Thermodynamic marketing intelligent analysis method, system and equipment based on multi-source data fusion and medium

Abstract

The invention relates to the technical field of thermodynamic analysis, and discloses a thermodynamic marketing intelligent analysis method, system and device based on multi-source data fusion and a medium, and the method comprises the steps: integrating multi-dimensional data obtained from a first layer, a second layer and a third layer through a data middle platform, and building a user information database; based on the user information database, constructing a first portrait model by combining a first statistical algorithm and a first label rule, and classifying and identifying the user; predicting heat consumption demands of different user groups through a classification result of the first portrait model, and formulating a thermal resource allocation strategy according to the heat consumption demands; establishing a correlation model of the work order response and the thermal resource allocation strategy, and judging and analyzing response delay and missing work orders based on the correlation model so as to optimize the marketing service process and the resource allocation strategy; according to the invention, service transformation from passive response to active prediction is realized, and an intelligent and refined operation management means is provided for a heat enterprise.

Description

Technical Field

[0001] This invention relates to the field of thermal analysis technology, and in particular to a method, system, device and medium for intelligent thermal marketing analysis based on multi-source data fusion. Background Technology

[0002] In traditional thermal marketing management systems, core business modules such as customer service, billing, and production typically employ independent IT architectures. The lack of effective data interaction mechanisms between these systems creates severe information silos, hindering both operational efficiency and service level improvement. Due to the inability to share data, enterprises face numerous difficulties in areas such as user information retrieval, work order processing, billing management, and production scheduling. For example, existing systems often employ a distributed database design, storing key data such as user basic information, payment records, work order data, and production operation indicators across different business systems, lacking unified data standards and integration interfaces. Traditional systems generally rely on manual operation and static report analysis. For instance, customer service work order allocation often uses a manual dispatch model, which is inefficient and susceptible to subjective factors; billing management lacks intelligent overdue payment warnings and collection strategies, relying mainly on experience-based judgment; and production scheduling is based on historical data and fixed algorithms, making it difficult to dynamically respond to changes in user demand or abnormal operating conditions. Furthermore, existing technologies have weak capabilities in analyzing user behavior, typically only achieving simple data statistics, unable to construct user profiles through multi-dimensional data fusion, let alone predict potential user needs or optimize service strategies.

[0003] Traditional systems often employ locally deployed client / server (C / S) or basic browser / server (B / S) architectures, resulting in limited data processing capabilities and poor scalability. Some heating companies are still using early standalone billing software, which cannot support mobile queries or real-time data synchronization; customer service systems may only have basic telephone access functions, lacking intelligent voice navigation, multi-channel work order integration, or automated knowledge base support. Furthermore, existing systems have a weak application of big data analytics and artificial intelligence technologies, with data mining largely limited to simple queries and report generation, lacking deep analysis capabilities based on machine learning.

[0004] Therefore, in order to address the significant shortcomings of existing thermal marketing management systems in terms of data integration, intelligent analysis, and business collaboration, it is urgent to introduce multi-source data fusion technology to break down information silos and build a unified intelligent analysis method. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides a method, system, device, and medium for intelligent analysis of heat marketing based on multi-source data fusion, which solves the problems of data silos, low level of intelligence, and poor business collaboration capabilities in traditional heat marketing management systems. It solves the technical bottlenecks of data fragmentation between systems, weak analysis capabilities, and low service efficiency through multi-source data fusion technology.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a heat map marketing intelligent analysis method based on multi-source data fusion, including:

[0009] By integrating multi-dimensional data obtained from the first, second, and third levels through a data platform, a user information database is established.

[0010] Based on the user information database, a first profile model is constructed by combining a first statistical algorithm and a first tagging rule to classify and identify users.

[0011] The heat demand of different user groups is predicted based on the classification results of the first profile model, and a heat resource allocation strategy is formulated based on the heat demand.

[0012] Establish a correlation model between work order response and the aforementioned thermal resource allocation strategy. Based on the correlation model, determine and analyze response delays and missing work orders to optimize marketing service processes and resource allocation strategies.

[0013] As a preferred embodiment of the heat marketing intelligent analysis method based on multi-source data fusion described in this invention, the step of classifying and identifying users by constructing a first profile model by combining a first statistical algorithm and a first tagging rule includes:

[0014] Extract the first basic attribute features and the first heating behavior features of the users from the user information database;

[0015] The first statistical algorithm is used to analyze the first basic attribute features and the first heat consumption behavior features to identify key behavioral features;

[0016] Users are initially categorized based on the key behavioral characteristics and the first labeling rules;

[0017] The user groups after the initial classification are subjected to feature aggregation to generate representative user profile templates and assigned unique identifier tags.

[0018] The beneficial effects of this preferred technical solution are: accurately identifying the characteristics of user groups and differences in heating demand, making the allocation of heat resources more scientific and rational.

[0019] As a preferred embodiment of the intelligent analysis method for heat marketing based on multi-source data fusion described in this invention, the method includes: predicting the heat demand of different user groups based on the classification results of the first user profile model, which includes:

[0020] Based on the classification results of the first profile model, historical heating data of different user groups are extracted, and combined with regional distribution data, the differences in heating behavior of user groups in different geographical areas are analyzed to identify regional heating patterns.

[0021] A first prediction model was constructed, and time series analysis was used to predict the changing trends of heating demand for different user groups in future periods.

[0022] The forecast results are aggregated by region to generate a heat map of regional heat demand distribution, marking high-demand and low-demand areas.

[0023] As a preferred embodiment of the intelligent analysis method for heat marketing based on multi-source data fusion described in this invention, the step of formulating a heat resource allocation strategy according to the heat demand includes:

[0024] Based on the heat demand distribution heat map of the region, and combined with the coverage of the heat exchange stations, the priority of heat source allocation is divided, a first scheduling instruction is generated and sent to each heat exchange station for execution.

[0025] As a preferred embodiment of the heat marketing intelligent analysis method based on multi-source data fusion described in this invention, the integration of multi-dimensional data obtained from the first, second, and third levels through a data platform includes:

[0026] The first layer consists of customer service system data, including user call records, work order processing information, and customer feedback data; the second layer consists of billing system data, including user payment records, outstanding payment information, and heat usage classification; the third layer consists of production system data, including heat exchange station operating parameters, room temperature monitoring data, and heating network dispatch records.

[0027] By using a data platform, data from the customer service system, billing system, and production system are linked and integrated according to user ID and timestamp fields to build a unified user information database and establish a mapping relationship between user payment behavior and customer service interaction.

[0028] The merged data is deduplicated, completed, and outlier detected, and a data update mechanism is established.

[0029] As a preferred embodiment of the intelligent analysis method for heat marketing based on multi-source data fusion described in this invention, the establishment of the correlation model between work order response and the heat resource allocation strategy includes:

[0030] The core factors affecting the warning time can be summarized into different dimensions, including work order type, work order priority / severity, applicant identity, and the system involved;

[0031] Each rule dimension is divided into segments, and preliminary time thresholds are set for each sub-scenario based on past data and SLA requirements;

[0032] Examine the coordination of rules from different dimensions, and formulate superimposed judgment rules for scenarios with overlapping effects.

[0033] As a preferred embodiment of the heat marketing intelligent analysis method based on multi-source data fusion described in this invention, the optimization of marketing service processes and resource allocation strategies includes:

[0034] The work orders are clustered into multiple service areas based on their geographical location, and the service distance between each work order and the center of each service area is calculated.

[0035] Distribute alert work orders to the nearest service area with the lowest current load, including:

[0036] The overall score by which a work order is assigned to a service area is evaluated based on the current load and distance weighting coefficient of the service area.

[0037] For each work order, find the service area with the lowest comprehensive score;

[0038] Intelligent sorting is performed within the region using a multi-weighted priority queue model;

[0039] For the highest-level red alert work orders, emergency dispatch between regions is initiated: for each red alert work order, the current load of the region to which it belongs is determined. If the current load exceeds the preset threshold, service personnel are drawn from the set of neighboring regions.

[0040] The beneficial effects of this preferred technical solution are: by establishing an intelligent correlation model between work order response and resource allocation, the service process and scheduling strategy are dynamically optimized, which significantly improves the accuracy and response efficiency of marketing services.

[0041] Secondly, the present invention provides a heat map marketing intelligent analysis system based on multi-source data fusion, comprising:

[0042] The data integration module is used to integrate multi-dimensional data obtained from the first, second, and third levels through the data platform to establish a user information database;

[0043] The classification and recognition module is used to classify and recognize users based on the user information database by constructing a first profile model by combining a first statistical algorithm and a first labeling rule.

[0044] The allocation strategy formulation module is used to predict the heating demand of different user groups based on the classification results of the first profile model, and formulate a heating resource allocation strategy based on the heating demand.

[0045] The optimization strategy module is used to establish a correlation model between work order response and the thermal resource allocation strategy. Based on the correlation model, it judges and analyzes response delays and missing work orders to optimize the marketing service process and resource allocation strategy.

[0046] Thirdly, the present invention provides an electronic device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor executes the computer-executable instructions to implement the steps of a heat marketing intelligent analysis method based on multi-source data fusion.

[0047] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of a heat marketing intelligent analysis method based on multi-source data fusion.

[0048] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a method, system, equipment, and medium for intelligent analysis of heat marketing based on multi-source data fusion. It integrates multi-dimensional data from customer service, billing, and production systems through a data platform to construct a unified user information database, achieving deep integration and sharing of cross-system data. Based on statistical algorithms and tagging rules, it constructs a user profile model to accurately identify user group characteristics and differences in heating demand, making the allocation of heat resources more scientific and rational. By establishing an intelligent correlation model between work order response and resource allocation, it dynamically optimizes service processes and scheduling strategies, significantly improving the accuracy and response efficiency of marketing services. The method provided by this invention effectively solves the problems of data fragmentation and insufficient analytical capabilities in traditional heat marketing systems, realizing a service transformation from passive response to proactive prediction, and providing heat companies with intelligent and refined operation and management tools. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the overall process logic of the intelligent analysis method for heat marketing based on multi-source data fusion according to an embodiment of the present invention. Detailed Implementation

[0051] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0052] Example 1, referring to Figure 1 As one embodiment of the present invention, a heat marketing intelligent analysis method based on multi-source data fusion is provided, such as... Figure 1 The specific steps shown are as follows:

[0053] S100: Integrate multi-dimensional data obtained from the first, second, and third levels through a data platform to establish a user information database;

[0054] S200: Based on the user information database, a first profile model is constructed by combining the first statistical algorithm and the first labeling rules to classify and identify users;

[0055] S300: Predicts the heating demand of different user groups based on the classification results of the first profile model, and formulates a heating resource allocation strategy based on the heating demand.

[0056] S400: Establish a correlation model between work order response and thermal resource allocation strategy. Based on the correlation model, judge and analyze response delays and missing work orders to optimize marketing service processes and resource allocation strategies.

[0057] It should be noted that, in order to solve the problems of data silos, low level of intelligence and poor business collaboration capabilities in traditional heat marketing management systems, this invention solves the technical bottlenecks of data fragmentation between systems, weak analysis capabilities and low service efficiency through the multi-source data fusion technology in the above steps S100 to S400.

[0058] It should be noted that this invention integrates multi-dimensional data from customer service, billing, and production systems through a data platform to construct a unified user information database, achieving deep integration and sharing of cross-system data. Based on statistical algorithms and tagging rules, a user profile model is built to accurately identify user group characteristics and differences in heating demand, making the allocation of heating resources more scientific and rational. By establishing an intelligent correlation model between work order response and resource allocation, service processes and scheduling strategies are dynamically optimized, significantly improving the accuracy and response efficiency of marketing services. The method provided by this invention effectively solves the problems of data fragmentation and insufficient analytical capabilities in traditional heating marketing systems, realizing a service transformation from passive response to proactive prediction, and providing heating companies with intelligent and refined operation and management tools.

[0059] Example 2, based on the previous example, provides a specific implementation method for the heat marketing intelligent analysis method based on multi-source data fusion, to illustrate the technical means used in this method.

[0060] In this embodiment of the application, step S100 above, which integrates multi-dimensional data obtained from the first, second, and third levels through a data platform to establish a user information database, includes:

[0061] Specifically, in this embodiment, the first layer is customer service system data, including user call records, work order processing information, and customer feedback data; the second layer is billing system data, including user payment records, overdue payment information, and heat usage classification; and the third layer is production system data, including heat exchange station operating parameters, room temperature monitoring data, and heat network dispatch records.

[0062] Specifically, the data platform will be used to link and merge the data from the customer service system, billing system, and production system according to the user ID and timestamp fields, to build a unified user information database and establish a mapping relationship between user payment behavior and customer service interaction;

[0063] In this embodiment of the application, the data after association and fusion is deduplicated, completed, and outlier detected, and a data update mechanism is established; the specific steps include:

[0064] To address the issue of data duplication, deduplication rules are established based on key fields such as user ID and timestamp. Duplicate records are identified and merged through hash comparison and similarity matching algorithms, while retaining the most complete and up-to-date data version.

[0065] For missing data, a multi-dimensional completion strategy is adopted: on the one hand, the deducible fields are automatically filled through the data mapping relationship between related systems; on the other hand, for important fields that cannot be automatically completed, a manual review process is established and data quality monitoring alarms are set.

[0066] In the outlier detection stage, abnormal data is identified by combining business rules and statistical models, and intelligent correction or marking for processing is carried out by comparing with historical data and verifying data from related systems.

[0067] Establish a hierarchical data update mechanism, including: using message queues to trigger real-time updates for production data with high real-time requirements; setting up timed incremental synchronization tasks for batch-processed billing and customer service data; and establishing a data version control system to retain key historical change records.

[0068] It should be noted that the data platform automatically monitors the quality indicators of each data source. When abnormal data fluctuations or synchronization failures are detected, an early warning is triggered and a self-healing process is initiated.

[0069] It should be noted that step S100 above achieves standardized integration of multi-source heterogeneous data by building a data middle platform, breaking down the data barriers between customer service, billing and production systems in traditional heat marketing, forming a complete user information database, so that user behavior analysis and resource allocation decisions can be based on comprehensive and real-time data support, significantly improving data utilization efficiency and business collaboration capabilities.

[0070] In this embodiment of the application, the above step S200, based on the user information database, constructs a first profile model by combining a first statistical algorithm and a first tagging rule, and classifies and identifies users, including the following sub-steps B1 to B4:

[0071] In B1: Extract the first basic attribute features and first heating behavior features of users from the user information database;

[0072] In this embodiment of the application, structured feature data is extracted from the user information database. The first basic attribute features include static information such as user age, address, and house area, while the first heating behavior features cover dynamic data such as historical heating fluctuation curves, on-time payment rate, room temperature adjustment frequency, and work order complaint types.

[0073] In an optional embodiment, the first basic attribute features may also include in-depth static information such as user occupation type, family composition, building age, insulation material type, and heating equipment model; the first heating behavior features may further include refined dynamic indicators such as seasonal heating mode switching time, response speed to abnormal heating events, multi-channel service usage preferences, participation in promotional activities, and equipment failure repair cycle.

[0074] In this embodiment of the application, the feature extraction process includes:

[0075] The raw data is preprocessed. For numerical features, Z-score standardization is used to eliminate the influence of units, and one-hot encoding is used to transform categorical features. By establishing a unified time axis, the work order timestamps of the customer service system, the payment cycle of the billing system, and the minute-level monitoring data of the production system are uniformly aligned to the hourly granularity. Linear interpolation is used to fill in the missing data points for the time period to ensure that all time series features have a consistent comparison benchmark.

[0076] For device-level data in the production system, activate the spatial feature conversion engine;

[0077] The spatial feature conversion engine loads the topology map of the heating network. Based on the node relationships in the graph database, it calculates the theoretical heating parameters for each user node according to the heat transfer model, using equipment parameters such as heat exchange station outlet water temperature and circulating pump frequency. Simultaneously, it combines measured data from room temperature monitoring points and performs data fusion using a Kalman filter algorithm to generate derived features reflecting the actual heating situation of users, such as "equivalent heating intensity" and "heat network transmission delay."

[0078] In B2: The first statistical algorithm is used to analyze the first basic attribute features and the first heat consumption behavior features to identify key behavioral features;

[0079] In this embodiment of the application, the first statistical algorithm is the principal component analysis algorithm, and the specific identification steps are as follows:

[0080] The first basic attribute feature and the first heat consumption behavior feature are constructed into an m×n dimensional feature matrix X, where m represents the number of users and n represents the feature dimension. The covariance matrix between features is calculated, where the covariance matrix reflects the degree of linear correlation and the magnitude of change between different heat consumption features.

[0081] The covariance matrix is ​​subjected to spectral decomposition to solve for its eigenvalues ​​and corresponding eigenvectors. The eigenvalues ​​are then sorted in descending order. Each eigenvector represents a principal component direction, and its corresponding eigenvalue represents the variance contribution of the data in that direction. The top k principal components whose cumulative contribution rate reaches a preset threshold are automatically retained to form a projection matrix.

[0082] The projection matrix is ​​transformed into k-dimensional new features through linear transformation, and the loading coefficients of each principal component are recorded. The correlation coefficients between the original features and the principal components are analyzed.

[0083] Based on the principal component loading matrix, identify the original features whose absolute loading values ​​are greater than a threshold for each retained principal component. For example, identify the second principal component as primarily reflecting the combined features of "frequency of room temperature adjustment" and "number of work order complaints". Establish a principal component-feature mapping table to label the behavioral pattern type represented by each principal component. For example, interpret the principal component with the largest variance contribution as "thermal stability dimension", and the second largest principal component as "service sensitivity dimension", etc.

[0084] It should be noted that the first statistical algorithm is used to perform dimensionality reduction and pattern mining on multidimensional features. By calculating the correlation coefficient matrix between features, the core feature combinations that affect users' heat usage behavior are identified, providing a statistical basis for subsequent classification.

[0085] In an optional embodiment, the first statistical algorithm may also be a K-means clustering algorithm, which automatically divides users with similar heat usage patterns into several clusters by calculating the Euclidean distance between user feature vectors, and identifies typical behavior patterns based on the cluster center features, such as discovering the feature combination of user clusters with high heat usage sensitivity.

[0086] In another alternative embodiment, the first statistical algorithm can also evaluate the importance of features in the random forest by constructing multiple decision trees and calculating the information gain of each feature when splitting at a node, thus quantifying the contribution of each feature to the prediction of heat usage behavior. For example, it can identify "house insulation performance" and "payment history" as key factors affecting the stability of user heat usage.

[0087] In B3: Users are initially categorized based on key behavioral characteristics and the first label rule;

[0088] Specifically, in the initial stage of user classification, a set of key behavioral features generated by principal component analysis and a pre-defined first label rule base are loaded. The label rules adopt a "condition-conclusion" logical expression structure; for example, rule R1 is defined as: "IF thermal stability dimension score ≥ 0.7 AND service sensitivity dimension score ≤ 0.3 THEN classify as 'stable and energy-saving'". Each rule is accompanied by a confidence weight and an applicable priority parameter, forming an scalable rule decision tree.

[0089] Specifically, a multi-level rule matching mechanism is used during classification. The first level handles exact matching scenarios, immediately assigning a corresponding label when a user's features fully meet all the conditions of a rule. The second level handles fuzzy matching scenarios, calculating the conformity score between the feature vector and each rule condition to probabilistically classify partially matched cases and label them with confidence scores. The third level handles rule conflict scenarios, determining the dominant classification based on preset priority parameters and weight calculation formulas when a user triggers multiple rules simultaneously. For outliers that fail to match any rule, the system automatically places them in a pending review queue and generates a feature difference report for subsequent rule optimization.

[0090] Specifically, a mapping index is established between classification results and original features, allowing for the verification of classification rationality through reverse queries. For example, all users classified as "sensitive complaint type" can be retrieved to check the actual distribution of their work order complaint counts. When users with the same combination of features are found to be assigned different classifications, the conflict detection and optimization process of the rule base is automatically triggered.

[0091] In an optional embodiment, the first label rule can also be a time-sensitive dynamic classification rule, such as rule R2 defined as: "IF seasonal heating fluctuation coefficient > 0.5 AND the number of days of payment delay in the last 3 times increases THEN classified as 'potential arrears risk'", which focuses on capturing the temporal change characteristics of user behavior.

[0092] In another optional embodiment, the first label rule can also be a composite service response rule R3: "IF annual average number of work orders ≥ 5 AND mobile service usage rate < 30% AND building age > 20 years THEN classified as 'traditional high demand'". This type of rule identifies groups that require special service strategies by cross-analyzing users' service channel preferences and building characteristics.

[0093] In B4: Feature aggregation is performed on the user groups after initial classification to generate representative user profile templates and assign unique identifier labels;

[0094] Specifically, the classification results are subjected to deep aggregation analysis, and feature embedding technology is used to project the multidimensional features of user groups into a low-dimensional space. Typical user profile templates are generated through density clustering.

[0095] Specifically, each typical user profile template is assigned a tag code with business semantics, and a profile version management mechanism is established. When a significant shift in group characteristics is detected, the template update process is automatically triggered to ensure that user classification always reflects the latest behavioral patterns.

[0096] It should be noted that step S200 above uses a combination of statistical algorithms and labeling rules to construct a user profile model, achieving intelligent classification and feature extraction of massive amounts of user data. This not only accurately identifies the heat usage behavior characteristics of different user groups but also uncovers potential patterns that are difficult to detect through traditional manual analysis, providing a scientific basis for personalized services and precision marketing.

[0097] In this embodiment of the application, the above step S300 predicts the heating demand of different user groups based on the classification results of the first profile model, and formulates a heating resource allocation strategy based on the heating demand, including the following sub-steps C1 and C2:

[0098] In C1: The heating demand of different user groups is predicted based on the classification results of the first user profile model, including:

[0099] Based on the classification results of the first profile model, historical heating data of different user groups are extracted, and combined with regional distribution data, the differences in heating behavior of user groups in different geographical areas are analyzed to identify regional heating patterns.

[0100] A first prediction model was constructed, and time series analysis was used to predict the changing trends of heating demand for different user groups in future periods.

[0101] The forecast results are aggregated by region to generate a heat map of regional heat demand distribution, marking high-demand and low-demand areas.

[0102] Specifically, the construction of the first prediction model includes: for each user group-region combination unit, establishing an ARIMA model to capture its basic heating consumption patterns, and introducing an LSTM neural network to handle nonlinear features. Model inputs include external variables such as historical heating data, weather forecasts, and holiday markers; the weight coefficients of each factor are determined through feature importance analysis. The system performs rolling time window training; each time heating demand is predicted for the next 24 hours, it automatically selects the data from the most recent 30 days as the training set and dynamically adjusts the model hyperparameters using a Bayesian optimization method. The prediction results are output in probability distribution form, including the expected value and confidence interval.

[0103] In an optional embodiment, the first prediction model may also employ an ensemble learning framework, such as stacking an XGBoost regression tree with a Prophet time series model. The former handles the nonlinear relationship between user profile features and heat consumption, while the latter captures periodic patterns such as holiday effects. Finally, the prediction results are fused through a meta-model.

[0104] In another alternative embodiment, the first prediction model may also employ a graph neural network model, taking the heating network topology as graph data input, utilizing the feature propagation mechanism of nodes and edges, and simultaneously modeling the spatial correlation and temporal dependence of user heating behavior to output demand prediction results that take into account the transmission characteristics of the network.

[0105] Specifically, the Kriging interpolation algorithm is used to consider spatial weighting factors such as pipeline transmission loss coefficient and building density, transforming discrete user point prediction values ​​into continuous thermal distribution surfaces; dynamic grading thresholds are set to automatically map thermal values ​​to three-color warning areas of high demand, medium demand, and low demand, and to mark the expected growth rate of demand in each area.

[0106] In C2: The strategy for allocating heat resources based on heat demand includes: based on the heat demand distribution heat map of the region, dividing the priority of heat source allocation according to the coverage of heat exchange stations, generating the first scheduling instruction and issuing it to each heat exchange station for execution.

[0107] In this embodiment of the application, reading the regional heat demand distribution heat map and performing spatial overlay analysis with the heat exchange station service area layer includes:

[0108] The radiation area of ​​each heat exchange station is divided by Voronoi diagram, and indicators such as the proportion of high-demand area within the coverage of each station and the predicted total load are calculated.

[0109] Based on the preset scheduling strategy tree, an instruction sequence is automatically generated: for primary stations where the proportion of high demand exceeds the threshold, the instructions include parameters such as increasing the water supply temperature and increasing the frequency of the circulating pump; for adjacent secondary stations, pre-scheduling parameters are set.

[0110] All instructions are sent to the PLC controllers at each station via the OPC UA protocol, and include an execution time window and a readback verification mechanism. The system monitors the instruction execution status in real time, and automatically triggers adjustment instructions or upgrades alarms when the deviation between the actual parameters and the instructions exceeds the tolerance.

[0111] In an optional embodiment, the first scheduling instruction may also be a dynamic optimization method based on reinforcement learning, which establishes a reward function between the heat exchange station operating parameters and user room temperature satisfaction, allowing the agent to autonomously explore the optimal scheduling strategy and generate a sequence of instructions that includes multi-parameter coordinated adjustment.

[0112] In another optional embodiment, the first scheduling instruction can also be a method of generating instructions that introduces a network hydraulic calculation model. Based on the heat map prediction results, the hydraulic conditions of the entire network are simulated first. Based on the differential pressure balance and heat balance calculations, a scheduling instruction package containing refined parameters such as pump linkage control and network pressure regulating valve opening is generated to ensure optimal matching between heat distribution and network operating status.

[0113] It should be noted that the above step S300, based on the predictive analysis of user profiles, achieves accurate prediction of heating demand. By combining regional distribution characteristics and time series analysis, it can dynamically predict changes in heating demand in different regions and at different times.

[0114] In this embodiment of the application, step S400 above establishes a correlation model between work order response and thermal resource allocation strategy, and judges and analyzes response delays and missing work orders based on the correlation model to optimize marketing service processes and resource allocation strategies, including the following sub-steps D1 to D3:

[0115] In D1: Establishing the correlation model between work order response and thermal resource allocation strategy includes:

[0116] The core factors affecting the warning time can be summarized into different dimensions, including work order type, work order priority / severity, applicant identity, and the system involved;

[0117] Each rule dimension is divided into segments, and preliminary time thresholds are set for each sub-scenario based on past data and SLA requirements;

[0118] Examine the coordination of rules from different dimensions, and formulate superimposed judgment rules for scenarios with overlapping effects.

[0119] In D2: Response delays and missing work orders are identified and analyzed based on the correlation model; specific steps include:

[0120] Extract all processed historical work order data from the work order management system, mark work orders with delayed or missing responses, and integrate the marking results with the original work order data to form a labeled dataset;

[0121] Analyze the characteristics of delayed / missing work orders from multiple dimensions, such as: which regions have more severe delays / missing orders, which types of work orders have more delays / missing orders, whether there are regular differences in work order creation time, and the poor performance of certain users or service personnel in related work orders; use statistical methods to quantify the degree of influence of different characteristics on delays / missing orders, and for the main influencing factors, analyze the root causes in conjunction with domain knowledge, such as: resource-scarce areas, insufficient personnel during peak hours, insufficient processing capacity for special work order types, and poor performance of individual service personnel, etc.

[0122] Based on the main reasons identified in the analysis, the existing marketing service process will be streamlined and optimized: the work order classification system will be improved, processing standards for special types of work orders will be developed, a work order processing knowledge base will be established, service personnel training will be strengthened, review steps will be added to key nodes to prevent omissions and delays, and on-site processing hardware and software facilities will be optimized to improve operational efficiency; the optimized new process will be piloted in some regions / departments, and the effects will be continuously tracked and improved cyclically.

[0123] Based on the analysis results, the resource allocation strategy was optimized, and the new and old strategies were compared in a data simulation environment. The solution with better overall response quality was selected, and the optimized allocation strategy was gradually promoted to all service areas.

[0124] In D3: Optimizing marketing service processes and resource allocation strategies includes:

[0125] The work orders are clustered into multiple service areas based on their geographical location, and the service distance between each work order and the center of each service area is calculated.

[0126] Distribute alert work orders to the nearest service area with the lowest current load, including:

[0127] The overall score by which a work order is assigned to a service area is evaluated based on the current load and distance weighting coefficient of the service area.

[0128] For each work order, find the service area with the lowest overall score;

[0129] Intelligent sorting is performed within the region using a multi-weighted priority queue model;

[0130] For the highest-level red alert work orders, emergency dispatch between regions is initiated: for each red alert work order, the current load of the region to which it belongs is determined. If the current load exceeds the preset threshold, service personnel are drawn from the set of neighboring regions.

[0131] It should be noted that step S400 above establishes an intelligent correlation model between work order response and resource allocation, which can automatically identify response delays and missing work orders, and perform intelligent scheduling based on multiple factors such as geographical location and service load to ensure efficient allocation of service resources.

[0132] Example 3: This example provides a heat marketing intelligent analysis system based on multi-source data fusion, including:

[0133] The data integration module is used to integrate multi-dimensional data obtained from the first, second, and third levels through the data platform to establish a user information database;

[0134] The classification and recognition module is used to classify and recognize users based on the user information database by combining the first statistical algorithm and the first labeling rules to construct a first profile model.

[0135] The allocation strategy formulation module is used to predict the heating demand of different user groups based on the classification results of the first profile model, and formulate a heating resource allocation strategy based on the heating demand.

[0136] The optimization strategy module is used to establish a correlation model between work order response and thermal resource allocation strategy. Based on the correlation model, it judges and analyzes response delays and missing work orders to optimize marketing service processes and resource allocation strategies.

[0137] It should be noted that the technical solution of the heat marketing intelligent analysis system based on multi-source data fusion is based on the same concept as the technical solution of the heat marketing intelligent analysis method based on multi-source data fusion described above. For details not described in detail in the technical solution of the heat marketing intelligent analysis system based on multi-source data fusion in this embodiment, please refer to the description of the technical solution of the heat marketing intelligent analysis method based on multi-source data fusion described above.

[0138] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0139] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a heat map marketing intelligent analysis method based on multi-source data fusion. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0140] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method proposed in the above embodiments.

[0141] The storage medium proposed in this embodiment belongs to the same inventive concept as the method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0142] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute the method of the embodiments of the present invention.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A heat-based marketing intelligent analysis method based on multi-source data fusion, characterized in that: include: By integrating multi-dimensional data obtained from the first, second, and third levels through a data platform, a user information database is established. Based on the user information database, a first profile model is constructed by combining a first statistical algorithm and a first tagging rule to classify and identify users. The heat demand of different user groups is predicted based on the classification results of the first profile model, and a heat resource allocation strategy is formulated based on the heat demand. Establish a correlation model between work order response and the aforementioned thermal resource allocation strategy. Based on the correlation model, determine and analyze response delays and missing work orders to optimize marketing service processes and resource allocation strategies.

2. The heat marketing intelligent analysis method based on multi-source data fusion as described in claim 1, characterized in that, The step of constructing a first profile model by combining a first statistical algorithm and a first labeling rule to classify and identify users includes: Extract the first basic attribute features and the first heating behavior features of the users from the user information database; The first statistical algorithm is used to analyze the first basic attribute features and the first heat consumption behavior features to identify key behavioral features; Users are initially categorized based on the key behavioral characteristics and the first labeling rules; The user groups after the initial classification are subjected to feature aggregation to generate representative user profile templates and assigned unique identifier tags.

3. The heat marketing intelligent analysis method based on multi-source data fusion as described in claim 2, characterized in that, The classification results of the first user profile model are used to predict the heating needs of different user groups, including: Based on the classification results of the first profile model, historical heating data of different user groups are extracted, and combined with regional distribution data, the differences in heating behavior of user groups in different geographical areas are analyzed to identify regional heating patterns. A first prediction model was constructed, and time series analysis was used to predict the changing trends of heating demand for different user groups in future periods. The forecast results are aggregated by region to generate a heat map of regional heat demand distribution, marking high-demand and low-demand areas.

4. The heat marketing intelligent analysis method based on multi-source data fusion as described in claim 3, characterized in that, The step of formulating a heat resource allocation strategy based on the heat demand includes: Based on the heat demand distribution heat map of the region, and combined with the coverage of the heat exchange stations, the priority of heat source allocation is divided, a first scheduling instruction is generated and sent to each heat exchange station for execution.

5. The heat marketing intelligent analysis method based on multi-source data fusion as described in claim 1, characterized in that, The integration of multi-dimensional data obtained from the first, second, and third levels through the data platform includes: The first layer consists of customer service system data, including user call records, work order processing information, and customer feedback data; the second layer consists of billing system data, including user payment records, outstanding payment information, and heat usage classification; the third layer consists of production system data, including heat exchange station operating parameters, room temperature monitoring data, and heating network dispatch records. By using a data platform, data from the customer service system, billing system, and production system are linked and integrated according to user ID and timestamp fields to build a unified user information database and establish a mapping relationship between user payment behavior and customer service interaction. The merged data is deduplicated, completed, and outlier detected, and a data update mechanism is established.

6. The heat marketing intelligent analysis method based on multi-source data fusion as described in claim 5, characterized in that, The correlation model between the work order response and the thermal resource allocation strategy includes: The core factors affecting the warning time can be summarized into different dimensions, including work order type, work order priority / severity, applicant identity, and the system involved; Each rule dimension is divided into segments, and preliminary time thresholds are set for each sub-scenario based on past data and SLA requirements; Examine the coordination of rules from different dimensions, and formulate superimposed judgment rules for scenarios with overlapping effects.

7. The heat marketing intelligent analysis method based on multi-source data fusion as described in claim 6, characterized in that, The strategies for optimizing marketing service processes and resource allocation include: The work orders are clustered into multiple service areas based on their geographical location, and the service distance between each work order and the center of each service area is calculated. Distribute alert work orders to the nearest service area with the lowest current load, including: The overall score by which a work order is assigned to a service area is evaluated based on the current load and distance weighting coefficient of the service area. For each work order, find the service area with the lowest comprehensive score; Intelligent sorting is performed within the region using a multi-weighted priority queue model; For the highest-level red alert work orders, emergency dispatch between regions is initiated: for each red alert work order, the current load of the region to which it belongs is determined. If the current load exceeds the preset threshold, service personnel are drawn from the set of neighboring regions.

8. A heat marketing intelligent analysis system based on multi-source data fusion, employing the heat marketing intelligent analysis method based on multi-source data fusion as described in any one of claims 1 to 7, characterized in that, include: The data integration module is used to integrate multi-dimensional data obtained from the first, second, and third levels through the data platform to establish a user information database; The classification and recognition module is used to classify and recognize users based on the user information database by constructing a first profile model by combining a first statistical algorithm and a first labeling rule. The allocation strategy formulation module is used to predict the heating demand of different user groups based on the classification results of the first profile model, and formulate a heating resource allocation strategy based on the heating demand. The optimization strategy module is used to establish a correlation model between work order response and the thermal resource allocation strategy. Based on the correlation model, it judges and analyzes response delays and missing work orders to optimize the marketing service process and resource allocation strategy.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the steps of the heat marketing intelligent analysis method based on multi-source data fusion as described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the computer-executable instructions are executed by the processor, they implement the steps of the heat marketing intelligent analysis method based on multi-source data fusion as described in any one of claims 1 to 7.

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