Kitchenware and cutlery market dynamic monitoring and decision support system

Abstract

This invention discloses a dynamic monitoring and decision support system for the kitchenware and sanitary ware market, relating to the field of market monitoring technology. It includes a conflict signal space modeling module, used to receive raw signal statements from multiple data sources for the same category of kitchenware and sanitary ware, convert each statement into standardized state nodes, and construct a directed graph based on semantic consistency or conflict relationships between nodes. This invention constructs a signed directed dependency graph, abstracting trend judgments from each data source into nodes and positive / negative / partially conflicting edges, and employs a depth-first search algorithm to traverse loops, using the parity of the number of negative edges in the loop as the criterion for cognitive deadlock. When a loop consisting of an odd number of negative edges is detected, the system can accurately identify logical paradoxes similar to process deadlock, thus distinguishing it from existing methods such as weighted average and majority voting, which repeatedly switch conclusions and fail to converge when there are three or more conflicting information sources.

Description

Technical Field

[0001] This invention relates to the field of market monitoring technology, specifically to a dynamic monitoring and decision support system for the kitchenware and sanitary ware market. Background Technology

[0002] With the rapid development of the kitchen and bathroom appliance market and the increasing diversification of sales channels, enterprises need to simultaneously monitor heterogeneous data from multiple sources, including e-commerce platforms (such as Tmall and JD.com), offline stores, engineering channels, and industry reports, in order to make accurate judgments on market trends. Among these, the effective integration of signals from various data sources has become a core technical aspect of market dynamic monitoring systems. Timely and accurate integration of multi-source signals, thereby providing a reliable basis for decisions on product strategy, inventory management, and marketing deployment, has become a crucial technical feature for ensuring enterprises maintain their competitive edge in complex markets.

[0003] In recent years, classic multi-source information fusion methods based on weighted average, majority voting, and DS evidence theory have been initially applied to the field of market trend analysis. These methods, by assigning fixed weights to different information sources or fusing based on the credibility of evidence, can, to some extent, handle simple contradictions between two information sources. However, when there are three or more data sources and the signals form cyclical contradictions—for example, information source A supports "sales increase," information source B supports "sales remain flat," and information source C supports "sales decrease," and there are direct or indirect logical conflicts between A and B, B and C, and C and A—existing fusion methods reveal a fundamental flaw: the system repeatedly switches conclusions between multiple information sources and cannot converge to a stable output. This phenomenon, similar to operating system process deadlock, lacks an effective detection and resolution mechanism in existing market monitoring and decision-making systems. The fundamental reason is that existing technologies lack the ability to structurally model complex dependencies between multi-source signals (especially signed adversarial relationships), cannot identify logical paradoxical loops composed of an odd number of contradictory relationships, and lack automated resolution strategies for such deadlocks. Therefore, when market data sources increase and signal differentiation between channels intensifies, traditional conflict resolution algorithms (such as multi-round voting and threshold comparison) will fall into an endless state of alternating adoption and rejection, resulting in oscillating decision outputs and loss of timeliness, which seriously affects the scientific nature and responsiveness of corporate strategy formulation. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic monitoring and decision support system for the kitchenware and sanitary ware market, in order to address the shortcomings in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a dynamic monitoring and decision support system for the kitchenware and sanitary ware market, comprising: The conflict signal space modeling module is used to receive raw signal statements from multiple data sources for the same category of kitchenware and sanitary ware, convert each statement into a standardized state node, and construct a directed graph based on the semantic consistency or conflict relationship between the nodes. The contradictory loop detection and deadlock determination module is used to detect loops in the directed graph using depth-first search. If a loop exists and the number of negative edges in the loop is odd, it is determined to be a cognitive deadlock, and the deadlock severity level is calculated. The deadlock resolution strategy selection module is used to automatically select at least one resolution strategy based on the deadlock severity level. The resolution strategies include: removing the source node with the lowest historical prediction accuracy in the loop, introducing an external anchor source to break the loop, resetting the initial weights of all sources in the loop to equal values ​​and adding balance constraints.

[0006] In a preferred embodiment, it further includes: The multi-source parallel path exploration and conflict marking module is used to traverse all source-contradictory path links from the root node in parallel based on the conflict-free source subset or balanced weight obtained after deadlock resolution, detect conflict signals at path intersections, calculate conflict intensity, and perform backtracking operation when the conflict intensity exceeds a preset threshold. The path pruning and comprehensive credibility calculation module is used to calculate the comprehensive credibility score for each complete path and prune the path according to the score threshold. The multi-version output and backtracking chain generation module is used to output the fusion conclusion corresponding to the optimal path, the list of alternative paths and their applicable scenarios, and generate a structured backtracking trajectory for each path, wherein the backtracking trajectory includes deadlock detection and resolution records.

[0007] In a preferred embodiment, the contradictory loop detection and deadlock determination module includes: The directed dependency graph construction unit is used to add directed edges based on the consistency or contradiction of the trend judgment of the same target category on the same indicator dimension between information sources, with each data source as a node. Specifically, if the trends are the same, a bidirectional positive edge is added; if the trends are opposite, a unidirectional negative edge is added; and if the trends are rising and remaining flat, directed edges with partial conflict weights are added. The cycle search unit is used to traverse the directed graph using a depth-first search, record the node visit status, and when an edge pointing to the currently visited node is found, record the cycle structure and the set of nodes and edge types it contains. The parity determination unit is used to count the number of negative edges in a loop. If the number is odd, it is determined to be a cognitive deadlock; if the number is even, it is determined to be a non-deadlock loop. The deadlock severity calculation unit is used to calculate the deadlock severity level, which is positively correlated with the ratio of the number of negative edges in the loop to the length of the loop and the conflict intensity of each edge in the loop.

[0008] In a preferred embodiment, the conflict intensity is determined by the degree of difference in the trend direction of the two information sources and their respective historical accuracy. The greater the difference in direction and the lower the historical accuracy, the higher the conflict intensity.

[0009] In a preferred embodiment, the deactivation strategy selection module includes: The lowest accuracy pruning unit is used to identify the source node with the lowest historical prediction accuracy in the loop when the deadlock severity level is lower than the first threshold, permanently delete it from the graph, and re-trigger the conflict loop detection. Anchor injection unit is used to select an external authoritative source as an anchor node when the deadlock severity level is higher than the second threshold or deadlock still exists after pruning. The anchor point is then connected to all sources in the loop with a strong constraint edge, and the edge is assigned a value that is significantly higher than the weight of a normal edge to force the loop to be broken. The dynamic weight reset unit is used to reset the initial speech weights of all sources in the loop to equal values ​​without deleting the sources. It also adds an intra-loop balance loss term to the loss function used to update the weights. This loss term is positively correlated with the degree of difference between the weight coefficients of each source in the loop. Through backpropagation, the weight distribution of sources in the loop tends to be uniform.

[0010] In a preferred embodiment, the weight of the strongly constrained edge is at least two orders of magnitude higher than the maximum value of the ordinary edge weight; the penalty coefficient in the in-loop balance loss term is adaptively adjusted according to the deadlock severity level.

[0011] In a preferred embodiment, before performing the backtracking operation, the multi-source parallel path exploration and conflict marking module first calls the conflict loop detection and deadlock determination module and the resolution strategy selection module, and uses the processed deadlock-free source subset as the input for path exploration, thereby avoiding infinite alternation caused by cyclical conflicts during the backtracking process.

[0012] In a preferred embodiment, when generating a structured backtracking chain, the backtracking trajectory generation module adds a deadlock detection node to the backtracking chain for nodes whose weights are removed or reset due to deadlock resolution. The information recorded by this node includes: loop structure, negative edge count, selected resolution strategy, removed source identifier and its historical accuracy, anchor source name, and distribution before and after weight reset.

[0013] In a preferred embodiment, the backtracking trajectory generation module also outputs natural language explanation text, wherein the deadlock resolution process is described using templated statements that include a description of the loop structure, the number of negative edges, the removal of information sources and their accuracy information.

[0014] In a preferred embodiment, the system further includes a deadlock pattern library and an incremental learning module, used to record the deadlock loop structure detected each time and the successfully adopted resolution strategy, and to calculate the graph edit distance between the new pattern and the existing patterns in the library. When the similarity between a newly emerging source combination and a pattern in the database exceeds a preset threshold, the historical de-detection strategy is directly reused, skipping the re-detection and strategy selection steps. The preset threshold is automatically determined based on statistical analysis of historical pattern matching success rates.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention constructs a signed directed dependency graph, abstracting the trend judgments of each data source into nodes and positive / negative / partially conflicting edges. A depth-first search algorithm is used to traverse loops, with the parity of the number of negative edges in the loop serving as the criterion for cognitive deadlock. When a loop consisting of an odd number of negative edges is detected, the system can accurately identify a logical paradox similar to process deadlock, thus distinguishing it from existing methods such as weighted average and majority voting, which repeatedly switch conclusions and fail to converge when there are three or more conflicting information sources.

[0016] This invention incorporates three differentiated deadlock resolution strategies: lowest accuracy pruning, anchor point injection, and dynamic weight reset, and automatically selects the optimal solution based on the deadlock severity level. For mild deadlocks, the source node with the lowest historical prediction accuracy in the loop is removed first to break the loop with minimal information loss. For severe deadlocks or scenarios where pruning is ineffective, an external authoritative anchor source is introduced to establish mandatory constraint edges, forcibly reconstructing the topology with weights two orders of magnitude higher. For flexible scenarios requiring the retention of all sources, the initial weights of all sources within the loop are reset to equal values, and an in-loop balancing loss term is added to the loss function to make the attention distribution more uniform through backpropagation. This hierarchical adaptive mechanism overcomes the limitations of traditional single pruning or voting strategies in complex scenarios, flexibly adapting to various operating conditions such as seasonal fluctuations in the kitchenware and sanitary ware market and raw material price anomalies, ensuring stable and reliable system output.

[0017] This invention, based on multi-source parallel path exploration, pruning, and credibility scoring, generates a structured backtracking trajectory for each retained analysis path. It forcibly embeds information such as the original deadlock detection log, resolution strategy execution records, and removed source identifiers, and automatically outputs natural language explanation text, making the entire decision-making process traceable and auditable. Simultaneously, the system is configured with a deadlock pattern library and an incremental learning module. It calculates the similarity between new and old deadlock loops using graph edit distance. When the matching degree exceeds an adaptive threshold, it directly reuses historically efficient resolution strategies, skipping the duplicate detection step. This closed-loop learning mechanism significantly reduces computational power consumption during long-term operation, improves the emergency response speed for monitoring sudden market conflicts, and fills the gaps in interpretability and self-evolution capabilities of existing decision support systems. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1, please refer to Figure 1 As shown in this embodiment, the kitchenware and sanitary ware market dynamic monitoring and decision support system includes: The Conflict Signal Space Modeling Module is the core adaptation unit for the system's front-end global information source access and standardization. It can routinely connect to various compliant commercial data sources, such as offline physical channel survey records for kitchen and bathroom appliances, online e-commerce platform transaction sentiment, industry supply chain capacity quotations, regional distributor distribution feedback, and third-party kitchen and bathroom industry research reports. It accurately collects real-time raw signal statements from all channels for single products, sub-categories, and clusters of all categories of kitchen and bathroom appliances, including woks, stainless steel sinks, integrated kitchen and bathroom sets, and anti-mildew bathroom hardware. The raw signal statements can cover a variety of market analysis information, including original text, structured data, and quantitative forecast annotations, such as monthly circulation volume predictions, terminal retail price fluctuation ranges, channel inventory levels, downstream procurement demand, and raw material matching and adaptation. Subsequently, relying on the built-in lightweight semantic adaptation and parsing algorithm, the kitchen and bathroom industry-specific entity lexicon, and market indicator normalization adaptation rules, redundant and invalid interference fields are stripped from each original signal statement, and irrelevant cross-category mixed data is eliminated. This data is then uniformly mapped and converted into standardized market status nodes with unified format, aligned dimensions, and traceable attributes. Each standardized status node can be independently bound to four basic traceability attributes: a unique code for the corresponding data source, a target category identifier for kitchen and bathroom appliances, a signal acquisition time stamp, and a core judgment indicator label. This avoids the problems of chaotic formats and misaligned definitions from multiple signal sources, preventing coordinated analysis. Simultaneously, relying on the kitchen and bathroom market-specific semantic similarity computing model and reverse conflict semantic discrimination rules, pairwise comparisons are made between standardized status nodes across the entire domain to determine the core judgment semantics, trend direction, and numerical threshold ranges. This accurately anchors the semantic consistency relationships between nodes in the same direction and the opposing semantic conflicts between nodes in opposite directions. Simultaneously, directional topological association logic is matched, and a batch of directed topological association graphs with full-domain closed-loop linkage are built, comprehensively collecting all source nodes and related edge basic topological data.

[0022] This module, when operating independently, can achieve comprehensive data standardization and storage of chaotic and multi-source market signals, and visualize topology networking. When linked with subsequent collaborative modules, it can build a solid foundation of standardized data for full-link contradiction analysis and deadlock tracing, thus avoiding the problems of subsequent monitoring and analysis deviations and computing power redundancy caused by massive non-standard market signals of kitchenware and sanitary ware from the source.

[0023] The conflict loop detection and deadlock determination module communicates and connects with the directed graph topology data formed by the aforementioned conflict signal space modeling module. It can be equipped with a lightweight, low-computing-power depth-first search traversal algorithm, suitable for batch parallel traversal operations of kitchenware and bathroom fixtures. It traces the source of closed-loop connections along the directed graph's directional edge paths, marking the topology node access sequence, edge direction, and node association binding source identity information in real time. It accurately intercepts all complete closed-loop units and simultaneously collects a list of all connected nodes, positive associated edges, and negative conflict edges within each loop group. After loop identification, it automatically quantifies and counts the actual cumulative number of negative conflict edges within each closed-loop group, accurately verifying the parity of the negative edge count. If real-time detection shows that the closed loop is effectively formed and the cumulative number of negative edges within the loop is odd, it is possible to accurately identify a cognitive deadlock anomaly in the market analysis of the corresponding kitchen and bathroom appliance category from multiple information sources, thus avoiding compliance analysis loopholes caused by misjudging deadlocks due to weak conflicts with even-numbered negative edges. Simultaneously, by combining multi-dimensional quantitative factors such as the overall loop topology link length, the real-time quantitative conflict weight of a single negative edge within the loop, and the interference coefficient of current market fluctuations in the kitchen and bathroom appliance sector, a layered and gradient-based detailed calculation of the specific severity level corresponding to the cognitive deadlock can be performed. This results in a four-level gradient deadlock classification standard: mild, moderate, severe, and extreme, providing a precise quantitative classification basis for subsequent graded adaptation and resolution strategies.

[0024] This module can independently complete seamless full-domain loop investigation and accurate deadlock classification, and pre-locate the core closed-loop contradiction points of multi-source analysis of kitchenware and sanitary ware. It effectively solves the practical pain points of traditional manual review, such as the inability to quickly locate deadlocks in information source analysis, lack of quantitative standards for classification, and delays in the timeliness of market dynamic monitoring.

[0025] The strategy selection module retrieves the global cognitive deadlock judgment results and gradient deadlock severity level quantification data from the aforementioned conflict loop detection and deadlock judgment modules in real time. Based on the built-in hierarchical strategy adaptation and scheduling logic, it can adaptively match and schedule at least one type of specific closed-loop conflict resolution strategy according to different deadlock severity levels. The three core resolution strategies can be called independently or in pairs for coordinated matching, adapting to all-gradient deadlock rectification scenarios. Among them, the strategy of removing the source node with the lowest historical prediction accuracy in the loop can selectively retrieve historical performance records of long-term kitchenware and sanitary ware category market forecasts, circulation calculations, and price fluctuation analysis from each source within the loop. It calculates the time-series comprehensive prediction accuracy source by source, accurately filtering out low-quality, distorted, and interfering sources, and quickly simplifying the loop topology to break the closed-loop conflict. By introducing external anchor sources to break the cycle strategy, this module can connect to compliant and highly credible external anchor sources such as official judgment databases of leading kitchen and bathroom industry associations and authoritative ledgers for bulk procurement of kitchen and bathroom products across the entire domain. It forcibly intervenes in the closed-loop reconstruction of topological constraints, solving the problem of severe deadlock and distortion in collective judgment from multiple sources. By resetting the initial weights of all sources within the loop to equal values ​​and adding balancing constraints, it can evenly resolve the initial biases in the power of sources without eliminating compliant and valid sources. Supporting constraint controls prevent secondary judgment conflicts caused by imbalances in the weights of local sources. This module offers tiered and adaptable strategies and flexible combinations, adapting to various market conditions, including stable off-season prices for kitchen and bathroom products, fluctuating peak season prices, and sudden changes in raw material prices. It quickly and efficiently resolves cognitive deadlocks across the entire domain, ensuring uninterrupted market monitoring and judgment and accurate and usable decision-making data.

[0026] In one embodiment, the system is also configured with a multi-source parallel path exploration and conflict marking module. This module is linked to the entire deadlock resolution process, prioritizing the retrieval of pure information sources that are topologically compliant and free from closed-loop contradictions after deadlock resolution, or the balanced weight full-domain benchmark dataset after weight balancing calibration as the core input base. Taking the core judgment root node of the entire kitchenware and sanitary ware market as the starting point, it is equipped with a multi-threaded parallel computing architecture to simultaneously traverse all compliant and valid information sources' independent contributions to the full-dimensional judgment path links, fully covering the entire judgment branches of the category supply side, distribution side, end consumer side, and supporting raw material side, and thoroughly investigating the complete judgment logic chain of a single path without omission. Simultaneously, at key core nodes where multiple analysis paths intersect and indicators are superimposed and coupled, the module performs secondary verification of the unidirectional and reversal compatibility of multi-dimensional analysis signals. It quantifies and calculates the real-time conflict intensity of points, benchmarking against the system's built-in dual standards: a fixed threshold for normalized industry-adaptive conflict intensity and a dynamically adjusted threshold. If the measured conflict intensity of a real-time verification point exceeds the preset threshold, a reverse backtracking stop-loss closed-loop operation is immediately triggered, reverting to the previous conflict-free standard analysis node to re-verify the local signal source analysis logic and correct path deviations. This module can efficiently and comprehensively investigate analysis paths in parallel, accurately intercept residual hidden conflicts, and avoid problems such as residual hidden signal conflicts after deadlock resolution, low efficiency of single-threaded traversal, and insufficient computing power for simultaneous monitoring of batch kitchenware and sanitary ware categories. This further solidifies the clean data foundation for subsequent path selection and credibility calculation.

[0027] In one embodiment, the system also includes a path pruning and comprehensive credibility calculation module. This module connects with the aforementioned multi-source parallel path exploration and conflict marking module to complete the backtracking correction and the complete set of compliance assessment paths without explicit or implicit conflicts. For each closed-loop complete market assessment path, it simultaneously extracts four core quantitative indicators: historical assessment and performance accuracy of the entire path's information sources, the degree of signal coupling and fit within the path, the adaptability to real-time market fluctuations, and the compliance adaptability coefficient for resolving previous deadlocks. These indicators are then weighted and fused to calculate a single path's exclusive comprehensive credibility quantification score. A higher score indicates that the corresponding assessment path closely matches the actual market conditions of kitchenware and sanitary ware and has higher reference commercial value. Simultaneously, it can flexibly set adjustable credibility compliance score screening thresholds based on the monthly industry market fluctuation intensity of kitchenware and sanitary ware and the critical level of decision-making accuracy required by government, enterprises, and merchants. It then performs automated and precise path pruning by comparing each path with the actual measured scores across the entire assessment domain, directly eliminating invalid, redundant, and inferior paths with low scores, distorted assessments, or those that interfere with decision-making, retaining only a set of high-credibility, highly adaptable, and low-interference core assessment paths. Among them, a standardized quantitative formula can be used to calculate the comprehensive credibility score for each path, which is compatible with batch path synchronous high-efficiency computing power operation and fits the high-frequency monitoring conditions of commercial applications.

[0028] The formula for calculating the overall credibility score is as follows: The overall credibility score Score(p) corresponding to a single analysis path p is calculated in compliance with the following weighted fusion quantification formula: Score(p) = ( )· ; Where len(p) represents the total number of valid topology nodes in the current path analysis domain, corresponding to the actual path analysis link length; To correspond to the category-specific weights of the information sources participating in the analysis, the unified constraint value range across the entire domain is [0, 1]. The weights of authoritative industry information sources are assigned higher values, while the weights of ordinary survey information sources are assigned lower values. To ensure the accuracy of the time-series historical comprehensive analysis of the target categories of kitchenware and sanitary ware from a single information source, the value is strictly anchored to the interval [0, 1]. The total quantitative value of the comprehensive penalty accumulated across all implicit and explicit conflict nodes along the entire assessment path can be obtained by weighted summation and normalization of the measured conflict intensity at each point along the path. Based on this formula, the superiority or inferiority levels of different path assessments can be objectively quantified. This module can efficiently reduce redundant and ineffective assessment computational power consumption, refine core and effective assessment data, and significantly improve the accuracy and efficiency of subsequent market decision-making outputs, meeting the commercial necessity of high-frequency dynamic monitoring of batch categories of kitchenware and sanitary ware.

[0029] In one embodiment, the system is also configured with a multi-version output and backtracking chain generation module. This module retrieves the high-quality core judgment path set purified by the aforementioned path pruning and comprehensive credibility calculation module, automatically selects the optimal judgment path with the highest comprehensive credibility score, and simultaneously extracts the core conclusions of the full-dimensional market judgment within the path. It then standardizes and encapsulates the output to provide exclusive integrated judgment conclusions for various commercial scenarios such as government and enterprise centralized procurement, brand manufacturer production scheduling, channel distributor inventory preparation, and e-commerce platform pricing. Simultaneously, it sorts and organizes multiple tiers of alternative compliance judgment paths according to credibility gradients, and annotates each alternative path with its corresponding specific applicable scenarios such as peak and off-peak seasons, regional sinking markets, high-end kitchen and bathroom products, and essential affordable kitchenware, facilitating differentiated and on-demand retrieval and use. Simultaneously, it can structurally break down the entire process operation nodes for all retained valid analysis paths across the entire domain, generating dedicated encrypted, traceable, replayable, and auditable structured backtracking trajectory archives. Each backtracking trajectory archive is forcibly embedded with the original ledger of deadlock detection at the front end, complete verification records of contradictory loops, and corresponding deadlock resolution process operation trace data, enabling reverse traceability and replay of the entire process. This module can output diversified decision-making analysis results at different levels and with supporting full-process traceability vouchers, which can not only meet the differentiated decision-making needs of different levels of market control and business entities, but also provide full-process traceability and compliance auditing, adapting to the standardized market control traceability compliance requirements of the kitchen and bathroom industry.

[0030] In one embodiment, the aforementioned contradiction loop detection and deadlock determination module is further refined and integrated into four types of dedicated collaborative operation units. Among them, the directed dependency graph construction unit is the core unit for building the underlying topology. It can collect all compliant and valid data sources participating in the kitchenware and bathroom product category assessment as independent core nodes of a single topology. It focuses on the same kitchenware and bathroom product target sub-category and the same core indicators of circulation inventory / price / demand, and compares the core results of real-time market trend assessment from different information sources in pairs. If the trend assessments of the two information sources are completely consistent, they can automatically build symmetrical positive correlation directed edges in both directions. If the trend assessments of the two information sources are completely opposite and contradictory, they can automatically build unidirectional forced constraint negative correlation directed edges. If the trends of the two information sources show a partially differentiated assessment situation of upward growth and stable change respectively, they can be matched with dedicated conflict weights that fit the actual degree of divergence, and differentiated tilted correlation directed edges can be built. Finally, a refined full-domain directed dependency topology graph that fits the kitchenware and bathroom assessment scenario is formed in an integrated manner. The loop search unit can be equipped with a lightweight depth-first search program adapted for batch topology operations. It traverses all nodes and edges of the aforementioned refined directed dependency graph, dynamically recording the temporal access status and hierarchical access trajectory of each topology node in real time. Once a directional edge is captured pointing in the opposite direction to the currently active topology node being accessed in real time, the prototype of the closed loop is instantly locked in. Simultaneously, the entire topology structure of the closed loop, the complete set of connected nodes within the loop, and the accurate ledger of various positive / negative / partial conflict edges are completely preserved, ensuring that the original loop data is retained without any omissions. The parity determination unit can specifically retrieve the detailed ledger of single-group loop edge bodies archived by the loop search unit, automatically count and statistically analyze the effective cumulative number of all negative conflict edges within the closed loop, and accurately verify the parity attribute of the number. If the verification result is odd, it can be legally determined that the corresponding closed loop has a core hidden danger of multi-source cognitive deadlock. If the verification result is even, it can be directly determined as a normal weak conflict non-deadlock ordinary loop, without the need to initiate a special deadlock resolution procedure, and accurately classify and divert different loop conditions. The deadlock level calculation unit can simultaneously retrieve multi-dimensional data such as the measured cumulative number of negative edges within the loop, the total length of the overall loop topology links, and the differentiated conflict intensity of a single conflict edge. It can also calculate the actual proportion of the number of negative edges to the overall length of the loop, and the coupling coefficient. It can also perform weighted quantification of the real-time conflict intensity of each edge, and finally collaboratively calculate the gradient deadlock severity level. The higher the proportion and the greater the conflict intensity of a single edge, the higher the corresponding deadlock severity level, providing quantitative support for the graded adaptation of front-end strategies.

[0031] Deadlock severity level calculation formula: The deadlock level calculation unit can rely on a unified standardized quantification formula to accurately calculate the deadlock severity level DeadlockLevel corresponding to a single closed loop. The specific calculation expression is as follows: DeadlockLevel=( )·( )· ; Where k precisely corresponds to the actual total number of negative conflict edges obtained from the verification statistics within the current closed-loop topology; L corresponds to the total number of connected topology nodes across the entire closed-loop, which is equivalently defined as the actual topology length of the loop; and n corresponds to the total number of all valid associated edges within the current loop. Each associated edge along the corresponding loop has its own independently measured conflict intensity value; after full-domain calculation, the deadlock severity level can be stably constrained within the standard range of [0, 1]. The closer the calculated value is to 1, the more prominent the contradiction in the current multi-source closed-loop analysis and the more severe the cognitive deadlock condition. The collaborative closed-loop operation of the four types of units can refine the compliance and accuracy of the entire deadlock analysis process, avoiding misjudgments caused by extensive topology networking and fuzzy loop verification.

[0032] In one embodiment, the conflict intensity calculated by the aforementioned directed dependency graph construction unit, parity determination unit, and deadlock level calculation unit can be generated by coupling and quantifying two-dimensional core factors, and matched with a dedicated standardized formula to unify computing power calculation, ensuring that the conflict intensity is consistent across the entire domain, traceable, and reproducible.

[0033] Conflict intensity calculation formula: Determining the conflict intensity between two sets of related information sources point-to-point. The calculation is performed according to the industry-specific formula specific to this system: =( )·(1-min( , )); Δdir is defined as the difference between the market trend analysis codes corresponding to the two sets of comparison sources. The system presets a unified industry trend coding standard: the market is rising and the value is fixed at 1; the market is generally flat and stable and the value is fixed at 0; the market is falling and the value is fixed at -1. The maximum limit value of the trend coding difference theory is 2, and the global fixed constant value is 2. , The system sequentially assigns two sets of paired information sources to assess the long-term, time-series historical prediction accuracy of the same kitchen and bathroom appliance target category. Both accuracy values ​​are strictly controlled and remain within the compliant range of [0, 1]. The overall calculation logic closely matches real-world scenarios of discrepancies in multi-source assessments of kitchen and bathroom appliances. The greater the directional difference and the lower the historical accuracy of the corresponding information source, the higher the real-time calculation of the conflict intensity value, ensuring that subsequent deadlock levels and strategy adaptations align with actual market conditions.

[0034] In one embodiment, the aforementioned resolution strategy selection module integrates three types of dedicated execution units with different functions and collaborative capabilities. Among them, the lowest accuracy pruning unit is adapted to mild deadlock emergency response scenarios. It can pre-set the first-level low-grade deadlock severity judgment threshold built into the system. When the measured deadlock severity level quantification value is lower than the preset first threshold, it is judged as a mild closed-loop judgment deadlock or a local low-quality source interference-induced condition. It immediately performs targeted source tracing and retrieval of the time-series historical prediction and fulfillment ledger of all access sources within the closed loop, accurately sorts and compares the accuracy of each source, locks the inferior interference source node with the lowest accuracy and the highest judgment distortion frequency within the loop, automatically performs a permanent topology removal and deletion operation, simultaneously refreshes the entire domain directed graph topology architecture, and triggers a secondary reverse callback to perform a re-inspection and verification process for contradictory loops, thus quickly and cost-effectively resolving mild deadlock anomalies. The anchor injection unit is adapted to complex scenarios involving moderate to severe deadlocks. It can pre-set the system's built-in second-tier high-level deadlock severity judgment threshold. When the measured deadlock level is higher than this second threshold, or when the loop deadlock anomaly is not eliminated after completing the pre-processing of low-accuracy information source pruning and optimization, and the closed-loop deadlock is still detected in the second inspection, it can automatically link with external, highly credible and compliant external anchor information sources such as the official judgment database of the kitchen and bathroom industry association, the authoritative ledger of bulk procurement of leading kitchen and bathroom products, and the national-level light industrial kitchenware market monitoring platform. The anchor information sources are newly embedded into the global topology graph, and high-strength forced constraint directed edges are built one by one with all existing information source nodes in the problem loop. The edge weights are assigned manually at the same time, which greatly strengthens the core judgment power of the anchor information sources, forcibly reverses the closed-loop contradictory logic, and quickly solves stubborn severe cognitive deadlocks. The dynamic weight reset unit adapts to flexible processing scenarios where all information sources are retained in compliance with regulations and no removal or intervention is required. It does not delete or block any compliant and valid assessment sources throughout the process, directly normalizing and resetting the original initial assessment weights of all information sources within the problem loop to equal and balanced values ​​with a single click. Simultaneously, a dedicated loop-wide balance constraint loss term is added to the system's backend computing power loss function, with a standardized formula to precisely control the constraint strength. This loss term can be positively correlated in real-time with the degree of dispersion difference in the real-time attention fitting coefficients of each information source within the loop; the greater the dispersion deviation, the stronger the penalty. Subsequent iterations and optimizations, relying on AI backpropagation and lightweight computing power, continuously converge and adjust the attention distribution of information sources within the loop, ultimately achieving a uniform and balanced distribution of information source discourse power across the entire domain. This flexibly and smoothly resolves cognitive deadlock induced by closed-loop weight imbalance, ensuring the complete retention and traceability of all information source data. The attention weight coefficients are calculated through a multi-head attention mechanism, where the query vector represents category differentiation features, and the key-value pairs represent information source feature vectors.

[0035] In one embodiment, the high-strength forced constraint edge constructed by the aforementioned anchor injection unit has specially assigned and controlled edge weight parameters. The basic weight value of the strong constraint edge can be uniformly set to be at least two standard orders of magnitude higher than the measured maximum weight value of ordinary conventional associated edges in the entire system. This maximizes the effectiveness of the forced guidance constraint of external anchor source information, prevents the reverse interference of poor source information in the original loop with the anchor analysis guidance, and ensures that the forced loop breaking effect of severe deadlock is stable and does not fail. At the same time, the loop balance loss term matched with the aforementioned dynamic weight reset unit has a built-in adaptive dynamic penalty coefficient. It can synchronously retrieve the measured value of the current deadlock severity level in real time. The higher the deadlock severity level, the more automatically and adaptively the penalty coefficient is increased to strengthen the constraint. The lower the level, the smaller the coefficient is decreased to adapt to flexible control. This dynamically adapts to the balance control needs of different deadlock conditions, taking into account both computing power consumption and rectification convergence efficiency.

[0036] After resetting the initial weights, the dynamic weight reset unit can define a loss function that includes an in-loop balancing loss term. This balancing loss term is positively correlated with the degree of difference between the currently assigned weight coefficients of each source within the loop. In one exemplary implementation, the sum of squares of the pairwise differences between each weight coefficient can be used to quantify this degree of difference. By iteratively optimizing this loss function through the backpropagation algorithm, the weight coefficients of each source within the loop can gradually tend towards a uniform distribution.

[0037] The formula for calculating the balance loss term in dynamic weight reset: The in-loop balance loss term introduced with the dynamic weight reset unit. The adaptive weighted squared deviation calculation formula can be defined as follows: =λ· ; Wherein, λ is the adaptive dynamic penalty coefficient, which can be adjusted up or down in real time according to the actual deadlock severity level in the current period. The higher the deadlock severity level, the larger the penalty coefficient can be to strengthen the constraint. The lower the level, the coefficient can be appropriately lowered to adapt to flexible control. , The attention fitting weight coefficients (or weight coefficients) correspond to the real-time outputs of different compliance assessment information sources within the closed-loop problem circuit; summation symbol This involves iterating and accumulating the pairwise combinations (i.e., unordered pairs) of all information sources within the loop. The system continuously iterates backpropagation through backend computing power to minimize this balancing loss term, steadily smoothing out the differences in attention weights among information sources within the loop, ultimately achieving a uniform and balanced distribution of attention across all information sources within the loop. With three types of units for hierarchical adaptation and a combination of hardware and software measures, it can fully cover all levels of deadlock rectification scenarios (light, medium, and heavy), flexibly adapting to different operational and maintenance computing power budgets and information source retention compliance requirements.

[0038] In one embodiment, the aforementioned multi-source parallel path exploration and conflict marking module has a built-in pre-linkage interlocking call logic. Before formally triggering the reverse backtracking and loss-stopping closed-loop operation, it can automatically prioritize and call the locally equipped conflict loop detection and deadlock judgment module and the resolution strategy selection module. This allows for the rapid pre-emptive completion of secondary investigation and pre-emptive resolution of residual implicit micro-deadlocks and local weak closed-loop conflicts. Only the pure and compliant information source subset that has been fully processed and has no deadlock residue is used as the sole legal input base for subsequent multi-source parallel path full-domain exploration. This effectively avoids the practical risks of repeated occurrence of implicit closed-loop conflicts, repeated triggering of invalid backtracking, idle computing resources, and path analysis interruption crashes during the subsequent long-link path traversal and cross-judgment backtracking process from the pre-emptive source, significantly improving the stability, continuity, and efficiency of the entire path exploration process.

[0039] In one embodiment, the aforementioned multi-version output and backtracking chain generation module's backtracking trajectory generation sub-unit specifically targets the inferior source nodes that have been permanently removed from the topology and the existing compliant source nodes whose weights have been dynamically reset in the equalization process during the deadlock resolution procedure. In the backend structured backtracking dedicated chain, an independent encrypted deadlock source detection sub-node is specifically embedded. This type of sub-node collects and encrypts core traceability information across all dimensions. Specifically, it can completely record the original problem closed-loop topology network structure ledger, the precise cumulative number of negative conflict edges within the ring as verified and counted in the early stage, the complete process name and actual operation flow of the exclusive deadlock hierarchical resolution strategy automatically matched and selected by the system in the current period, the exclusive unique identity of the eliminated inferior interference sources and the corresponding historical judgment original accuracy archive data, the official filing full name of the external authoritative anchor source empowered by emergency intervention, the ledger of the differentiated original weight distribution curve of the sources within the ring before the weight reset process is executed, and the detailed data of the equalized new weight distribution after the weight balance reset. All information is tamper-proof and leaves a complete trace, which is convenient for later compliance audits, work status reviews, and strategy iteration and optimization.

[0040] In one embodiment, the aforementioned backtracking trajectory generation sub-unit can also be linked with a built-in kitchen and bathroom industry-specific natural language template generation engine to automatically generate readable and easy-to-understand natural language visual explanation text with one click, based on structured backtracking chain ledger data. During the text generation process, for the core procedures of front-end full-process deadlock closed-loop detection and hierarchical policy resolution, the preset standardized special statement template is directly called to accurately embed simplified description statements of loop topology, actual statistical quantification values ​​of negative edges, removed inferior information source numbers and corresponding accuracy rate core ledger desensitized information, and automatically splices together a smooth and compliant natural language explanation text that can be quickly understood by non-professional operation and maintenance personnel. This balances the professionalism of technical tracing with the convenience of operation and maintenance viewing, and is suitable for collaborative viewing by personnel in multiple positions in government and enterprises.

[0041] In one embodiment, the system is also equipped with a deadlock pattern library and an incremental learning module. The module has a built-in local encrypted dedicated storage database as a deadlock standard pattern library, which can automatically archive and retain the complete topology of various differentiated deadlock closed loops detected by the system in each on-site test, as well as the full-process practical solutions of efficient adaptation and resolution strategies that have been successfully implemented on-site, and package them into standardized historical deadlock sample patterns. Simultaneously, it is equipped with a lightweight graph structure computing power comparison algorithm to calculate in real time the graph editing and fitting distance between the topology of new sudden information source combinations and new deadlock loop patterns on site and the archived historical standard patterns in the pattern library. The shorter the distance, the higher the pattern fitting similarity. The system can rely on the background big data statistics and review to statistically analyze the effectiveness of successful implementation and rectification of pattern matching in multiple rounds of history. Based on the actual success efficiency, it dynamically and adaptively fine-tunes the exclusive judgment preset threshold for pattern matching. When the measured fitting similarity between a new sudden information source combination or a new deadlock condition on site and the archived high-quality historical patterns in the library exceeds the limit and the adaptive preset threshold, the module can directly and intelligently reuse the mature and efficient historical resolution strategies implemented in the past with one click. It automatically skips redundant secondary full-domain loop re-detection and repeated hierarchical strategy judgment and selection of all redundant steps in the process, and quickly closes the loop to resolve new deadlock anomalies of the same kind. It continuously relies on incremental learning to iteratively optimize the system's response and handling efficiency. Long-term operation can significantly reduce the system's computing power and energy consumption and improve the emergency response speed for monitoring sudden conflicts in the kitchenware and sanitary ware market.

[0042] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A dynamic monitoring and decision support system for the kitchenware and sanitary ware market, characterized in that, include: The conflict signal space modeling module is used to receive raw signal statements from multiple data sources for the same category of kitchenware and sanitary ware, convert each statement into a standardized state node, and construct a directed graph based on the semantic consistency or conflict relationship between the nodes. The contradictory loop detection and deadlock determination module is used to detect loops in the directed graph using depth-first search. If a loop exists and the number of negative edges in the loop is odd, it is determined to be a cognitive deadlock, and the deadlock severity level is calculated. The deadlock resolution strategy selection module is used to automatically select at least one resolution strategy based on the deadlock severity level. The resolution strategies include: removing the source node with the lowest historical prediction accuracy in the loop, introducing an external anchor source to break the loop, resetting the initial weights of all sources in the loop to equal values ​​and adding balance constraints.

2. The kitchenware and sanitary ware market dynamic monitoring and decision support system according to claim 1, characterized in that: Also includes: The multi-source parallel path exploration and conflict marking module is used to traverse all source-contradictory path links from the root node in parallel based on the conflict-free source subset or balanced weight obtained after deadlock resolution, detect conflict signals at path intersections, calculate conflict intensity, and perform backtracking operation when the conflict intensity exceeds a preset threshold. The path pruning and comprehensive credibility calculation module is used to calculate the comprehensive credibility score for each complete path and prune the path according to the score threshold. The multi-version output and backtracking chain generation module is used to output the fusion conclusion corresponding to the optimal path, the list of alternative paths and their applicable scenarios, and generate a structured backtracking trajectory for each path, wherein the backtracking trajectory includes deadlock detection and resolution records.

3. The kitchenware and sanitary ware market dynamic monitoring and decision support system according to claim 1, characterized in that: The contradictory loop detection and deadlock determination module includes: The directed dependency graph construction unit is used to add directed edges based on the consistency or contradiction of the trend judgment of the same target category on the same indicator dimension between information sources, with each data source as a node. Specifically, if the trends are the same, a bidirectional positive edge is added; if the trends are opposite, a unidirectional negative edge is added; and if the trends are rising and remaining flat, directed edges with partial conflict weights are added. The cycle search unit is used to traverse the directed graph using a depth-first search, record the node visit status, and when an edge pointing to the currently visited node is found, record the cycle structure and the set of nodes and edge types it contains. The parity determination unit is used to count the number of negative edges in a loop. If the number is odd, it is determined to be a cognitive deadlock; if the number is even, it is determined to be a non-deadlock loop. The deadlock severity calculation unit is used to calculate the deadlock severity level, which is positively correlated with the ratio of the number of negative edges in the loop to the length of the loop and the conflict intensity of each edge in the loop.

4. The kitchenware and sanitary ware market dynamic monitoring and decision support system according to claim 3, characterized in that: The intensity of the conflict is determined by the degree of difference in the trend direction of the two sources and their respective historical accuracy. The greater the difference in direction and the lower the historical accuracy, the higher the intensity of the conflict.

5. The kitchenware and sanitary ware market dynamic monitoring and decision support system according to claim 1, characterized in that: The deactivation strategy selection module includes: The lowest accuracy pruning unit is used to identify the source node with the lowest historical prediction accuracy in the loop when the deadlock severity level is lower than the first threshold, permanently delete it from the graph, and re-trigger the conflict loop detection. Anchor injection unit is used to select an external authoritative source as an anchor node when the deadlock severity level is higher than the second threshold or deadlock still exists after pruning. The anchor point is then connected to all sources in the loop with a strong constraint edge, and the edge is assigned a value that is significantly higher than the weight of a normal edge to force the loop to be broken. The dynamic weight reset unit is used to reset the initial speech weights of all sources in the loop to equal values ​​without deleting the sources. It also adds an intra-loop balance loss term to the loss function used to update the weights. This loss term is positively correlated with the degree of difference between the weight coefficients of each source in the loop. Through backpropagation, the weight distribution of sources in the loop tends to be uniform.

6. The kitchenware and sanitary ware market dynamic monitoring and decision support system according to claim 5, characterized in that: The weight of the strongly constrained edge is at least two orders of magnitude higher than the maximum value of the ordinary edge weight; the penalty coefficient of the in-loop balance loss term is adaptively adjusted according to the deadlock severity level.

7. The kitchenware and sanitary ware market dynamic monitoring and decision support system according to claim 2, characterized in that: Before performing the backtracking operation, the multi-source parallel path exploration and conflict marking module first calls the conflict loop detection and deadlock determination module and the resolution strategy selection module, and uses the processed deadlock-free source subset as the input for path exploration, thereby avoiding infinite alternation caused by cyclical conflicts during the backtracking process.

8. The kitchenware and sanitary ware market dynamic monitoring and decision support system according to claim 2, characterized in that: When generating a structured backtracking chain, the backtracking trajectory generation module adds a deadlock detection node to the backtracking chain for nodes whose weights are removed or reset due to deadlock resolution. The information recorded by this node includes: loop structure, negative edge count, selected resolution strategy, removed source identifier and its historical accuracy, anchor source name, and distribution before and after weight reset.

9. The kitchenware and sanitary ware market dynamic monitoring and decision support system according to claim 8, characterized in that: The backtracking trajectory generation module also outputs natural language explanation text, in which the deadlock resolution process is described using templated statements that include a description of the loop structure, the number of negative edges, the removal of information sources and their accuracy information.

10. The kitchenware and sanitary ware market dynamic monitoring and decision support system according to claim 1, characterized in that: The system also includes a deadlock pattern library and an incremental learning module, which are used to record the deadlock loop structure detected each time and the successfully adopted resolution strategy, and to calculate the graph edit distance between the new pattern and the existing patterns in the library. When the similarity between a newly emerging source combination and a pattern in the database exceeds a preset threshold, the historical de-detection strategy is directly reused, skipping the re-detection and strategy selection steps. The preset threshold is automatically determined based on statistical analysis of historical pattern matching success rates.

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