Multi-platform live broadcasting room data visualization and optimization decision support method and system

Abstract

The invention provides a multi-platform live broadcast room data visualization and optimization decision support method and system, and the method comprises the steps: collecting the historical live broadcast data of live broadcast rooms on different platforms, visualizing the live broadcast data, constructing a multi-dimensional benefit evaluation index system based on a heterogeneous data source, taking the exposure times of the live broadcast rooms as an agent index for generating benefits, and carrying out the visualization of the live broadcast data. A benefit function of a live broadcast room is introduced to model a fund optimization problem into a multi-objective optimization model, benefit function maximization is used as an objective function to solve an optimal distribution strategy, and a particle swarm optimization algorithm is used for iterative solution to enable the benefit to reach the optimal exposure times of the live broadcast room. Live broadcast data are analyzed through quantized historical data and a multi-objective optimization algorithm, and a visual distribution scheme is sorted out through a computer, so that a user is helped to make a reasonable decision. Through optimization and decision support, a decision maker is helped to flexibly adjust a strategy in a dynamically changing market environment, blind investment is avoided, and decision accuracy and overall operation benefits are improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a multi-platform live broadcast room data visualization and optimization decision support method and system. Background Art

[0002] In the current cross-platform live broadcasting of e-commerce, merchants who broadcast live are faced with the dilemma that live broadcast data is scattered and difficult to aggregate. The lack of clear and intuitive live broadcast data display makes it difficult for merchants to effectively rely on data analysis results when making operational decisions, resulting in inefficient decision-making and even affecting sales results.

[0003] The live broadcast data of each platform is usually stored in different systems or databases, and the data structure and format are different, which makes data integration, aggregation and analysis very complicated. In addition, the existing live broadcast data display usually lacks sufficient visualization tools, resulting in merchants being unable to fully and clearly see the operating conditions of different platforms when making cross-platform operation decisions. System users often rely on cumbersome manual summaries and unintuitive data reports, and it is difficult to quickly grasp the overall trend and key indicators of the live broadcast effect, which affects the quality of their decision-making. Therefore, there is an urgent need for a method that can quantify and visualize the data of each live broadcast platform to help merchants maximize the benefits of the live broadcast rooms on each platform through reasonable capital allocation strategies, thereby improving the overall operation effect and sales performance. Summary of the invention

[0004] The present invention proposes a multi-platform live broadcast room data visualization and optimization decision support method and system, which collects historical live broadcast data of live broadcast rooms on different platforms and visualizes the live broadcast data, analyzes the live broadcast data through quantitative historical data and multi-objective optimization algorithm, and sorts out a visualized allocation plan, thereby helping users make reasonable decisions to solve the above-mentioned problems. The technical solutions provided by the present invention are as follows: A multi-platform live broadcast room data visualization and optimization decision support method includes the following steps: Step 1: Collect real-time and historical data of live broadcast rooms on multiple platforms and build a data matrix of multiple live broadcast rooms; Step 2: Standardize the collected live broadcast room data through data preprocessing and feature extraction technology to obtain quantitative indicators with unified measurement standards and time dimensions; Step 3: According to the quantitative indicators, a multi-objective optimization problem is set to maximize the overall benefits of all live broadcast rooms in the live broadcast matrix; Step 4, using a particle swarm optimization algorithm to solve the multi-objective optimization problem, finding the optimal resource allocation for each live broadcast room through the particle swarm algorithm, and obtaining an optimal allocation plan that maximizes the overall benefit of the live broadcast matrix; Step 5: Use visualization tools to present quantitative indicators and optimized allocation plans to decision makers.

[0005] Preferably, the specific steps of step 2 are: Step 2.1: Collect data through the API interface opened by the live broadcast platform, and use the Pandas library in Python to clean the collected data to remove duplicate, missing and invalid data; Step 2.2, extract features from the collected data, and use the Max-Min method to standardize each indicator so that the value range of each indicator is consistent. The steps are: Suppose the data matrix A contains n samples and m features. a ij is the data in the i-th row and j-th column of the matrix, , for the jth column in the data matrix, calculate the minimum and maximum values ​​of the feature:

[0006] Application formula: , a ij ’ is standardized data, and the value of each feature is scaled to the range of [0, 1].

[0007] Preferably, step 2 further selects indicators that are valuable for subsequent decision-making from the quantitative indicators, and the specific steps are: Step 2.3, calculate the covariance matrix C of the data matrix A, the formula is ; Then calculate the eigenvalues ​​of the covariance matrix C λ and the eigenvector v , satisfying the following equation: , where the eigenvector v Indicates the main direction of the data, eigenvalue λ Indicates the data variance in this direction; Step 2.4, select the eigenvectors corresponding to the largest k eigenvalues ​​from the calculated eigenvalues, and project the data matrix onto the selected k principal components. The projection formula is: , where A' is the quantized data matrix, V k is the matrix composed of the selected k principal component eigenvectors, and Z is the data matrix after dimensionality reduction.

[0008] Preferably, the specific process of step 3 is: Step 3.1: The total amount of funds used to invest in the live broadcast room exposure is B. L i The exposure cost is x i , live broadcast roomL i The unit exposure cost is s i , this coefficient is determined by the traffic statistics after the last live broadcast. For live broadcast L i The number of impressions available for purchase; Step 3.2, define the resource matrix R:

[0009] in, r i It is i The resource allocation coefficient of the live broadcast room satisfies ,in P i It's a live broadcast room L i The priority is set based on user activity and revenue contribution factors; U i is user engagement; C i is the load requirement of the live broadcast room, which is related to the content complexity and the number of viewers in the live broadcast room; α , β and γ It is a tuning factor that controls the impact of priority, user participation, and load requirements on resource allocation; P i , U i and C i After a live broadcast ends, the resource allocation coefficient of the live broadcast room before the next live broadcast is calculated through the collected historical data. r i Cost per unit exposure s i There is a positive correlation; Step 3.3, use effective exposure times Replace the benefit function y i , the optimization problem is to maximize the overall benefits of all live broadcast rooms in the live broadcast matrix, and the objective function is:

[0010] All constraints need to be met: .

[0011] Preferably, the benefit function is further set y i Effective exposure times There is a nonlinear relationship between: , the objective function is:

[0012] in b i Indicates i The basic benefit offset of each live broadcast room.

[0013] Preferably, step 4 is specifically: Step 4.1, in the particle swarm, each particle represents a possible optimization scheme, and the position of the particle represents the number of exposures in each live broadcast room , the particle speed v It indicates the change in the number of exposures; Step 4.2, the fitness of the particle is calculated by the total benefit function, that is:

[0014] Step 4.3, set the initial particle speed to 0, so that the particle swarm algorithm can explore the solution space at a slower speed in the initial iteration. The particle speed determines the moving direction and stride of the particle in the solution space, which will be updated in subsequent iterations. When the fitness of the particle is better than its historical optimal value, update the individual optimal solution of the particle. p best ; When the fitness of the particle is better than the global optimal value, update the group optimal solution g best ; Particle velocity update formula and position update formula:

[0015]

[0016] Among them is w Inertia weight, c 1 , c 2 is the acceleration constant, r 1 , r 2 is a random number, t is the number of iterations. The particle swarm updates the position of the particles through multiple iterations until the fitness change is less than the convergence threshold or the number of iterations reaches the maximum. The algorithm stops iterating and obtains the optimal optimization allocation plan.

[0017] Preferably, step 6 is further added: according to the optimal optimization allocation solution solved by the particle swarm algorithm in step 4, the resource matrix is ​​optimized by analyzing historical data R , constantly update live data, analyze and update optimization models to ensure that capital allocation plans and resource allocation are always based on the latest historical data and market dynamics.

[0018] A multi-platform live broadcast room data visualization and optimization decision support system, characterized in that it is used to implement the steps in the above-mentioned multi-platform live broadcast room data visualization and optimization decision support method, including a data acquisition module, a data visualization module, an allocation plan optimization module, a data processing module, and a decision support module.

[0019] A computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the steps in the above-mentioned multi-platform live broadcast room data visualization and optimization decision support method are implemented.

[0020] A computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the above-mentioned multi-platform live broadcast room data visualization and optimization decision support method are implemented.

[0021] Compared with the prior art, the beneficial effects achieved by the present invention are: by analyzing the live broadcast data with quantified historical data, the system can visualize the key operating data of each platform, helping decision makers to comprehensively evaluate the live broadcast effects of different platforms. Through the visual interface, decision makers can intuitively feel the effect of optimization and adjust the budget or other strategic parameters as needed to improve operational efficiency.

[0022] At the same time, the system applies particle swarm algorithm through multi-objective optimization to obtain the fund allocation plan with the greatest benefit. Through allocation optimization and intelligent decision support, decision makers can flexibly adjust strategies in a dynamically changing market environment, avoid blind investment, and improve decision accuracy and overall operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a schematic diagram of the overall process of the method provided by the present invention; Figure 2 It is a logical schematic diagram of the particle swarm optimization algorithm adopted by the present invention; Figure 3 It is a structural schematic diagram of the system provided by the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] In order to make the above-mentioned objects, features and effects of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] Embodiment 1: A multi-platform live broadcast room data visualization and optimization decision support method, such as Figure 1 As shown, the following steps are included: Step 1: Collect real-time and historical data of live broadcast rooms on multiple platforms and build a data matrix of multiple live broadcast rooms. N Live broadcast rooms are broadcasted simultaneously, and real-time and historical data of each live broadcast room are collected and stored. Real-time data includes the number of exposures, number of viewers, interaction frequency, user behavior, etc. of each live broadcast room, and historical data includes audience behavior data, sales conversion rate, live broadcast duration, etc. of the live broadcast room. Data format is optimized according to different characteristics of the platform, and live broadcast room data is aggregated into a central database to build a live broadcast matrix , which is convenient for subsequent processing and analysis.

[0027] Step 2: Standardize the collected live broadcast room data through data preprocessing and feature extraction technology to obtain quantitative indicators with unified measurement standards and time dimensions. The specific steps are as follows: Step 2.1: Collect data through the API interface opened by the live broadcast platform, and use the Pandas library in Python to clean the collected data to remove duplicate, missing and invalid data; Step 2.2: Use the principal component analysis (PCA) algorithm to extract features from the collected data. According to the features extracted from the collected data, each feature is converted into a specific quantitative index. Use the Max-Min method to standardize the quantified indicators to ensure that the numerical range of each indicator is consistent, which is convenient for subsequent comparison and analysis. Specifically: Suppose the data matrix A contains n samples and m features. a ij is the data in the i-th row and j-th column of the matrix, For the jth column in the data matrix, calculate the minimum and maximum values ​​of the feature:

[0028] Apply the Max-Min normalization formula: ,a ij ’ It is the standardized data, and the value of each feature is scaled to the range of [0,1].

[0029] Furthermore, indicators that are valuable for subsequent decision-making are selected from the above quantitative indicators. The specific steps are as follows: Step 2.3, calculate the covariance matrix C of the data matrix A, the formula is ; Then calculate the eigenvalues ​​of the covariance matrix C λ and the eigenvector v , satisfying the following equation: , where the eigenvector v Indicates the main direction of the data, eigenvalue λ Indicates the data variance in this direction; Step 2.4, select the eigenvectors corresponding to the largest k eigenvalues ​​from the calculated eigenvalues, because they can explain more variance in the data. Project the data matrix onto the selected k principal components, and the projection formula is: , where A' is the standardized data matrix, V k is the matrix composed of the selected k principal component eigenvectors, and Z is the data matrix after dimensionality reduction.

[0030] Step 3: Based on the quantified data, a multi-objective optimization problem is set to maximize the overall benefits of all live broadcast rooms in the live broadcast matrix, and the resources of the live broadcast rooms are intelligently allocated to optimize the resource investment and scheduling of the live broadcast rooms. Specifically: Step 3.1: The total amount of funds used to invest in the live broadcast room exposure is B. L i The exposure cost is x i , live broadcast room L i The unit exposure cost is s i , this coefficient is determined by the traffic statistics after the last live broadcast. For live broadcast L i The number of impressions available for purchase; Step 3.2, define the resource matrix R:

[0031] in, r i It is i The resource allocation coefficient of the live broadcast room satisfies ,in P i It's a live broadcast roomL i The priority is set based on factors such as user activity and revenue contribution; U i is user engagement (number of viewers, frequency of interactions, etc.); C i It is the load requirement of the live broadcast room, which is related to the content complexity and the number of viewers in the live broadcast room. α , β and γ It is a tuning factor that controls the impact of priority, user participation, and load requirements on resource allocation; P i , U i and C i After a live broadcast ends, the resource allocation coefficient of the live broadcast room before the next live broadcast is calculated through the collected historical data. r i Cost per unit exposure s i There is a positive correlation; Step 3.3, Live Room L i The benefit function is y i , to simplify the model, the effective exposure times are introduced Replace the benefit function y i , because in actual situations, the effective exposure times of the live broadcast room are usually nonlinearly related to the benefits, so we further assume that the benefit function y i Effective exposure times There is a relationship between: ,in b i Indicates i The basic benefit offset of each live broadcast room to ensure that the number of effective exposures can accurately replace the benefit function; Step 3.4: The optimization problem is to maximize the overall benefits of all live broadcast rooms in the live broadcast matrix. The objective function is:

[0032] All constraints need to be met: .

[0033] Step 4: Use the particle swarm optimization algorithm to solve the multi-objective optimization problem. The particle swarm optimization algorithm is used to find the optimal resource allocation for each live broadcast room, and obtain the optimal allocation plan that maximizes the overall benefit of the live broadcast matrix, such as Figure 2 As shown, the specific steps are: Step 4.1, in the particle swarm, each particle represents a possible optimization scheme, and the position of the particle represents the number of exposures in each live broadcast room , the particle speed v It indicates the change in the number of exposures; Step 4.2, the fitness of the particle is calculated by the total benefit function, that is:

[0034] Step 4.3, set the initial particle speed to 0, so that the particle swarm algorithm can explore the solution space at a slower speed in the initial iteration. The particle speed determines the moving direction and stride of the particle in the solution space, which will be updated in subsequent iterations. In actual scheduling, the speed vector can control the range of change of the optimization scheme. In the case of tight funds, set a smaller initial speed to ensure that the optimization scheme is adjusted more finely. When funds are more abundant, set a larger initial speed to speed up the search progress. When the fitness of the current particle is better than its historical optimal value, update the individual optimal solution of the particle. p best ; The fitness of the current particle is better than the global optimal value, and the group optimal solution is updated g best ; Particle velocity update formula and position update formula:

[0035]

[0036] Among them is w Inertia weight, c 1 , c 2 is the acceleration constant, r 1 , r 2 is a random number, t is the number of iterations. The particle swarm updates the position of the particles through multiple iterations until the fitness change is less than the convergence threshold or the number of iterations reaches the maximum, the algorithm stops iterating, and the optimal optimization allocation solution is obtained.

[0037] Step 5, the quantified data and the optimized allocation plan are displayed to the decision maker through a visualization tool. Data display includes but is not limited to real-time charts, trend analysis, allocation plan comparison and other contents. Decision makers can view the real-time data, benefit forecast and optimized fund allocation plan of each platform through an interactive interface. The visualization interface allows decision makers to query data, adjust strategies and view the optimization effect in real time according to different needs, thereby helping decision makers make more reasonable operational decisions. The present invention imports Matplotlib and Seaborn libraries in Python to draw charts.

[0038] Step 6: According to the optimal allocation solution obtained by the particle swarm algorithm in step 4, optimize the resource allocation matrix by analyzing historical data. R , constantly updating live broadcast data, analyzing and updating optimization models to ensure that funding allocation plans and resource allocation are always based on the latest historical data and market dynamics. Through the dynamic optimization process, decision makers can track the benefits of each platform based on the optimal results obtained, and continuously optimize the resource investment and allocation efficiency of the live broadcast room to adjust decisions.

[0039] Embodiment 2: A multi-platform live broadcast room data visualization and optimization decision support system, such as Figure 3 As shown, it includes data acquisition module, data visualization module, allocation scheme optimization module, data processing module and decision support module, among which: Data collection module: collects historical live broadcast data from multiple platforms through the open API of the live broadcast platform, builds a multi-dimensional benefit evaluation index system based on heterogeneous data sources, combines real-time interactive data, and forms multi-dimensional indicators based on user participation, product conversion rate, and server system load. When evaluating resource allocation, the priority of live broadcast rooms on different platforms is established; Data visualization module: After the collected data is standardized, it is displayed in intuitive ways such as charts and trend analysis to assist allocation decisions in a quantitative manner; Allocation plan optimization module: Use the number of live broadcast room exposures as a proxy indicator for benefits, introduce the benefit function of the live broadcast room to model the capital optimization problem as a multi-objective optimization model, sort out a visual allocation plan through a computer, and solve the optimal allocation strategy with the maximization of the benefit function as the objective function; Data processing module: standardize the collected data in Python; and apply the particle swarm optimization algorithm to iteratively solve the number of live broadcast room exposures that achieve the best benefits based on the multi-objective optimization model; Decision support module: Based on the solution results of the particle swarm algorithm, the optimal exposure times of the live broadcast rooms on different platforms are optimized to assist system users in making decisions.

[0040] Embodiment 3: The computer-readable storage medium of this embodiment stores a computer program thereon, which, when executed by a processor, implements the steps in a multi-platform live broadcast room data visualization and optimization decision support method of Embodiment 1.

[0041] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0042] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0043] Embodiment 4: The computer device of this embodiment includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of a multi-platform live broadcast room data visualization and optimization decision support method in Embodiment 1 are implemented.

[0044] In this embodiment, the processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, readily available programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0045] Those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on this understanding, the above technical solution can be essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-platform live broadcast room data visualization and optimization decision support method, characterized in that: The following steps are involved: Step 1: Collect real-time and historical data of live broadcast rooms on multiple platforms and build a data matrix of multiple live broadcast rooms; Step 2: Standardize the collected live broadcast room data through data preprocessing and feature extraction technology to obtain quantitative indicators with unified measurement standards and time dimensions; Step 3: According to the quantitative indicators, a multi-objective optimization problem is set to maximize the overall benefits of all live broadcast rooms in the live broadcast matrix; Step 4, using a particle swarm optimization algorithm to solve the multi-objective optimization problem, finding the optimal resource allocation for each live broadcast room through the particle swarm algorithm, and obtaining an optimal allocation plan that maximizes the overall benefit of the live broadcast matrix; Step 5: Use visualization tools to present quantitative indicators and optimized allocation plans to decision makers.

2. According to claim 1, a multi-platform live broadcast room data visualization and optimization decision support method is characterized in that: Step 2 The specific steps are: Step 2.1: Collect data through the API interface opened by the live broadcast platform, and use the Pandas library in Python to clean the collected data to remove duplicate, missing and invalid data; Step 2.2, extract features from the collected data, and use the Max-Min method to standardize each indicator so that the value range of each indicator is consistent. The steps are: Suppose the data matrix A contains n samples and m features. a ij is the data in the i-th row and j-th column of the matrix, , for the jth column in the data matrix, calculate the minimum and maximum values ​​of the feature: ； Application formula: , a ij ’ is standardized data, and the value of each feature is scaled to the range of [0, 1].

3. According to claim 2, a multi-platform live broadcast room data visualization and optimization decision support method is characterized in that: Step 2 is to further select indicators that are valuable for subsequent decision-making from the quantitative indicators. The specific steps are: Step 2.3, calculate the covariance matrix C of the data matrix A, the formula is ; Then calculate the eigenvalues ​​of the covariance matrix C λ and the eigenvector v , satisfying the following equation: , where the eigenvector v Indicates the main direction of the data, eigenvalue λ Indicates the data variance in this direction; Step 2.4, select the eigenvectors corresponding to the largest k eigenvalues ​​from the calculated eigenvalues, and project the data matrix onto the selected k principal components. The projection formula is: , where A' is the quantized data matrix, V k is the matrix composed of the selected k principal component eigenvectors, and Z is the data matrix after dimensionality reduction.

4. According to claim 1, a multi-platform live broadcast room data visualization and optimization decision support method is characterized in that: The specific process of step 3 is: Step 3.1: The total funds used to invest in the live broadcast room exposure is B. L i The exposure cost is x i , live broadcast room L i The unit exposure cost is s i , this coefficient is determined by the traffic statistics after the last live broadcast. For live broadcast L i The number of impressions available for purchase; Step 3.2, define the resource matrix R: ； in, r i It is i The resource allocation coefficient of the live broadcast room satisfies ,in P i It's a live broadcast room L i The priority is set based on user activity and revenue contribution factors; U i is user engagement; C i is the load requirement of the live broadcast room, which is related to the content complexity and the number of viewers in the live broadcast room; α , β and γ It is a tuning factor that controls the impact of priority, user participation, and load requirements on resource allocation; P i , U i and C i After a live broadcast ends, the resource allocation coefficient of the live broadcast room before the next live broadcast is calculated through the collected historical data. r i Cost per unit exposure s i There is a positive correlation; Step 3.3, use effective exposure times Replace the benefit function y i , the optimization problem is to maximize the overall benefits of all live broadcast rooms in the live broadcast matrix, and the objective function is: ; All constraints need to be met: 。 5. According to claim 4, a multi-platform live broadcast room data visualization and optimization decision support method is characterized in that: Further setting the benefit function y i Effective exposure times There is a nonlinear relationship between: , the objective function is: ; in b i Indicates i The basic benefit offset of each live broadcast room.

6. A multi-platform live broadcast room data visualization and optimization decision support method according to claim 5, characterized in that: Step 4 is as follows: Step 4.1, in the particle swarm, each particle represents a possible optimization scheme, and the position of the particle represents the number of exposures in each live broadcast room , the particle speed v It indicates the change in the number of exposures; Step 4.2, the fitness of the particle is calculated by the total benefit function, that is: ； Step 4.3, set the initial particle speed to 0, so that the particle swarm algorithm can explore the solution space at a slower speed in the initial iteration. The particle speed determines the moving direction and stride of the particle in the solution space, which will be updated in subsequent iterations. When the fitness of the particle is better than its historical optimal value, update the individual optimal solution of the particle. p best ; When the fitness of the particle is better than the global optimal value, update the group optimal solution g best ; Particle velocity update formula and position update formula: ； ； Among them is w Inertia weight, c 1 , c 2 is the acceleration constant, r 1 , r 2 is a random number, t is the number of iterations. The particle swarm updates the position of the particles through multiple iterations until the fitness change is less than the convergence threshold or the number of iterations reaches the maximum. The algorithm stops iterating and obtains the optimal optimization allocation plan.

7. The method for data visualization and optimization decision support of multi-platform live broadcast rooms according to claim 1, characterized in that: Further add step 6: According to the optimal optimization allocation solution solved by the particle swarm algorithm in step 4, optimize the resource matrix by analyzing historical data R , constantly update live data, analyze and update optimization models to ensure that capital allocation plans and resource allocation are always based on the latest historical data and market dynamics.

8. A multi-platform live broadcast room data visualization and optimization decision support system, characterized in that: The method is used to implement the steps in a multi-platform live broadcast room data visualization and optimization decision support method as described in any one of claims 1-7, including a data acquisition module, a data visualization module, an allocation plan optimization module, a data processing module, and a decision support module.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in a multi-platform live broadcast room data visualization and optimization decision support method as described in any one of claims 1-7 are implemented.

10. A computer device comprising a processor, a memory and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the multi-platform live broadcast room data visualization and optimization decision support method as described in any one of claims 1-7 are implemented.

Images