of each intangible cultural heritage project is output based on the relative proximity.

[0055] The establishment of the big data model includes the following steps:

[0056] Assume that there are m intangible cultural heritage projects in the region and the historical data of the intangible cultural heritage projects is n. A decision matrix X is constructed based on the intangible cultural heritage projects and historical data as follows: In the formula, x ij represents the value of the i-th intangible cultural heritage project on the j-th historical data. The data in the decision matrix is standardized into dimensionless data. The standardization formula is: In the formula, yij represents the standardized value of the \(i\)-th intangible cultural heritage item on the \(j\)-th historical data, \(x\) ij represents the value of the \(i\)-th intangible cultural heritage item on the \(j\)-th historical data, and \(m\) is the number of intangible cultural heritage items;

[0057] Calculate the positive solution and the negative solution, and the expressions are:

[0058] In the formula, \(A\) + is the positive solution, \(A\) - is the negative solution, \(\max(*)\) represents taking the maximum value, and \(\min(*)\) represents taking the minimum value;

[0059] Calculate the distances of each intangible cultural heritage item from the positive solution and the negative solution, and the expressions are:

[0060] In the formula, represents the distance of the \(i\)-th intangible cultural heritage item from the positive solution, represents the distance of the \(i\)-th intangible cultural heritage item from the negative solution, \(n\) is the number of historical data, \(y\) ij represents the standardized value of the \(i\)-th intangible cultural heritage item on the \(j\)-th historical data, represents the positive solution value of the \(j\)-th historical data, represents the negative solution value of the \(j\)-th historical data;

[0061] Calculate the relative closeness based on the distances of the intangible cultural heritage item from the positive solution and the negative solution, and the expressions are:

[0062] In the formula, \(C\) i is the relative closeness of the \(i\)-th intangible cultural heritage item, represents the distance of the \(i\)-th intangible cultural heritage item from the positive solution, represents the distance of the \(i\)-th intangible cultural heritage item from the negative solution;

[0063] Calculate the priority index of each intangible cultural heritage item based on the relative closeness of the intangible cultural heritage item, and the expressions are: In the formula, \(SR\) i is the priority index of the \(i\)-th intangible cultural heritage item, and \(m\) is the number of intangible cultural heritage items.

[0064] Substitute the pedestrian flow index, project success rate, deadline urgency index, and all intangible cultural heritage items into the big data model. Assume there are \(m\) intangible cultural heritage items in the region, and construct a decision matrix \(X\) based on the intangible cultural heritage items and historical data as: In the formula, \(x\) ij represents the value of the \(i\)-th intangible cultural heritage item on the \(j\)-th historical data. Standardize the data in the decision matrix into dimensionless data, and the standardization formula is: In the formula, \(y\) ijrepresents the standardized value of the $i$-th intangible cultural heritage item on the $j$-th historical data, $x$ ij represents the value of the $i$-th intangible cultural heritage item on the $j$-th historical data, and $m$ is the number of intangible cultural heritage items;

[0065] Calculate the positive solution and the negative solution, and the expression is:

[0066] In the formula, $A$ + is the positive solution, $A$ - is the negative solution, $\max(*)$ represents taking the maximum value, $\min(*)$ represents taking the minimum value, $y$ i1 represents the pedestrian flow index of the $i$-th intangible cultural heritage item, $y$ i2 represents the project success rate of the $i$-th intangible cultural heritage item, $y$ i3 represents the deadline urgency index of the $i$-th intangible cultural heritage item;

[0067] Calculate the distance between each intangible cultural heritage item and the positive solution and the negative solution, and the expression is:

[0068] In the formula, represents the distance between the $i$-th intangible cultural heritage item and the positive solution, represents the distance between the $i$-th intangible cultural heritage item and the negative solution, $y$ ij represents the standardized value of the $i$-th intangible cultural heritage item on the $j$-th historical data, represents the positive solution value of the $j$-th historical data, represents the negative solution value of the $j$-th historical data;

[0069] Calculate the relative closeness based on the distances between the intangible cultural heritage items and the positive solution and the negative solution, and the expression is:

[0070] In the formula, $C$ i is the relative closeness of the $i$-th intangible cultural heritage item, represents the distance between the $i$-th intangible cultural heritage item and the positive solution, represents the distance between the $i$-th intangible cultural heritage item and the negative solution;

[0071] Calculate and obtain the priority index of each intangible cultural heritage item based on the relative closeness of the intangible cultural heritage items, and the expression is: In the formula, $SR$ i is the priority index of the $i$-th intangible cultural heritage item, and $m$ is the number of intangible cultural heritage items.

[0072] The acquisition logic of the pedestrian flow index is: After obtaining the pedestrian flows of the intangible cultural heritage item at multiple historical time points, calculate the mean value and the standard deviation of the pedestrian flows based on the pedestrian flows at multiple time points, and calculate and obtain the pedestrian flow index according to the mean value and the standard deviation of the pedestrian flows, and the expression is: In the formula, $y$ 1Let \(I\) be the pedestrian flow index, \(\mu\) be the average pedestrian flow, and \(\sigma\) be the standard deviation of pedestrian flow. The larger the pedestrian flow index, the greater the overall historical pedestrian flow of the intangible cultural heritage project, that is, the higher the priority of the intangible cultural heritage project;

[0073] The larger the pedestrian flow index, it indicates that the intangible cultural heritage project has attracted more pedestrian flow in history, reflecting the cultural influence and social attention of the project.

[0074] Cultural value: Intangible cultural heritage projects with a large pedestrian flow usually have high cultural value or historical significance and are easily recognized and supported by the public.

[0075] Economic benefits: Projects with a large pedestrian flow may bring more economic benefits (such as ticket sales, derivative sales, etc.) and are more attractive in resource allocation.

[0076] Dissemination potential: Projects with a large pedestrian flow have stronger dissemination capabilities, can attract more social resources and media attention, and thus further enhance the popularity and influence of intangible cultural heritage projects.

[0077] The calculation logic of the project success rate is as follows: Obtain the total number of historical handling times and the total number of successful times of the intangible cultural heritage project. Divide the total number of successful times by the total number of handling times to obtain the project success rate. The larger the project success rate, the greater the probability of successful handling of the intangible cultural heritage project in history, and the higher the priority;

[0078] The project success rate is calculated by the ratio of the total number of historical handling times to the number of successful handling times, reflecting the probability of the intangible cultural heritage project being successfully completed in history.

[0079] Implementation feasibility: Projects with a high success rate have performed well in past implementations, indicating that their plans and resource allocations have high execution efficiency and are suitable as objects for priority resource allocation.

[0080] Risk reduction: Selecting projects with a high historical success rate can reduce the risk of failure in current management and ensure that the plans of intangible cultural heritage projects can be completed on schedule and produce expected benefits.

[0081] Demonstration effect: Projects with a high success rate are likely to form good social feedback and be promoted as successful cases, which helps to enhance the overall image of intangible cultural heritage protection work.

[0082] The calculation logic of the deadline urgency index is as follows: obtain the deadline of the intangible cultural heritage project, calculate the remaining time by subtracting the current time from the deadline, obtain the number of unfinished tasks for handling the intangible cultural heritage project, normalize the remaining time and the number of unfinished tasks so that the value ranges of the remaining time and the number of unfinished tasks are mapped to the range of [0, 1], obtain the normalized value of the remaining time and the normalized value of the number of unfinished tasks, sum the normalized value of the remaining time and the normalized value of the number of unfinished tasks to obtain the deadline urgency index. The larger the deadline urgency index, the more urgent the tasks and time of the intangible cultural heritage project, and the higher the priority.

[0083] By calculating the normalized values of the remaining time and the number of unfinished tasks of the project, these two factors are combined into an index reflecting the urgency of time and tasks.

[0084] Time sensitivity: The management and activities of intangible cultural heritage projects are usually restricted by time (such as festival activities, protection work, etc.). Projects with a short remaining time need to be processed as soon as possible to avoid affecting their final completion or activity implementation.

[0085] Task load pressure: A project with a large number of unfinished tasks means that more workload needs to be invested. Considering the time factor, more urgent projects should be processed first to ensure timely completion.

[0086] Dynamic nature: The deadline urgency index reflects the changes in the project time and tasks in real time, which can help the management system dynamically adjust the priority and flexibly allocate resources.

[0087] Generate the initial scheduling plan by combining the priority index and regional resource information, and use the python tool to generate several alternative scheduling plans based on the initial scheduling plan. Mark both the initial scheduling plan and several alternative scheduling plans as the initial scheduling plan. The content of the initial scheduling plan includes fund allocation, management personnel allocation, and the time priority of activity arrangements, including the following steps:

[0088] Mark the amount of funds in the regional resources as A and the number of management personnel as B. Then, the initial allocated fund amount and the initial allocated management personnel number generated for each intangible cultural heritage project according to the priority index are respectively: In the formula, a i is the initial allocated fund amount for the i-th intangible cultural heritage project, is the initial allocated management personnel number for the i-th intangible cultural heritage project, and SR i is the priority index of the i-th intangible cultural heritage project;

[0089] After sorting all intangible cultural heritage projects according to the priority index from large to small, generate a project sorting table, and generate a time arrangement table for all intangible cultural heritage projects in ascending order according to the project sorting table. That is, the higher the ranking of the intangible cultural heritage project in the project sorting table, the earlier the time arrangement of the intangible cultural heritage project.

[0090] Based on the initial scheduling scheme, the neighborhood scheduling scheme is derived in the following ways:

[0091] Funds allocation: Introduce random perturbations to the values of funds allocation while ensuring that the total funds budget remains unchanged.

[0092] Managers allocation: Re-adjust the number of managers allocated to each project according to certain rules (such as random selection).

[0093] Mark the initial scheduling scheme and the set of derived neighborhood scheduling schemes as the initial scheduling scheme for subsequent quality value calculation and optimization.

[0094] An example of Python tool code is as follows:

[0095] Import-random

[0096] Import-copy

[0097] # Example of the initial scheduling scheme

[0098] initial_schedule = {

[0099] "projects":

[0100] {"id":1,"funds":50000,"managers":3,"priority":1},

[0101] {"id":2,"funds":30000,"managers":2,"priority":2},

[0102] {"id":3,"funds":20000,"managers":1,"priority":3}

[0103] ,

[0104] "total_budget":100000,

[0105] "total_managers":6

[0106] }

[0107] # Derive the neighborhood scheduling scheme

[0108] Def-generate_neighborhood_schedules(initial_schedule,num_variants = 5):schedules =

[0109] for_in-range(num_variants):

[0110] # Copy the initial plan

[0111] new_schedule = copy.deepcopy(initial_schedule)

[0112] total_budget = new_schedule["total_budget"]

[0113] total_managers = new_schedule["total_managers"]

[0114] # Randomly adjust the fund allocation while keeping the total budget unchanged

[0115] funds = [p["funds"] for p in new_schedule["projects"]]

[0116] adjustment = random.uniform(-0.1, 0.1) # Random adjustment ratio

[0117] funds = [f + f * adjustment for f in funds]

[0118] factor = total_budget / sum(funds) # Normalize the adjustment

[0119] For i, project in enumerate(new_schedule["projects"]):

[0120] project["funds"] = round(funds[i] * factor, 2)

[0121] # Randomly adjust the manager allocation while keeping the total number of people unchanged

[0122] managers = [p["managers"] for p in new_schedule["projects"]]

[0123] adjustments = random.choices(range(-1, 2), k = len(managers)) # Random adjustment range

[0124] For - i, adj in enumerate(adjustments):

[0125] managers[i] = max(0, managers[i] + adj) # Ensure non - negative

[0126] While sum(managers) != total_managers:

[0127] diff = total_managers - sum(managers)

[0128] managers[random.randint(0, len(managers) - 1)] += diff

[0129] For - i, project in enumerate(new_schedule["projects"]):

[0130] project["managers"] = managers[i]

[0131] schedules.append(new_schedule)

[0132] Return schedules

[0133] # Example run

[0134] neighborhood_schedules = generate_neighborhood_schedules(initial_schedule, num_variants = 5)

[0135] # Output result

[0136] For - i, schedule in enumerate(neighborhood_schedules):

[0137] print(f"Schedule {i + 1}:")

[0138] For project in schedule["projects"]:

[0139] print(f"Project{project['id']}:Funds={project['funds']},Managers={project['managers']},Priority={project['priority']}")

[0140] print("-"*50)

[0141] Funding allocation adjustment: After introducing random perturbations, perform normalization to ensure that the total budget remains unchanged. For the funding allocation value of each project, add a certain percentage of random value changes.

[0142] Manager allocation adjustment: Adjust the manager allocation based on priority or random selection. Ensure that the total number of managers after adjustment is the same as the initial total.

[0143] Generation of alternative scheduling plans: Alternative plans have diversity in funding allocation and manager allocation, but maintain the time priority of activity arrangements unchanged.

[0144] Output results: The detailed information of each alternative scheduling plan includes project funds, the number of managers, and time priority.

[0145] Calculate the quality value of each initial scheduling plan through a quality function, including the following steps:

[0146] Obtain the allocation conflict rate and constraint violation rate of each initial scheduling plan, and substitute the allocation conflict rate and constraint violation rate into the quality function to calculate the quality value. The expression is: In the formula, zl is the quality value, θ is the allocation conflict rate, is the constraint violation rate, α and β are the proportionality coefficients of the allocation conflict rate and constraint violation rate respectively, and both α and β are greater than 0.

[0147] The calculation logic of the allocation conflict rate is: In the formula, θ is the allocation conflict rate, E ij represents the resource allocation conflict amount (including funding and manager allocation conflicts) between intangible cultural heritage project i and intangible cultural heritage project j, R i represents the resource allocation amount of intangible cultural heritage project i, m is the number of intangible cultural heritage projects. The larger the allocation conflict rate, the more conflicts there are between intangible cultural heritage projects in this scheduling plan, that is, the lower the quality.

[0148] The calculation expression of the constraint violation rate is: is the constraint violation rate, R i represents the resource allocation amount of intangible cultural heritage project i, L i represents the upper limit of the resource limit of intangible cultural heritage project i, N iIt represents the lower limit of the resource constraint of the intangible cultural heritage project i. The larger the value of the constraint breakthrough rate, the more unreasonable the resource allocation in the scheduling plan.

[0149] Based on the quality value, part of the initial scheduling plan is retained and randomized to obtain the final scheduling plan. All the final scheduling plans are used to establish a plan set. The steps of calculating the quality value, retaining the plan, and randomizing the operation for the final scheduling plan in the plan set are repeated iteratively. When the convergence condition is met, all the plan sets are output, and the final scheduling plan with the largest quality value in all the plan sets is selected to manage the intangible cultural heritage projects in the region, including the following steps:

[0150] After sorting all the initial scheduling plans in descending order according to the quality value, a plan list is generated. G initial scheduling plans are selected in the positive order according to the plan list for retention, where G is the quantity threshold. After randomly perturbing the amount of capital allocation and the component of management personnel for all the retained initial scheduling plans, the final scheduling plan is obtained. All the final scheduling plans are used to establish a plan set;

[0151] The steps of calculating the quality value, retaining the plan, and randomizing the operation for the final scheduling plan in the plan set are repeated iteratively. When the number of iterations is equal to the iteration threshold or there is a final scheduling plan with a quality value greater than or equal to the quality threshold in any plan set, it is determined that the convergence condition is met. All the plan sets are output, and the final scheduling plan with the largest quality value in all the plan sets is selected to manage the intangible cultural heritage projects in the region.

[0152] Embodiment 3: An intangible cultural heritage project management system based on a big data model described in this embodiment includes an initial plan generation module, a processing module, and a management module:

[0153] Initial plan generation module: Obtain the information of intangible cultural heritage projects in the region through the API interface of the regional platform. The information of intangible cultural heritage projects includes the number of intangible cultural heritage projects and the content of intangible cultural heritage projects. After obtaining the historical data of each intangible cultural heritage project through the big database, generate a priority index for each intangible cultural heritage project based on the big data model, combine the priority index with the regional resource information to generate the initial generation scheduling plan, and use the python tool to spread several collateral generation scheduling plans based on the initial generation scheduling plan. Mark both the initial generation scheduling plan and several collateral generation scheduling plans as the initial scheduling plan. The content of the initial scheduling plan includes capital allocation, management personnel allocation, and the priority of activity arrangement time. The initial scheduling plan is sent to the processing module;

[0154] Processing module: Calculate the quality value of each initial scheduling plan through the quality function, and based on the quality value, retain part of the initial scheduling plans and perform randomization operations to obtain the final scheduling plan. All the final scheduling plans are used to establish a plan set. The plan set is sent to the management module;

[0155] Management module: Repeatedly iterate through the steps of calculating the quality value, retaining the solution, and randomizing the final scheduling solutions in the solution set. When the convergence condition is met, output all solution sets and select the final scheduling solution with the largest quality value in all solution sets to manage the intangible cultural heritage projects in the region.

[0156] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0157] It should be understood that the term "and / or" in this article is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0158] It should be understood that in various embodiments of the present application, the magnitude of the sequence numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0159] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but this implementation should not be considered to exceed the scope of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0160] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for managing intangible cultural heritage projects based on a big data model, characterized by: The management method comprises the following steps: The management system obtains information about intangible cultural heritage projects in the region through the API interface of the regional platform. After obtaining the historical data of each intangible cultural heritage project through the big database, it generates a priority index for each intangible cultural heritage project based on the big data model. Combine the priority index and regional resource information to generate the initial scheduling plan, and use Python tools to diffuse several alternative scheduling plans based on the initial scheduling plan, and mark the initial scheduling plan and several alternative scheduling plans as initial scheduling plans; The quality value of each initial scheduling plan is calculated through the quality function, and the final scheduling plan is obtained after retaining some initial scheduling plans according to the quality value and performing randomization operations. A plan set is established for all the final scheduling plans, and the quality value calculation, plan retention and randomization steps of the final scheduling plans in the plan set are repeated. When the convergence conditions are met, all plan sets are output, and the final scheduling plan with the largest quality value in all plan sets is selected to manage the intangible cultural heritage projects in the region.

2. According to claim 1, a method for managing intangible cultural heritage projects based on a big data model is characterized by: After obtaining the historical data of each intangible cultural heritage item through the big database, a priority index is generated for each intangible cultural heritage item based on the big data model, including the following steps: Obtain historical data of each intangible cultural heritage project through a large database, including the flow index, project success rate, and deadline urgency index; The traffic index, project success rate, deadline urgency index and all intangible cultural heritage projects are substituted into the big data model. After the big data model calculates the relative proximity of each intangible cultural heritage project, it outputs the priority index of each intangible cultural heritage project based on the relative proximity.

3. According to claim 2, a method for managing intangible cultural heritage projects based on a big data model is characterized in that: Substitute the traffic index, project success rate, deadline urgency index and all intangible cultural heritage projects into the big data model. Suppose there are m intangible cultural heritage projects in the region. The decision matrix X is constructed based on the intangible cultural heritage projects and historical data: In the formula, x ij Represents the value of the i-th intangible cultural heritage item in the j-th historical data. The data in the decision matrix is ​​standardized into dimensionless data. The standardization formula is: In the formula, y ij represents the standardized value of the i-th intangible cultural heritage item based on the j-th historical data, x ij represents the value of the i-th intangible cultural heritage item in the j-th historical data, and m is the number of intangible cultural heritage items; Calculate the positive solution and negative solution, and calculate the distance between each intangible cultural heritage item and the positive solution and the negative solution. Calculate the relative proximity based on the distance between the intangible cultural heritage item and the positive solution and the negative solution. Obtain the priority index of each intangible cultural heritage item based on the relative proximity of the intangible cultural heritage item. The expression is: In the formula, SR i is the priority index of the i-th intangible cultural heritage item, and m is the number of intangible cultural heritage items.

4. The intangible cultural heritage project management method based on the big data model according to claim 3 is characterized by: Calculate the positive and negative solutions, the expression is: In the formula, A + is the forward solution, A - is a negative solution, max(*) means taking the maximum value, min(*) means taking the minimum value, y i1 represents the flow index of the i-th intangible cultural heritage item, y i2 represents the success rate of the i-th intangible cultural heritage project, y i3 Represents the deadline urgency index of the i-th intangible cultural heritage item.

5. The method for managing intangible cultural heritage projects based on a big data model according to claim 4 is characterized in that: Calculate the distance between each non-legacy item and the positive solution and negative solution. The expression is: In the formula, represents the distance between the i-th intangible cultural heritage item and the forward solution, represents the distance between the i-th intangible cultural heritage item and the negative solution, y ij represents the standardized value of the i-th intangible cultural heritage item based on the j-th historical data, represents the forward solution value of the j-th historical data, Represents the negative solution value of the j-th historical data; The relative proximity is calculated based on the distance between the intangible cultural heritage item and the positive solution and the negative solution. The expression is: In the formula, C i is the relative proximity of the i-th intangible cultural heritage item.

6. The intangible cultural heritage project management method based on big data model according to claim 5 is characterized by: Combining the priority index with the regional resource information to generate the initial scheduling plan includes the following steps: The content of the initial scheduling plan includes the allocation of funds, the allocation of management personnel, and the priority of activity scheduling; The amount of funds in the regional resources is marked as A, and the number of managers is marked as B. Then the initial amount of funds allocated and the initial number of managers allocated for each intangible cultural heritage project based on the priority index are: In the formula, a i is the initial amount of funds allocated to the i-th intangible cultural heritage project, is the initial number of managers allocated to the i-th intangible cultural heritage project, SR i is the priority index of the i-th intangible cultural heritage item; After all the intangible cultural heritage items are sorted from large to small according to the priority index, a project sorting table is generated, and a time schedule for all the intangible cultural heritage items is generated in positive order according to the project sorting table.

7. The intangible cultural heritage project management method based on big data model according to claim 6 is characterized by: The quality value of each initial scheduling solution is calculated by the quality function, including the following steps: Obtain the allocation conflict rate and constraint breakthrough rate of each initial scheduling scheme, substitute the allocation conflict rate and constraint breakthrough rate into the quality function to calculate the quality value, the expression is: In the formula, zl is the quality value, θ is the allocation conflict rate, is the constraint breakthrough rate, α and β are the proportional coefficients of the allocation conflict rate and constraint breakthrough rate, respectively, and both α and β are greater than 0.

8. The method for managing intangible cultural heritage projects based on a big data model according to claim 7 is characterized in that: The final scheduling plan is obtained by randomizing the initial scheduling plan according to the quality value, including the following steps: After sorting all the initial scheduling plans from large to small according to the quality value, a plan list is generated, and G initial scheduling plans are selected and retained according to the positive order of the plan list, where G is the quantity threshold, and the final scheduling plan is obtained after the capital allocation amount and the management personnel component of all the retained initial scheduling plans are randomly disturbed, and all the final scheduling plans are established as a plan set; Repeat the quality value calculation, scheme retention and randomization steps for the final scheduling scheme in the scheme set. When the number of iterations is equal to the number threshold or there is a final scheduling scheme with a quality value greater than or equal to the quality threshold in any scheme set, it is judged that the convergence condition is met, all scheme sets are output, and the final scheduling scheme with the largest quality value in all scheme sets is selected to manage the intangible cultural heritage projects in the region.

9. The method for managing intangible cultural heritage projects based on a big data model according to claim 5, characterized in that: The logic for obtaining the flow index is as follows: after obtaining the flow of people at multiple time points in the history of the intangible cultural heritage project, the mean flow and the standard deviation of the flow are calculated based on the flow of people at multiple time points, and the flow index is calculated based on the mean flow and the standard deviation of the flow, and the expression is: In the formula, y1 is the human flow index, μ is the human flow mean, and σ is the human flow standard deviation; The calculation logic of the project success rate is as follows: obtain the total number of times the intangible cultural heritage project has been handled and the total number of successes in history, and obtain the project success rate by dividing the total number of successes by the total number of times it has been handled; The calculation logic of the deadline urgency index is as follows: obtain the deadline of the intangible cultural heritage project, obtain the remaining time by subtracting the current time from the deadline, obtain the number of unfinished tasks handled by the intangible cultural heritage project, normalize the remaining time and the number of unfinished tasks so that the value range of the remaining time and the number of unfinished tasks are mapped to between [0,1], obtain the normalized value of the remaining time and the normalized value of the number of unfinished tasks, and sum the normalized value of the remaining time and the normalized value of the number of unfinished tasks to obtain the deadline urgency index.

10. An intangible cultural heritage project management system based on a big data model, used to implement the management method according to any one of claims 1 to 9, characterized in that: Including initial solution generation module, processing module and management module: Initial plan generation module: obtains the information of intangible cultural heritage projects in the region through the API interface of the regional platform, obtains the historical data of each intangible cultural heritage project through the big database, generates a priority index for each intangible cultural heritage project based on the big data model, combines the priority index with the regional resource information to generate the initial scheduling plan, and uses Python tools to spread several alternative scheduling plans based on the initial scheduling plan, and marks the initial scheduling plan and several alternative scheduling plans as initial scheduling plans; Processing module: Calculate the quality value of each initial scheduling plan through the quality function, retain some initial scheduling plans according to the quality value, perform randomization operations to obtain the final scheduling plan, and establish a plan set for all the final scheduling plans; Management module: Repeat the quality value calculation, solution retention and randomization operation steps for the final scheduling solution in the solution set. When the convergence conditions are met, output all solution sets and select the final scheduling solution with the largest quality value in all solution sets to manage the intangible cultural heritage projects in the region.

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