Enterprise service demand prediction and resource optimization configuration system based on artificial intelligence

Abstract

The invention discloses an enterprise service demand prediction and resource optimization configuration system based on artificial intelligence, and relates to the technical field of artificial intelligence. According to the method, the multi-source heterogeneous data is integrated through the multi-modal data fusion algorithm, the demand change rule is accurately captured through the time sequence prediction algorithm, the demand prediction precision is remarkably improved, and the problem that the demand perception ability is limited due to insufficient data utilization in a traditional method is solved; the resource optimization allocation algorithm is combined with the comprehensive utility function of the resource utilization rate and the customer satisfaction, the resource combination is dynamically adjusted, the resource allocation efficiency is greatly improved, the defect that a traditional resource allocation mode is static and lack of flexibility is overcome, and the user experience is improved by continuously monitoring customer interaction and service feedback data. Model parameters are dynamically updated, a resource allocation algorithm is optimized, continuous optimization of resource allocation is achieved, the problem that an existing method lacks an effective feedback mechanism is solved, long-term efficient operation of a system is ensured, and more competitive operation support is provided for enterprises.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of artificial intelligence, and particularly relates to an enterprise service demand prediction and resource optimization allocation system based on artificial intelligence. BACKGROUND

[0002] Enterprise service resource allocation is a core link of modern business operation, and directly affects the competitiveness and customer satisfaction of enterprises. With the increasingly complex market environment, the service demand faced by enterprises presents high dynamicity and uncertainty, and the traditional static resource allocation mode cannot meet the requirements of rapid response and accurate matching.

[0003] The current mainstream resource allocation methods generally have the problem of insufficient data utilization depth. These methods are often limited to processing structured historical data, and cannot effectively integrate multi-source heterogeneous information such as customer interaction records, supply chain sensor data and social media public opinion, resulting in limited perception ability of market demand changes. At the same time, the existing prediction models have insufficient ability in dealing with spatio-temporal correlation, and cannot accurately capture the complex rules of demand evolution, especially in identifying customer implicit demand. SUMMARY

[0004] The purpose of the present application is to provide an enterprise service demand prediction and resource optimization allocation system based on artificial intelligence, which significantly improves the demand prediction accuracy, resource allocation efficiency and system continuous optimization ability through multi-modal data fusion, dynamic resource optimization and closed-loop feedback mechanism.

[0005] The purpose of the present application can be achieved by the following technical solutions: The present application provides an enterprise service demand prediction and resource optimization allocation system based on artificial intelligence, comprising: A data acquisition and preprocessing module acquires enterprise internal customer interaction records, supply chain sensor data and external social media public opinion information, and performs format standardization processing on heterogeneous information sources through a multi-modal data fusion algorithm to obtain a fusion data set represented by a unified feature vector. A demand prediction module analyzes the mapping relationship between customer behavior patterns and demand change trends according to the spatio-temporal correlation features in the fusion data set, extracts periodic feature parameters if a periodic fluctuation of explicit demand is detected, and constructs a latent demand probability distribution model if an implicit demand evolution signal is identified. A customer value evaluation module calculates the service demand prediction value in a future time window through the demand probability distribution model, prioritizes the predicted demand according to the customer value level classification results, and determines the core service demand of high-value customers and the basic service demand of ordinary customers. The resource adaptation module obtains the current service personnel skill matrix and equipment load status data, calculates the task adaptation score of each service personnel based on the skill requirements matching degree of the predicted demand, and includes them in the candidate resource pool if the adaptation score exceeds the preset threshold. The resource optimization and allocation module uses a resource optimization allocation algorithm to dynamically combine and optimize human resources and equipment resources in the candidate resource pool. By calculating the comprehensive utility function of resource utilization and customer satisfaction, the optimal resource allocation scheme is obtained. The resource scheduling and adjustment module adjusts the resource scheduling strategy based on the personnel allocation results in the optimal resource configuration scheme and combined with real-time monitoring data of equipment load status. The model optimization and feedback module continuously monitors customer interaction records and service execution feedback data to update the feature weight parameters of the multimodal data fusion model.

[0006] Furthermore, the data acquisition and preprocessing module includes: Structured information such as customer interaction records and supply chain sensor data are obtained from within the enterprise, while unstructured data of public opinion information is crawled from external social media platforms to obtain heterogeneous datasets. The heterogeneous datasets are preprocessed and features are extracted. All features are then standardized. The standardized feature vectors are integrated through a weighted fusion method to obtain a fused dataset with a unified feature vector representation.

[0007] Furthermore, the demand forecasting module includes: Spatiotemporal correlation features are obtained from the fused dataset. Principal component analysis is used to reduce the dimensionality of the features to obtain high-dimensional spatiotemporal feature vectors. Long short-term memory network algorithm is used to analyze the mapping relationship between customer behavior patterns and demand change trends to obtain behavior trend prediction results. If the behavioral trend prediction results show periodic fluctuations, the Fourier transform method is used to extract periodic feature parameters to obtain a set of periodic feature parameters. If the behavioral trend prediction results do not show periodic fluctuations, the latent demand signal is analyzed through a hidden Markov model to obtain a potential demand state sequence. A Gaussian mixture model is used to construct the potential demand probability distribution, resulting in a demand probability distribution model. Probability distribution parameters are extracted from the demand probability distribution model and combined with a set of periodic feature parameters. A weighted fusion method is used to generate a comprehensive demand forecast result. Time series smoothing technology is used to optimize the forecast result and obtain the final demand change trend.

[0008] Furthermore, the customer value assessment module includes: The service demand value within the future time window is calculated by using a demand probability distribution model. A Bayesian probability model is used, and historical service data and time series features are input to obtain the predicted service demand value. Combined with a pre-established customer value hierarchy classification model, a K-means clustering algorithm is used, and customer behavior data and consumption records are input to determine the customer value hierarchy. For high-value customers and ordinary customers, a weighted sorting algorithm is used to input the predicted service demand value and customer value weight to obtain a demand priority sequence. If the proportion of high-value customer demand in the demand priority sequence is higher than the preset threshold, the core service demand of high-value customers is determined by inputting the priority sequence through the core service allocation rule. If the proportion of ordinary customer demands in the demand priority sequence exceeds a preset threshold, then the basic service demands of ordinary customers are determined according to the basic service allocation rules and the input priority sequence. Based on the core service demands and basic service demands, a service resource matching algorithm is used, with the service resource pool and demand priority sequence as input, to obtain a service resource allocation scheme. By combining real-time business data and employing time series analysis, along with input allocation schemes and business feedback data, dynamic adjustment values ​​for service demand are obtained.

[0009] Furthermore, the resource adaptation module includes: The service personnel skill matrix and equipment load status data are obtained. The service personnel skill feature vector and the current operating status parameters of the equipment are extracted from the database to generate a structured dataset. The cosine similarity algorithm is used to calculate the matching degree between the service personnel skill feature vector and the predicted demand skill vector to obtain the skill matching score of each service personnel. By combining equipment load status data, a weighted average method is used to calculate the task suitability score to determine the overall suitability of service personnel for the task. If the task suitability score exceeds a preset threshold, the corresponding service personnel will be included in the candidate resource pool to generate a candidate resource list. Obtain the current task allocation and equipment load status of each service personnel, and use a linear regression algorithm to predict the performance of service personnel in future tasks to obtain a predicted performance score; For service personnel in the candidate resource pool, a task allocation sequence is generated through a priority sorting algorithm to determine the final task allocation scheme. Real-time equipment load status and task execution progress are obtained to dynamically adjust the allocation of service personnel and generate optimized resource scheduling results.

[0010] Furthermore, the resource optimization and allocation module includes: Attribute data of human resources and equipment resources are obtained from the candidate resource pool. The initial allocation of human resources and equipment resources is calculated using a linear programming algorithm. By solving the linear programming algorithm, the preliminary resource allocation combination is obtained and the preliminary resource allocation combination is determined. If the resource utilization rate of the initial resource allocation combination is lower than the preset threshold, the genetic algorithm is used to iteratively optimize the configuration combination to obtain the optimized resource allocation combination. The resource utilization rate is calculated based on the optimized resource allocation combination, and the comprehensive utility value is obtained by combining the weighted summation method of the comprehensive utility function with the customer satisfaction index. If the overall utility value does not reach the preset threshold, the resource allocation weights are adjusted, and the genetic algorithm is used again for optimization to obtain the updated overall utility value. The utility scores of different resource configuration combinations are compared to determine the optimal resource allocation scheme and generate a resource allocation execution plan, and the final configuration result is output.

[0011] Furthermore, the resource scheduling and adjustment module includes: Acquire real-time monitoring data, extract equipment load data from equipment operating status, and if the equipment load data exceeds the safe operating threshold, trigger load over-limit judgment, and determine the triggering conditions for resource reallocation through load over-limit judgment; Data fusion analysis is employed to integrate personnel allocation results with equipment load data to obtain the basis for adjusting resource scheduling strategies. A linear regression algorithm is applied to predict the changing trend of resource scheduling strategies. Based on the changing trend, the resource allocation scheme is updated to obtain optimized resource allocation results. If the optimized resource allocation results meet the safe operation threshold, the final resource scheduling strategy is output.

[0012] Furthermore, the model optimization and feedback module includes: Real-time collection of customer interaction records and service feedback data is used to construct a multimodal dataset, obtain an initial dataset, process it using a multimodal data fusion model, update the feature weight parameters, and determine the optimized feature weights. By comparing customer satisfaction scores with preset thresholds, if the score is lower than the preset threshold, the parameters of the demand forecasting model are adjusted, an adjustment signal is obtained, the parameter configuration of the demand forecasting model is backtracked and optimized, and updated model parameters are generated. By re-predicting customer demand using the updated model parameters, the demand prediction results are obtained. The support vector machine algorithm is then used to classify the demand prediction results, and the accuracy of the prediction results is judged to obtain the classification evaluation results. The input data weights of the multimodal data fusion model are dynamically adjusted to obtain the optimized data fusion model.

[0013] Furthermore, it also includes a system optimization module, which optimizes the constraints and objective function weights in the resource allocation algorithm based on historical data of resource configuration execution effects, forming a closed-loop feedback mechanism.

[0014] Furthermore, the system optimization module includes: Acquire historical resource configuration data and execution performance data, construct a multidimensional dataset containing time series and resource utilization, analyze the multidimensional dataset using a linear regression algorithm, extract the correlation features between resource configuration and execution performance, and obtain feature weights; If the feature weights exceed the preset threshold, the constraints in the allocation algorithm are adjusted, the resource allocation limit range is updated, a new set of constraints is determined, the objective function is optimized using a genetic algorithm, the function weights are adjusted, the optimized objective function is obtained, a new resource allocation scheme is generated through the optimized objective function, the execution effect data of the allocation scheme is recorded, and the multidimensional dataset is updated. The updated multidimensional dataset is analyzed using a sliding window method to detect the trend of configuration efficiency changes and judge the optimization effect. If the trend does not reach the preset efficiency threshold, the feature weights and constraints are iteratively adjusted and the optimization process is repeated to obtain the final allocation scheme.

[0015] The beneficial effects of this invention are as follows: This invention employs a multimodal data fusion algorithm to integrate heterogeneous data from multiple sources both inside and outside an enterprise, and utilizes a time-series forecasting algorithm to accurately capture patterns of demand changes. This addresses the problem that traditional resource allocation methods, limited by structured historical data, struggle to integrate multi-source information such as customer interaction records, supply chain sensor data, and social media sentiment, resulting in limited perception of market demand changes. By comprehensively integrating multi-source data and advanced forecasting technologies, the accuracy and foresight of demand forecasting are significantly improved, providing enterprises with a more scientific basis for resource allocation and enhancing their adaptability and competitiveness in complex market environments. By combining a resource optimization allocation algorithm with a comprehensive utility function that considers resource utilization and customer satisfaction, the combination of human resources and equipment resources is dynamically adjusted. This solves the problem that traditional resource allocation models are static and lack flexibility, making it difficult to achieve efficient resource utilization in a dynamic market environment, leading to resource waste and decreased customer satisfaction. By dynamically optimizing resource allocation schemes, resources are ensured to be used efficiently while meeting customer needs, significantly improving resource allocation efficiency, enhancing enterprise operational benefits, and helping enterprises achieve optimal resource allocation in a dynamic market environment. By continuously monitoring customer interaction records and service execution feedback data, the feature weight parameters of the multimodal data fusion model are dynamically updated. Based on historical data of resource allocation performance, the resource allocation algorithm is optimized. This addresses the problem that existing resource allocation methods lack an effective feedback mechanism, making it impossible to dynamically adjust model parameters and optimization strategies based on actual performance, resulting in insufficient continuous improvement capabilities for resource allocation solutions. Through a closed-loop feedback mechanism, the actual effect of resource allocation can be perceived in real time, and optimization strategies can be dynamically adjusted based on feedback data. This enables continuous optimization of resource allocation solutions, ensuring long-term efficient system operation, providing enterprises with more competitive operational support, and continuously improving enterprise resource allocation efficiency and service quality. Attached Figure Description

[0016] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0017] Fig. 1 A schematic diagram of the structure of the AI-based enterprise service demand forecasting and resource optimization system provided for this application; Fig. 2 A flowchart illustrating the demand forecasting module of the AI-based enterprise service demand forecasting and resource optimization system provided in this application; Fig. 3 A flowchart illustrating the resource optimization and allocation module of the AI-based enterprise service demand forecasting and resource optimization and allocation system provided in this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0019] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0020] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0021] Example

[0022] Please see Figs. 1-3 This embodiment provides an artificial intelligence-based enterprise service demand prediction and resource optimization allocation system, including: The data acquisition and preprocessing module acquires internal customer interaction records, supply chain sensor data, and external social media sentiment information. It uses a multimodal data fusion algorithm to standardize the format of heterogeneous information sources and obtain a fusion dataset with a unified feature vector representation.

[0023] Furthermore, the data acquisition and preprocessing module includes: The system acquires structured information from internal customer interaction records and supply chain sensor data, while simultaneously scraping unstructured public opinion data from external social media platforms, resulting in a heterogeneous dataset. This dataset undergoes preprocessing and feature extraction, including data cleaning, noise reduction, missing value imputation, and word segmentation and stop word removal for text data. Features are extracted for different data types: numerical features are directly extracted from structured data, while unstructured text data is converted into semantic vectors using methods such as TF-IDF or Word2Vec. All features are then normalized or Z-score standardized to eliminate dimensional differences. The standardized feature vectors are then integrated through weighted fusion or feature concatenation to obtain a fused dataset with a unified feature vector representation.

[0024] Specifically, by integrating heterogeneous data from multiple sources and performing standardized processing, the integration difficulties caused by the single data source and inconsistent formats in traditional methods are effectively solved. This not only improves the efficiency and accuracy of data processing, but also provides a high-quality, uniformly formatted data foundation for subsequent analysis and modeling, significantly enhancing the system's ability to process complex data and its overall performance.

[0025] The demand forecasting module analyzes the mapping relationship between customer behavior patterns and demand change trends using time-series forecasting algorithms based on the spatiotemporal correlation characteristics of the fused dataset. If periodic fluctuations in explicit demand are detected, periodic feature parameters are extracted. If implicit demand evolution signals are identified, a potential demand probability distribution model is constructed.

[0026] Furthermore, the demand forecasting module includes: S11. Obtain spatiotemporal correlation features from the fused dataset, use principal component analysis to reduce the dimensionality of the features to obtain high-dimensional spatiotemporal feature vectors, and use the long short-term memory network algorithm to analyze the mapping relationship between customer behavior patterns and demand change trends to obtain behavior trend prediction results. S12. If the behavioral trend prediction results show periodic fluctuations, the Fourier transform method is used to extract periodic feature parameters to obtain a set of periodic feature parameters. If the behavioral trend prediction results do not show periodic fluctuations, the latent demand signal is analyzed through the hidden Markov model to obtain the potential demand state sequence. S13. Based on the potential demand state sequence, a Gaussian mixture model is used to construct the potential demand probability distribution, resulting in a demand probability distribution model. Probability distribution parameters are extracted from the demand probability distribution model, and combined with a set of periodic feature parameters. A weighted fusion method is used to generate a comprehensive demand forecast result. Time series smoothing technology is used to optimize the forecast result, resulting in the final demand change trend.

[0027] The process of constructing the probability distribution of potential demand using a Gaussian Mixture Model (GMM) mainly includes: extracting feature vectors related to potential demand from the fused dataset. These feature vectors contain information about customer behavior patterns and demand change trends. Then, these feature vectors are used as input data, and the GMM algorithm is used to model the data. GMM is a probability-based clustering method that assumes the data is generated by a mixture of multiple Gaussian distributions, each representing a potential demand state. The parameters of the GMM are iteratively optimized using the Expectation-Maximization (EM) algorithm to obtain parameters such as the mean, covariance matrix, and mixture weights for each Gaussian distribution; these parameters together constitute the demand probability distribution model. Finally, probability distribution parameters are extracted from the demand probability distribution model, such as the parameters of the probability density function (mean, covariance matrix) and mixture weights for each potential demand state. These parameters can be used to describe the characteristics and probability of occurrence of different potential demand states. Through these parameters, potential demand can be quantitatively analyzed and predicted, providing a basis for subsequent resource allocation.

[0028] Specifically, the demand forecasting module analyzes the spatiotemporal correlation features in the fused dataset and uses Long Short-Term Memory (LSTM) networks to capture customer behavior patterns and demand change trends, significantly improving the accuracy and foresight of demand forecasting. It can identify the periodic fluctuations of explicit demand and extract periodic feature parameters. At the same time, it analyzes implicit demand signals through Hidden Markov Models (HMM) and Gaussian Mixture Models (GMM) to construct a potential demand probability distribution model. Through weighted fusion and time series smoothing techniques, it optimizes the forecast results, providing a more scientific and accurate forecast basis for resource allocation, effectively solving the shortcomings of traditional methods in handling spatiotemporal correlations and identifying implicit demand.

[0029] The customer value assessment module calculates the predicted service demand within a future time window using a demand probability distribution model. It then prioritizes the predicted demand based on the customer value hierarchy classification results, determining the core service needs of high-value customers and the basic service needs of ordinary customers.

[0030] Furthermore, the customer value assessment module includes: The service demand value within the future time window is calculated by using a demand probability distribution model. A Bayesian probability model is used, and historical service data and time series features are input to obtain the predicted service demand value. Combined with a pre-established customer value hierarchy classification model, a K-means clustering algorithm is used, and customer behavior data and consumption records are input to determine the customer value hierarchy. The calculation of service demand within a future time window begins with the initial modeling of potential demand using a demand probability distribution model. This model captures the uncertainty of demand based on historical data and feature analysis. Next, a Bayesian probability model is employed, using historical service data and time-series features as input. This model combines prior knowledge and data-driven analysis to predict service demand. The predicted service demand within the future time window is then obtained through posterior probability calculation using the Bayesian model. This process not only considers historical trends but also introduces the quantification of demand uncertainty through a probability distribution model, thereby improving the accuracy and reliability of the prediction. To construct a pre-established customer value hierarchy classification model, multi-dimensional customer data was first collected, including consumption behavior, purchase frequency, purchase amount, customer feedback, and interaction records with the company. This data was then preprocessed, including data cleaning, noise reduction, and feature extraction, to ensure data quality and usability. Appropriate feature vectors, such as customer purchase amount, purchase frequency, and feedback satisfaction, were selected as input to the model. The K-means clustering algorithm was used to classify customers, and by iteratively optimizing the cluster centers, customers were divided into different value levels, such as high-value customers, medium-value customers, and low-value customers. Finally, the clustering results were evaluated and validated to ensure the model's accuracy and reliability, thus providing a basis for subsequent resource allocation and customer service strategies.

[0031] By classifying customers by value level, a weighted sorting algorithm is used to input the predicted service demand value and customer value weight for high-value customers and ordinary customers to obtain a demand priority sequence. If the proportion of demand from high-value customers in the demand priority sequence is higher than a preset threshold, the core service allocation rules are used to input the priority sequence to determine the core service demand of high-value customers. In this process, high-value customers are assigned higher weights, while ordinary customers are assigned lower weights. Then, the predicted service demand is multiplied by the customer's value weight to obtain a demand priority score for each customer. Finally, all customers are ranked according to their demand priority scores to form a demand priority sequence, thus providing a basis for resource allocation.

[0032] If the proportion of ordinary customer demands in the demand priority sequence exceeds a preset threshold, then the basic service demands of ordinary customers are determined according to the basic service allocation rules and the input priority sequence. Based on the core service demands and basic service demands, a service resource matching algorithm is used, with the service resource pool and demand priority sequence as input, to obtain a service resource allocation scheme. By combining service resource allocation schemes with real-time business data and employing time series analysis, and inputting allocation schemes and business feedback data, dynamic adjustment values ​​for service demands are obtained.

[0033] Specifically, by using demand probability distribution models and Bayesian probability models to accurately predict future service demands, and combining them with customer value hierarchy classification models to prioritize demands, the core service demands of high-value customers and the basic service demands of ordinary customers are determined. This solves the problems of insufficient customer value identification and inaccurate demand prioritization in traditional resource allocation. It not only improves the accuracy of resource allocation and customer satisfaction, but also optimizes the overall efficiency of resource allocation, providing higher-quality services to high-value customers, while ensuring the flexibility and adaptability of the resource allocation plan.

[0034] The resource adaptation module obtains the current service personnel skill matrix and equipment load status data, calculates the task adaptation score for each service personnel based on the skill requirements matching degree of the predicted demand, and includes them in the candidate resource pool if the adaptation score exceeds the preset threshold.

[0035] Furthermore, the resource adaptation module includes: The service personnel skill matrix and equipment load status data are obtained. The service personnel skill feature vector and the current operating status parameters of the equipment are extracted from the database to generate a structured dataset. The cosine similarity algorithm is used to calculate the matching degree between the service personnel skill feature vector and the predicted demand skill vector to obtain the skill matching score of each service personnel. The process of calculating the matching degree between service personnel skill feature vectors and predicted demand skill vectors involves first extracting service personnel skill feature vectors and predicted demand skill vectors from the database. These vectors typically contain multiple dimensions, each representing a specific skill level or demand. Then, the cosine similarity algorithm is used to calculate the cosine value of the angle between the two vectors, quantifying the matching degree between the service personnel's skills and task requirements, and obtaining a skill matching score for each service personnel. This score reflects the service personnel's suitability for a specific task, providing an important reference for subsequent resource allocation.

[0036] Based on the skill matching score and combined with equipment load status data, the task suitability score is calculated using a weighted average method to determine the overall suitability of service personnel for the task. If the task suitability score exceeds a preset threshold, the corresponding service personnel will be included in the candidate resource pool to generate a candidate resource list. The calculation of the task suitability score involves first obtaining the skill matching score of each service personnel and the current equipment load status data. The skill matching score reflects the degree of fit between the service personnel's skills and task requirements, while the equipment load status data represents the current operating pressure of the equipment. Weights are then set based on business needs and experience. Typically, the skill matching score has a higher weight because the fit between skills and tasks is a key factor, but the equipment load status cannot be ignored because it affects the actual work efficiency of the service personnel. The skill matching score and the equipment load status data are multiplied by their respective weights and then added together to obtain the task suitability score. This score comprehensively considers the skill level of the service personnel and the actual operating status of the equipment, enabling a more comprehensive assessment of the overall suitability of the service personnel for the task.

[0037] From the candidate resource list, obtain the current task allocation and equipment load status of each service personnel, and use a linear regression algorithm to predict the performance of service personnel in future tasks to obtain a predicted performance score; Based on the predicted performance score, a task allocation sequence is generated for the service personnel in the candidate resource pool using a priority sorting algorithm. The final task allocation scheme is determined, real-time equipment load status and task execution progress are obtained, and the allocation of service personnel is dynamically adjusted to generate optimized resource scheduling results.

[0038] Specifically, by accurately matching service personnel skills with task requirements and dynamically adjusting resource allocation based on equipment load status, the problem of inaccurate matching of personnel and tasks and insufficient flexibility in resource scheduling in traditional resource allocation is effectively solved. The skill matching score is calculated using the cosine similarity algorithm to select highly suitable service personnel into the candidate resource pool. Then, the future performance is predicted through linear regression to generate a task allocation sequence, achieving efficient resource allocation. At the same time, real-time monitoring of equipment load and task progress and dynamic adjustment of resource allocation improve the flexibility and adaptability of resource scheduling, significantly improving resource utilization efficiency and service quality.

[0039] The resource optimization and allocation module uses a resource optimization allocation algorithm to dynamically combine and optimize human and equipment resources in the candidate resource pool. By calculating the comprehensive utility function of resource utilization and customer satisfaction, the optimal resource allocation scheme is obtained.

[0040] Furthermore, the resource optimization and allocation module includes: S21. Obtain attribute data of human resources and equipment resources from the candidate resource pool, including the skill level, work efficiency, and current task load of service personnel, as well as the operating status, performance indicators, and current load of equipment. Use a linear programming algorithm to calculate the initial allocation of human resources and equipment resources. The goal of linear programming is to maximize resource utilization while meeting customer needs and resource constraints. By solving the linear programming algorithm, obtain the initial resource configuration combination and determine the initial resource configuration combination. S22. If the resource utilization rate of the initial resource allocation combination is lower than the preset threshold, a genetic algorithm is used to iteratively optimize the allocation combination to obtain an optimized resource allocation combination. The genetic algorithm performs encoding, crossover, mutation, and selection operations on the resource allocation scheme by simulating the process of natural selection, gradually improving the resource utilization rate. The resource utilization rate is calculated based on the optimized resource allocation combination, and the comprehensive utility value is obtained by combining the weighted summation method of the comprehensive utility function with the customer satisfaction index. The overall utility value requires determining the weights of two key indicators: resource utilization and customer satisfaction. These weights can be adjusted according to the company's strategic goals and actual needs. For example, if the company places more emphasis on customer satisfaction, it can give customer satisfaction a higher weight by multiplying resource utilization and customer satisfaction by their respective weights and then summing the weighted values ​​to obtain the overall utility value.

[0041] S23. If the overall utility value does not reach the preset threshold, adjust the resource allocation weights, re-optimize using the genetic algorithm, obtain the updated overall utility value, compare the utility scores of different resource configuration combinations, determine the optimal resource allocation scheme, generate a resource allocation execution plan, and output the final configuration result.

[0042] Specifically, by dynamically optimizing the combination of human and equipment resources, resource utilization and customer satisfaction have been significantly improved. Resource attribute data is obtained from the candidate resource pool, and a preliminary resource allocation plan is generated using a linear programming algorithm. By combining a comprehensive utility function with a customer satisfaction index evaluation plan, the optimal resource allocation plan is determined and an execution plan is generated. This effectively solves the problems of low resource utilization and insufficient customer satisfaction in traditional resource allocation, ensuring the scientific and practical nature of resource allocation.

[0043] The resource scheduling and adjustment module adjusts the resource scheduling strategy based on the personnel allocation results in the optimal resource configuration scheme and the real-time monitoring data of equipment load status. If the equipment load exceeds the safe operation threshold, the resource reallocation mechanism is triggered.

[0044] Furthermore, the resource scheduling and adjustment module includes: Acquire real-time monitoring data and extract equipment load data from equipment operating status. If the equipment load data exceeds the safe operating threshold, trigger a load over-limit judgment, and determine the triggering conditions for resource reallocation based on the load over-limit judgment; Data fusion analysis is employed to integrate personnel allocation results with equipment load data to obtain the basis for adjusting resource scheduling strategies. A linear regression algorithm is applied to predict the changing trend of resource scheduling strategies. Based on the changing trend, the resource allocation scheme is updated to obtain optimized resource allocation results. If the optimized resource allocation results meet the safe operation threshold, the final resource scheduling strategy is output.

[0045] This method employs data fusion analysis technology to integrate personnel allocation results with equipment load data. The personnel allocation results provide detailed information on current task assignments, including the skill level of service personnel, task priority, and allocation status. The equipment load data reflects the real-time operating status and current load of the equipment. Through data fusion analysis, the overall status of current resource scheduling can be comprehensively assessed, thereby obtaining the basis for adjusting resource scheduling strategies. The linear regression algorithm is applied to analyze the fused data to predict the changing trends of resource scheduling strategies. By establishing a mathematical model, the linear regression algorithm analyzes the linear relationship between data to predict future changes in resource demand and equipment load trends. Based on the prediction results, resource allocation can be adjusted in advance, scheduling strategies can be optimized, and efficient resource utilization and safe equipment operation can be ensured.

[0046] Specifically, by monitoring the equipment load status in real time, the risk of equipment overload is solved. When the equipment load exceeds the safety threshold, the module triggers a resource reallocation mechanism. It combines personnel allocation results and equipment load data for data fusion analysis, uses linear regression algorithm to predict the changing trend of scheduling strategy, and updates the resource configuration scheme accordingly. The final output resource scheduling strategy ensures the safety of equipment operation and the flexibility of resource scheduling, thereby improving the stability and efficiency of enterprise operation.

[0047] The model optimization and feedback module continuously monitors customer interaction records and service execution feedback data to update the feature weight parameters of the multimodal data fusion model. If the customer satisfaction score is lower than expected, the parameter configuration of the demand forecasting model is adjusted retrospectively.

[0048] Furthermore, the model optimization and feedback module includes: Real-time collection of customer interaction records and service feedback data is used to construct a multimodal dataset, obtain an initial dataset, process the initial dataset using a multimodal data fusion model, update the feature weight parameters, and determine the optimized feature weights. Customer interaction records include data from various touchpoints between customers and the company, such as customer service chat logs, telephone call records, and online customer service interactions; service feedback data covers customer satisfaction ratings, complaint records, and product usage feedback.

[0049] By comparing customer satisfaction scores with preset thresholds, if the score is lower than the preset threshold, the parameters of the demand forecasting model are adjusted, an adjustment signal is obtained, the parameter configuration of the demand forecasting model is backtracked and optimized, and updated model parameters are generated. By updating the model parameters, customer demand is re-predicted to obtain demand prediction results. The support vector machine algorithm is used to classify the demand prediction results, and the accuracy of the prediction results is judged to obtain classification evaluation results. The input data weights of the multimodal data fusion model are dynamically adjusted to obtain the optimized data fusion model.

[0050] Specifically, by monitoring customer interaction and service feedback data in real time, the feature weight parameters of the multimodal data fusion model are dynamically adjusted to ensure that the model can adapt to market changes in a timely manner. Furthermore, the support vector machine algorithm is used to classify and evaluate demand forecasting results, dynamically adjusting the input weights of the data fusion model to optimize model performance. Through a closed-loop feedback mechanism, the model's predictive ability and resource allocation adaptability are continuously improved, significantly enhancing customer satisfaction and resource allocation efficiency.

[0051] Furthermore, it also includes a system optimization module, which optimizes the constraints and objective function weights in the resource allocation algorithm based on historical data of resource configuration execution performance, forming a closed-loop feedback mechanism to continuously improve overall configuration efficiency.

[0052] Furthermore, the system optimization module includes: Acquire historical resource configuration data and execution performance data, construct a multidimensional dataset containing time series and resource utilization, analyze the multidimensional dataset using a linear regression algorithm, extract the correlation features between resource configuration and execution performance, and obtain feature weights; This process involves acquiring historical resource allocation and execution performance data from the enterprise's resource management and business execution systems. This includes multi-dimensional information such as time-series resource allocation, resource utilization, task completion, and customer satisfaction. These data are then integrated into a multidimensional dataset containing time-series data and resource utilization. A linear regression algorithm is then used to analyze this dataset. A linear model is established to quantify the relationship between resource allocation and execution performance. Through regression analysis, relevant features between resource allocation and execution performance are extracted, and the weight of each feature is calculated. These weights reflect the importance of different features in resource allocation performance, providing a scientific basis for subsequent resource optimization and allocation.

[0053] If the feature weights exceed the preset threshold, the constraints in the allocation algorithm are adjusted, the resource allocation limit range is updated, a new set of constraints is determined, the objective function is optimized using a genetic algorithm, the function weights are adjusted, the optimized objective function is obtained, a new resource allocation scheme is generated through the optimized objective function, the execution effect data of the allocation scheme is recorded, and the multidimensional dataset is updated. This study employs a genetic algorithm to optimize the objective function and adjust its weights. Specifically, the resource allocation problem is modeled as an objective function that comprehensively considers key indicators such as resource utilization and customer satisfaction. A population is then initialized, with each individual representing a resource allocation scheme, represented by a binary string or other suitable encoding method. Next, the fitness of each individual, i.e., the objective function value, is calculated; individuals with higher fitness are more likely to be selected. A new population is generated through selection, crossover, and mutation operations. Selection selects individuals based on fitness, crossover exchanges some genes between individuals, and mutation randomly alters some genes in individuals, introducing new genetic variations. These operations are iterated repeatedly to progressively optimize the objective function value, adjusting the function weights to balance the importance of different indicators. Finally, the individual with the highest fitness is selected as the optimized resource allocation scheme, yielding the optimized objective function and providing the optimal solution for resource allocation.

[0054] A sliding window method is used to analyze the updated cube, detect the trend of configuration efficiency changes, and judge the optimization effect. If the trend does not reach the preset efficiency threshold, the feature weights and constraints are iteratively adjusted, and the optimization process is repeated to obtain the final allocation scheme.

[0055] Specifically, the system optimization module uses a closed-loop feedback mechanism to construct a multidimensional dataset using historical resource allocation data and execution performance data. It analyzes the correlation characteristics between resource allocation and execution performance, dynamically adjusts the constraints and objective function weights in the allocation algorithm, optimizes the objective function using a genetic algorithm to generate new resource allocation schemes, and detects the changing trend of allocation efficiency using a sliding window method. This solves the problem of the lack of dynamic adjustment and continuous optimization capabilities in traditional resource allocation methods. Through the closed-loop feedback mechanism, it continuously improves the efficiency and adaptability of resource allocation, ensuring the long-term efficient operation of the system.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An enterprise service demand forecasting and resource optimization system based on artificial intelligence, characterized in that: include: The data acquisition and preprocessing module acquires internal customer interaction records, supply chain sensor data, and external social media sentiment information. It uses a multimodal data fusion algorithm to standardize the format of heterogeneous information sources and obtain a fusion dataset with a unified feature vector representation. The demand forecasting module analyzes the mapping relationship between customer behavior patterns and demand change trends using time-series forecasting algorithms based on the spatiotemporal correlation characteristics in the fused dataset. If periodic fluctuations in explicit demand are detected, periodic feature parameters are extracted. If signals of implicit demand evolution are identified, a probability distribution model of potential demand is constructed. The customer value assessment module calculates the predicted service demand within a future time window using a demand probability distribution model, and prioritizes the predicted demand based on the customer value hierarchy classification results to determine the core service needs of high-value customers and the basic service needs of ordinary customers. The resource adaptation module obtains the current service personnel skill matrix and equipment load status data, calculates the task adaptation score of each service personnel based on the skill requirements matching degree of the predicted demand, and includes them in the candidate resource pool if the adaptation score exceeds the preset threshold. The resource optimization and allocation module uses a resource optimization allocation algorithm to dynamically combine and optimize human resources and equipment resources in the candidate resource pool. By calculating the comprehensive utility function of resource utilization and customer satisfaction, the optimal resource allocation scheme is obtained. The resource scheduling and adjustment module adjusts the resource scheduling strategy based on the personnel allocation results in the optimal resource configuration scheme and combined with real-time monitoring data of equipment load status. The model optimization and feedback module continuously monitors customer interaction records and service execution feedback data to update the feature weight parameters of the multimodal data fusion model.

2. The enterprise service demand prediction and resource optimization system based on artificial intelligence according to claim 1, characterized in that: The data acquisition and preprocessing module includes: Structured information such as customer interaction records and supply chain sensor data are obtained from within the enterprise, while unstructured data of public opinion information is crawled from external social media platforms to obtain heterogeneous datasets. The heterogeneous datasets are preprocessed and features are extracted. All features are then standardized. The standardized feature vectors are integrated through a weighted fusion method to obtain a fused dataset with a unified feature vector representation.

3. The enterprise service demand prediction and resource optimization allocation system based on artificial intelligence according to claim 1, characterized in that: The demand forecasting module includes: Spatiotemporal correlation features are obtained from the fused dataset. Principal component analysis is used to reduce the dimensionality of the features to obtain high-dimensional spatiotemporal feature vectors. Long short-term memory network algorithm is used to analyze the mapping relationship between customer behavior patterns and demand change trends to obtain behavior trend prediction results. If the behavioral trend prediction results show periodic fluctuations, the Fourier transform method is used to extract periodic feature parameters to obtain a set of periodic feature parameters. If the behavioral trend prediction results do not show periodic fluctuations, the latent demand signal is analyzed through a hidden Markov model to obtain a potential demand state sequence. A Gaussian mixture model is used to construct the potential demand probability distribution, resulting in a demand probability distribution model. Probability distribution parameters are extracted from the demand probability distribution model and combined with a set of periodic feature parameters. A weighted fusion method is used to generate a comprehensive demand forecast result. Time series smoothing technology is used to optimize the forecast result and obtain the final demand change trend.

4. The enterprise service demand prediction and resource optimization allocation system based on artificial intelligence according to claim 1, characterized in that: The customer value assessment module includes: The service demand value within the future time window is calculated by using a demand probability distribution model. A Bayesian probability model is used, and historical service data and time series features are input to obtain the predicted service demand value. Combined with a pre-established customer value hierarchy classification model, a K-means clustering algorithm is used, and customer behavior data and consumption records are input to determine the customer value hierarchy. For high-value customers and ordinary customers, a weighted sorting algorithm is used to input the predicted service demand value and customer value weight to obtain a demand priority sequence. If the proportion of high-value customer demand in the demand priority sequence is higher than the preset threshold, the core service demand of high-value customers is determined by inputting the priority sequence through the core service allocation rule. If the proportion of ordinary customer demands in the demand priority sequence exceeds a preset threshold, then the basic service demands of ordinary customers are determined according to the basic service allocation rules and the input priority sequence. Based on the core service demands and basic service demands, a service resource matching algorithm is used, with the service resource pool and demand priority sequence as input, to obtain a service resource allocation scheme. By combining real-time business data and employing time series analysis, along with input allocation schemes and business feedback data, dynamic adjustment values ​​for service demand are obtained.

5. The enterprise service demand prediction and resource optimization allocation system based on artificial intelligence according to claim 1, characterized in that: The resource adaptation module includes: The service personnel skill matrix and equipment load status data are obtained. The service personnel skill feature vector and the current operating status parameters of the equipment are extracted from the database to generate a structured dataset. The cosine similarity algorithm is used to calculate the matching degree between the service personnel skill feature vector and the predicted demand skill vector to obtain the skill matching score of each service personnel. By combining equipment load status data, a weighted average method is used to calculate the task suitability score to determine the overall suitability of service personnel for the task. If the task suitability score exceeds a preset threshold, the corresponding service personnel will be included in the candidate resource pool to generate a candidate resource list. Obtain the current task allocation and equipment load status of each service personnel, and use a linear regression algorithm to predict the performance of service personnel in future tasks to obtain a predicted performance score; For service personnel in the candidate resource pool, a task allocation sequence is generated through a priority sorting algorithm to determine the final task allocation scheme. Real-time equipment load status and task execution progress are obtained to dynamically adjust the allocation of service personnel and generate optimized resource scheduling results.

6. The enterprise service demand prediction and resource optimization allocation system based on artificial intelligence according to claim 1, characterized in that: The resource optimization and allocation module includes: Attribute data of human resources and equipment resources are obtained from the candidate resource pool. The initial allocation of human resources and equipment resources is calculated using a linear programming algorithm. By solving the linear programming algorithm, the preliminary resource allocation combination is obtained and the preliminary resource allocation combination is determined. If the resource utilization rate of the initial resource allocation combination is lower than the preset threshold, the genetic algorithm is used to iteratively optimize the configuration combination to obtain the optimized resource allocation combination. The resource utilization rate is calculated based on the optimized resource allocation combination, and the comprehensive utility value is obtained by combining the weighted summation method of the comprehensive utility function with the customer satisfaction index. If the overall utility value does not reach the preset threshold, the resource allocation weights are adjusted, and the genetic algorithm is used again for optimization to obtain the updated overall utility value. The utility scores of different resource configuration combinations are compared to determine the optimal resource allocation scheme and generate a resource allocation execution plan, and the final configuration result is output.

7. The enterprise service demand prediction and resource optimization system based on artificial intelligence according to claim 1, characterized in that: The resource scheduling and adjustment module includes: Acquire real-time monitoring data, extract equipment load data from equipment operating status, and if the equipment load data exceeds the safe operating threshold, trigger load over-limit judgment, and determine the triggering conditions for resource reallocation through load over-limit judgment; Data fusion analysis is employed to integrate personnel allocation results with equipment load data to obtain the basis for adjusting resource scheduling strategies. A linear regression algorithm is applied to predict the changing trend of resource scheduling strategies. Based on the changing trend, the resource allocation scheme is updated to obtain optimized resource allocation results. If the optimized resource allocation results meet the safe operation threshold, the final resource scheduling strategy is output.

8. The enterprise service demand prediction and resource optimization system based on artificial intelligence according to claim 1, characterized in that: The model optimization and feedback module includes: Real-time collection of customer interaction records and service feedback data is used to construct a multimodal dataset, obtain an initial dataset, process it using a multimodal data fusion model, update the feature weight parameters, and determine the optimized feature weights. By comparing customer satisfaction scores with preset thresholds, if the score is lower than the preset threshold, the parameters of the demand forecasting model are adjusted, an adjustment signal is obtained, the parameter configuration of the demand forecasting model is backtracked and optimized, and updated model parameters are generated. By re-predicting customer demand using the updated model parameters, the demand prediction results are obtained. The support vector machine algorithm is then used to classify the demand prediction results, and the accuracy of the prediction results is judged to obtain the classification evaluation results. The input data weights of the multimodal data fusion model are dynamically adjusted to obtain the optimized data fusion model.

9. The enterprise service demand prediction and resource optimization allocation system based on artificial intelligence according to claim 1, characterized in that: It also includes a system optimization module, which optimizes the constraints and objective function weights in the resource allocation algorithm based on historical data of resource configuration execution effects, forming a closed-loop feedback mechanism.

10. The enterprise service demand prediction and resource optimization system based on artificial intelligence according to claim 9, characterized in that: The system optimization module includes: Acquire historical resource configuration data and execution performance data, construct a multidimensional dataset containing time series and resource utilization, analyze the multidimensional dataset using a linear regression algorithm, extract the correlation features between resource configuration and execution performance, and obtain feature weights; If the feature weights exceed the preset threshold, the constraints in the allocation algorithm are adjusted, the resource allocation limit range is updated, a new set of constraints is determined, the objective function is optimized using a genetic algorithm, the function weights are adjusted, the optimized objective function is obtained, a new resource allocation scheme is generated through the optimized objective function, the execution effect data of the allocation scheme is recorded, and the multidimensional dataset is updated. The updated multidimensional dataset is analyzed using a sliding window method to detect the trend of configuration efficiency changes and judge the optimization effect. If the trend does not reach the preset efficiency threshold, the feature weights and constraints are iteratively adjusted and the optimization process is repeated to obtain the final allocation scheme.

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