Method for intelligent matching of trauma center construction plan resources

Abstract

The application discloses a method for intelligent matching of trauma center construction scheme resources, and particularly relates to the technical field of medical resource allocation optimization, and comprises the following steps: establishing a standard template library and performing structured coding; obtaining multi-source latest standard information, and dynamically adjusting index weights by incremental learning after structured signature verification; generating a feature vector from the resource information input by a target hospital by lightweight coding, and performing cosine similarity calculation on the feature vector and a template vector to obtain a matching degree; constructing a three-state mutual control type robust weighting model to dynamically correct the matching degree; and finally outputting a candidate scheme that is most matched to the resource structure of the target hospital; the application keeps the template library synchronized with the latest standards through multi-source information acquisition, structured signature verification and incremental learning weight adjustment; and through the construction of the robust weighting model, the matching degree score is dynamically corrected to ensure that the output scheme has credibility, balance and regional adaptability, and the scientificity, practicality and landing feasibility of the recommended result are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical resource allocation optimization, and more particularly, to a method for intelligent matching of resources for a trauma center construction plan. BACKGROUND

[0002] With the continuous improvement of trauma treatment system in China, the national and provincial health administrative departments have successively issued the trauma center construction standards and regional medical center setting specifications. As an important hub for dealing with critically ill patients, the construction quality of the trauma center is directly related to the trauma treatment efficiency and patient survival rate. However, in the actual construction process, hospitals often face the following difficulties when formulating the construction plan:

[0003] The standards are frequently updated and come from various sources, and different versions of policy documents differ in index requirements, dimension division and review points, so it is difficult for hospitals to capture and apply the latest standards in time, which may lead to a lag of the construction plan behind the policy requirements. The resource allocation is significantly different, and there are great differences in department setting, equipment configuration and personnel qualifications among hospitals at different levels. If only relying on manual comparison of standard documents, it is difficult to form a precisely matched construction plan, and there are problems of "high standards not applicable" or "resource underestimation and insufficient configuration". The plan matching lacks intelligence and dynamics, and the existing methods often only rely on fixed templates or manual experience, lack of automatic matching means based on feature vector mapping and data mining, and cannot realize the precise association between hospital resources and standard templates. There is also a lack of dynamic regulation mechanism to balance the template credibility, resource dimension rationality and regional adaptability. Therefore, the present application proposes a method for intelligent matching of resources for a trauma center construction plan to solve the above problems. SUMMARY

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0005] The method for intelligent matching of resources for a trauma center construction plan comprises the following steps:

[0006] A standard template library is established based on trauma center construction related standard documents, and structured construction indexes are extracted to generate initial templates, which are stored in the form of multi-dimensional features;

[0007] The latest standard information is obtained from multiple trauma center construction related information sources, the credibility is verified by comparing the structure signature, the weight of each template index in the standard template library is adjusted by using an incremental learning algorithm, and the standard template library is dynamically and iteratively updated;

[0008] The resource characteristic information input by the target hospital is acquired and converted into a resource characteristic vector through a lightweight encoding method. A characteristic vector mapping algorithm is used to calculate the cosine similarity between the resource characteristic vector and the attribute characteristic vector of each template in the standard template library, and the matching degree score between the resource and the template is obtained.

[0009] Three factors are generated respectively: template credibility index, resource dimension balance index, and regional suitability index. A three-state mutual control robust weighting model is constructed to dynamically adjust the weights of each matching dimension. Among them:

[0010] The template credibility index determines the legality of the template source according to multi-source cross signature and variable frequency hash chain;

[0011] The resource dimension balance index evaluates the dimension weight rationality of the resource characteristics according to the semantic compression rate and information entropy distribution;

[0012] The regional suitability index evaluates the applicability of the recommended template according to the regional medical resource quantity, traffic accessibility, and population density;

[0013] The above three factors jointly control the matching weight correction function and dynamically intervene in the matching degree score calculation process;

[0014] Templates with matching degree scores exceeding the preset matching threshold are screened, and at least one trauma center construction candidate scheme most matched with the resource structure of the target hospital is output.

[0015] In a preferred embodiment, the step of establishing a standard template library based on trauma center construction related standard documents includes:

[0016] Trauma center construction standard documents are obtained from multiple publishing channels. Construction elements related to multiple preset construction elements are extracted and structured. Each construction element is classified and labeled according to its corresponding dimension. A multi-dimensional feature system is established, and the attribute type, numerical range, and priority of each dimension are defined. The classified construction elements are digitally represented using attribute vector encoding, and initial weight values are assigned based on historical review cases. Finally, a complete standard template library is generated.

[0017] In a preferred embodiment, the step of verifying the credibility of the comparison structure signature includes:

[0018] The structure level and key field summary of the standard content are extracted to generate a hash signature, and the acquisition timestamp and source domain name are labeled;

[0019] The historical standard signature database is called to compare the same type of standard signatures in multiple versions, and the signature repetition rate, update frequency, and domain name authority level are calculated;

[0020] If the signature is inconsistent with the history and the source domain name is not a state-controlled domain, or the update frequency exceeds the preset standard, it is determined as a low credibility standard and the source is prohibited from participating in the template library update.

[0021] In a preferred embodiment, adjusting the weight of each template index in the standard template library using the incremental learning algorithm includes the following steps:

[0022] The construction index in the newly added standard file is compared and classified with the existing index in the standard template library, and the mapping relationship is established according to the word similarity and whether the dimension belongs to the same dimension. The determination method of the coincidence degree is: for each new index, the number of items that can be mapped to the existing index is counted, and the ratio operation is performed with the total number of new indexes. The obtained ratio is the structural coverage of the new index relative to the existing system;

[0023] An importance change scoring model is constructed, including two key influence factors: the frequency of the new index appearing in the historical standard version as the basic importance factor; the second is whether there is a weight strong prompt word language label in the same dimension in the adjacent two standard updates. If there is, give the index an importance promotion label. After standardizing the two factors, the change importance score of each new index is obtained according to the set linear weighting relationship;

[0024] A weighted update function is constructed to adjust the original weight of the index in the existing template library, and for the old index that no longer appears in the new standard, if it is not referenced in the last three updates, gradually reduce its weight according to the preset equal gradient reduction value until it is reduced to the set minimum reserved value.

[0025] In a preferred embodiment, the weighted update function is executed based on the following principles:

[0026] When a certain existing index forms semantic coincidence with a new index, and the importance score of the new index is higher than the set threshold, the weight of the existing index is increased by an increment based on the original basis. The increment is obtained by multiplying the importance score by the structural coverage. If a new index has no mapping item in the existing system, and the importance score is still higher than the preset importance threshold, the new index is added to the template library, and the initial weight value is obtained by multiplying the importance score by the initial weight coefficient set by the system.

[0027] In a preferred embodiment, the step of converting the resource characteristic information input by the target hospital into a resource characteristic vector and performing a matching degree score includes:

[0028] The original resource information input by the target hospital is structured and vector labeled according to the preset field standard. The free text field is classified into the corresponding structure label using semantic parsing rules.

[0029] The lightweight coding mode based on word weight sparse mapping is applied to convert the structure label into a resource feature vector. In this process, a dimension balance adjustment factor is introduced. The value of the adjustment factor is the ratio of the information entropy value of each dimension resource item to the vector proportion value. According to the frequency and influence of each dimension feature in the hospital resource data, dynamic normalization adjustment is performed. If the coding length or numerical weight of a certain dimension is significantly higher than those of other dimensions and the entropy value is lower than the set threshold, the system sets its adjustment factor as an inhibition coefficient less than one to reduce the influence of the dimension on the overall matching, and ensures the relative balance of the weights of different types of resources in the vector space.

[0030] The resource feature vector and the attribute feature vector of each template in the standard template library are calculated by cosine similarity one by one, and a context sensitivity adjustment factor is introduced to correct the matching degree. The context sensitivity adjustment factor is obtained based on a three-state mutual control type robust weighting model constructed by a template credibility index, a resource dimension balance index, and a regional suitability credibility. Finally, the adjusted matching degree score is output as the basis for candidate template screening.

[0031] In a preferred embodiment, the calculation of the template credibility index includes:

[0032] The standard file source associated with each template is extracted, the source credibility level is set based on the source domain name, and the number of unchanged standard versions of the source within a specified period is counted.

[0033] After standardizing the source credibility level and the number of unchanged standard versions, the template credibility index is obtained by weighted average according to the set proportion.

[0034] In a preferred embodiment, the calculation of the resource dimension balance index includes:

[0035] The resource information input by the target hospital is classified according to the preset dimensions, and semantic normalization processing is performed to eliminate duplicates. The number of valid entries in each dimension and the proportion of the overall resource information are counted. The information density and the difference after compression between different dimensions are compared to judge the distribution balance between resource dimensions. Specifically, the information density of each resource dimension is calculated, which is defined as the ratio between the number of actual filled items in the dimension and the theoretical maximum number of indicators in the dimension.

[0036] Semantic compression processing is performed to combine similar resource items, and the retention ratio before and after compression is calculated. The compression retention rate is defined as the ratio of the number of retained items after compression to the number of original items before compression.

[0037] For each resource dimension, calculate its equilibrium score. The equilibrium score is the product of the information density and compression retention rate of that dimension. It is used to comprehensively evaluate the completeness and diversity of that dimension. After collecting the equilibrium scores of all dimensions, calculate the variance of all dimensions. Finally, take the score after subtracting the variance normalization value from the score and use it as the resource dimension equilibrium index.

[0038] In a preferred embodiment, the calculation of the geographic fit confidence includes:

[0039] The weighted difference between the number of permanent residents, traffic accessibility indicators, and the number of surrounding medical resources in the hospital's location and the applicable parameters preset in the template is calculated. The weighted absolute difference is used as the adaptation distance metric, and the results are standardized into a percentage score to obtain the regional adaptation reliability.

[0040] In a preferred embodiment, the three-state mutually controlled robust weighting model refers to:

[0041] The weighted average of the template credibility index, regional suitability credibility, and resource dimension balance index is calculated to obtain the context sensitivity adjustment factor. The cosine similarity between the resource feature vector and the attribute feature vector of each template is calculated to obtain the cosine similarity value. When the context sensitivity adjustment factor is lower than the preset adjustment threshold, the original cosine similarity value is negatively corrected by multiplying the cosine similarity value by an adjustment coefficient less than one. The adjustment coefficient is determined by the normalized score of the adjustment factor. Finally, the adjusted matching score is obtained.

[0042] The technical effects and advantages of this invention are as follows:

[0043] This invention establishes a standard template library based on relevant standard documents for trauma center construction, generates initial templates using structured construction indicators, and stores them through attribute encoding in the form of multi-dimensional features. This transforms requirements originally scattered across different documents and textual descriptions into a quantifiable and comparable multi-dimensional feature system. This approach avoids the subjectivity and inefficiency of manual interpretation of policy documents, enabling rapid access and unified management of standard content. In practical applications, hospital input information can be directly compared with template vectors, reducing redundant interpretation steps, improving the objectivity and accuracy of data processing, and ensuring that the construction plan has a complete and standardized foundation from the initial stage.

[0044] This invention obtains the latest standard information from multiple trauma center construction-related sources and introduces structural signatures for credibility verification. Simultaneously, it utilizes an incremental learning algorithm to dynamically adjust the weights of each template indicator, achieving continuous adaptive updates to the template library. This approach ensures that template content remains synchronized with the latest official standards, avoiding erroneous recommendations caused by using outdated or unverified documents. In practical applications, when national or local governments issue new construction indicators, the system can instantly extract key content, verify the legality of the sources, and gradually adjust the weights without overturning the entire template library. This maintains the continuity of historical indicators and enables rapid response to policy changes, thereby ensuring the long-term validity and reliability of the recommendation results.

[0045] This invention introduces three factors—template credibility index, resource dimension balance index, and regional suitability reliability—and constructs a three-state mutually controlled robust weighting model to dynamically correct the matching score. This ensures that the final recommendation solution not only relies on the numerical value of cosine similarity but also comprehensively considers the credibility of the data source, the balance of hospital resource input across dimensions, and the adaptability of the template to the regional medical environment. For example, if a hospital inputs extremely detailed information in the equipment resource dimension but relatively insufficient information in the personnel qualification dimension, the model will automatically correct the score using the resource dimension balance index to prevent distortion due to excessive information in a single dimension. Similarly, if a template is highly matched numerically but its design is suitable for large cities while the target hospital is located in a remote mountainous area, the regional suitability reliability will trigger a correction mechanism, lowering the recommendation priority of that template. Through this multi-factor dynamic intervention, the output candidate solutions are more in line with the actual conditions and application scenarios of hospitals, improving the scientific validity, practicality, and feasibility of the recommendation results. Attached Figure Description

[0046] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0047] Figure 1 This is a schematic diagram of the intelligent resource matching method for the trauma center construction scheme in this invention. Detailed Implementation

[0048] 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0049] Reference Figure 1 The following examples were obtained:

[0050] Example 1: A method for intelligent resource matching in trauma center construction plans, comprising the following steps:

[0051] A standard template library was established based on relevant standard documents for trauma center construction, and structured construction indicators were extracted to generate initial templates, which were then stored using attribute encoding in the form of multi-dimensional features. This step aims to extract quantifiable construction elements, such as departmental setup requirements, equipment configuration lists, and personnel qualification standards, through in-depth analysis of national guidelines for trauma center construction, and convert them into structured data, which is then stored in the standard template library. The multi-dimensional feature encoding method ensures that each template is not merely textual information, but rather an attribute vector that can participate in calculations, facilitating rapid matching and inference judgments by subsequent algorithms.

[0052] The system obtains the latest standard information from multiple trauma center construction-related sources, verifies its credibility by comparing structural signatures, and uses an incremental learning algorithm to adjust the weights of each template indicator in the standard template library, dynamically updating the library iteratively. By periodically retrieving the latest standard documents from multiple authoritative data sources (such as the National Health Commission's website and provincial policy databases), the system can determine the credibility of standards based on structural signature comparisons and source credibility levels. Simultaneously, through the incremental learning algorithm, the system fine-tunes the weights of affected indicators without reconstructing the entire template system, thereby achieving "dynamic adaptive evolution" of the knowledge base and keeping the matching logic synchronized with national policies.

[0053] The system acquires resource feature information from the target hospital and transforms it into resource feature vectors using a lightweight encoding method. A feature vector mapping algorithm is then used to calculate the cosine similarity between the resource feature vectors and the attribute feature vectors of each template in the standard template library, obtaining a matching score between the resource and the template. The original information input by the hospital, such as equipment models, number of personnel, and department structure, is transformed into resource feature vectors that can be recognized and processed by the algorithm through structured labeling and semantic normalization, ensuring data dimension uniformity and format consistency. Subsequently, feature vector mapping and cosine similarity calculation are used to determine the association strength between hospital resources and standard templates in the vector space, thereby obtaining the matching degree between each template and the hospital, providing a quantitative basis for subsequent recommendations.

[0054] Three factors are generated: template credibility index, resource dimension balance index, and regional suitability credibility. A three-state mutually controlled robust weight adjustment model is constructed to dynamically adjust the weights of each matching dimension, wherein:

[0055] The template credibility index determines the legitimacy of the template's source based on multi-source cross-signatures and the hash chain of change frequency.

[0056] The resource dimension balance index evaluates the rationality of its dimension weights based on the semantic compression rate and information entropy distribution of resource characteristics.

[0057] The reliability of regional suitability assessment is based on the availability of regional medical resources, accessibility, and population density to evaluate the applicability of the recommended template.

[0058] The above three factors jointly control the matching weight correction function, dynamically intervening in the matching score calculation process; a multi-factor adjustment mechanism is introduced to achieve context-awareness in the matching process. The template credibility index ensures the template source is legitimate and the version is stable; the resource dimension balance index corrects potential structural biases in the hospital's input data; and the regional suitability reliability reflects whether the template is suitable for the hospital's objective geographical and resource environment. By merging and regulating these three factors through a three-state mutual control model, the matching logic not only depends on the resources themselves but also responds to the underlying usage context, improving the actual usability and rationality of the matching results. The matching degree is no longer a single calculation result but a dynamically adjusted output value after considering contextual factors. The three factors form a "context-sensitive adjustment factor" through weighted averaging, adjusting the intensity of the cosine similarity score, enabling the system to judge real-world scenarios and achieve true "contextual matching" rather than purely mathematical matching.

[0059] Templates with matching scores exceeding a preset matching threshold are filtered, and at least one candidate plan for trauma center construction that best matches the resource structure of the target hospital is output. The final result is generated by ranking the matching scores, but only templates with scores higher than the system's set threshold are retained to ensure that the output templates are not only theoretically matching but also practically applicable. The output results may include one or more templates for hospitals to refer to, providing highly accurate, compliant, and implementable decision-making basis for their trauma center construction plans.

[0060] In this invention, the process of establishing a standard template library can be implemented through the following steps to ensure that the constructed templates have structural integrity, accurate expression, and dynamic adaptability. Policy documents, guiding standards, and review reference materials related to the construction of trauma centers are collected from multiple authoritative sources, including the official website of the National Health Commission, provincial health commission release platforms, and databases of medical industry academic associations. To ensure the diversity of sources and the legality of content, keyword filtering and publishing unit identification rules are set during the collection process, such as requiring that the document title must simultaneously contain the words "trauma center," "establishment standard," or "construction specification." In this way, more than twenty highly credible construction standard documents, including the "National Trauma Regional Medical Center Establishment Standard (Trial)," can be obtained, and the structural paragraphs, table content, and appendix indicators of each document are extracted as preprocessing content.

[0061] Based on the acquired documents, information on key elements related to specific construction requirements is extracted, structured, and each element is categorized and labeled in multiple dimensions according to its technical attributes. The categorization process is based on the functional dimension of the construction element, dividing it into six dimensions: personnel configuration, department setup, equipment list, diagnostic and treatment capabilities, information system configuration, and site environment. Each construction element is further refined into its attribute type (e.g., "equipment model" or "professional title"), data type (e.g., "numerical" or "enumerated"), unit (e.g., "person", "unit", or "square meter"), and priority (e.g., "mandatory", "recommended", or "optional"). For example, if the standard states that "the emergency department must be equipped with three physicians with associate senior professional titles or above," this information will be parsed as follows: dimension "personnel configuration", attribute type "professional title level", data type "enumerated", unit "person", and priority "mandatory".

[0062] Based on the categorized and standardized construction elements, a vectorized coding method is used to digitally represent them. Specifically, enumerated indicators are represented by one-hot encoding, and numerical indicators are normalized and encoded as continuous vector elements. Each template is represented as a multi-dimensional vector structure, with each dimension corresponding to the existence or quantity value of a specific element. For example, a complete template can be transformed into a feature vector containing 120 dimensions, where the fifteenth dimension represents "number of CT devices" with a value of five, and the thirtieth dimension represents "whether an anesthesiology department is established" with a value of one (indicating that it has been established). Simultaneously, for each dimension, the frequency of its appearance in standard documents and the mandatory level of relevant regulations are recorded.

[0063] By combining historical trauma center review cases and data from accepted construction units, an initial weight value is assigned to each dimension indicator in the standard template to represent its importance and reference priority in the overall plan. The specific weighting strategy is based on a comprehensive evaluation of two dimensions: first, the frequency of the indicator's recurrence across multiple standards; and second, whether it was a key deduction item in historical reviews. For example, the "independent establishment of a trauma green channel" is explicitly required in over 90% of policy documents, and it was penalized 36 times in 50 review reports for non-compliance with this item; therefore, its initial weight is set at 0.95. Through this process, a standard template library with high structural integrity, weight differentiation, and practical relevance is ultimately formed, serving as the core data support for subsequent resource matching and recommendations.

[0064] In the process of dynamically collecting and verifying the credibility of standard documents related to the construction of trauma centers, to ensure that the collected content is from legitimate sources, stable in version, and possesses structural consistency and computability, a credibility verification mechanism based on structural signatures can be implemented through the following steps: For each newly acquired standard document, structural extraction and semantic segmentation are performed, dividing the document into multiple logical levels, including chapter titles, clause content, table information, and appendix descriptions. Key fields with decision-making value are extracted using natural language processing methods, such as "required department names," "minimum equipment configuration standards," and "review deduction items." The text content of each paragraph or field, along with its hierarchical path, forms a structural fragment, and these structural fragments are encoded using a hash digest algorithm to generate a structural signature. Each standard document will correspond to one or more sets of structural signatures, while also recording its collection timestamp and source URL. For example, a document signature obtained from "www.nhc.gov.cn" can be labeled as a "nationally controlled source" category.

[0065] A historical standard document signature comparison dataset is established, storing the structural signatures of previously collected and approved standard documents in a unified comparison index. Each signature entry should include: document name, publication date, standard category, extracted structural signature content, and its frequency record. For example, the "2021 National Trauma Center Setup Standard" contains a total of 150 signature fragments, and the frequency record shows that "configuration requirements for independent emergency operating rooms" frequently appears in subsequent versions. By constructing a signature comparison network across multiple standard versions, it is possible to analyze whether a particular structural signature is consistent across historical versions and whether it has been frequently modified, thereby assessing the stability and evolution trend of the signature.

[0066] For newly collected standard documents, their structural signatures will be compared one by one with the historical signature index, and the overlap and frequency of change will be calculated. If a signature fragment has a large number of inconsistencies with historical signatures, and the document's source domain is not controlled by a national agency (such as a commercial platform like "health-consulting.cn" or "doc-share.org"), then the structural signature will be marked as "credibility pending review." If more than 40% of the signature fragments in the document are marked as suspicious, or if the source has published more than five document changes in the past three months, then the entire document will be classified as a potentially low-credibility document.

[0067] By combining signature difference analysis results with source authority ratings, credibility is assessed. A standard document is classified as a low-credibility standard if it exhibits both excessively high structural signature volatility and originates from a non-official domain. Low-credibility standards are not permitted to participate in subsequent template weight updates and feature fusion, nor are they included in the standard template library. Furthermore, the source URL is added to a monitoring list, automatically triggering a review mechanism upon future document releases. For example, if the source domain "www.med-policy123.cn" pushes three consecutive standard drafts with severely drifting structural signatures, its data is automatically identified and blocked from entering the subsequent processing flow. This process achieves credibility verification at the structural signature level, ensuring that standard documents entering the template construction and weight learning process possess authority, stability, and consistency, effectively preventing unofficial documents, draft versions, or tampered content from interfering with the accuracy of the generated scheme.

[0068] To dynamically optimize the weights of various construction indicators in the standard template library, an incremental learning algorithm can be used for weight adjustment. This process is based on structural coverage analysis, semantic evolution trend identification, and importance scoring quantification, and involves the following steps to update the weights: For newly collected trauma center construction standard documents, construction indicator entries are extracted, and each new indicator is semantically matched and categorized with existing indicators in the current template library. The semantic matching process is based on a natural language similarity model and keyword synonym mapping rules to ensure that indicator items with different expressions can be uniformly identified as the same indicator group. Subsequently, based on the principle of dimensional consistency, similar indicators are grouped into the same construction dimension, and a mapping relationship table is established. On this basis, the structural coverage rate is calculated, which is the ratio of the number of new indicators that can be successfully mapped to the current template to the total number of new indicators. For example, if 120 indicators are extracted from a new standard document, and 90 of them can be mapped to existing entries in the template library, then the structural coverage rate is 0.75. This coverage rate reflects the compatibility of the existing template system with the new standard content and provides a reference benchmark for subsequent weight adjustments.

[0069] A scoring model for the importance changes of newly added indicators is constructed to measure the value evolution trend of each indicator during the transition between old and new standards. This model includes two key influencing factors: first, the frequency of the indicator's appearance in historical standard versions, reflecting its continuity and policy acceptance; for example, "establishment of a green channel for trauma rescue" has appeared in the past five versions, thus assigning it a high basic importance value; second, whether the context of the indicator in the latest version of the standard contains strongly directive terms, such as "must," "should be established," or "as core construction content," etc. Such expressions will trigger an importance enhancement flag. After normalization of the two factors, a weighted score is calculated according to a set weight ratio to obtain the comprehensive importance score for each newly added indicator, which guides whether it should be included in the weight optimization process and the extent of adjustment.

[0070] Based on semantic mapping relationships and importance scores, a weighted update function is constructed to fine-tune the weights of corresponding indicators in the existing template library. For existing indicators that have a strong semantic overlap with the new indicator and whose scores are higher than the system threshold, their original weights are increased by multiplying the importance score by the structural coverage rate. For example, if the original weight of an old indicator is 0.6, and the matching new indicator has an importance score of 0.8 and a coverage rate of 0.9, its final new weight is 0.72, and the updated weight is 1.82. Furthermore, if a new indicator has no mapping object in the existing system, but its score is significantly higher than the average level, the indicator is introduced into the template system as a new entry and assigned an initial weight value, usually determined by multiplying its score by a preset coefficient.

[0071] When dealing with "legacy indicators" that are no longer present in the new standards, if an indicator has not appeared in three consecutive standard versions, its importance is considered to have gradually decreased, and a "decay mechanism" should be activated. The decay mechanism is set to a uniform gradient linear decrease, meaning that the weight value is reduced by a fixed percentage with each update until its weight reaches a preset minimum retention value. For example, the weight might decrease by 0.1% each time, with a minimum retention value of 0.15%. This approach ensures historical compatibility while preventing invalid indicators from occupying matching space for extended periods, ensuring that the template structure remains consistent with policy requirements. Through the coordinated implementation of the above steps, dynamic, gradual, and structural optimization of construction indicator weights can be achieved, enabling the template library to adapt to policy evolution trends over the long term and maintain a high degree of coupling with actual construction needs, providing a more accurate data foundation for intelligent matching.

[0072] To achieve dynamic adjustment and rational evolution of the standard template weight structure, a weighted update function based on importance score and structural coverage can be designed. This function not only supports weight enhancement of existing indicators but also supports intelligent injection of new high-value indicators. Specifically, the weight update process can be implemented in the following four steps: Based on the aforementioned semantic mapping results and structural coverage calculation results, for each indicator already existing in the template, it is determined whether it semantically overlaps with any indicator in the currently added standard file. The criteria for determining semantic overlap are that the text similarity score reaches the system's set threshold (e.g., 0.85) and the corresponding construction dimensions are completely identical. For example, "high-energy damage screening" and "high-speed injury mechanism assessment" appear in different standards, but are identified by the model as the same clinical assessment element, belonging to the "emergency treatment capability" dimension. After identifying the semantically overlapping item, it is further confirmed whether the importance score of the newly added indicator is higher than the set threshold (e.g., 0.6). If the above conditions are met, it is considered qualified to adjust the weights of existing indicators.

[0073] The incremental weight value is calculated based on a weighted update function, which is grounded in the following mathematical principle: for each semantically overlapping indicator that meets the conditions, the incremental weight increase should be the product of its importance score and its structural coverage rate. For example, if an indicator has an importance score of 0.75 and a structural coverage rate of 0.8, then the incremental weight increase for that indicator should be 0.6. If the original weight is 0.95, then the adjusted weight becomes 1.55. To prevent the weights of certain high-frequency indicators from expanding indefinitely, the system can set a maximum single weight increase limit (e.g., not exceeding 0.5) and a capping threshold for the final weight value (e.g., not exceeding 2.0), thereby maintaining the stability and controllability of the weight system.

[0074] For new indicators that have no semantic mapping in the current template system, their introduction value is further assessed. The criterion for introduction value is whether the indicator's importance score is higher than the threshold for new indicator entries (e.g., 0.7). If this condition is met, the indicator is identified as a "high-value new item." Such indicators will be directly added to the template system as new dimension entries in the vector coding structure, and their initial weights will be set based on their importance score multiplied by the initial weight coefficient set by the system. For example, a "regional trauma bed prediction ratio model" is a completely new indicator with a score of 0.85. If the initial coefficient is set to 1 / 5, its initial weight will be set to 1 / 275.

[0075] It should be noted that, in actual implementation, consistency checks and normalization should be performed on all sets of indicators undergoing update operations. Considering that some templates were built during the old standard period, their overall weight distribution may experience structural drift due to the concurrent injection of multiple new indicators. Therefore, the normalization ratio of the overall weight distribution of the template needs to be recalculated after each weight update to ensure that the sum of weight values ​​remains consistent with the original template structure, avoiding impact on the accuracy of subsequent similarity calculations. Simultaneously, for all newly added or adjusted indicators, their change path, source file, and change timestamp should be recorded to support subsequent version backtracking and dynamic iteration control. Through the orderly execution of the above steps, the standard template library can retain its historically valid structure while rapidly responding to policy updates and industry evolution, achieving intelligent adjustment of template indicator weights, and providing a more accurate knowledge base for subsequent resource matching.

[0076] To achieve efficient integration between target hospital resource information and standard templates, the raw resource information needs to be structured, vectorized, and its dimensional weights adjusted using an entropy weighting mechanism to ensure the accuracy and robustness of the matching score process. Specifically, this involves structuring the resource data provided by the target hospital and constructing a standardized field tagging system. Input information may come from various data sources, including uploaded forms, completed forms, and free text descriptions. To unify the format, various types of information are first parsed into basic structural fields for hospital resources, such as "number of emergency department beds," "number of advanced imaging devices," and "proportion of intensive care nursing qualified personnel." For fields in free text format, semantic parsing rules are used to categorize them into the closest structured tags. For example, "the hospital has the only 24-hour interventional platform in the region" can be parsed into the "whether it has 24 / 7 interventional capability" sub-item under the "interventional platform settings" field, assigned the value "yes." This ultimately forms a set of resource tags with a consistent field structure.

[0077] The structured tag set is mapped to resource feature vectors, and a lightweight encoding method based on word weight sparse mapping is applied to achieve a low-redundancy, highly adaptable vector representation. The encoding method employs a sparse mapping strategy, assigning positive weights only to tags with significant matching influence, while leaving secondary tags unweighted or setting them to zero, thus avoiding diluting key features in the high-dimensional vector space. For example, for the field "number of trauma operating rooms," if the hospital has two independent operating rooms, this dimension is weighted at two, while other unfilled related dimensions are kept at zero. A dimension balance adjustment factor is introduced during the encoding process, setting a weight adjustment benchmark for each dimension. The calculation method is the ratio between the information entropy value corresponding to the dimension and its proportion in the overall vector, used to measure the expression saturation and structural importance of that dimension.

[0078] Based on the obtained adjustment factors, the vector structure is normalized and adjusted. The system statistically analyzes the frequency of each resource dimension in the hospital input and its impact on the final matching result. Combining its encoding length and information entropy value, it determines whether there is a risk of "weight inflation" for that dimension. If a dimension, such as "medical equipment model code," is found to have a significantly longer encoding length than other dimensions, while its entropy value is below a set threshold (e.g., below 0.2), it indicates that although this dimension has complex values, its information contribution is limited. In this case, the adjustment factor for this dimension is set to a suppression coefficient less than one (e.g., 0.6) to reduce the influence weight of this dimension on the matching score in the final calculation. Conversely, for high-entropy, high-influence dimensions such as the "personnel qualification level" field, its adjustment factor can be set to an amplification coefficient slightly higher than one (e.g., 1.2) to increase its participation intensity in the matching process.

[0079] The adjusted resource feature vectors are compared with the attribute feature vectors of each template in the standard template library using cosine similarity calculation to obtain a preliminary matching score. This provides basic data support for subsequent refinement of the score by introducing a context-sensitive adjustment factor. This method ensures that the relative weight of each resource dimension in the matching calculation more accurately reflects its impact on the adaptability of the trauma center construction plan, avoiding interference from redundant or scarce expression of individual data items, thereby significantly improving the accuracy and context adaptability of candidate plan recommendations. The resource feature vectors are compared with the attribute feature vectors of each template in the standard template library using cosine similarity calculation, and a context-sensitive adjustment factor is introduced to correct the matching score. The context-sensitive adjustment factor is obtained based on a three-state mutually controlled robust weighting model constructed from the template credibility index, resource dimension balance index, and regional suitability reliability. Finally, the adjusted matching score is output as the basis for candidate template selection.

[0080] In this invention, the resource feature information input by the target hospital needs to be transformed into a computable feature vector through lightweight encoding to achieve matching with attribute feature vectors in a standard template library. Lightweight encoding refers to a processing method that, when digitally representing hospital resource information, uses techniques such as semantic normalization, feature compression, and weight adjustment to transform complex, redundant, and diverse input data into a low-dimensional, sparse, and discriminative vector structure. Its core features are: on the one hand, maintaining the integrity of key information and avoiding semantic loss due to data compression; on the other hand, reducing data dimensionality and storage redundancy, reducing computational overhead, and enabling resource features to participate in large-scale matching calculations at a lower cost. The application of lightweight encoding ensures that hospital input information can be quickly transformed into standardized, comparable data representations without relying on complex interface connections. Its core idea is to map complex structured and unstructured resource information into a sparse and discriminative vector structure through semantic compression and weight allocation.

[0081] At the information processing level, the lightweight coding method uses semantic parsing and classification to uniformly map inputs from different sources and in different formats to preset fields. For example, "equipped with spiral CT" and "owns two CT scanning devices" are identified as the same type of indicator and mapped to the "radiological equipment" dimension. In this process, key feature word extraction and synonym merging methods are used to ensure that the data remains consistent across different hospitals.

[0082] At the feature compression level, lightweight encoding reduces data dimensionality through sparse mapping and weight reduction. For example, hospital input may contain dozens of different brands and models of patient monitors, but during the matching process, it only needs to be represented by two sub-dimensions: "patient monitors are available" and "quantity level". This preserves the discriminative nature of the indicators while avoiding dimensionality explosion caused by invalid information, thus improving computational efficiency.

[0083] At the weight adjustment level, the lightweight encoding method introduces a dimensionality balancing mechanism, which measures the influence of each dimension in the overall feature space through information entropy and feature proportion. When the feature information of a certain dimension is too high but the entropy value is too low, the system automatically weakens its weight, thereby avoiding the excessive dominance of a single dimension on the overall matching result and ensuring a more objective and balanced matching result.

[0084] After lightweight encoding, the resource information is transformed into a multi-dimensional vector form. To achieve matching between the resource vector and the template vector, this invention employs a feature vector mapping algorithm. The feature vector mapping algorithm maps hospital resource information and standard template construction indicators to a unified feature space, and uses mathematical calculations to measure the similarity between the two. In this algorithm, both the hospital's resource data and the template's attribute data are transformed into multi-dimensional feature vectors, with each dimension corresponding to a specific construction element. By calculating the cosine of the angle between the resource vector and the template vector, the degree of matching between the two in the overall feature space can be quantitatively reflected. The closer the value is to one, the more consistent the hospital resources and the template are in structure and attributes. This algorithm not only enables rapid association between resources and templates but can also be combined with a dynamic weighting mechanism to generate matching results that balance accuracy and applicability.

[0085] During execution, the two vectors are first normalized to ensure that features with different dimensions and value ranges can be compared in the same space. Secondly, the dot product of the two vectors is calculated; this value represents the degree of overlap between the two sets of features in the multidimensional space. Finally, the dot product result is divided by the product of the magnitudes of the two vectors to obtain a similarity value ranging from zero to one. The closer the value is to one, the closer the target hospital resource is to a certain standard template in the feature space, i.e., the higher the matching degree. In this invention, feature vector mapping not only relies on basic cosine similarity but also incorporates a context-sensitive adjustment factor for correction. That is, during the matching process, if the template credibility is low, the resource dimensions are unbalanced, or the regional adaptability is insufficient, even if the cosine similarity score is high, it will be negatively corrected to avoid the erroneous result of "high similarity but unusable". This mechanism significantly improves the robustness and real-world adaptability of the matching process. The lightweight encoding method solves the problem of heterogeneous and redundant input resource information, while the feature vector mapping algorithm provides the mathematical basis for intelligent matching between resources and templates. The combination of the two constitutes the key support point for the technical implementation of this invention.

[0086] To ensure the high credibility of the template content, a systematic evaluation of its source, stability, and update history is necessary to generate a quantifiable template credibility index. This credibility index not only filters low-quality template sources but also serves as a core component of the context-sensitive adjustment factor, directly impacting the matching score. Specifically, the template credibility index is calculated using the following steps: Extracting the basic source information of the construction standard documents associated with each standard template, focusing on the original publishing platform's website domain, publication timestamp, version number, and the identity verification information of the publishing unit and the person responsible for signing. For example, the "National Regional Medical Center Construction Standards (2021 Edition)" published by the National Health Commission on its official website has the domain "www.nhc.gov.cn," which belongs to the national controlling agency and can be preset to level one credibility. When creating a template, the corresponding original standard document is automatically annotated with its source information, and its acquisition time, structural summary, and key content summary are recorded simultaneously.

[0087] A source trust level assessment table is established for each source domain. The trust level is set based on multi-dimensional standards, including five dimensions: the administrative affiliation of the domain, whether it is a platform directly under a national agency, whether it has HTTPS encryption, whether there is a history of content tampering, and whether there has been a high frequency of 404 errors. Each dimension is scored out of ten, with a total score ranging from 0 to 50. For example, "www.nhc.gov.cn," as a centrally administered authoritative domain, has full marks in all five categories and can be assigned a perfect score of 50, classifying it as a Level 1 trusted source; while a source from an academic forum might only receive 22 points due to lack of encryption and official identification, classifying it as a Level 3 trusted source.

[0088] The stability of the standard document corresponding to each template is statistically analyzed over the past two years. The method is as follows: fingerprint hashes are compared for version snapshots of the same standard document within the past 730 days. If the hash digests remain consistent, it is considered an "unchanged version." If the digests differ slightly but the release date is marked as "draft for comments" or "internal draft," it is recorded as an "informal change" and is not counted in the change count. The stability is ultimately quantified using the "number of consecutive unchanged versions." For example, if a standard document associated with a template remains unchanged for the past 24 months, its stability is recorded as 24 unchanged versions. The source trust level score (out of 50) and the version stability index (e.g., 24 times) are standardized and then weighted according to a set ratio. For example, the trust level is converted to a ratio score from 0 to 1 (50 points to 1.0), and 24 unchanged versions are standardized to 0.8 with a set upper limit of 30. The weighting ratio is set at 60:4, meaning the source level accounts for 60% and version stability accounts for 40%. The final template credibility index is calculated as: (1.0 × 0.6) + (0.8 × 0.4) = 0.92. This value will be used in the context sensitivity correction judgment during subsequent matching processes.

[0089] To avoid imbalances in overall matching results due to an overemphasis on resource data in one dimension, a resource dimension balance index needs to be introduced during vectorized modeling. This index measures the uniformity of distribution and completeness of the resource information submitted by hospitals across different dimensions, ensuring a relatively balanced impact of each dimension on the matching score during the similarity calculation phase. The construction of this index can be achieved through the following steps: The original resource data input by the target hospital is divided into dimensions according to a predefined resource system, forming clear dimension boundaries, and semantic normalization is performed on the entries within each dimension. For example, the predefined resource dimensions may include four categories: "bed resource dimension," "personnel qualification dimension," "equipment configuration dimension," and "emergency access dimension," each covering several specific fields. When the text submitted by the hospital contains semantically similar expressions such as "5 senior anesthesiologists" and "5 senior anesthesiologists," the system identifies their synonymy using natural language processing rules and uniformly categorizes them into the "number of senior anesthesiologists" field, avoiding duplicate entries and ensuring consistency and statistical compatibility of dimension expressions.

[0090] The normalized resource data is statistically analyzed to calculate the information density of each dimension. Information density is defined as the ratio between the actual number of fields filled in a dimension and the theoretical maximum number of indicators for that dimension. For example, the "Equipment Configuration Dimension" includes 15 preset indicator items, such as the number of CT scanners, whether MRI equipment is available, whether mobile X-rays are supported, and the number of trauma surgeries. If a hospital fills in 9 items under this dimension, the information density is nine divided by fifteen, which is 0.6. This value reflects the degree of information saturation in that dimension and can serve as an important reference factor for assessing completeness.

[0091] Building upon information density calculations, semantic compression is further applied to resource entries within each dimension. Similar items are merged, and redundant content is removed to obtain the retention ratio before and after compression. Taking the "Personnel Qualifications" dimension as an example, a hospital might have filled in three semantically similar but differently formatted descriptions: "Anesthesiologist," "Anesthesia Professionals," and "Anesthesiologist." The system uses a semantic clustering algorithm to merge these into a single entry, "Anesthesia Professionals." Before compression, there were three items; after compression, only one is retained, resulting in a compression retention rate of one-third. The compression retention rate reflects the uniqueness of the information and the actual amount of expression after deduplication, providing a quantitative description of diversity and redundancy.

[0092] The information density is multiplied by the compression retention rate to obtain the equilibrium score for each dimension. A higher equilibrium score indicates higher quality in terms of information completeness and representational diversity for that dimension. For example, if the information density of the "equipment configuration dimension" is 0.6 and the compression retention rate is 0.75, the equilibrium score is 0.45. After aggregating the equilibrium scores of all dimensions, their standard deviation is calculated. Large fluctuations in scores across dimensions indicate uneven resource distribution, resulting in a higher variance. The system normalizes the variance and uses the result of subtracting the normalized value from one as the final resource dimension equilibrium index. This index ranges from zero to one; the closer the value is to one, the more evenly the hospital's resource data is distributed across dimensions, and the stronger the representativeness of the data for matching. In practical applications, if the hospital data has extremely high scores in the "bed" and "equipment" dimensions, but severely lacks data in the "personnel qualification" dimension, the equilibrium index will significantly decrease. This triggers a weight adjustment mechanism for the hospital's data during the matching correction phase, improving the overall robustness and fairness of the matching process.

[0093] To ensure accurate matching of the recommended construction template to the geographical environment, it is necessary to calculate the regional suitability reliability based on the geographical characteristics and regional conditions of the hospital's location. This reliability measures the degree of closeness between the geographical application scenario set by the template and the actual regional conditions of the target hospital, thereby avoiding the incorrect recommendation of templates designed for densely populated cities to medical institutions in remote areas. The calculation of regional suitability reliability can be implemented through the following steps: collecting basic regional data on the area where the target hospital is located, including but not limited to three core indicators: resident population, transportation accessibility, and the number of surrounding medical resources. The resident population is based on the latest publicly available data from the National Bureau of Statistics. The transportation accessibility indicator can be obtained by calculating the average travel time to emergency centers within a certain radius of the hospital. The number of surrounding medical resources is calculated based on the total number of secondary and above hospitals within a 30-kilometer radius. Taking a municipal central hospital as an example, its urban area has a resident population of 750,000, an average travel time of 15 minutes, and 18 hospitals within 30 kilometers.

[0094] The system extracts the baseline regional requirements by comparing the "recommended applicable area parameters" set in the standard template. For example, if a template's standard applicable scenario is "a resident population exceeding 500,000, a medium or higher traffic accessibility index, and more than ten surrounding medical institutions," the system would express the template's regional vector as (500,000, standard value of 60, 10 institutions). The actual hospital parameters (750,000, actual score of 78, 18 institutions) are then compared item by item with the template parameters, calculating the absolute differences between the three types of indicators. For example, the population difference is 250,000, the traffic accessibility difference is 18 points, and the medical resource difference is 8 institutions.

[0095] To prevent imbalances in the influence of indicators, weighting coefficients are introduced for weighted difference calculations. The weighting standard for each indicator is set based on its sensitivity to the template's applicability. For example: the resident population, as the basis for the service capacity of trauma centers, is weighted at 40%; transportation accessibility significantly impacts the speed of emergency patient transfer, so it is weighted at 30%; and medical resources, as supporting capacity, are weighted at 30%. Based on this weighting, the three differences are weighted and calculated: the population difference of 250,000 is converted to 30 points on a 100-point scale, multiplied by 0.4 equals 12 points; the transportation difference of 18 points is multiplied by 0.3 equals 5.4; and the resource difference of 8 points is converted to 20 points on a 100-point scale, multiplied by 0.3 equals 6 points. The total weighted sum of the three is 23.4.

[0096] The obtained weighted adaptation distance values ​​are standardized into a percentage-based adaptation reliability score. Based on a set adaptation tolerance range, the system linearly normalizes the weighted distance scores, and the result of subtracting the normalized score from one is taken as the final regional adaptation reliability. For example, the weighted adaptation distance score of the aforementioned hospital is 23.4. In a normalized model with a maximum distance score of 50 points, its normalized value is 0.468, and the final regional adaptation reliability is 1 minus 0.468, which is 0.532, or 53.2 points on a percentage scale. The regional adaptation reliability not only reflects whether the hospital's geographical attributes match the template's set environment, but also avoids misjudgments caused by extreme parameters in remote areas or megacities through difference logic and weight constraints. This provides important contextual correction input for the three-state mutually controlled robust weighting system, improving the interpretability and regional adaptability of the recommendation results.

[0097] To effectively improve the real-world adaptability and technical robustness of matching recommendation results, a context-aware dynamic weighting mechanism needs to be introduced on top of basic similarity calculation. This involves adjusting the standard matching process using a three-state mutually controlled robust weighting model. Based on the calculation results of three core confidence factors, this model forms a context-sensitive adjustment factor that plays a negative corrective role in the matching score calculation between resources and templates. Specifically, the execution of this model may include the following steps: calculating the template credibility index, regional suitability reliability, and resource dimension balance index associated with each candidate template. The template credibility index is obtained based on the source credibility level and historical stability of the standard document corresponding to the template; the regional suitability reliability is standardized by comparing the difference between three indicators—resident population, transportation accessibility, and surrounding medical resources—of the target hospital's geographical location and the regional applicability parameters set in the template; the resource dimension balance index is evaluated by weighted integration of the entropy values, compression rates, and variance analysis results of the hospital's input resources across various dimensions. For example, if a hospital's resources are highly concentrated in the "beds" dimension but sparse in the "personnel qualifications" dimension, the balance index will be lowered. All three scores are standardized to between zero and one, serving as the basis for subsequent weighting calculations. A weighted average of the three values ​​is then used to form the context sensitivity adjustment factor. The weighting strategy can be prioritized based on the actual application scenario; for example, in resource-rich but geographically constrained areas, the weighting ratio of regional suitability reliability can be increased. The default weighting configuration is set as follows: template reliability accounts for 40%, regional suitability reliability accounts for 30%, and the resource dimension balance index accounts for 30%. In a specific case, if the three indicators are 0.8, 0.65, and 0.55 respectively, their weighted average is 0.685, which is the context sensitivity adjustment factor used to dynamically adjust the basic matching score.

[0098] The cosine similarity between the resource feature vector and each template attribute feature vector is calculated to obtain a preliminary matching score. The cosine similarity is calculated by dividing the vector dot product by the product of their magnitudes; the result, between zero and one, represents the degree of similarity between the angles of the two vectors. For example, the cosine similarity between the resource feature vector and the template feature vector of a particular hospital is 0.93. This value represents a high degree of similarity in the pure feature space; however, it may contain matching biases due to contextual factors such as geographical location, data structure, or uneven information distribution. Therefore, the aforementioned context-sensitive adjustment factor needs to be introduced to correct this score.

[0099] The initial cosine similarity score is adjusted based on the relationship between the context sensitivity adjustment factor and a set threshold. When the adjustment factor is higher than the set threshold (e.g., 0.7), the matching score remains unchanged or is appropriately amplified (multiplied by a small coefficient of one or more). When the adjustment factor is lower than the threshold, the template is considered to have contextual risk, and a negative correction is performed. Specifically, the initial cosine similarity is multiplied by an adjustment coefficient less than one, which is calculated from the normalized difference of the context sensitivity adjustment factor. For example, if the cosine similarity is 0.93, the adjustment factor is 0.65, and the set threshold is 0.7, the normalized difference is 0.05. This value is multiplied by the adjustment coefficient (e.g., 0.5) to generate a negative decay value, adjusting the original matching score to 0.93 multiplied by 0.975, resulting in 0.9075, which serves as the final scoring basis for template selection and recommendation.

[0100] After the matching score is calculated, all template scores participating in the matching process need to be screened and sorted to select one or more construction plans that best fit the target hospital's resource structure. These will then be pushed out as feasible candidate suggestions. A preset threshold for the matching score is set, and all calculated matching scores are initially screened. The matching score is the final score after adjusting for cosine similarity and context sensitivity factors, typically ranging from zero to one. To ensure the recommended solutions have high adaptability and practical value, the system defaults to a matching threshold of 0.8. This means that only templates with a final matching score equal to or higher than 0.8 can enter the candidate pool. For example, if one template has a matching score of 0.83 and another has 0.75, only the former meets the threshold requirement. All templates entering the candidate pool are then ranked by matching score, arranged in descending order from highest to lowest.

[0101] Preferably, at least one candidate trauma center construction plan that best matches the resource structure of the target hospital is output according to a preset strategy. For example, to avoid determining output priority solely based on numerical values, a weighted approach based on the hospital's priority dimensions is introduced for re-ranking. For instance, if a target hospital currently focuses on "completeness of trauma treatment pathway coverage," and this dimension has a high weight in a candidate template, it can be moved forward in the ranking even if its overall score is slightly lower. In practice, the system can set "configurable focus dimensions," such as bed structure, staffing, and information integration level, and simultaneously record the responsiveness of each template in each focus dimension during the matching process. After ranking, a structural difference re-evaluation mechanism is executed to ensure that the recommended templates not only have excellent scores but also structural adaptability. This step performs a difference analysis based on the sparse matrix of the target hospital's resource structure and the construction indicator matrix of each candidate template, calculating the structural coverage difference, i.e., the proportion of indicators with non-zero differences between the two across all dimensions. For example, if the target hospital has only three beds in the "intensive care unit" dimension, while a template requires no less than ten, the difference in this dimension is negative seven. If such difference items account for more than 30%, the template will still be judged as structurally incompatible and removed, even if it scores highly.

[0102] Finally, among the remaining templates that meet the structural adaptability requirements, based on the score ranking and the hospital's focus weight, at least one template with the highest matching degree and the most suitable structure is output as the final recommended solution. If the matching degree scores are very close (e.g., the difference is less than 0.05), two or three candidate solutions can be output side by side for further review and selection by humans or experts. In a practical application, after a top-tier hospital in western China submitted resource information, the system calculated and screened out five templates with scores higher than 0.8. Among them, two templates performed outstandingly in the "regional medical network linkage capability" dimension, and their scores were both higher than 0.85. Therefore, the system recommended these two templates side by side for evaluation, along with the reasons for the recommendation and the expected configuration risk warning, to improve the feasibility and security of the solution implementation.

[0103] The above algorithms or formulas are all dimensionless and numerical calculations, and the results are obtained by software simulation based on a large amount of collected data to obtain the most recent real-world results. The preset parameters are set by those skilled in the art according to the actual situation.

[0104] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0105] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0107] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for intelligent resource matching in trauma center construction plans, characterized in that, Includes the following steps: A standard template library was established based on relevant standard documents for the construction of trauma centers, and structured construction indicators were extracted to generate initial templates, which were then stored with attribute encoding in the form of multi-dimensional features. The latest standard information is obtained from multiple trauma center construction-related information sources, and the credibility is verified by comparing structural signatures. An incremental learning algorithm is used to adjust the weights of each template indicator in the standard template library and dynamically iterate and update the standard template library. The resource feature information input by the target hospital is obtained and converted into a resource feature vector through a lightweight encoding method. The feature vector mapping algorithm is used to calculate the cosine similarity between the resource feature vector and the attribute feature vector of each template in the standard template library to obtain the matching degree score between the resource and the template. Three factors are generated: template credibility index, resource dimension balance index, and regional suitability reliability. A robust weight adjustment model with three-state mutual control is constructed to dynamically adjust the weights of each matching dimension. Specifically: the template credibility index determines the legality of the template source based on multi-source cross-signature and change frequency hash chain; the resource dimension balance index evaluates the rationality of its dimension weights based on the semantic compression rate and information entropy distribution of resource features; and the regional suitability reliability evaluates the applicability of the recommended template based on the amount of regional medical resources, transportation accessibility, and population density. The above three factors jointly control the matching weight correction function and dynamically intervene in the matching score calculation process. The three-state mutual control robust weight adjustment model refers to: The weighted average of the template credibility index, regional suitability credibility, and resource dimension balance index is calculated to obtain the context sensitivity adjustment factor. The cosine similarity between the resource feature vector and the attribute feature vector of each template is calculated to obtain the cosine similarity value. When the context sensitivity adjustment factor is lower than the preset adjustment threshold, the original cosine similarity value is negatively corrected by multiplying the cosine similarity value by an adjustment coefficient less than one. The adjustment coefficient is determined by the normalized score of the adjustment factor. Finally, the adjusted matching score is obtained. Templates with matching scores exceeding a preset matching threshold are filtered, and at least one candidate scheme for the construction of a trauma center that best matches the resource structure of the target hospital is output. The final result of the candidate schemes is generated by sorting the matching scores, but only templates with matching scores exceeding the preset matching threshold are retained. The output results include one or more templates for the hospital to refer to.

2. The method for intelligent resource matching in the construction scheme of a trauma center according to claim 1, characterized in that, The steps to establish a standard template library based on relevant standard documents for the construction of trauma centers include: We obtained standard documents for the construction of trauma centers from multiple publishing channels, extracted and structured multiple pre-set construction elements, classified and labeled each construction element according to its own dimension, established a multi-dimensional feature system, and defined the attribute type, numerical range and priority of each dimension. We used attribute vectorization coding to digitally express the classified construction elements, and assigned initial weights based on historical review cases, and finally generated a complete standard template library.

3. The method for intelligent resource matching in the construction scheme of a trauma center according to claim 1, characterized in that, The steps for verifying trustworthiness by comparing structural signatures include: Extract the structural hierarchy and key field digest of the standard content to generate a hash signature, and mark the timestamp and source domain name; Call the historical standard signature database to compare multiple versions of similar standard signatures and count their signature duplication rate, update frequency and domain authority level. If the signature is inconsistent with the history and the source domain is not a country-controlled domain, or the update frequency exceeds the preset standard, it is judged as a low-trust standard and the source is prohibited from participating in the template library update.

4. The method for intelligent resource matching in the construction scheme of a trauma center according to claim 1, characterized in that, Adjusting the weights of each template metric in the standard template library using an incremental learning algorithm includes the following steps: The construction indicators in the newly added standard documents are semantically compared and classified with the existing indicators in the standard template library. The mapping relationship is established based on the similarity of word meaning and whether the dimensions belong to them are consistent. The method for determining the overlap is: for each new indicator, count the number of entries that can be mapped to the existing indicators, and calculate the ratio with the total number of new indicators. The obtained ratio is the structural coverage rate of the new indicator relative to the existing system. An importance change scoring model is constructed, which includes two key influencing factors. The first is the frequency of the new indicator in the historical standard version, which serves as its basic importance factor. The second is whether there are language tags with strong weighted prompt words in the description of the indicator in the same dimension in two adjacent standard updates. If so, the indicator is given an importance enhancement tag. After standardizing the two factors, the change importance score of each new indicator is obtained according to the set linear weighting relationship. A weighted update function is constructed to adjust the original weights of indicators in the existing template library. For old indicators that no longer appear in the new standard, if they have not been referenced in the last three updates, their weights are gradually reduced according to a preset gradient reduction value until they are reduced to the set minimum retention value.

5. The method for intelligent resource matching in the construction scheme of a trauma center according to claim 4, characterized in that, The weighted update function is executed based on the following principles: When an existing indicator and a new indicator have semantic overlap, and the importance score of the new indicator is higher than a set threshold, the weight of the existing indicator will be increased by an increment, which is obtained by multiplying the importance score by the structural coverage rate. If a new indicator has no mapping in the existing system and its importance score is still higher than the preset importance threshold, then the new indicator is added to the template library, and its importance score is multiplied by the initial weight coefficient set by the system as the initial weight value.

6. The method for intelligent resource matching in the construction scheme of a trauma center according to claim 5, characterized in that, The steps for converting the resource feature information input from the target hospital into a resource feature vector and then performing a matching score include: The raw resource information input by the target hospital is structured and vectorized according to preset field standards. Semantic parsing rules are used to classify free text fields into corresponding structural tags. A lightweight encoding method based on word weight sparse mapping is applied to transform structural labels into resource feature vectors. In this process, a dimension balance adjustment factor is introduced. The value of the adjustment factor is the ratio of the information entropy value of each resource item to the vector proportion value. The resource feature vector is compared with the attribute feature vector of each template in the standard template library by cosine similarity calculation. A context sensitivity adjustment factor is introduced to correct the matching degree. The context sensitivity adjustment factor is obtained based on a three-state mutually controlled robust weight adjustment model constructed by template credibility index, resource dimension balance index and regional suitability credibility. Finally, the adjusted matching degree score is output as the basis for candidate template selection.

7. The method for intelligent resource matching in the construction scheme of a trauma center according to claim 6, characterized in that, The calculation of the template credibility index includes: Extract the standard file source associated with each template, set the source trust level based on the source domain name, and count the number of times the standard version of the source remains unchanged within a given period; After standardizing the source credibility level and the number of times the standard version has not changed, the template credibility index is obtained by weighting the average according to the set ratio.

8. The method for intelligent resource matching in the construction scheme of a trauma center according to claim 7, characterized in that, The calculation of the resource-dimensional balance index includes: The resource information input by the target hospital is categorized according to preset dimensions and semantic normalization is performed to eliminate duplicates. The number of valid entries in each dimension and the proportion of overall resource information are counted. The information density and the degree of difference after compression between different dimensions are compared to determine the distribution balance between resource dimensions. Specifically, the information density of each resource dimension is calculated and defined as the ratio between the number of actual items filled in the dimension and the theoretical maximum number of indicators in the dimension. Semantic compression is performed to merge similar resource items and calculate the retention ratio before and after compression. The compression retention rate is defined as the ratio of the number of items retained after compression to the number of items in the original state before compression. For each resource dimension, calculate its equilibrium score. The equilibrium score is the product of the information density and compression retention rate of that dimension. It is used to comprehensively evaluate the completeness and diversity of that dimension. After collecting the equilibrium scores of all dimensions, calculate the variance of all dimensions. Finally, take the score after subtracting the variance normalization value from the score and use it as the resource dimension equilibrium index.

9. The method for intelligent resource matching in the construction scheme of a trauma center according to claim 8, characterized in that, The calculation of the location fit reliability includes: The weighted difference between the number of permanent residents, traffic accessibility indicators, and the number of surrounding medical resources in the hospital's location and the applicable parameters preset in the template is calculated. The weighted absolute difference is used as the adaptation distance metric, and the results are standardized into a percentage score to obtain the regional adaptation reliability.

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