Solid waste resource technology cross-domain matching method based on substance metabolism map

Abstract

This invention belongs to the field of solid waste comprehensive utilization and treatment technology. Specifically, it discloses a cross-domain matching method for solid waste resource utilization technology based on a material metabolism map. The method includes: first, acquiring solid waste data and performing conflict elimination to determine a safe subset of materials; then, searching for target core technology nodes in the process map and extracting their preceding physical state envelopes. By comparing the current state of the solid waste with the envelope and performing zero-value truncation difference operations to extract out-of-bounds difference data, the system reversely searches the map library for pretreatment equipment that meets the adjustment range and throughput requirements. The system further constructs directed acyclic equipment links by sequentially assembling each pretreatment device and core node according to pretreatment rule priorities, and instantiates operation parameters using the node's built-in engine, finally serializing and outputting structured production line assembly drawings. This solution achieves full automation in the design of complex solid waste production lines.

Description

Technical Field

[0001] This invention belongs to the field of solid waste comprehensive utilization and treatment technology, and relates to a cross-domain matching method for solid waste resource utilization technology based on material metabolism maps. Background Technology

[0002] Solid waste resource recovery refers to the process of converting waste materials generated during industrial production and urban life into reusable energy or materials through physical, chemical, or thermal technologies. When planning and designing resource recovery treatment lines, engineers typically need to utilize computer-aided process planning systems to analyze the material composition of solid waste based on fuzzy descriptions of its sources and to match safe, efficient, and suitable equipment chains from complex treatment process knowledge. Material metabolism mapping, a technical knowledge graph inspired by ecological metabolic networks, abstracts solid waste components, treatment equipment, process parameters, and product destinations into a network structure of nodes and edges to support the reasoning and analysis of resource recovery paths.

[0003] In existing technologies, matching solid waste resource utilization processes typically relies on domain experts consulting manuals or performing simple screening based on spreadsheets. Some information systems use relational databases to manage solid waste entries and treatment process templates, allowing users to obtain preset general treatment route suggestions by entering the solid waste category name for keyword search. These systems often use flat solid waste classification tables and process comparison tables in their data organization, and physical state adaptability analysis and cross-equipment combination design still mainly rely on manual judgment.

[0004] Existing technical solutions have significant limitations. First, there is a lack of systematic and precise mapping between solid waste names and chemical elements, making it impossible to automatically analyze the deep chemical composition of materials and predict the risk of material conflicts during the co-processing of multi-source solid waste from a reaction safety perspective. Second, the selection of technical equipment does not fully consider the specific mass budget and physical state parameters of the solid waste, failing to dynamically verify whether the material mass meets the minimum start-up throughput of the equipment, and also unable to automatically identify physical state differences based on furnace requirements such as moisture content and particle size. This results in the output process route potentially failing to start or exceeding the equipment's allowable range in actual operation. Furthermore, the process schemes generated by existing methods are mostly static text descriptions, lacking the ability to directly generate structured equipment link diagrams and data, making it difficult to directly interface with automated design software or production execution systems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above objectives, the present invention proposes the following technical solution: a cross-domain matching method for solid waste resource utilization technology based on material metabolism map, comprising: S1, receiving a multi-source solid waste query request containing macroscopic name, initial mass and current physical state, using a pre-built tree mapping table and hierarchical hash code to parse the macroscopic name into a set of microscopic elements, and summarizing the initial mass as the total mass budget.

[0006] S2. Input multiple sets of micro-elements into a pre-built conflict adjacency matrix, perform a full permutation cross-comparison of micro-elements of solid waste from different sources, lock the conflict source based on the Boolean value of the rejection reaction, add negation logic to the query request of multi-source solid waste, generate a safe material subset that eliminates the conflict source, and simultaneously deduct the initial mass of the conflict source from the total mass budget to obtain the effective total mass budget.

[0007] S3. Using a subset of safe materials as the starting search node and the effective total mass budget as the traversal constraint, perform a depth-first search on the core technology node in the graph database, accumulate the path consumption mass, and obtain the minimum device startup throughput of the next hop technology node. When the remaining mass after subtracting the accumulated path consumption mass from the effective total mass budget is lower than the minimum device startup throughput, terminate the search for the next hop technology node, and retain the target core technology node and its search path.

[0008] S4. Extract the envelope of the physical state of the target core technology node before it enters the furnace, perform a difference operation with the current physical state, and generate a state difference vector.

[0009] S5. Read the fields with values ​​greater than zero in the state difference vector, convert them into a reverse query statement consisting of processing tasks, processing values, and processing throughput, and perform a reverse search in the preprocessing device library to obtain the matching preprocessing device nodes.

[0010] S6. Following the physical processing order, reverse the preprocessing equipment nodes and connect them to the search path before the target core technology node to generate a directed acyclic graph of equipment links, and serialize and output the structured production line assembly drawing data.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention constructs a tree-like mapping table from macro solid waste ledger names to micro chemical elements and calculates hierarchical hash codes, thereby reducing the semantically ambiguous solid waste source description to an accurate set of element identifiers. At the same time, it uses a conflict adjacency matrix to perform a full permutation chemical compatibility cross-comparison of multi-source substances, which can proactively identify and exclude solid waste combinations with a risk of violent reaction before planning the resource utilization treatment path, ensuring the safety of subsequent process selection and avoiding production accidents caused by material incompatibility.

[0012] (2) The present invention introduces a depth-first search based on dual constraints of quality budget and minimum equipment start-up throughput in the graph database. During the search process, the material consumption is accumulated in real time and the minimum start-up throughput of each technical node is compared. The core processing path with more efficient material utilization is automatically selected, which eliminates the reliance on the manual selection experience of technical experts and improves the efficiency and objectivity of solid waste resource utilization technology solutions.

[0013] (3) This invention generates a state difference vector by performing a difference operation between the physical state envelope of the core equipment and the current physical state of the solid waste, and performs a reverse query driven by task, value and throughput in the pretreatment equipment library accordingly. This can accurately locate and reversely splice the necessary pretreatment equipment, so that the final output directed acyclic graph equipment link completely covers all conditioning processes from raw materials to final products. The output structured production line assembly drawing data can be directly parsed and executed by the downstream engineering system, shortening the production line design cycle. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. 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.

[0015] Figure 1 This is a schematic diagram of the implementation steps of the method of the present invention.

[0016] Figure 2 This is a schematic diagram of the rejection matrix verification and dynamic elimination logic of the present invention.

[0017] Figure 3 This is a schematic diagram of the path depth-first search and dynamic pruning optimization logic of the present invention.

[0018] Figure 4 This is a schematic diagram of the physical state difference calculation and zero-value truncation logic of the present invention. Detailed Implementation

[0019] 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, and 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.

[0020] Please see Figure 1As shown, the present invention proposes a cross-domain matching method for solid waste resource utilization technology based on material metabolism map, which includes: S1, receiving a multi-source solid waste query request containing macroscopic name, initial mass and current physical state, using a pre-built tree mapping table and hierarchical hash code to parse the macroscopic name into a set of microscopic elements, and summarizing the initial mass as the total mass budget.

[0021] In a preferred embodiment, resolving a macroscopic name into a set of microscopic elements includes: extracting keywords from the macroscopic name, mapping them to the macroscopic solid waste ledger name level of a tree-like mapping table, and obtaining the corresponding mesoscopic phase component nodes; traversing the microscopic chemical element branches under the mesoscopic phase component nodes and extracting the microscopic chemical element identifiers of all leaf nodes; combining all the extracted microscopic chemical element identifiers and associating them with the hash prefix calculated based on the hierarchical hash code to form a set of microscopic elements.

[0022] Specifically, when the system receives a multi-source solid waste query request, this request includes a macro-level description of the solid waste source, the initial mass of the solid waste from that source, and the current physical state of the solid waste at the time of its generation. The macro-level name is usually a common term for waste in industrial or residential sectors, such as "waste copper-clad laminate" or "fly ash from municipal solid waste incineration." The initial mass represents the total weight of the solid waste from that source to be processed, in kilograms. The current physical state is a set of data describing the immediate physical characteristics of the solid waste, such as moisture content, particle size, or calorific value. This data is typically collected in real-time by a front-end material weighing system or on-site testing equipment and uploaded with the request.

[0023] To transform this broad macroscopic name into elemental information suitable for microscopic chemical reaction analysis, the system pre-constructs a tree-like mapping table based on the National Chemical Substance Standard Database and historical solid waste component analysis records. This mapping table organizes data using three levels: macroscopic solid waste ledger name, mesoscopic phase component, and microscopic chemical element. The tree-like mapping table is a non-cyclic hierarchical data structure. The first level under the root node is the macroscopic solid waste ledger name node. Each macroscopic solid waste ledger name node connects to one or more mesoscopic phase component nodes, and each mesoscopic phase component node connects to one or more microscopic chemical element leaf nodes. Simultaneously, the system calculates and stores the corresponding hierarchical hash code for each node path in this tree-like mapping table. The hierarchical hash code is a fixed-length bit string calculated by concatenating the node identifier text from the macroscopic to the microscopic level and applying a collision-resistant hash function. It is used to uniquely and compactly identify a complete tree path.

[0024] Upon receiving a query request, the system performs a dimensionality reduction parsing operation. This process decomposes the macroscopic name into a set of microscopic element identifiers, and linearly sums the initial masses of all solid waste sources in the query request to directly derive the total mass budget. The initial mass of each solid waste source maintains a consistent dimension, with the unit being kilograms. The specific dimensionality reduction parsing process is as follows: The system first extracts keywords from the macroscopic name. This keyword extraction process removes prefixes or suffixes like "waste" or "used," which lack substantial distinguishing meaning, resulting in the core substance name string. Subsequently, the system performs text matching between this core substance name string and all nodes at the macroscopic solid waste ledger name level in the tree-structured mapping table. The matching process employs either exact string comparison or fuzzy matching combining edit distance and cultural feature weighting coefficients. Edit distance measures the minimum number of operations required to convert two strings into each other through insertion, deletion, and replacement. The cultural feature weighting coefficient is a value between 0 and 1 pre-defined in a mapping dictionary constructed based on regional industrial slang and dialectal habits, used to adjust tolerance for specific spelling differences, thus avoiding matching failures due to regional variations. Once a matching macroscopic solid waste ledger name node is found, the system locates that node and retrieves all its subordinate mesoscopic phase component nodes. Mesoscopic phase components describe the main components of the solid waste in terms of phase or material composition, such as "glassy material," "metallic element," "resin polymer," or "halogen salt."

[0025] For the set of mesophase component nodes obtained through the matching process, the system traverses them. The traversal employs a depth-first strategy, visiting each branch structure under each mesophase component node one by one. Under each mesophase component node, the system continues to explore downwards until it reaches the leaf node at the microscopic chemical element level. A leaf node is a terminal node in the tree structure with no child nodes, and it contains a microscopic chemical element identifier. The microscopic chemical element identifier is a unique code representing a specific chemical element; for example, "Si" represents silicon, "Cu" represents copper, and "Cl" represents chlorine. The system extracts the microscopic chemical element identifiers of all leaf nodes encountered during the traversal and temporarily stores them in the system's memory working area.

[0026] Next, the system deduplicates and combines all extracted microscopic chemical element identifiers to form a list without duplicate elements. Simultaneously, the system needs to calculate a hash prefix associated with this list to ensure the tamper resistance and version consistency of the multi-source material data. The calculation process is as follows: The system reads the hierarchical hash codes corresponding to all paths from the previously matched macroscopic solid waste ledger name nodes to each leaf node visited during traversal. These hierarchical hash codes are then sorted in ascending alphabetical order and used as input to ensure the determinism of the hash calculation. Finally, a core data digest hash function is used for further calculation.

[0027] in, This represents the path from the macroscopic node to the leaf nodes of all related microscopic elements. The hierarchical hash code of the complete path; b represents the total number of valid paths that can be extended from the specific macroscopic solid waste ledger name node to the microscopic chemical element leaf node; symbol This indicates a sequential concatenation operation for bit strings or byte streams. For the selected quadratic hash function, the SHA-256 algorithm can be used to output a 256-bit hash value. The first 64 bits of the calculated hash value are used as the hash prefix. This hash prefix Together with the deduplicated list of microscopic chemical element identifiers, these are packaged as a verification prefix to form a complete set of microscopic elements. This set is represented in data structure as an array of identifiers associated with version verification tags. This step is repeated for all sources of solid waste, ultimately resulting in multiple sets of microscopic elements, which are then aggregated to obtain the total mass budget.

[0028] For example, the system receives a multi-source solid waste query request, which includes a macro-name for one source called "waste copper-clad laminate" with an initial mass of 500 kg, and a macro-name for another source called "municipal solid waste incineration fly ash" with an initial mass of 200 kg. According to step S1, the system first calculates the total mass budget, that is, adds the 500 kg and 200 kg together to obtain a total mass budget of 700 kg.

[0029] For the term "waste copper-clad laminate," the system extracts the keyword "copper-clad laminate" and precisely matches the node "waste copper-clad laminate" in the macroscopic solid waste ledger name hierarchy of the tree-structured mapping table. This node has sub-nodes for mesoscopic phase components, including "epoxy resin" and "copper foil." The system traverses these two mesoscopic phase component nodes. The leaf nodes for microscopic chemical elements under the "epoxy resin" node contain the identifiers "C," "H," "O," and "Br," while the leaf nodes under the "copper foil" node contain the identifier "Cu." The system then extracts these five microscopic chemical element identifiers: "C," "H," "O," "Br," and "Cu." The system retrieves the hierarchical hash codes for five paths: from "waste copper-clad laminate" to "epoxy resin," then to "C," "H," "O," and "Br," and finally to "copper foil" and then to "Cu." These five hierarchical hash codes are concatenated and input into the SHA-256 function; the first 64 bits are taken to obtain the hash prefix. Therefore, the set of microscopic elements obtained from the analysis of "waste copper-clad plates" is {prefix: Elements: [C,H,O,Br,Cu]}.

[0030] For the term "municipal solid waste incineration fly ash," the system extracts the keyword "municipal solid waste incineration fly ash" and matches it to the corresponding macroscopic node. The mesoscopic phase component nodes under this node include "silicate matrix" and "chloride salt." After traversal, "Si," "O," "Al," and "Ca" are extracted from "silicate matrix," and "Cl," "Na," and "K" are extracted from "chloride salt." Subsequently, the seven identifiers "Si," "O," "Al," "Ca," "Cl," "Na," and "K" are extracted, and the hash prefix is ​​calculated similarly. This forms the second set of micro-elements {prefix: The elements are: [Si, O, Al, Ca, Cl, Na, K]. At this point, the parsing process is complete, outputting a total mass estimate of 700 kg and two sets of microscopic elements associated with hash prefixes.

[0031] S2. Input multiple sets of micro-elements into a pre-built conflict adjacency matrix, perform a full permutation cross-comparison of micro-elements of solid waste from different sources, lock the conflict source based on the Boolean value of the rejection reaction, add negation logic to the query request of multi-source solid waste, generate a safe material subset that eliminates the conflict source, and simultaneously deduct the initial mass of the conflict source from the total mass budget to obtain the effective total mass budget.

[0032] In a preferred embodiment, step S2 specifically includes: creating a cursor corresponding to the number of micro-elements in the set, traversing the conflict adjacency matrix, and reading the Boolean value in the intersection cell of any two micro-elements from different sources one by one; when the read Boolean value is the first logical value representing the existence of a repulsion reaction, marking the solid waste source to which the pair of micro-elements belongs as a conflict source; after all cursor traversals are completed, collecting the identifiers of all marked conflict sources, constructing a negation conditional clause, and appending the negation conditional clause to the query statement of the multi-source solid waste query request with a logical AND relation, re-executing the query to filter out conflict sources, and obtaining a subset of safe substances.

[0033] In a further preferred embodiment, the pre-built conflict adjacency matrix is ​​constructed through the following steps: obtaining mutually exclusive substance pairs that pose a risk of violent chemical reaction as defined in chemical safety regulations; mapping the mutually exclusive substance pairs to microscopic element identifiers to construct a two-dimensional matrix with the microscopic element identifiers as rows and columns; in the two-dimensional matrix, assigning the intersecting cells of mutually exclusive rows and columns to Boolean values ​​indicating the presence of a repulsive reaction, and assigning the remaining intersecting cells to Boolean values ​​indicating the absence of a repulsive reaction, thus obtaining the conflict adjacency matrix.

[0034] Specifically, after completing the aforementioned analysis of the micro-element set and initially summarizing the total mass budget, the system needs to detect potential chemical incompatibilities between micro-elements from different solid waste sources. Chemical incompatibility refers to the potential for dangerous chemical reactions, such as violent oxidation, combustion, and the generation of toxic gases, that may occur between certain chemical elements contained in different solid wastes under the physical conditions of co-processing. To accomplish this detection, the system uses a pre-constructed conflict adjacency matrix. The conflict adjacency matrix is ​​a square two-dimensional Boolean matrix, with the number of rows and columns equal to the total number of micro-element identifiers recognized by the system. The rows and columns of the matrix are indexed by the same sequence of micro-element identifiers. The construction of this matrix is ​​independent of the query process and is completed during system initialization. Specifically, it includes: firstly, extracting mutually exclusive substance pairs with a risk of violent chemical reactions from chemical safety regulations. The data sources for the chemical safety regulations are the "Technical Guidelines for Hazardous Waste Compatibility" and the "Chemical Interaction Safety Database," compiled based on 200 typical industrial solid waste co-disposal accident cases. Mutually exclusive material pairs are two specific chemical substances or elements that, when exposed to certain conditions, will trigger a hazardous reaction; for example, a mixture of chlorine gas and aluminum powder may cause a violent combustion. The system then maps these mutually exclusive material pairs to microscopic element identifiers. During the mapping process, if the mutually exclusive material is an element or can be classified into a specific element, the identifier of that element is directly used; if the mutually exclusive material is a compound, its core reacting element is analyzed, for example, sodium chlorate is classified as the element "Cl". This results in a set of mutually exclusive element pairs, containing only those combinations of elements whose repulsive reactions have been verified by a safety database.

[0035] Then, the system constructs a two-dimensional matrix of size 80×80 using all the micro-element identifiers (containing a total of 80 identifiers) as rows and columns. For any cell in the matrix The element identifier corresponding to its row is The element identifier corresponding to the column is Assign values ​​according to the following core logic rules:

[0036] Boolean value Indicates the presence of a rejection response, i.e., the first logical value; Boolean value. This indicates no rejection response, i.e., the second logical value. Because this matrix symmetrically expresses rejection relationships, it satisfies... This matrix is ​​the conflict adjacency matrix, which resides in memory for fast random access.

[0037] The aforementioned execution process begins by inputting the parsed K sets of micro-elements into the matrix for computation, where K is a set constant. The system performs a full permutation cross-comparison between micro-elements of solid waste from different sources to pinpoint the source of the conflicting solid waste. The specific steps are as follows: The system creates cursors corresponding to the number of micro-element sets. A cursor is a traversal control structure that points to the current element position within a set. Each cursor is bound to a micro-element set and can be moved sequentially to retrieve element identifiers within the set. The full permutation cross-comparison employs a double-loop mechanism: the index control of the outer and inner loops ensures that each pair of sets is compared only once and that the two cursors always point to sets from different sources. During traversal, for the element identifiers retrieved by the outer and inner cursors, the system directly uses them as row and column indices to read the Boolean values ​​in the corresponding cross cells of the conflict adjacency matrix. This operation corresponds to a constant-time matrix index lookup.

[0038] When the read boolean value equals When the system reaches the first logical value representing a repulsive reaction, it determines that the coexistence of the two elements currently being traversed is dangerous. At this point, the system marks the respective solid waste sources to which these micro-elements belong as conflict sources. Each set of micro-elements is associated with a clear source identifier during the resolution phase. The source identifier is a unique string carried in the query request to distinguish different solid waste sources, such as "waste copper-clad laminate". The system records the two source identifiers that currently trigger the conflict and adds them to the conflict source set through the mapping relationship between sets and sources. If multiple element pairs trigger the same source, the source identifier will only be recorded once to maintain the uniqueness of the set.

[0039] After all nested cursor traversals are completed, the system collects all source identifiers in the conflict source set. Based on these identifiers, the system constructs a negation clause. A negation clause is a filtering logic applied to a query instruction, such as using the "NOTIN" semantics of a structured query language. Specifically, it is constructed by enclosing each identifier in the conflict source set in quotation marks and separating them with commas, generating a negation list string in the format "NOTIN('identifier1','identifier2',...)". The system then opens the query statement corresponding to the original multi-source solid waste query request and appends the negation clause to the condition part of that statement using a logical AND relation. For example, the original query statement might contain "SELECT sourceFROM waste_sources WHERE…", which becomes "…AND source NOT IN('identifier1','identifier2')" after appending. The system then re-executes the query operation once using the modified query statement, directly filtering out all solid waste sources marked as conflict sources from the data source. The query returns a list of solid waste sources that do not contain any conflict sources. The sets of micro-elements corresponding to these sources and the material combinations they constitute constitute the safe material subset. The safe material subset is the union of sets of micro-elements from different solid waste sources that exclude endogenous hazardous chemical reactions, ensuring that the material basis upon which subsequent process routing is based is inherently chemically safe. Simultaneously, to prevent flux misjudgments during subsequent core technology equipment matching, the system synchronously deducts the initial mass corresponding to the filtered conflict sources from the total mass budget, resulting in a dynamically updated effective total mass budget.

[0040] For example, continuing the foregoing description and further expanding, assume that the system is currently processing a multi-source solid waste query request containing three solid waste sources. In addition to the already parsed "waste copper-clad laminate" (initial mass 500 kg) and "municipal solid waste incineration fly ash" (initial mass 200 kg), the system also receives a third source, macroscopically named "waste aluminum shavings," with an initial mass of 300 kg. The initial total mass estimate is 1000 kg. The three sets of microscopic elements obtained after parsing are as follows: Set A is associated with the source "waste copper-clad laminate," with element identifiers including "C," "H," "O," "Br," and "Cu"; Set B is associated with the source "municipal solid waste incineration fly ash," with element identifiers including "Si," "O," "Al," "Ca," "Cl," "Na," and "K"; Set C is associated with the source "waste aluminum shavings," with element identifiers including "Al," "Mg," "Si," and "Fe." In the pre-constructed conflict adjacency matrix of the system, mutually exclusive substance pairs extracted according to the "Technical Guidelines for Hazardous Waste Compatibility" are mapped to mutually exclusive element pairs, including mutually exclusive pairs consisting of aluminum "Al" and chlorine "Cl", because mixing fine aluminum powder with chlorine-containing active substances before disposal may trigger a violent exothermic reaction. The value of the cross cells corresponding to "Al" and "Cl" in the matrix is ​​1.

[0041] The system creates three cursors bound to sets A, B, and C. First, the system cross-compares elements in set A with elements in set B: when iterating through "Cu" and "Cl", the corresponding matrix value is 0, indicating no conflict; after combining all elements from A and B, no conflict is detected. Next, it compares set A with set C: no mutually exclusive element pairs are found in any combinations. Finally, it compares set B with set C: when iterating through element "Cl" from set B and element "Al" from set C, the matrix cell value is 1, triggering a hazard assessment. The system marks the solid waste sources to which this pair of elements belongs, "municipal solid waste incineration fly ash" and "waste aluminum shavings", as conflict sources, with the conflict source set being {"municipal solid waste incineration fly ash", "waste aluminum shavings"}. The system then constructs a negative condition clause "NOTIN('municipal solid waste incineration fly ash', 'waste aluminum shavings')", appends it as a logical AND relation to the query statement of the multi-source solid waste query request, and re-executes the query. After filtering, only the source "waste copper-clad laminate" remained unrejected, and its corresponding set of micro-elements, A, constitutes the safe material subset. Finally, the system precisely deducts 200 kg of fly ash and 300 kg of aluminum shavings from the initial budget of 1000 kg, updating the effective total mass budget to 500 kg. This safe material subset and the updated effective total mass budget will serve as the starting material node and traversal constraints for subsequent library searches.

[0042] Please see Figure 2 As shown, Figure 2This is a schematic diagram of the exclusion matrix verification and dynamic rejection logic of the present invention. As shown in the figure, when the system merges and evolves heterogeneous waste sets, it uses a pre-set chemical element conflict adjacency matrix for cross-comparison. When it detects that chlorine (Cl) in set B (incineration fly ash) meets aluminum (Al) in set C (waste aluminum scrap), the Boolean value 1 at the corresponding coordinate in the matrix is ​​hit, thereby triggering the hazard rejection mechanism. Based on this rejection signal, the system automatically concatenates and generates an SQL dynamic rejection statement containing the condition "NOT IN('incineration fly ash', 'waste aluminum scrap')" to remove the hazard source from the current processing batch. Subsequently, the system executes constraint parameter updates, deducting the rejected 200kg and 300kg from the initial total budget of 1000kg, calculating the remaining evolution budget of the retained node (set A, waste copper-clad laminate) as 500kg, and passing it as the safe material set to the starting point of the DFS search for the next hop.

[0043] S3. Using a subset of safe materials as the starting search node and the effective total mass budget as the traversal constraint, perform a depth-first search on the core technology node in the graph database, accumulate the path consumption mass, and obtain the minimum device startup throughput of the next hop technology node. When the remaining mass after subtracting the accumulated path consumption mass from the effective total mass budget is lower than the minimum device startup throughput, terminate the search for the next hop technology node, and retain the target core technology node and its search path.

[0044] In a preferred embodiment, when the remaining quality after subtracting the accumulated path consumption quality from the effective total quality budget is lower than the minimum device startup throughput, the search for the next-hop technology node is terminated. This includes: when the depth-first search extends to the next-hop technology node, obtaining the node quality consumption coefficient and the node's minimum device startup throughput; subtracting the sum of the quality consumption of all visited nodes on the current search path from the effective total quality budget to obtain the current remaining quality budget; comparing the current remaining quality budget with the node's minimum device startup throughput, and if the current remaining quality budget is less than the node's minimum device startup throughput, performing a pruning operation to release the memory space occupied by the search branch of the next-hop technology node.

[0045] In a further preferred embodiment, step S3 further includes: during the depth-first search process, if multiple paths meet their respective minimum device startup throughput requirements, then the final remaining quality of each path is compared, and the technical node on the path with the largest final remaining quality is selected as the target core technical node.

[0046] Specifically, after eliminating the aforementioned conflict sources and obtaining a subset of safe substances and a dynamically updated effective total mass budget, the system possesses a chemically compatible combination of substances. The task of the aforementioned process is to find a target core process route in a pre-built graph database that can transform this subset of safe substances into the final resource-based product. A graph database is a non-relational data storage system that uses nodes to represent entities and edges to represent the connections between entities. The graph database used here is built based on an attribute graph model, where nodes represent core technology nodes and edges represent directed process connections where materials flow from one technology node to another.

[0047] Core technology nodes are digital representations of core resource-processing equipment abstracted from graph databases. Each such node is bound to a set of attribute data during storage, including equipment identifier, technology type, node mass consumption coefficient, minimum equipment startup throughput, and a reference to the envelope of the physical state of the pre-feeding material in the furnace related to subsequent steps. The node mass consumption coefficient is a dimensionless scaling factor, representing the ratio of the mass of input material consumed per unit mass of input material passing through the technology node due to inherent process losses (such as high-temperature gasification loss, slag physical retention, and other non-target product consumption). The minimum equipment startup throughput is the minimum input material mass required to maintain the normal startup and stable operation of the corresponding equipment at the technology node; its unit is kilogram, consistent with the total mass budget.

[0048] The system uses a subset of safe materials as the starting search node, and sets the node in the graph database that has the highest similarity to the combination of micro-elements in that subset as the search starting point. The search process employs a depth-first search algorithm with pruning. Depth-first search is a graph traversal strategy that starts from the starting node and explores as deeply as possible along a path until it can no longer expand before backtracking to try other branches. In this context, the system's search process accumulates the consumed mass of each node on the path in real time and obtains the minimum device startup throughput bound to each next-hop technology node. The total mass budget is used as a traversal constraint to determine the expansion and termination of the search boundary.

[0049] When expanding from the currently visited technology node to the next hop technology node in a depth-first search, the system first obtains the node quality consumption coefficient of that node through the attribute reading interface of the graph database. and node minimum device startup throughput Node quality consumption coefficient The setting is based on the average actual loss rate statistically derived from long-term operating data of the equipment, measured using 200 sets of industrial production line equipment operation records. Minimum equipment startup throughput at the node. Extracted directly from the equipment engineering design specifications, it represents the critical economic threshold between the equipment operating under no-load conditions and handling extremely small amounts of material.

[0050] Next, the system needs to evaluate whether the current material quality is sufficient to drive the next hop technology node. In the layer-by-layer recursion of the depth-first search, the system maintains a node feed quality variable that is dynamically updated with the path depth. For the first technical node after the starting node, This equals the effective total mass budget inherited from the previous processing stage. The system will use the feed mass of the current node. Minimum device startup throughput of the acquired node Perform a preliminary comparison.

[0051] The logic of the comparison is: if This means that even if all remaining materials are input into the device, the minimum startup requirements cannot be met. At this point, the system determines that the next-hop node is unreachable and performs a pruning operation on it. The pruning operation immediately stops expanding the next-hop node, prevents it from being added to the search path, and releases the candidate search state object, neighbor list buffer, and stack frames allocated during downward recursion that were prepared for the node.

[0052] if If the next-hop node meets the device startup requirements, the system accepts the node into the current search path. Subsequently, the system dynamically calculates the output quality that can be passed to the next-hop node after the device has consumed the material. This calculation process does not require re-accumulating the entire path; instead, it directly executes the core state recursion formula with constant time complexity based on the variables maintained in the state object. ,in, This represents the effective output mass, expressed in kilograms, remaining after the inherent losses of this technology node and usable as actual input for the next level node. After calculation, the system will... The value is assigned to the feed quality variable of the next level of recursion, allowing the search to continue to extend to subsequent branches of that node.

[0053] Since a graph database may contain multiple paths that connect the starting material node to the terminal node representing the final resource product, depth-first search explores multiple feasible paths. Each complete path has a final residual mass upon reaching the terminal node, equal to the output mass of that terminal node. If multiple paths satisfy the minimum equipment startup throughput requirements for each node along their respective paths, the system selects the target path using a comparison rule. Specifically, the rule compares the final residual masses of each path and selects the technical node on the path with the largest final residual mass as the target core technical node. The rationale for selecting the path with the largest final residual mass is that the corresponding process has the highest resource conversion rate for the input material, minimal inherent losses, and maximizes the yield of the final resource product while satisfying all equipment constraints, avoiding the excessive conversion of valuable solid waste into ineffective slag or waste gas, thus preventing objective waste of production capacity. After the search is complete, the system retains the identifier sequence of all technical nodes on this target path, along with the connection edges between nodes and the in-degree and out-degree information of each node, as the search path for the target core technical node.

[0054] For example, following the steps above that excluded the scenarios of "fly ash from municipal solid waste incineration" and "waste aluminum shavings," the safe material subset is the set of micro-elements corresponding to "waste copper-clad laminate." After deducting the conflict source from the initial budget of 1000 kg, the system updates the inherited effective total mass budget to 500 kg. In the graph database, the system will use the main constituent elements of "waste copper-clad laminate"—C, H, O, Br, and Cu—as matching conditions to locate the starting material node. From this starting node, there are two candidate technical paths available for depth-first search exploration.

[0055] Path 1 includes a core technology node called a "high-temperature incinerator," with a node mass consumption coefficient of 0.12 and a minimum equipment startup throughput of 100 kg. When searching along this path, the system acquires this consumption coefficient and startup throughput. The current remaining mass budget is calculated; since this is the first node, the current feed mass is the updated effective total budget of 500 kg. The system first compares the feed mass with the startup throughput; since 500 ≥ 100, the condition is met, and the node is reachable. Subsequently, the system calculates the output mass after passing through the high-temperature incinerator node using a recursive formula, multiplying 500 by (1 minus 0.12), resulting in an output mass of 440 kg. This path subsequently connects to the terminal node "glass solidified body," which has no additional mass consumption coefficient; therefore, the final remaining mass of Path 1 is 440 kg.

[0056] Path 2 includes a core technology node called a "low-temperature pyrolysis furnace," with a node mass consumption coefficient of 0.25 and a minimum equipment startup throughput of 300 kg. During search expansion, the system retrieves the corresponding attributes. The current feed mass is 500 kg. The system compares 500 ≥ 300, and the condition is met. The output mass after passing through the low-temperature pyrolysis furnace node is updated to 500 multiplied by (1 minus 0.25), resulting in an output mass of 375 kg. This path subsequently connects to the terminal node "pyrolysis oil and solid carbon," therefore, the final remaining mass of Path 2 is 375 kg.

[0057] Now, suppose a new core technology node, "Plasma Melting Furnace," is added to the graph database. Its node mass consumption coefficient is 0.08, but its minimum equipment startup throughput is 600 kg. After the "High-Temperature Incinerator" node, the system attempts to use the Plasma Melting Furnace node as the next hop. At this point, the feed mass arriving at the Plasma Melting Furnace node is 440 kg after consumption in the high-temperature incinerator. The system compares 440 and 600 and finds that 440 is less than 600, meaning the condition is not met! This means that the remaining material after processing in the high-temperature incinerator is insufficient to meet the minimum startup standard of the plasma melting furnace. The system immediately triggers a pruning operation, stopping the downward exploration of this branch (designated as path three).

[0058] The search concluded, and the system identified two feasible paths with final remaining masses of 440 kg and 375 kg, respectively. Based on the aforementioned comparison rules, the system selected path one, corresponding to the largest remaining mass of 440 kg—the path with the "high-temperature incinerator" as the core technology node—as the target path. The "high-temperature incinerator" node on this path was marked as the target core technology node, and its complete node attributes and upstream / downstream connections were preserved and output for subsequent physical state matching analysis.

[0059] Please see Figure 3 As shown, Figure 3This is a schematic diagram of the depth-first search and dynamic pruning optimization logic for the processing path in this invention. As shown in the figure, the starting node of the search is a set of safe materials with a mass of 500 kg. The system traverses the candidate branches downwards: Candidate branch 1 on the left (high-temperature incinerator) meets the minimum start-up flux threshold of 100 kg, and the output mass is calculated to be 440 kg based on the consumption coefficient; when evolving downwards from this node, one of the lower-level branches attempts to connect to the (plasma melting furnace) node, but because the mass of 440 kg is less than the start-up flux of 600 kg for this node, a dynamic pruning operation is triggered and memory is released; while the other valid branch successfully connects to the terminal "glass solidified body". Candidate branch 2 on the right (low-temperature pyrolysis furnace) meets the start-up threshold, and the calculated output mass is 375 kg, connecting to the terminal "pyrolysis oil and solid carbon". Finally, based on the comparison and optimization logic of the DFS search results, since 440 kg is greater than 375 kg, the system preferentially backtracks to branch 1 with the largest remaining mass as the target path.

[0060] S4. Extract the envelope of the physical state of the target core technology node before it enters the furnace, perform a difference operation with the current physical state, and generate a state difference vector.

[0061] In a preferred embodiment, step S4 specifically includes: parsing the attribute data of the target core technology node, obtaining the physical state envelope of the pre-entry furnace bound to it, wherein the physical state envelope of the pre-entry furnace includes one or more physical state indicators and their upper or lower threshold values; extracting the current physical state value corresponding to the physical state indicator from the current physical state of the multi-source solid waste query request; for each physical state indicator, subtracting the corresponding upper threshold value from the current physical state value, or subtracting the current physical state value from the lower threshold value, and truncating it using a zero-value truncation function, calculating and generating one or more difference fields representing the degree of difference, thus forming a state difference vector.

[0062] Specifically, after identifying the target core technology nodes and their search paths, the system needs to verify whether the current comprehensive physical state exhibited by the subset of safety materials before entering the equipment corresponding to the core technology node is within the design tolerance range of the equipment. This process is accomplished by extracting the envelope of the physical state of the pre-entry furnace bound to the target core technology node and performing a difference operation between it and the received current physical state.

[0063] The pre-feed physical state envelope is a set of data objects associated with and stored at each core technology node in the graph database. It defines the physical characteristic boundaries of the equipment at that technology node that can stably accept materials at the furnace inlet. This envelope contains one or more physical state indices, each with a clearly defined upper or lower threshold. The upper threshold represents the critical value that the material's physical state value must not exceed; for example, if the moisture content is too high, the pyrolysis furnace will not be able to maintain its heat. The lower threshold represents the critical value that the material's physical state value must not fall below; for example, if the particle size is too small, it may cause losses due to airflow within the furnace. Physical state indices are parameter names describing the physical characteristics of solid waste materials, including but not limited to moisture content, average particle size, lower heating value, density, and ash content. Their setting is based on the compilation of equipment engineering design parameters and accumulated furnace inlet permit index data. The data is extracted from the structured operation manuals of 60 typical pyrolysis, incineration, and melting equipment.

[0064] The current physical state is a set of key-value pairs carried verbatim from the multi-source solid waste query request. When the system receives a query request, the initial mass and current physical state, in addition to the macroscopic name, are submitted together. The data structure of the current physical state is a mapping table, where the key is a string of physical state indicators, and the value is the measured value of the corresponding indicator at the current moment. The values ​​are provided by online monitoring instruments or offline laboratory analysis at the solid waste generation site, based on the average expression precision of 200 sets of industrial solid waste sampling analysis, and uniformly retained to two decimal places.

[0065] The system's differential operation process is executed sequentially according to the following logic: First, the system accesses the attribute data block of the previously determined target core technology node in memory. The attribute data block is a structured object deserialized along with the node returned by the graph query, containing a field named "Pre-entry Furnace Physical State Envelope". The system parses this field to obtain a list of physical state indicators and the threshold type and threshold value bound to each indicator. The threshold type is marked with an enumeration value, for example, "UPPER" represents the upper limit threshold, and "LOWER" represents the lower limit threshold. Each physical state indicator... The threshold is denoted as The units are consistent with the units of the corresponding indicators in the current physical state. Simultaneously, the system records the set of names for this batch of physical state indicators. .

[0066] Subsequently, the system extracts the data from the current physical state mapping table carried in the multi-source solid waste query request, and... The physical state index name must be an exact match for each current physical state value. For each physical state index... The extracted current physical state value is denoted as Its value is provided by on-site measurement, and its dimensions correspond to the threshold. Similarly, if a certain indicator is missing in the current physical state mapping table, the system will refuse to arbitrarily assign a value of 0 to avoid causing serious subsequent control misjudgments. Instead, the system will initiate a secondary query to the preset chemical substance standard database based on the macroscopic solid waste ledger name corresponding to the safety substance subset obtained from the aforementioned analysis, and retrieve the historical experience average value of this type of solid waste under the same working conditions for interpolation to ensure the completeness of the differential operation and the safety of the project.

[0067] Next, the system targets the set of indicators. Each physical state index in Calculate its current physical state value With the corresponding envelope threshold The degree of difference between them generates a difference field. To simplify the computational logic and directly filter out safe data that meets the envelope requirements, the system uses a unified core state difference formula with zero-value truncation for calculation:

[0068] in, This is the current physical state value; This refers to the corresponding upper or lower threshold, both using the same unit, such as mass percentage for moisture content and millimeters for average particle size. The difference field is obtained after subtraction. natural units and Consistency. By introducing The zero-truncation function automatically truncates the difference result to 0 when the material index does not exceed the threshold limit; it only truncates it to 0 when the index does not exceed the threshold limit. Only then can it be clearly stated that the current physical state of the material has violated the envelope constraint, and the positive difference value is... .

[0069] For indicators that do not distinguish between upper and lower limits and only require equal value matching, the same processing logic as the upper threshold can be uniformly adopted, treating the threshold as a nominal value and using the absolute value of the difference in the calculation. The system combines all these difference fields into a data object, which is the state difference vector, using the physical state indicator name as the key and the difference value as the value. Due to the truncation characteristic of the above formula, the state difference vector will necessarily contain fields with a value of 0. These fields with a value of 0 represent compliant indicators that do not require preprocessing and will be directly ignored in subsequent steps. Only fields with a value greater than 0 are retained to trigger preprocessing matching.

[0070] For example, following the example scenario in step S3 above, the system has selected the "high-temperature incinerator" as the target core technology node, and this node is marked with a final remaining mass of 440 kg in the aforementioned process, satisfying the minimum equipment startup throughput. The current physical state data of "waste copper-clad laminate" included in the multi-source solid waste query request are: moisture content 18.50% by mass, average particle size 5.20 mm, and lower heating value 11.30 MJ / kg. These three physical state values ​​are all from real-time sampling analysis conducted at the solid waste generation end before shipment.

[0071] During the attribute parsing phase, the system parses the attributes of the "high-temperature incinerator" node and reads that the physical state envelope of the pre-loaded furnace bound to this node contains three physical state indicators. The high-temperature incinerator requires the material to be dry to maintain the furnace temperature; therefore, the moisture content indicator is the upper limit threshold, which is 10% by mass. Suspended combustion requires the material not to be too large; therefore, the average particle size indicator is the upper limit threshold, which is 50 mm. The combustion process needs to maintain a minimum heat of combustion; therefore, the lower heating value indicator is the lower limit threshold, which is 15 MJ per kilogram.

[0072] The system extracts three current values ​​from the current physical state: the current moisture content is 18.50% by mass, the current average particle size is 5.20 mm, and the current lower heating value is 11.30 MJ per kilogram.

[0073] The system then invokes the core state difference formula to calculate the difference. For moisture content, which is an upper threshold type, the system subtracts the upper threshold (18.50 minus 10) from the current value, calculating a moisture content difference of 8.50% by mass. This result is greater than 0, indicating that the moisture content exceeds the standard. For average particle size, also an upper threshold type, the system subtracts the upper threshold (5.20 minus 50) from the current value. Since the current value does not exceed the upper limit, the calculation result is truncated to 0 mm, indicating that the particle size is within the safe range. For lower heating value, a lower threshold type, the system subtracts the current value (15 minus 11.30) from the lower threshold, calculating a calorific value difference of 3.70 MJ / kg. This result is greater than 0, indicating a calorific value shortage. The resulting state difference vector contains three fields: moisture content difference 8.50, particle size difference 0, and calorific value difference 3.70. The difference between moisture content and calorific value being greater than 0 clearly indicates the extent to which the current moisture content of the material exceeds the upper limit of the envelope of the high-temperature incinerator, and the degree of shortage of calorific value that has not reached the lower limit of ignition. This difference vector containing non-zero values ​​will drive the subsequent reverse search for dehydration pretreatment and calorific value enhancement equipment.

[0074] Please see Figure 4 As shown, Figure 4This is a schematic diagram of the physical state difference calculation and zero-value truncation logic of the present invention. As shown in the figure, the calculation logic performs differentiated comparisons for physical state indicators of different threshold types. For physical state indicator 1 (moisture content), which is set as the upper limit type, the current physical state exceeds the state envelope threshold, the system calculates a difference of 8.50 and triggers dehydration preprocessing; for physical indicator 2 (average particle size), which is not out of bounds, the system uses a zero-value truncation function to forcibly truncate the calculated negative difference to 0, determining that no processing is needed; for physical indicator 3 (lower heating value), which is set as the lower limit type, the current state is below the threshold, the system calculates a difference of 3.70 and triggers calorific value compensation preprocessing. Finally, the calculation module only extracts fields greater than 0, encapsulates them into a state difference vector (moisture content difference: 8.50, lower heating value difference: 3.70), and passes it to subsequent steps for reverse preprocessing device matching.

[0075] S5. Read the fields with values ​​greater than zero in the state difference vector, convert them into a reverse query statement consisting of processing tasks, processing values, and processing throughput, and perform a reverse search in the preprocessing device library to obtain the matching preprocessing device nodes.

[0076] In a preferred embodiment, step S5 specifically includes: filtering out the difference fields in the state difference vector where the value is greater than zero, reading the field name as the processing task, and reading the field value as the processing value; obtaining the effective total quality budget and determining it as the processing throughput; encapsulating the processing task, processing value, and processing throughput into a reverse query statement, matching and retrieving the functional attributes and processing capability attributes of the device nodes in the preprocessing device library of the graph database, and returning the preprocessing device nodes that meet the processing requirements.

[0077] Specifically, after generating the state difference vector in the aforementioned process, the system begins to identify which physical state indicators have actually exceeded the limits, and accordingly proposes specific requirements for the pretreatment of solid waste materials. In the state difference vector, due to the protection of the pre-truncation function, the difference field values ​​corresponding to some compliant indicators are 0. These fields correspond to physical state indicators that are within the acceptable envelope of the core equipment and do not require additional intervention. However, the values ​​of other difference fields are greater than 0, meaning that the current value of the corresponding physical state indicator has exceeded the permissible threshold of the envelope. The core of this stage is to convert these fields exceeding the threshold one by one into a reverse query statement oriented towards equipment functions and capabilities, and to perform the retrieval in an independently maintained graph structure.

[0078] The pretreatment equipment library of the graph is a pre-built attribute graph database. Each node in this library represents a type of standard or non-standard equipment that can be used for solid waste pretreatment, collectively referred to as a pretreatment equipment node. Each pretreatment equipment node is bound to functional attributes and processing capacity attributes upon entry into the database. Functional attributes are recorded using multi-valued text tags to describe the types of processing tasks the equipment can perform, such as "dewatering," "crushing," "screening," "mixing," "calorific value compensation," and "magnetic separation." Tag naming follows the industry standard "Classification and Coding of Solid Waste Treatment Equipment." The processing capacity attribute is a set of key-value pairs or sub-objects that, for each processing task the equipment can perform, record the upper limit of the adjustment range for that task and the upper limit of the overall processing capacity of the equipment. The upper limit of the adjustment range refers to the upper limit of the absolute value of the corresponding physical state index that the equipment can change during a single pass or online operation. It covers two modes: physical stripping (such as dewatering) and positive addition (such as calorific value compensation). Its physical unit is consistent with the unit of the difference value of the corresponding index in the state difference vector. The upper limit of the processing range indicates the maximum mass of material that the equipment can process under standard operating conditions, expressed in kilograms.

[0079] Before performing the reverse lookup, the system first filters out the difference fields in the state difference vector that have values ​​greater than 0. The filtering operation traverses all elements of the vector, discarding elements with values ​​of 0. For each element that remains greater than 0, the system reads the field name directly as the matching source for the processing task. Here, the field name is the original name of the physical state indicator, such as "moisture content" or "lower heating value". The processing task is an operation name representing the direction of target adjustment. In actual execution, the system uses a fixed task mapping table to convert the physical state indicator name into a standard processing task label used in the equipment's functional attributes. For example, the physical state indicator "moisture content" is mapped to the standard label "dehydration", the physical state indicator "average particle size" may be mapped to "crushing" or "screening", and the physical state indicator "lower heating value" is mapped to "calorific value compensation". The system records this mapped label as the processing task identifier. At the same time, the field value is read as the processing value. The physical meaning of the processing value is that in order to bring the physical state indicator back into the envelope, at least the adjustment amount generated by the preprocessing equipment needs to be preprocessed. The unit of this value is consistent with that used in the aforementioned difference calculation.

[0080] Next, the system obtains the effective total mass budget dynamically inherited from the conflict source elimination step output and determines it as the processing throughput. All solid waste contained in the entire safe material subset will flow through the pretreatment equipment; therefore, any selected pretreatment equipment must be able to handle this current effective total material mass. The unit of processing throughput is kilograms.

[0081] Subsequently, the system encapsulates the three data items—processing task, processing value, and processing throughput—into a reverse query statement. The encapsulation process is performed independently for each difference field greater than 0. However, if multiple difference fields have the same processing task label, the maximum processing value can be merged into a single query. Structurally, the reverse query statement is a graph node matching query carrying attribute constraints. Its constraint logic requires the target preprocessing device node to simultaneously meet the following three conditions: First, the identifier of the processing task to be queried must be included in the device node's function label set; second, the upper limit of the adjustment range recorded by the device node for the processing task must be greater than or equal to the requested processing value; third, the upper limit of the device node's processing range must be greater than or equal to the current effective processing throughput. The system converts this constraint into query syntax supported by the graph preprocessing device library, for example, constructing a "MATCH" statement in the attribute graph query language and writing the corresponding attribute value comparison in the conditional clause.

[0082] Subsequently, the system uses this reverse query statement to perform a fixed-point reverse retrieval in the preprocessing device library. Fixed-point reverse retrieval means that the query process does not start from a known node to expand the path, but directly uses the attribute values ​​of the device nodes as the retrieval target, filtering out a set of nodes that meet the constraints from all preprocessing device nodes in the graph. Since the query conditions already include precise attribute thresholds, the retrieval results form a list of matching preprocessing device nodes. If multiple device nodes meet the conditions for the same difference field, these nodes are all retained as candidates for subsequent steps to further select the best based on factors such as processing order or efficiency. If, during the retrieval, no single device in the graph's preprocessing device library has an adjustment range upper limit that meets the processing value, the system triggers a multi-level concatenation supplementation mechanism, using a greedy algorithm to select multiple preprocessing device nodes under the same processing task identifier for concatenation matching until the sum of the adjustment range upper limits of these multiple devices is greater than or equal to the processing value. All difference fields greater than 0, after being queried, are processed by preprocessing device nodes or node concatenation groups, ultimately forming a candidate set of device nodes to be used in subsequent steps (i.e., the device link splicing step).

[0083] For example, continuing with the aforementioned core technology node of "high-temperature incinerator," the system has already obtained a state difference vector, which includes a moisture content difference field of 8.5% by mass (greater than 0), an average particle size difference field of 0 mm (truncated due to not exceeding the standard, not greater than 0), and a lower heating value difference field of 3.7 MJ / kg (greater than 0). The system performs a filtering operation to extract two fields greater than 0: moisture content difference and lower heating value difference.

[0084] For the moisture content difference, the system reads the field name "Moisture Content," converts it to the processing task label "Dehydration" through the task mapping table, and reads the field value 8.50 as the processing value, indicating that the material moisture content needs to be reduced by at least 8.50 mass percentages. For the lower heating value difference, the system reads the field name "Lower Heating Value," maps it to the processing task label "Heating Value Compensation" through the task mapping table, and reads the field value 3.70 as the processing value. Simultaneously, the system obtains the current effective total mass budget of 500 kg and determines it as the processing throughput.

[0085] The system encapsulates reverse query statements. For dehydration tasks, it combines the task "dehydration," the processing value of 8.50, and the processing throughput of 500. The pretreatment equipment library in the atlas contains multiple dehydration equipment nodes. Taking the "paddle dryer" node as an example, its function label set includes "dehydration," and the upper limit of the adjustment range for the "dehydration" task is 10.00 mass percentage, with an upper limit of the processing capacity of 1200 kg. Constraints checked: label matching; adjustment range 10.00. 8.50 is satisfied; processing capacity 1200 500 is satisfied. Therefore, this node meets the requirements. Similarly, the upper limit of the adjustment range for the "hollow paddle dryer" node is 12.00% by mass, and the upper limit of the processing capacity is 1500 kg, which also satisfies all constraints.

[0086] For the calorific value compensation task, the task "calorific value compensation," the value 3.70, and the throughput 500 are combined. The system retrieves the "high calorific value waste liquid blender" node, which contains the "calorific value compensation" tag, with an adjustment range upper limit of 5.00 MJ / kg and a processing capacity upper limit of 800 kg. All constraints are checked and satisfied; this node is then included as a candidate.

[0087] The query returns multiple matching pretreatment device nodes that cover dehydration and calorific value compensation functions, for subsequent use. At this point, this stage of the operation provides the necessary and fully compliant pretreatment device candidates for subsequent device link splicing.

[0088] S6. Following the physical processing order, reverse the preprocessing equipment nodes and connect them to the search path before the target core technology node to generate a directed acyclic graph of equipment links, and serialize and output the structured production line assembly drawing data.

[0089] In a preferred embodiment, step S6 specifically includes: creating an empty device linked list and adding the target core technology node to the end of the device linked list; inserting the preprocessed device node as a new node into the front of the current target core technology node in the device linked list one by one; constructing directed edges from the input raw materials to the final resource-based products according to the order of the nodes in the device linked list, forming a directed acyclic graph device link; traversing the directed acyclic graph device link, extracting the device identifier, operation parameters and connection relationships of each node, encapsulating the structure according to preset key values, and generating production line assembly drawing data.

[0090] Specifically, after the aforementioned process completes the reverse retrieval of the pretreatment equipment library and returns the matching pretreatment equipment nodes, the system possesses all the equipment elements constituting a complete solid waste treatment production line: the target core technology nodes and their search paths obtained through graph traversal, and several pretreatment equipment nodes obtained through state differential reverse query. These equipment nodes themselves have a clear sequential dependency relationship in the physical process—solid waste materials must first undergo pretreatment to adjust their physical state before being sent to the core technology nodes for resource transformation. The task of this stage is to organize these two parts of equipment nodes according to the correct physical processing sequence, forming an equipment link without loops, and serializing it into structured production line assembly drawing data that can be directly read by downstream engineering systems.

[0091] First, the system creates an empty device linked list. A device linked list is a linear data structure that supports efficient insertion and deletion at any position. Here, a doubly linked list is used, and each element contains a pointer to the predecessor node, a pointer to the successor node, and a reference to the device node itself. The head and tail pointers are null when the list is created. Next, the system adds the target core technology node to the tail of the device linked list. The addition operation is performed when the list is empty; therefore, the target core technology node is both the head and tail node of the list. At this point, the list contains only one device node, representing the core transformation step in the entire resource processing.

[0092] Next, the system inserts all matching pretreatment equipment nodes obtained from the previous retrieval as new nodes one by one at the front of the current target core technology node in the equipment chain. The insertion order is determined by the potential physical processing order among the pretreatment equipment nodes. The physical processing order refers to the logical sequence in which solid waste materials should undergo various pretreatment operations during actual processing. For example, materials are usually crushed or screened first to adjust particle size, then dehydrated to adjust moisture content, and finally mixed or compensated for calorific value to homogenize composition. This order is determined based on the pretreatment rule base, which is extracted and summarized from the upstream and downstream relationships of pretreatment units in 200 actual layout cases of industrial solid waste resource utilization production lines. For each pretreatment equipment node to be inserted, the system queries the pretreatment rule base according to the type of processing task it undertakes to obtain a processing priority ordinal number. Processing tasks with lower ordinal numbers are placed earlier in the physical process; for example, the priority ordinal number of "crushing" is 1, the priority ordinal number of "dehydration" is 3, and the priority ordinal number of "calorific value compensation" is 5. The system sorts all preprocessing device nodes to be inserted in ascending order of their processing priority. Then, starting with the node with the lowest priority, it searches for the insertion position in the device list sequentially, ensuring that the node sequence from beginning to end maintains a non-decreasing order of processing priority at any given time. If multiple preprocessing device nodes have the same processing priority, the system triggers a secondary sorting rule, performing a second sort according to the alphabetical order of the device node identifiers to ensure the uniqueness and determinism of the generated directed acyclic graph device link topology sequence.

[0093] Subsequently, the system constructs directed edges from the input raw materials to the final resource-based product, forming a directed acyclic graph (DAG) equipment link, following the order of nodes in the equipment chain from head to tail. The construction process traverses the equipment chain; for each pair of adjacent predecessor and successor nodes, the system creates a directed edge from the output material port of the predecessor node to the input material port of the successor node. The input raw materials are the raw solid waste materials corresponding to the untreated safe material subset determined in the aforementioned conflict source elimination step. The system creates a virtual material source node at the front of the first node in the chain and adds a directed edge between it and the first node. The final resource-based product is the successor terminal node connected to the target core technology node in the resource-based graph search. The system draws a directed edge from the tail node of the chain (i.e., the target core technology node) to this terminal node. The entire structure satisfies the definition of a directed acyclic graph: composed of nodes and directed edges, and there is no closed-loop path that starts from a node, traverses several edges, and returns to that node. The linear order of the equipment chain naturally guarantees acyclicity.

[0094] Finally, the system traverses the generated directed acyclic graph (DAG) device links, extracting the device identifier, operating parameters, and connection relationships for each node. The device identifier is a globally unique string assigned to each device node in the graph database or graph hierarchy, such as "EQUIP_DRYER_PADDLE_01". Operating parameters are a set of process parameter settings directly related to the solid waste material being processed by the device. These parameters are read from the device node attributes and instantiated by calling the device node's built-in mass and heat transfer calculation engine, substituting the previously generated processing values ​​(such as the percentage of moisture content to be reduced) and the total mass budget into the transfer function. For example, the drying temperature of a paddle dryer is set to 145 degrees Celsius based on the moisture content difference and processing throughput using a built-in process parameter calculation formula. Connection relationships record the starting and ending node identifiers of each directed edge, as well as the path description of material transport. The system encapsulates these three types of information according to a preset key-value pair structure. The preset key-value pair structure is a hierarchical JSON template, with the top layer containing three keys: "Production Line Name", "Node List", and "Edge List". The "Node List" is an array, where each element is an object containing subkeys for "Node Identifier," "Equipment Name," and "Operation Parameters." Similarly, the "Edge List" is an array, where each element contains subkeys for "Start Point Identifier," "End Point Identifier," and "Material Description." After traversing all nodes and edges, the system uses a JSON serializer to convert the encapsulated data objects into string-based production line assembly drawing data. This data describes the complete equipment layout and connection topology from raw materials to the final resource-based product, and can be directly parsed and used by industrial control systems or production line design software.

[0095] Furthermore, after generating the directed acyclic graph (DAG) device link, the system triggers a secondary full-link throughput verification mechanism. Based on the quality stripping attributes of preprocessing equipment (such as dewatering and screening equipment) in the link, the actual feed mass to the target core technology node is recalculated. If the actual feed mass is lower than the minimum equipment startup throughput, a backtracking mechanism is triggered, the current device link is abandoned, and the process returns to step S3. The system then reselects the path with the second-highest remaining mass from the remaining feasible paths for matching until the full-link throughput verification passes. If the verification still fails after traversing all feasible paths, an error message indicating device matching failure is output.

[0096] For example, continuing from steps S1 to S5, the system identifies the target core technology node as "high-temperature incinerator" in the aforementioned optimization process. In the reverse search, it obtains matching pretreatment equipment nodes: "paddle dryer" (responsible for dehydration) and "high-calorific-value waste liquid mixer" (responsible for calorific-value compensation). The system creates an empty device linked list and adds the "high-temperature incinerator" node to the end of the list, resulting in the list named [high-temperature incinerator]. Next, the system obtains the processing task tags of the pretreatment equipment nodes and queries the pretreatment rule base to find that the priority ordinal number for the dehydration task is 3, the priority ordinal number for the calorific-value compensation task is 5, and the priority ordinal number for the high-temperature incinerator is 100. The system then inserts the "paddle dryer" and "high-calorific-value waste liquid mixer" sequentially before the "high-temperature incinerator," changing the linked list to [paddle dryer, high-calorific-value waste liquid mixer, high-temperature incinerator]. The system then traverses the linked list, creating virtual material source nodes, constructing directed edges from the material source to the "paddle dryer," then from the "paddle dryer" to the "high-calorific-value waste liquid blender," followed by a directed edge from the "high-calorific-value waste liquid blender" to the "high-temperature incinerator," and finally, a directed edge from the "high-temperature incinerator" to the resource-based product terminal node "high-temperature flue gas and slag." This directed acyclic graph device link consists of 5 nodes and 4 edges, and does not contain any cyclic paths. During the parameter extraction and serialization stage, the equipment identifier for the "paddle dryer" is extracted as "EQUIP_DRYER_PADDLE_A1", and its operating parameters are instantiated based on a processing value of 8.50% by mass and a processing throughput of 500 kg, resulting in a drying temperature of 145 degrees Celsius and a spindle speed of 12 rpm. The equipment identifier for the "high-calorific-value waste liquid blender" is extracted as "EQUIP_MIXER_HEAT_B1", and its operating parameters are instantiated based on a calorific value compensation difference of 3.70 MJ / kg, resulting in a blending valve opening of 35%. The equipment identifier for the "high-temperature incinerator" is extracted as "EQUIP_INCIN_HIGH_F1", and its operating parameters are a combustion chamber temperature of 1100 degrees Celsius and a residence time of 2 seconds. The connection relationships include the solid material input from the material source to the dryer, the dried material conveying from the dryer to the blender, the high-calorific-value mixed material conveying from the blender to the incinerator, and the high-temperature flue gas and slag output from the incinerator to the product.The system fills all information into a JSON template and serializes it, finally outputting the following JSON production line assembly drawing data: {"Production Line Name":"Solid Waste Resource Utilization Production Line 01","Node List":[{"Node Identifier":"SRC_SOLID_INPUT","Equipment Name":"Material Source","Operating Parameters":{}},{"Node Identifier":"EQUIP_DRYER_PADDLE_A1","Equipment Name":"Paddle Dryer","Operating Parameters":{"Drying Temperature":145,"Spindle Speed":12}},{"Node Identifier":"EQUIP_MIXER_HEAT_B1","Equipment Name":"High Calorific Value Waste Liquid Blender","Operating Parameters":{"Valve Opening":35}},{"Node Identifier":"EQUIP_INCIN_HIGH_F1","Equipment Name":"High Temperature Incinerator","Operating Parameters":{"Combustion Chamber Temperature":1100,"Residence Time":2}},{"Node Identifier":"DST_PRO DUCT_GAS_SLAG","Equipment Name":"High Temperature Flue Gas and Slag","Operating Parameters":{}}],"Side List":[{"Start Point Identifier":"SRC_SOLID_INPUT","End Point Identifier":"EQUIP_DRYER_PADDLE_A1","Material Description":"Safety Subset Mixture"},{"Start Point Identifier":"EQUIP_DRYER_PADDLE_A1","End Point Identifier":"EQUIP_MIXER_HEAT_B1","Material Description":"Dehydrated Material"},{"Start Point Identifier":"EQUIP_MIXER_HEAT_B1","End Point Identifier":"EQUIP_INCIN_HIGH_F1","Material Description":"Calcination Product"}]}. This data is the final output of the production line assembly drawings, completing a fully automated process from multi-source solid waste query requests to complete equipment production line design.

[0097] 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A cross-domain matching method for solid waste resource utilization technologies based on material metabolism maps, characterized in that, include: S1. Receive a multi-source solid waste query request containing macroscopic name, initial mass and current physical state. Utilize a pre-built tree-structured mapping table and hierarchical hash code to parse the macroscopic name into a set of microscopic elements and summarize the initial mass as the total mass budget. S2. Input multiple sets of micro-elements into the pre-built conflict adjacency matrix, perform full permutation cross-comparison of micro-elements of solid waste from different sources, lock the conflict source based on the Boolean value of the rejection reaction, add negation logic to the query request of multi-source solid waste, generate a safe material subset that eliminates the conflict source, and simultaneously deduct the initial mass of the conflict source from the total mass budget to obtain the effective total mass budget. S3. Using a subset of safe materials as the starting search node and the effective total mass budget as the traversal constraint, perform a depth-first search on the core technology node in the graph database, accumulate the path consumption mass, and obtain the minimum device startup throughput of the next hop technology node. When the remaining mass after subtracting the accumulated path consumption mass from the effective total mass budget is lower than the minimum device startup throughput, terminate the search for the next hop technology node, and retain the target core technology node and its search path. S4. Extract the envelope of the physical state of the target core technology node before it enters the furnace, perform a difference operation with the current physical state, and generate a state difference vector. S5. Read the fields with values ​​greater than zero in the state difference vector, convert them into a reverse query statement consisting of processing task, processing value and processing throughput, perform a reverse search in the preprocessing device library, and obtain the matching preprocessing device node. S6. Following the physical processing order, reverse the preprocessing equipment nodes and connect them to the search path before the target core technology node to generate a directed acyclic graph of equipment links, and serialize and output the structured production line assembly drawing data.

2. The cross-domain matching method for solid waste resource utilization technology based on material metabolism maps according to claim 1, characterized in that, The macroscopic name is resolved into a set of microscopic elements, including: Extract keywords from macroscopic names and map them to the macroscopic solid waste ledger name level of the tree mapping table to obtain the corresponding mesoscopic phase component nodes; Traverse the micro-chemical element branches under the meso-phase composition nodes and extract the micro-chemical element identifiers of all leaf nodes; All extracted microscopic chemical element identifiers are combined and associated with hash prefixes calculated based on hierarchical hash codes to form a set of microscopic elements.

3. The cross-domain matching method for solid waste resource utilization technology based on material metabolism maps according to claim 1, characterized in that, Step S2 specifically includes: Create a cursor corresponding to the number of micro-elements in the set, traverse the conflict adjacency matrix, and read the Boolean value in the intersection cell of any two micro-elements from different sources one by one; When the read Boolean value is the first logic value representing the existence of a repulsive reaction, the solid waste source to which the pair of micro-elements belong is marked as a conflict source; After all cursor traversals are completed, the identifiers of all marked conflict sources are collected, a negative condition clause is constructed, and the negative condition clause is appended to the query statement of the multi-source solid waste query request using a logical AND relation. The query is then re-executed to filter out conflict sources and obtain a subset of safe substances.

4. The cross-domain matching method for solid waste resource utilization technology based on material metabolism maps according to claim 1, characterized in that, The pre-built conflict adjacency matrix is ​​constructed through the following steps: Obtain mutually exclusive substance pairs that pose a risk of violent chemical reaction as defined in chemical safety regulations; Mutually exclusive matter pairs are mapped to microscopic element identifiers to construct a two-dimensional matrix with the microscopic element identifiers as rows and columns; In a two-dimensional matrix, the cells where rows and columns intersect with mutual exclusion are assigned Boolean values ​​indicating a repulsive response, and the remaining cells are assigned Boolean values ​​indicating no repulsive response, thus obtaining a conflict adjacency matrix.

5. The cross-domain matching method for solid waste resource utilization technology based on material metabolism maps according to claim 1, characterized in that, When the remaining mass after subtracting the cumulative path consumption mass from the effective total mass budget is lower than the minimum device startup throughput, the search for the next-hop technology node is terminated, including: When the depth-first search extends to the next-hop technology node, obtain the node quality consumption coefficient and the minimum device startup throughput of that node; The remaining quality budget is obtained by subtracting the sum of the quality consumption of all visited nodes on the current search path from the effective total quality budget. The remaining quality budget is compared with the minimum device startup throughput of the node. If the remaining quality budget is less than the minimum device startup throughput of the node, a pruning operation is performed to release the memory space occupied by the search branch of the next hop technology node.

6. The cross-domain matching method for solid waste resource utilization technology based on material metabolism maps according to claim 1, characterized in that, Step S3 also includes: during the depth-first search process, if multiple paths meet their respective minimum device startup throughput requirements, the final remaining quality of each path is compared, and the technical node on the path with the largest final remaining quality is selected as the target core technical node.

7. The cross-domain matching method for solid waste resource utilization technology based on material metabolism maps according to claim 1, characterized in that, Step S4 specifically includes: Analyze the attribute data of the target core technology node to obtain the physical state envelope of the pre-entry furnace bound to it. The physical state envelope of the pre-entry furnace contains one or more physical state indicators and their upper or lower threshold values. Extract the current physical state value corresponding to the physical state index from the current physical state of the multi-source solid waste query request; For each physical state index, the current physical state value is subtracted from the corresponding upper limit threshold, or the lower limit threshold is subtracted from the current physical state value, and then truncated using a zero-value truncation function. This process generates one or more difference fields representing the degree of difference, forming a state difference vector.

8. The cross-domain matching method for solid waste resource utilization technology based on material metabolism maps according to claim 7, characterized in that, Step S5 specifically includes: Filter out the difference fields in the state difference vector with values ​​greater than zero, read the field name as the processing task, and read the field value as the processing value; Obtain the effective total mass budget and determine it as the processing throughput; The processing task, processing value, and processing throughput are encapsulated into a reverse query statement. The functional attributes and processing capability attributes of the device nodes are matched and retrieved in the preprocessing device library of the graph database, and the preprocessing device nodes that meet the processing requirements are returned.

9. The cross-domain matching method for solid waste resource utilization technology based on material metabolism maps according to claim 1, characterized in that, Step S6 specifically includes: Create an empty device list and add the target core technology node to the end of the device list; The preprocessing device nodes are treated as new nodes and inserted one by one into the front of the current target core technology node in the device chain; Based on the order of nodes in the equipment chain, construct directed edges from the input raw materials to the final resource-based products, forming a directed acyclic graph equipment chain; Traverse the directed acyclic graph device links, extract the device identifier, operation parameters and connection relationships of each node, encapsulate the structure according to preset key values, and generate production line assembly drawing data.

Images