Method for intelligently matching trauma center construction scheme resources

Abstract

The invention discloses a trauma center construction scheme resource intelligent matching method, and particularly relates to the technical field of medical resource configuration optimization, and the method comprises the following steps: building a standard template library, and carrying out the structural coding; obtaining multi-source latest standard information, and dynamically adjusting the index weight by adopting incremental learning after structural signature verification; carrying out lightweight coding on resource information input by a target hospital to generate a feature vector, and carrying out cosine similarity calculation on the feature vector and the template vector to obtain a matching degree; constructing a three-state mutual control type robust weight adjustment model to dynamically correct the matching degree; and finally outputting a candidate scheme which is most matched with the target hospital resource structure. Through multi-source information acquisition, structural signature verification and incremental learning weight adjustment, a template library is kept synchronous with the latest standard; by constructing a robust weight adjustment model and dynamically correcting a matching degree score, an output scheme is ensured to have credibility, balance and regional suitability, and scientificity, practicability and feasibility of a recommendation result are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical resource allocation optimization, more specifically, the present application relates to a method for intelligent matching of resources of 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, the hospital often faces the following difficulties when formulating the construction plan: The standards are updated frequently and come from various sources, and there are differences in index requirements, dimension division and review points among different versions of policy documents, so it is difficult for the hospital to capture and apply the latest standards in time, which may lead to the 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 qualification among hospitals at all levels. If only relying on manual comparison of standard documents, it is difficult to form a precise and adaptive construction plan, and there are problems of "high standard inapplicability" or "resource underestimation and insufficient configuration". The plan matching lacks intelligence and dynamics, and the existing method often only relies on fixed templates or manual experience, lacks automatic matching means based on feature vector mapping and data mining, cannot realize the precise association between hospital resources and standard templates, and also lacks a 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 of a trauma center construction plan, in order to solve the above problems. SUMMARY

[0003] To achieve the above purpose, the present application provides the following technical scheme: The method for intelligent matching of resources of a trauma center construction plan comprises the following steps: A standard template library is established based on the standard documents related to trauma center construction, and structured construction indicators are extracted to generate initial templates, which are stored in the form of multi-dimensional features; The latest standard information is obtained from multiple information sources related to trauma center construction, the credibility is verified by comparing the structure signature, the weight of each template indicator in the standard template library is adjusted by using an incremental learning algorithm, and the standard template library is dynamically and iteratively updated; The resource feature information input by the target hospital is obtained, and is converted into a resource feature vector by a lightweight coding method, the cosine similarity between the resource feature vector and the attribute feature vector of each template in the standard template library is calculated by using a feature vector mapping algorithm, and the matching degree score between the resource and the template is obtained; Three factors are generated respectively: template credibility index, resource dimension balance index and regional suitability index, and a three-state mutual control robust weighting model is constructed to dynamically adjust the weight of each matching dimension, wherein: The template credibility index determines the legitimacy of the template source according to the multi-source cross signature and the variable frequency hash chain; The resource dimension balance index evaluates the rationality of the dimension weight according to the semantic compression rate and information entropy distribution of the resource characteristics; The regional suitability index evaluates the applicability of the recommended template according to the regional medical resource quantity, traffic accessibility and population density; The above three factors jointly control the matching weight correction function and dynamically intervene in the matching degree score calculation process; Templates with a matching degree score 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.

[0004] In a preferred embodiment, the step of establishing a standard template library based on trauma center construction related standard documents includes: Obtain trauma center construction standard documents from multiple publishing channels, extract multiple preset construction elements involved, and perform structured processing, classify and label each construction element according to its belonging dimension, establish a multi-dimensional feature system, and define the attribute type, numerical range and priority of each dimension, digitize the classified construction elements using attribute vectorization coding method, and combine historical review cases to perform initial weight assignment, and finally generate a complete standard template library.

[0005] In a preferred embodiment, the step of verifying the credibility of the comparison structure signature includes: Extract the structure level and key field summary of the standard content to generate a hash signature, and mark the acquisition timestamp and source domain name; Call the historical standard signature database to compare the same type of standard signatures, and calculate the signature repetition rate, update frequency and domain name authority level; 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.

[0006] In a preferred embodiment, adjusting the weight of each template index in the standard template library using an incremental learning algorithm includes the following steps: The construction indicators in the newly added standard file are compared and classified with the existing indicators in the standard template library, and the mapping relationship is established according to the similarity of word meaning and whether the dimensions belong to the same dimension. The determination method of the coincidence degree is: for each new indicator, the number of items that can be mapped to the existing indicators is counted, and the ratio operation is performed with the total number of new indicators. The ratio obtained is the structural coverage of the new indicators relative to the existing system; An importance change scoring model is constructed, including two key influence factors. One is the frequency of the new indicator appearing in the historical standard version, which is used 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, the importance of the indicator is improved. The two factors are standardized, and 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 weight of the indicators in the existing template library. For old indicators that no longer appear in the new standard, if they are not referenced in the last three updates, their weight is gradually reduced according to the preset equal gradient reduction value until it is reduced to the set minimum reserved value.

[0007] In a preferred embodiment, the weighted update function is executed based on the following principles: When a certain existing indicator forms semantic coincidence with a new indicator, and the importance score of the new indicator is higher than the set threshold, the weight of the existing indicator 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 indicator has no mapping item in the existing system, and the importance score is still higher than the preset importance threshold, the new indicator 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.

[0008] 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: 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. The lightweight coding mode based on the word weight sparse mapping is applied to convert the structural 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 the 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 the adjustment factor of the dimension to an inhibition coefficient less than one to reduce the influence of the dimension on the overall matching, and ensures that the weights of different types of resources in the vector space are relatively balanced. 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 template credibility index, a resource dimension balance index, and a regional suitability reliability, and a three-state mutual control type robust weighting model. Finally, the adjusted matching degree score is output as the basis for screening candidate templates.

[0009] In a preferred embodiment, the calculation of the template credibility index comprises: 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 given period is counted. 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.

[0010] In a preferred embodiment, the calculation of the resource dimension balance index comprises: 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 determine 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. 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. For each resource dimension, the balance score is calculated, which is the product of the information density and the compression retention rate, used to comprehensively evaluate the completeness and diversity of the dimension. After collecting the balance scores of all dimensions, the variance value of all dimensions is calculated. Finally, the score obtained by subtracting the variance normalization value from one is taken as the resource dimension balance index.

[0011] In a preferred embodiment, the calculation of the regional suitability reliability comprises: The number of permanent residents in the hospital location, the traffic accessibility index and the number of surrounding medical resources are respectively weighted difference calculated with the applicable parameters preset in the template, the weighted absolute difference is used as the adaptation distance measurement standard, and the obtained result is standardized into a percentage score, and finally the regional adaptation confidence is obtained.

[0012] In a preferred embodiment, the three-state mutual control robust weighting model refers to: The weighted average value of the template credibility index, the regional adaptation confidence and the resource dimension balance index is calculated to obtain the context sensitivity adjustment factor, the resource feature vector and the attribute feature vector of each template are subjected to cosine similarity calculation to obtain a cosine similarity value, when the context sensitivity adjustment factor is lower than the preset adjustment threshold, the original cosine similarity value is subjected to negative correction, that is, the cosine similarity value is multiplied by an adjustment coefficient less than one, the adjustment coefficient is determined by the normalized score of the adjustment factor, and finally the adjusted matching degree score is obtained.

[0013] The technical effects and advantages of the present application are: The present application establishes a standard template library based on the standard documents related to trauma center construction, generates an initial template based on structured construction indicators, and stores the attributes by multi-dimensional feature form, so that the requirements originally scattered in different files and text expressions are converted into a multi-dimensional feature system that can be quantified and compared. This method avoids the subjectivity and inefficiency of manual analysis of policy documents, and can realize fast calling and unified management of standard content. In specific application, the input information of the hospital can be directly compared with the template vector, reducing the redundant explanation link, improving the objectivity and accuracy of data processing, and ensuring that the construction scheme has integrity and standardization at the starting point.

[0014] The present application obtains the latest standard information from multiple trauma center construction related information sources, introduces a structure signature for credibility verification, and dynamically adjusts the weights of each template indicator using an incremental learning algorithm, thereby realizing continuous self-adaptive updating of the template library. This method ensures that the template content is synchronized with the latest official standards, avoiding errors caused by using outdated or unverified documents. In practical application, when new construction indicators are issued by the state or local government, the system can immediately extract the key content, verify the legality of the source, and gradually adjust the weights without overhauling the entire template library, thus maintaining the continuity of historical indicators and realizing rapid response to policy changes, thereby ensuring long-term effectiveness and reliability of the recommended results.

[0015] The present application realizes dynamic correction of the matching degree score by introducing template credibility index, resource dimension balance index and regional suitability credibility, and constructing a three-state mutual control robust adjustment model, so that the final recommended scheme not only depends on the numerical value of the cosine similarity, but also comprehensively considers the credibility of the data source, the balance of the hospital resource input among the dimensions, and the adaptation degree of the regional medical environment to the template. For example, if a hospital inputs information in the equipment resource dimension in great detail and the personnel qualification dimension is relatively insufficient, the model will automatically correct the score through the resource dimension balance index to prevent the result from being distorted due to the overemphasis of single dimension information. For another example, when a template is highly matched in numerical value, but it is designed for large cities and the target hospital is located in a remote mountainous area, the regional suitability credibility will trigger the correction mechanism to reduce the recommended priority of the template. Through this multi-factor dynamic intervention, the output candidate scheme is more in line with the actual conditions and application scenarios of the hospital, and the scientificity, practicality and feasibility of the recommended result are improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to facilitate the understanding of those skilled in the art, the present application will be further described below in conjunction with the drawings; Figure 1 The principle diagram of the method for intelligent matching of trauma center construction scheme resources in the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0018] Reference Figure 1 The following embodiments are obtained: Embodiment 1: A method for intelligent matching of trauma center construction scheme resources, comprising the following steps: A standard template library is established based on trauma center construction related standard files, and structured construction indicators are extracted to generate initial templates, which are stored in the form of multi-dimensional feature attributes. The purpose of this step is to deeply analyze the trauma center construction guidance files published by the state, extract construction elements with quantifiable characteristics such as department setting requirements, equipment configuration list, personnel qualification standards, etc., and convert them into structured data and store them in the standard template library. The multi-dimensional feature coding method makes each template not only text information, but also an attribute vector that can participate in calculation, which is convenient for subsequent algorithm to realize fast matching and reasoning judgment.

[0019] 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 the incremental learning algorithm, and the standard template library is dynamically iterated and updated. By regularly grabbing the latest standard files from multiple authoritative data sources (such as the official website of the health commission, provincial policy library, etc.), the system can determine whether the standard can be adopted according to the structure signature comparison and the source credibility level. At the same time, through the incremental learning algorithm, the affected index weight is fine-tuned without reconstructing the entire template system, so as to realize the "dynamic adaptive evolution" of the knowledge base, and keep the matching logic and national policy synchronized.

[0020] The resource characteristic information input by the target hospital is obtained and converted into a resource characteristic vector through a lightweight coding method. The cosine similarity between the resource characteristic vector and the attribute characteristic vector of each template in the standard template library is calculated using the characteristic vector mapping algorithm to obtain the matching degree score between the resource and the template. The original input information of the hospital, such as equipment model, personnel quantity, department structure, etc., is processed through structured labels and semantic normalization to convert it into a resource characteristic vector that can be recognized and processed by the algorithm, ensuring uniform data dimensions and consistent format. Then, using the characteristic vector mapping and cosine similarity calculation, the correlation strength between hospital resources and standard templates in the vector space is determined, and the matching degree between each template and the hospital is obtained, providing a quantitative basis for subsequent recommendations.

[0021] Three factors are generated respectively: template credibility index, resource dimension balance index and regional configuration confidence, and a three-state mutual control robust weighting model is constructed to dynamically adjust the weight of each matching dimension, wherein: The template credibility index determines the legality of the template source according to the multi-source cross signature and the variable frequency hash chain; The resource dimension balance index evaluates the dimension weight rationality according to the semantic compression rate and information entropy distribution of resource characteristics; The regional configuration confidence evaluates the applicability of the recommended template according to the regional medical resource quantity, traffic accessibility and population density; The three factors jointly control the matching weight correction function, dynamically intervene in the matching degree score calculation process; the multi-factor adjustment mechanism is introduced to realize the context awareness ability of the matching process. The template credibility index ensures that the template source is legal and the version is stable; the resource dimension balance index is used to correct the possible structural bias of the hospital in the input data; and the regional suitability reliability reflects whether the template is suitable for the objective geographical and resource environment of the hospital. The three are combined and controlled through the three-state mutual control model, so that the matching logic not only depends on the resource itself, but also responds to the use context behind it, improving the actual availability and rationality of the matching result. The matching degree is no longer a single calculation result, but a dynamically corrected output value after considering the context factors. The three factors form a "context sensitivity adjustment factor" through weighted averaging, which adjusts the intensity of the cosine similarity score, so that the system has the ability to judge the actual scene and realize real "context matching" rather than pure mathematical matching.

[0022] The 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. The final result is generated by matching degree score sorting, but only templates with scores higher than the system set threshold are retained to ensure that the output templates not only match in theory, but also have application feasibility. The output result can include one or more templates for the hospital to use for reference, providing highly accurate, compliant, and landable decision-making basis for formulating trauma center construction schemes.

[0023] In the present application, the process of establishing a standard template library can be implemented by the following steps to ensure that the constructed templates have structural integrity, expression accuracy and dynamic adaptability. Policy documents, guidance standards and review reference materials related to trauma center construction are collected from multiple authoritative publishing channels, which can include the official website of the National Health Commission, the provincial health commission publishing platform, and the medical industry academic association database, etc. To ensure source diversity and content legality, keyword filtering and publishing unit identification rules are set during the collection process, such as limiting the file title to contain "trauma center", "setting standards" or "construction specifications" at the same time. In this way, more than 20 high-credibility construction standard documents including "National Trauma Regional Medical Center Setting Standards (Trial)" can be obtained, and the structural paragraphs, table contents and appendix indicators in each document are extracted as preprocessed content.

[0024] On the basis of the acquired files, the element information related to specific construction requirements is extracted, structured, and each element is classified and labeled according to its technical attributes. The classification process is based on the functional dimension of the construction element, which is divided into six dimensions: personnel allocation, department setting, equipment list, diagnosis and treatment capacity, information system configuration, and site environment. Each construction element is further refined in terms of attribute type (such as "equipment model" or "professional title"), data type (such as "numeric" or "enumeration"), unit (such as "person", "unit", or "square meter"), and priority (such as "mandatory", "recommended", or "optional"). For example, the standard mentions "three doctors with a vice-senior title above must be equipped in the emergency department", such information will be parsed as the dimension of "personnel allocation", the attribute type of "title level", the data type of "enumeration", the unit of "person", and the priority of "mandatory".

[0025] Based on the classified and standardized construction elements, a vectorization coding method is used to represent them digitally. Specifically, enumeration indicators are represented by a hot encoding, and numerical indicators are normalized and coded as continuous vector elements. Each template is represented as a multi-dimensional vector structure, with each dimension corresponding to the existence or quantity of a specific element. For example, a complete template can be converted into a feature vector containing one hundred and twenty dimensions, where the fifteenth dimension represents the "number of CT equipment" with a value of five, and the thirtieth dimension represents whether the "anesthesia department is established" with a value of one (indicating that it is established). At the same time, for each dimension indicator, the frequency of its appearance in the standard literature and the mandatory level of the relevant regulations are recorded.

[0026] Combining historical trauma center review cases and data from accepted construction units, each dimension indicator in the standard template is assigned an initial weight value, which represents its importance and reference priority in the overall scheme. The specific weighting strategy is based on two dimensions: the frequency of the indicator appearing in multiple standards, and whether it is a key item for deduction in historical reviews. For example, "independent setting of trauma green channel" is required in more than 90% of policy documents, and in 50 review reports, it has been deducted for 36 times due to non-compliance, so its initial weight is set to 0.95. Through this process, a standard template library with high structural integrity, weight differentiation, and realistic direction is formed, serving as the core data support for subsequent resource matching and recommendation.

[0027] In the process of dynamically collecting and verifying the credibility of the relevant standard documents for the construction of trauma centers, in order to ensure that the collected content is legal, stable in version, and has structural consistency and calculability, the following steps can be taken to realize the credibility verification mechanism based on structural signature: Extract the structure and segment the semantics of each newly acquired standard document content, divide the document into multiple logical levels, including chapter title, clause content, table information, and appendix description. Extract the key field summary with decision value through natural language processing method, such as "must set department name", "minimum equipment configuration standard", "review deduction item" and other content. The text content of each paragraph or field and its hierarchical path constitute a structural fragment, and the hash digest algorithm is used to encode these structural fragments to generate structural signatures. Each standard document will correspond to one or more structural signature sets, while recording its acquisition timestamp and source website, for example, the document signature obtained from "www.nhc.gov.cn" can be marked as "national master source" category.

[0028] Establish a historical standard document signature comparison data set, and store the structural signatures of standard documents that have been collected and approved in the past in the comparison index library. Each signature entry should include: document name, release time, standard category, extracted structural signature content and its frequency record. For example, "2021 version of national trauma center setting standard" contains a total of one hundred and fifty signature fragments, and the frequency record shows that "independent emergency operation room configuration requirements" frequently appear in subsequent versions. By constructing a signature comparison network between multiple versions of standards, it can be analyzed whether a structural signature is consistent in historical versions or frequently modified, and then the stability and evolution trend of the signature can be evaluated.

[0029] For the current newly collected standard documents, their structural signatures will be compared one by one with the historical signature index library, and the coincidence degree and change frequency will be counted. If a signature fragment is largely inconsistent with the historical signature, and the source domain name of the document is not controlled by the national institution (such as "health-consulting.cn" or "doc-share.org" commercial platform), the structural signature will be marked as "credibility to be reviewed"; if more than forty percent of the signature fragments in the document are marked as suspicious, or the source has published more than five content change documents in the past three months, the entire document will be classified as a potentially low-credible document.

[0030] The credibility is judged by combining the signature difference analysis results and the source authority level score. When a standard document has both the characteristics of high structural signature fluctuation and non-official domain name, it will be judged as low credibility standard. The low credibility standard will not be allowed to participate in the subsequent template weight update and feature fusion, and will not be written into the standard template library. At the same time, the source website will be added to the monitoring list, and the review mechanism will be automatically triggered when the document is published again in the future. For example, a source domain name "www.med-policy123.cn" pushed a standard draft containing serious structural signature drift for three times, which was automatically identified and blocked from entering the subsequent processing flow. Through the above processing flow, the credibility of the structural signature level is verified, ensuring that the standard documents entering the template construction and weight learning process have authority, stability and consistency, effectively preventing the interference of informal documents, draft versions or tampered content on the accuracy of scheme generation.

[0031] To realize the dynamic optimization of the weight of each construction index 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 the weight update is completed through the following steps. For the newly collected trauma center construction standard documents, extract the construction index items therein, and perform semantic matching and classification archiving for each new index and the existing index in the current template library. The semantic matching process is based on the natural language similarity model and the keyword synonym mapping rules to ensure that index items in different expressions can be identified as the same index group. Then, according to the dimension consistency principle, similar indexes are classified into the same construction dimension, and a mapping relationship table is established. On this basis, the structural coverage rate is calculated, that is, the number of successfully mapped indexes from all new indexes to the current template is calculated, and the ratio operation is performed with the total number of new indexes. For example, from a newly added standard document, 120 index items are extracted, of which 90 can be mapped to the existing items in the template library, so the structural coverage rate is 0.75. This coverage rate reflects the compatibility of the existing template system to the new standard content, providing a reference benchmark for subsequent weight adjustment.

[0032] An importance change scoring model for new indicators is constructed to measure the value evolution trend of each indicator in the replacement of old and new standards. The model includes two key influencing factors: one is the frequency of the indicator appearing in the historical standard version, reflecting its persistence and policy recognition degree, for example, "trauma rescue green channel setting" has appeared in the past five versions, which can be given a high basic importance value; the second is whether the context of the indicator in the latest version of the standard contains strong directive words such as "must", "should be set", "as the core construction content" and the like. Such expressions will trigger importance promotion marks. After normalization processing, the two factors are calculated according to the set weight ratio to obtain the comprehensive importance score of each new indicator, which is used to guide whether it is included in the weight optimization process and its adjustment range.

[0033] According to the semantic mapping relationship and importance score result, a weighted update function is constructed to fine-tune the weight of the corresponding indicator in the existing template library. For the existing indicators that form a high-intensity semantic overlap relationship with the new indicators and have a score higher than the system threshold, the original weight will be increased by "importance score multiplied by structure coverage". For example, the original weight of an old indicator is zero point six, the importance score of the matching new indicator is zero point eight, and the coverage is zero point nine. Then the final new weight is zero point seventy-two, and the updated weight is one point eighty-two. In addition, if the new indicator has no mappable object in the existing system, but its score is significantly higher than the average level, the indicator will be introduced into the template system as a new item, and an initial weight value is assigned. Usually, the initial weight value is determined by multiplying the score by a preset coefficient.

[0034] When dealing with "legacy indicators" that no longer appear in the new standard, if an indicator does not appear in three consecutive standard versions, it is determined that its importance is gradually declining, and the "decay mechanism" should be started. The decay mechanism is set to linearly decrease by a fixed proportion each time, and the weight value is decreased by a fixed proportion each time until it is reduced to a preset minimum reserved value, for example, set to decrease by zero point one each time, and the minimum reserved value is zero point fifteen. This way ensures historical compatibility and avoids invalid indicators occupying matching space for a long time, ensuring that the template structure is consistent with policy requirements. Through the linkage implementation of the above steps, dynamic, gradual and structural optimization of indicator weights can be realized, so that the template library can adapt to the policy evolution trend for a long time and maintain a high coupling with the real construction demand, providing a more accurate data basis for intelligent matching.

[0035] To achieve dynamic adjustment and reasonable evolution of the standard template weight structure, a class of weighted update functions based on importance score and structure coverage can be designed. This function not only supports the weight enhancement of existing indicators, but also supports the intelligent injection of new high-value indicators. Specifically, the weight update process can be implemented in the following four steps: According to the semantic mapping results and structure coverage calculation results, for each existing indicator in the template, it is determined whether it is semantically coincident with any indicator in the newly added standard file. The basis for judging semantic coincidence is that the text similarity score reaches the system set threshold (such as 0.85), and the construction dimension belongs to the same dimension. For example, "high energy damage screening" and "high speed injury mechanism evaluation" appear in different standards, but are identified as the same clinical evaluation element by the model, and belong to the "emergency treatment capacity" dimension. After identifying the semantically coincident items, it is further determined whether the importance score of the newly added indicator is higher than the set threshold (such as 0.6). If the above conditions are met, it is considered to have the qualification to adjust the weight of the existing indicator.

[0036] According to the weighted update function, the incremental weight value is calculated, which is based on the following mathematical principle: for each semantically coincident indicator that meets the conditions, the weight increment that should be added is the product of the importance score of the indicator and its structure coverage. For example, if the importance score of an indicator is 0.75 and the structure coverage is 0.8, the weight increment that should be added to the indicator is 0.6. If the original weight is 0.95, the adjusted weight of the indicator becomes 1.45. To prevent some high-frequency indicators from having unlimited weight expansion, the system can set a maximum weight growth limit (such as not more than 0.5) for a single time, and set a ceiling threshold for the maximum weight value (such as not more than 2.0), so as to maintain the stability and controllability of the weight system.

[0037] For new indicators that have no semantic mapping items in the current template system, it is further determined whether they have introduction value. The judgment standard for introduction value is whether the importance score of the indicator is higher than the introduction threshold of the newly added indicator (such as 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 the initial weight is set according to the importance score multiplied by the system set initial weight coefficient. For example, a "regional trauma bed prediction ratio model" is a newly introduced indicator with a score of 0.85, and if the initial coefficient is set to 1.45, its initial weight is set to 1.275.

[0038] It should be noted that during actual execution, consistency checks and normalization processing should also be performed on all index sets that perform update operations. Considering that some templates were built during the old standard period, the overall weight distribution may shift due to the concurrent injection of multiple new indicators, so the normalization ratio of the overall weight distribution of the template after each weight update needs to be recalculated to ensure that the sum of the weight values remains consistent with the original template structure, avoiding affecting the accuracy of subsequent similarity calculations. At the same time, for all new or adjusted indicators, record their change path, source file, and timestamp of change to support subsequent version backtracking and dynamic iteration control. Through the orderly execution of the above steps, the standard template library can quickly respond to policy updates and industry evolution while preserving historical effective structures, enabling intelligent adjustment of template indicator weights and providing a more accurate knowledge base for subsequent resource matching.

[0039] To achieve efficient docking between target hospital resource information and standard templates, the original resource information needs to be structured and vectorized, and the dimension weight is adjusted through the entropy weight mechanism to ensure the accuracy and robustness of the matching degree scoring process. Specifically, the resource data provided by the target hospital is structured and a standardized field tag system is constructed. The input information may come from various data sources, including table upload, form filling, and free text description. To unify the format, first parse various information into the basic structure fields of hospital resources, such as "number of emergency department beds", "number of advanced imaging equipment", "proportion of critical care qualified personnel", etc. For fields in free text form, use semantic parsing rules to classify them into the closest structured tags. For example, "the hospital has the only 24-hour intervention platform in the region" can be parsed as "whether the interventional platform has all-day interventional capability" under the "interventional platform setting" field, with a value of "yes". Finally, a set of resource tags with consistent field structure is formed.

[0040] The structured tag set is mapped to a resource feature vector, and a lightweight encoding method based on word weight sparse mapping is applied to realize low redundancy and high adaptation of vector representation. The encoding method uses a sparse mapping strategy, i.e., only labels with significant matching influence are given positive weights, while secondary labels are not assigned or set to zero, avoiding dilution of key features in high-dimensional vector space. For example, for the "number of trauma operating rooms" field, if the hospital has two independent operating rooms, the dimension is assigned a weight of two, and the remaining related dimensions are kept at zero. During encoding, a dimension balance adjustment factor is introduced to set the weight adjustment benchmark for each dimension. The calculation method is the ratio between the information entropy value of the dimension and its proportion in the overall vector, which is used to measure the expression saturation and structural importance of the dimension.

[0041] Based on the obtained adjustment factor, the system statistics the frequency of each resource dimension appearing in the hospital input and its influence on the final matching result, judges whether the dimension has the risk of "weight expansion" by combining its code length and information entropy value. If it is found that a dimension such as "medical equipment model code" has a code length significantly higher than other dimensions, and its entropy value is lower than the set threshold (for example, lower than 0.2), it means that the dimension has complex values but limited information contribution. At this time, the adjustment factor of the dimension is set to a suppression coefficient less than one (for example, 0.6), which is used to reduce the influence weight of the dimension on the matching score in the final calculation. On the contrary, for high-entropy, high-influence dimensions such as the "personnel qualification level" field, the adjustment factor can be set to an amplification coefficient slightly higher than one (for example, 1.2) to enhance its participation intensity in the matching.

[0042] The resource feature vector after adjustment is calculated with the cosine similarity of the attribute feature vector corresponding to each template in the standard template, to obtain the preliminary matching score, and to provide basic data support for subsequent introduction of context sensitivity adjustment factor to correct the score result. This method ensures that the relative weight of each resource dimension in the matching calculation reflects its influence on the adaptability of the trauma center construction plan more truly, avoids interference with the matching process due to individual data item expression redundancy or scarcity, and significantly improves the accuracy and context adaptability of the candidate scheme recommendation. The resource feature vector and the attribute feature vector of each template in the standard template library are calculated one by one with the cosine similarity, and the context sensitivity adjustment factor is introduced to modify the matching degree. The context sensitivity adjustment factor is obtained based on a three-state mutual control type robust weighting model constructed by the template credibility index, the resource dimension balance index and the regional adaptability index. The final output is the adjusted matching score, which is used as the basis for screening candidate templates.

[0043] In the present application, the resource feature information of the target hospital input needs to be converted into a calculable feature vector through a lightweight coding method to realize matching with the attribute feature vector in the standard template library. The lightweight coding method refers to a processing method that converts complex, redundant and diversified input data into a low-dimensional, sparse and discriminative vector structure through semantic normalization, feature compression and weight adjustment when digitizing hospital resource information. Its core features are: on the one hand, maintaining the integrity of key information to avoid semantic loss due to data compression; on the other hand, reducing data dimension and storage redundancy, and reducing computational overhead, so that resource features can participate in large-scale matching calculation at a lower cost. The application of lightweight coding method ensures that the hospital input information can be quickly converted into standardized and comparable data expression without relying on complex interfaces. The core idea is to map complex structured and unstructured resource information into a sparse and discriminative vector structure through semantic compression and weight distribution.

[0044] At the information processing level, the lightweight coding method maps different sources and formats of input to the preset fields through semantic analysis and classification. For example, "equipped with spiral CT" and "owning two CT scanning devices" are identified as the same type of indicators and mapped to the "radiological equipment" dimension. In this process, key feature word extraction and synonym merging methods are used to ensure data consistency between different hospitals.

[0045] At the feature compression level, the lightweight coding method reduces the data dimension through sparse mapping and weight reduction. For example, a hospital input may contain dozens of different brands and models of monitors, but only needs to be represented as "monitor with" and "quantity level" two sub-dimensions in the matching process. This not only preserves the discriminability of the indicators, but also avoids dimension explosion caused by invalid information, improving the computing efficiency.

[0046] At the weight adjustment level, the lightweight coding method introduces a dimension balance adjustment 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 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.

[0047] After lightweight coding, the resource information is converted into a multi-dimensional vector form. To realize the matching between the resource vector and the template vector, the present application adopts a feature vector mapping algorithm. The feature vector mapping algorithm is a calculation method that maps hospital resource information and standard template construction indicators to a unified feature space and measures the similarity between the two through mathematical calculation methods. In this algorithm, the resource data of the hospital and the attribute data of the template are both converted into multi-dimensional feature vectors, and each dimension of the vector corresponds to a specific construction element. By calculating the cosine value between the resource vector and the template vector, the matching degree of the two in the overall feature space can be quantitatively reflected. The closer the numerical value is to one, the more consistent the hospital resources and the template are in structure and attributes. This algorithm not only enables fast association between resources and templates, but also can be combined with a dynamic weight adjustment mechanism to generate a matching result that balances accuracy and applicability.

[0048] In the execution process, first, the two vectors are normalized to ensure that features of different dimensions and different value ranges can be compared in the same space. Secondly, the dot product of the two vectors is calculated, which represents the degree of coincidence of the two groups of features in the multi-dimensional space. Finally, the dot product result is divided by the product of the two vector lengths to get a similarity value ranging from zero to one. The closer the value is to one, the closer the target hospital resources are to a certain standard template in the feature space, that is, the higher the matching degree. In the present application, the feature vector mapping not only depends on the basic cosine similarity, but also is modified by combining the context sensitivity adjustment factor. That is, in the matching process, if the template credibility is low, the resource dimension is unbalanced, or the regional adaptability is insufficient, even if the cosine similarity score is high, it will be negatively corrected to avoid the error result of "high similarity but unusable". This mechanism significantly improves the robustness and real-time adaptability of the matching process. The lightweight encoding method solves the problem of heterogeneous and redundant input resource information, and the feature vector mapping algorithm provides a mathematical basis for intelligent matching between resources and templates. The combination of the two constitutes the key support point of the present application in technical implementation.

[0049] To ensure that the dependent template content has high credibility, the source, stability and update history of the template content need to be systematically evaluated to generate a quantifiable template credibility index. The credibility index is not only used to filter low-quality template sources, but also serves as a key component of the context sensitivity adjustment factor, which directly affects the matching score. Specifically, the calculation of the template credibility index can be divided into the following steps: extracting the basic source information of the construction standard file associated with each standard template, including the website domain name of the original publishing platform, the publishing timestamp, the version number, the publishing unit and the identity authentication information of the responsible person. Taking the "National Regional Medical Center Construction Standard (2021 Edition)" published by the "National Health Commission" on its official website as an example, the domain name is "www.nhc.gov.cn", which belongs to the national control agency and can be preset as level one. When the template is established, the corresponding original standard file will be automatically labeled with source information, and the acquisition time, structure summary and key content summary will be recorded simultaneously.

[0050] A source credibility level evaluation table is established for each source domain name. The credibility level is set according to multi-dimensional standards, including the administrative attribution of the domain name, whether it is a platform directly under the state organ, whether it has HTTPS encryption, whether there is a history of content tampering, and whether there is a 404 invalid frequency. Each dimension is set to a score of 10, and the total score ranges from 0 to 50. For example, "www.nhc.gov.cn" as a central direct authority domain name has five full scores, which can be given a full score of 50 and classified as a first-level credible source. While a certain academic forum may only get 22 points due to lack of encryption and official identification, and be classified as a third-level credible source.

[0051] The stability of each template corresponding to the standard file in the past two years is counted. The counting method is as follows: for the version snapshots of the same standard file within the past 730 days, if the hash digest remains consistent, it is considered as "unchanged version"; if the digest has a slight difference but the release time is marked as "draft for comments" or "internal draft", it is recorded as "informal change" and is not counted in the number of changes. Finally, the "number of consecutive unchanged versions" is used as the stability index for quantification. For example, if a template associated standard file remains unchanged in content within the past 24 months, the stability is recorded as twenty-four times of unchanged. The source credibility level score (full score of fifty) and the version stability index (such as 24 times) are standardized and weighted average according to the set proportion. For example, the credibility level is converted into a proportion score of 0 to 1, i.e. 50 points is converted into 1.0, and 24 times of unchanged version is standardized to 0.8 according to the set upper limit of 30 times. The set weight distribution proportion is six to four, i.e. the source level weight accounts for sixty percent and the version stability accounts for forty percent. Therefore, the final template credibility index is calculated as follows: (1.0 x 0.6) + (0.8 x 0.4) = 0.92. This value will be involved in the subsequent context sensitivity correction judgment.

[0052] In order to avoid the problem of imbalance of the overall matching result caused by the bias of resource data in a certain dimension, a resource dimension balance index needs to be introduced in the vectorization modeling process. This index is used to measure the uniformity and completeness of the distribution of the resource information submitted by the hospital in different dimensions, to ensure that the influence of each dimension on the matching degree score remains relatively balanced in the similarity calculation stage. The construction of this index can be realized through the following steps: the original resource data input by the target hospital is divided into dimensions according to the pre-set resource system, forming clear dimension boundaries, and the entries in each dimension are subjected to semantic normalization processing. For example, the pre-set resource dimensions can include "bed resource dimension", "personnel qualification dimension", "equipment configuration dimension", "emergency access dimension", etc., which cover a number of specific fields. When there are expressions such as "5 senior anesthesiologists" and "5 senior anesthesiologists" in the text content submitted by the hospital, the system recognizes their synonymity through natural language processing rules and unifies them into the "number of senior anesthesiologists" field, avoiding repeated information counting, and ensuring the consistency and statistical nature of the dimension expression.

[0053] The normalized resource data is counted to calculate the information density of each dimension. The information density is defined as the ratio between the actual number of filled fields in a dimension and the theoretical maximum number of indicators in that dimension. For example, the "device configuration dimension" contains 15 preset indicator items, such as the number of CT devices, whether the MRI device is equipped, whether the mobile X-ray is supported, the number of trauma operating tables, etc. If the hospital fills in 9 items in this dimension, the information density is 9 divided by 15, i.e. 0.6. This value reflects the information saturation degree of the dimension and can be used as an important reference factor for evaluating completeness.

[0054] Based on the information density calculation, further semantic compression processing is performed on the resource items in each dimension to merge similar items and remove redundant content, and the retention ratio before and after compression is obtained. Taking the "personnel qualification dimension" as an example, the hospital may fill in "anesthetist", "anesthesia professional technical personnel" and "anesthesia physician", which are similar in semantics but different in format. The system merges them into a unified item "anesthesia professional personnel" through semantic clustering algorithm. The original number of items before compression is three, and the number of items retained after compression is one, so the compression retention rate is one-third. The compression retention rate reflects the uniqueness of information and the actual expression amount after deduplication, which is a quantitative description of diversity and expression redundancy.

[0055] The information density and compression retention rate are multiplied to obtain the balance score of each dimension. The higher the balance score, the higher the quality of the dimension in terms of information completeness and expression diversity. For example, if the information density of the "device configuration dimension" is 0.6 and the compression retention rate is 0.75, the balance score is 0.45. After collecting the balance scores of all dimensions, the standard deviation is calculated. If the scores of different dimensions fluctuate greatly, it means that the resource distribution is uneven, and the variance is high. The system normalizes the variance value, and the result of subtracting one from the normalized value is used as the final resource dimension balance index. The range of this index is zero to one, and the closer the value is to one, the more evenly the hospital's resource data is distributed in each dimension, and the more representative it is in the matching process. In practical applications, if the "bed dimension" and "device dimension" scores in the hospital data are extremely high, while the "personnel qualification dimension" is severely lacking, the balance index will be significantly reduced, triggering the weight adjustment mechanism for the hospital data in the matching correction stage, and improving the overall matching robustness and fairness.

[0056] To achieve the recommended construction template in the geographical environment of the applicability of precise matching, it is necessary to calculate the regional suitability based on the geographical features and regional conditions of the hospital. The confidence degree is used to measure the closeness between the geographical application scenario set by the template and the actual regional conditions of the target hospital, so as to avoid the error recommendation of the template designed for densely populated cities to remote medical institutions. The calculation of regional suitability can be implemented by the following steps: collecting the regional basic data of the target hospital, including but not limited to three core indicators: the number of permanent population, traffic accessibility index and the number of surrounding medical resources. The number of permanent population adopts the latest public data of the National Bureau of Statistics, the traffic accessibility index can be obtained by measuring the average traffic time of the reachable emergency center within a certain radius of the hospital; the number of surrounding medical resources is the total number of hospitals at the level of two and above within a radius of thirty kilometers. Taking a central hospital in a certain prefecture-level city as an example, the permanent population in its urban area is seven hundred and fifty thousand, the average traffic time is fifteen minutes, and the number of hospitals within thirty kilometers is eighteen.

[0057] According to the "recommended applicable regional parameters" set in the standard template, the set regional requirement benchmark value is extracted. For example, a template sets the standard applicable scenario as "permanent population more than five hundred thousand, traffic accessibility index above medium, and the number of surrounding medical institutions more than ten". The system expresses the regional vector of this template as (five hundred thousand, standard value sixty points, ten). Compare the actual parameters of the hospital (seven hundred and fifty thousand, actual seventy-eight points, eighteen) with the template parameters one by one, and calculate the absolute difference value between the three indicators. For example: the population difference is two hundred and fifty thousand, the traffic accessibility difference is eighteen points, and the medical resource difference is eight.

[0058] To prevent the imbalance of the influence between indicators, a weighting coefficient is introduced for weighted difference calculation. The weighting standard of each indicator is set based on its sensitivity to the applicability of the template. For example: the permanent population, as the basis for the service capacity of the trauma center, is weighted by four percent; the traffic accessibility has a significant impact on the speed of emergency patient transfer, and is set to three percent; the medical resources are set to three percent as supporting ability. With this weight distribution, the three difference values are weighted and calculated: the population difference value two hundred and fifty thousand is converted to thirty points in percentage, multiplied by zero point four to twelve points; the traffic difference eighteen points multiplied by zero point three is five point four; the resource difference eight is converted to twenty points in percentage, multiplied by zero point three to six points, and the weighted sum of the three is twenty-three point four.

[0059] The obtained weighted adaptation distance value is standardized into a percentage adaptation confidence score. Based on the set adaptation tolerance interval, the system linearly normalizes the weighted distance score, and the result of subtracting the normalized score from one is taken as the final regional adaptation confidence. For example, the weighted adaptation distance score of the above-mentioned hospital is 23.4, in a normalization model with a maximum distance score of 50 points, the normalized value is 0.468, and the final regional adaptation confidence is one minus 0.468, i.e. 0.532, which is 53.2% in percentage. The regional adaptation confidence not only reflects whether the geographical attribute of the hospital meets the template setting environment, but also avoids misjudgment in remote areas or super large cities due to extreme parameters through difference logic and weight constraints, provides important context correction input for the three-state mutual control robust weighting system, and improves the explainability and regional adaptability of the recommended results.

[0060] To effectively improve the real adaptability and technical robustness of the matching recommendation results, a context-aware dynamic weighting mechanism needs to be introduced based on the basic similarity calculation, that is, the standard matching process is adjusted through a three-state mutual control robust weighting model. Based on the calculation results of three core confidence factors, the context sensitivity adjustment factor is formed, which plays a negative correction role in the matching score calculation between resources and templates. Specifically, the execution of the model can include the following steps: calculate the template credibility index, regional adaptation confidence and resource dimension balance index related to each candidate template respectively. The template credibility index is obtained based on the source credibility level and historical stability of the standard file corresponding to the template; the regional adaptation confidence is obtained by comparing the difference between the target hospital geographical location, permanent population, traffic accessibility and surrounding medical resources three indicators and the regional application parameters set in the template; the resource dimension balance index is obtained by weighting and integrating the dimension entropy value, compression rate and variance analysis results of the hospital input resources. For example, if the resources of a hospital are extremely concentrated in the "bed" dimension but sparse in the "personnel qualification" dimension, the balance index will be lowered. The three score results are standardized to between zero and one, which are the basis for subsequent weighting calculation. The three values are weighted and averaged to form the context sensitivity adjustment factor. The weighting strategy can set priority weights according to actual application scenarios, for example, in resource-rich but geographically limited areas, the weighting proportion of regional adaptation confidence can be increased. The default weight configuration is set as follows: template credibility accounts for 40%, regional adaptation confidence accounts for 30%, and resource dimension balance index accounts for 30%. In a certain actual case, if the three indicators are 0.8, 0.65 and 0.55 respectively, the weighted average value is 0.685, which is the context sensitivity adjustment factor used to dynamically adjust the basic matching degree score.

[0061] The cosine similarity between the resource feature vector and the template attribute feature vector is calculated to obtain a preliminary matching degree score. The cosine similarity calculation method is the dot product of the vectors divided by the product of the vector lengths, and the obtained value is between zero and one, indicating the similarity of the angle between the two vectors. For example, for the resource feature vector and the template feature vector of a certain hospital, the cosine similarity is calculated to be 0.93. This value represents a high degree of similarity in the pure feature space, but there may be matching biases due to regional, data structure, or uneven information distribution context factors, so the aforementioned context sensitivity adjustment factor needs to be introduced to modify the score.

[0062] The preliminary cosine similarity score is modified according to the relationship between the value of the context sensitivity adjustment factor and the set threshold. When the adjustment factor is higher than the set threshold (such as 0.7), the matching degree score remains unchanged or is appropriately amplified (multiplied by a small coefficient greater than one); when the adjustment factor is lower than the threshold, it is considered that the template has context risks, and at this time, negative correction is performed. The specific method is to multiply the preliminary cosine similarity by an adjustment coefficient less than one, which is calculated from the normalized difference of the context sensitivity adjustment factor. For example, when 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, and the negative attenuation value is generated after multiplying the adjustment coefficient factor (such as 0.5). The original matching degree score is adjusted to 0.93 multiplied by 0.975, resulting in 0.9075, which is used as the final score basis for template screening and recommendation.

[0063] After the calculation of the matching degree score is completed, the score results of all templates participating in the matching need to be screened and sorted to select one or more construction schemes that best match the resource structure of the target hospital, and finally output as a feasibility candidate suggestion. A preset threshold of the matching degree score is set, and all calculated matching degree scores are initially screened. The matching degree score is the final score after the comprehensive action of the cosine similarity and the context sensitivity adjustment factor, and usually takes a value between zero and one. To ensure that the recommended scheme has high adaptability and practical value, the system defaults the matching threshold to 0.8, i.e. only when the final matching degree score of a template is equal to or higher than 0.8, it can enter the candidate screening pool. For example, the matching degree score of a template is 0.83, and that of another template is 0.75, only the former meets the threshold requirement. The templates in the candidate pool are sorted according to the matching degree score from high to low in descending order.

[0064] Preferably, at least one trauma center construction candidate scheme most matched with the target hospital resource structure is output according to the preset strategy, for example, to avoid determining the output priority only by the numerical value, while introducing the hospital attention priority dimension weight for reordering. For example, if a target hospital currently focuses on “trauma treatment path coverage completeness”, and the weight of this dimension in a certain candidate template is high, then even if the overall score is slightly low, it can be moved forward in the ordering. In practice, the system can set “configurable attention dimensions” such as bed structure, manpower configuration, and information integration degree, and record the response degree of each template in each attention dimension during the matching process. After completing the ordering, a structure difference re-evaluation mechanism is performed to ensure that the recommended template not only has a high score, but also has structural adaptability. This step is based on the resource structure sparse matrix of the target hospital and the construction index matrix of each candidate template to perform difference analysis and calculate the structure coverage difference, that is, the proportion of the number of non-zero difference indicators between the two in all dimensions. For example, if the target hospital has only three beds in the “intensive care bed” dimension, while a template requires at least ten beds, then the difference value of this dimension is negative seven, and if the proportion of such difference items exceeds thirty percent, then the template is determined to be structurally incompatible and is excluded even if it has a high score.

[0065] Finally, among the remaining templates that meet the structural adaptability requirements, at least one template with the highest matching degree and the most suitable structure is output as the final recommended scheme according to the score ordering and hospital attention weight. If the matching degree scores are highly close (such as a difference less than zero point zero five), then two or three candidate schemes can be output side by side for further review and selection by artificial or experts. In an actual application, after a western third-class hospital submits resource information, the system calculates and filters out five templates with scores higher than zero point eight, two of which perform outstandingly in the “regional medical network linkage ability” dimension and have scores higher than zero point eight five, so the system recommends these two templates side by side for evaluation, with recommended reasons and expected configuration risk prompts to improve the feasibility and safety of the scheme landing.

[0066] The above algorithms or formulas are dimensionless and their numerical values are calculated, and the results closest to the real situation are obtained by software simulation of a large amount of data, and the preset parameters are set by a person skilled in the art according to the actual situation.

[0067] It should be understood that the size of the sequence number of each process in various embodiments of the present application does not mean the order of execution, and 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 the present application.

[0068] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on 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 the present application.

[0069] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0070] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for intelligent matching of trauma center construction plan resources, characterized in that, The method comprises the following steps: Based on the relevant standard documents of trauma center construction, a standard template library is established, and structured construction indicators are extracted to generate initial templates, which are stored in the form of multi-dimensional characteristics and attribute coding; Obtain the latest standard information from multiple information sources related to trauma center construction, compare the structure signature for credibility verification, adjust the weight of each template indicator in the standard template library using incremental learning algorithm, and dynamically update the standard template library; Obtain the resource characteristic information input by the target hospital, and convert it into a resource characteristic vector through a lightweight coding method. Use the feature vector mapping algorithm to calculate the cosine similarity between the resource characteristic vector and the attribute characteristic vector of each template in the standard template library, and obtain the matching degree score between the resource and the template. Generate three factors: template credibility index, resource dimension balance index, and regional suitability confidence, and build a three-state mutual control robust weighting model to dynamically adjust the weight of each matching dimension. The template credibility index determines the legality of the template source according to the multi-source cross signature and the variable frequency hash chain; The resource dimension balance index evaluates the dimension weight rationality based on the semantic compression rate and information entropy distribution of the resource characteristics; the regional suitability confidence evaluates the applicability of the recommended template based on the regional medical resource quantity, traffic accessibility, and population density; the three factors jointly control the matching weight correction function and dynamically intervene in the matching degree score calculation process. Select the templates with matching degree scores exceeding the preset matching threshold, and output at least one trauma center construction candidate scheme that best matches the resource structure of the target hospital.

2. The method for intelligent matching of trauma center construction plan resources according to claim 1, characterized in that, The step of establishing a standard template library based on trauma center construction-related standard documents includes: Obtain trauma center construction standard documents from multiple channels, extract multiple preset construction elements, and perform structured processing. Classify each construction element according to its dimension, establish a multi-dimensional characteristic system, define the attribute type, numerical range, and priority of each dimension, digitize the classified construction elements using attribute vector coding, and assign initial weights based on historical review cases. Finally, a complete standard template library is generated.

3. The method for intelligent matching of trauma center construction plan resources according to claim 1, characterized in that, The step of comparing the structure signature for credibility verification includes: Extract the structure level and key field summary of the standard content to generate a hash signature, and mark the acquisition timestamp and source domain name; Call the historical standard signature database to compare the same type of standard signatures, and calculate the signature repetition rate, update frequency, and domain name authority level; 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-confidence standard and the source is prohibited from participating in the template library update.

4. The method for intelligent matching of trauma center construction plan resources according to claim 1, characterized in that, The step of adjusting the weight of each template indicator in the standard template library using the incremental learning algorithm includes the following steps: The construction indicators in the newly added standard file are compared and classified with the existing indicators in the standard template library, and the mapping relationship is established according to the similarity of word meaning and whether the dimensions belong to the same dimension. The determination method of the coincidence degree is: for each new indicator, the number of items that can be mapped to the existing indicators is counted, and the ratio is calculated with the total number of new indicators. The obtained ratio is the structural coverage of the new indicators relative to the existing system; An importance change scoring model is constructed, including two key influence factors. One is the frequency of the new indicator appearing in the historical standard version, which is the basic importance factor. The second is whether there is a language label with a strong weight prompt word for the indicator in the same dimension in the adjacent two standard updates. If there is, the importance of the indicator is improved. 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 weight of the indicators in the existing template library. For old indicators that no longer appear in the new standard, if they are not referenced in the last three updates, their weight is gradually reduced according to the preset equal gradient reduction value until it is reduced to the preset minimum reserved value.

5. The method for intelligent matching of trauma center building program resources according to claim 4, wherein, The weighted update function is executed based on the following principles: When a certain existing indicator forms semantic coincidence with a new indicator, and the importance score of the new indicator is higher than the set threshold, the weight of the existing indicator 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 indicator has no mapping item in the existing system, and the importance score is still higher than the preset importance threshold, the new indicator 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.

6. The method for intelligent matching of trauma center building program resources according to claim 5, wherein, The steps of converting the resource characteristic information input by the target hospital into a resource characteristic vector and performing a matching degree score include: 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. A lightweight coding method based on word weight sparse mapping is used to convert the structure label into a resource characteristic 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. The resource characteristic vector and the attribute characteristic vector of each template in the standard template library are calculated by cosine similarity one by one, and the context sensitivity adjustment factor is introduced to modify the matching degree. The context sensitivity adjustment factor is obtained based on a three-state mutual control type robust weighting model constructed by the template credibility index, the resource dimension balance index and the regional suitability credibility. The final output is the adjusted matching degree score, which is used as the basis for candidate template screening.

7. The method for intelligent matching of trauma center building program resources according to claim 6, wherein, The calculation of the template credibility index includes: The source of the standard file associated with each template is extracted, and the source credibility level is set based on the source domain name. At the same time, the number of unchanged standard versions in a given period is counted. The source credibility level and the number of unchanged standard versions are standardized, and the weighted average is obtained by weighting the set proportion to obtain the template credibility index.

8. The method for intelligent matching of trauma center building program resources according to claim 7, wherein, The calculation of the resource dimension balance index includes: 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 overall resource information proportion are counted. The information density and compression difference between different dimensions are compared to determine 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. 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. For each resource dimension, the balance score is calculated, which is the product of the information density and the compression retention rate of the dimension. It is used to comprehensively evaluate the completeness and diversity of the dimension. After collecting the balance scores of all dimensions, the variance value of all dimensions is calculated. Finally, the score obtained by subtracting the variance normalization value is taken as the resource dimension balance index.

9. The method for intelligent matching of trauma center construction plan resources according to claim 8, characterized in that, The calculation of the regional configuration confidence includes: The number of permanent residents, traffic accessibility indicators and the number of surrounding medical resources of the hospital location are weighted and subtracted from the preset applicable parameters of the template. The weighted absolute difference is used as the adaptive distance measurement standard, and the result is standardized to a percentage score. Finally, the regional configuration confidence is obtained.

10. The method for intelligent matching of trauma center building program resources according to claim 9, wherein, The three-state mutual control robust weighting model refers to: The weighted average of the template credibility index, regional configuration confidence and resource dimension balance index is calculated to obtain the context sensitivity adjustment factor. The resource feature vector and the attribute feature vector of each template are calculated by cosine similarity 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, that is, the cosine similarity value is multiplied by an adjustment coefficient less than one. The adjustment coefficient is determined by the normalized score of the adjustment factor. Finally, the adjusted matching degree score is obtained.

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