A high-rise building top space renewal demand generation method based on sparse relationship compensation and causal double-channel mapping

Abstract

A high-rise building top space renewal demand generation method based on sparse relationship compensation and causal double-channel mapping, the present application is to solve the problems that the existing multi-scene demand framework depends on artificial induction and is difficult to reproduce. The renewal demand generation method comprises the following steps: S1, structuring processing of multi-source data; S2, expanding the demand element influence relationship into a sparse direct influence matrix; S3, establishing a group direct influence matrix; S4, calculating a total influence matrix; S5, calculating the influence degree, the influenced degree, the center degree and the reason degree of each demand element again; S6, establishing a demand element-scene correlation matrix; S7, mapping the priority response weight and the front driving weight to each scene respectively; and S8, generating a structured renewal demand framework. The demand element-scene correlation matrix is used for automatically mapping the production, transportation and storage scenes, the overall causal calculation result is converted into a scenarized renewal demand framework, and the data structuring degree of demand analysis is improved.

Description

Technical Field

[0001] This invention belongs to the field of data processing and computer-aided decision-making technology for building renovation design. Specifically, it relates to a method for converting sparse influence relationship data of renovation demand elements of high-rise building rooftop space into a causal structure, priority response weight, pre-driving weight, and multi-scenario renovation demand framework for low-altitude economic scenarios. Background Technology

[0002] With the development of low-altitude economy-related industries, urban air traffic, drone logistics, emergency transport, and low-altitude service activities, the rooftop spaces of high-rise buildings are gradually transforming from traditional spaces for equipment placement, landscape viewing, or image shaping into potential nodes connecting urban low-altitude networks, building vertical transportation systems, and building internal functions. Existing high-rise building rooftop spaces, while accommodating these activities, need to simultaneously address multiple evolving requirements, including structural load-bearing capacity, take-off and landing platforms, passenger flow, freight distribution, energy supply, communication and sensing, noise and vibration mitigation, spatial flexibility, and integration with existing transportation systems.

[0003] The determination of existing top-level space update requirements typically relies on designers' experience, compilation of standard provisions, or evaluation of individual indicators. Because different update requirements are mutually reinforcing, mutually restrictive, and multi-level transmitted, simply ranking them by source frequency, average expert importance, or simple weighting makes it difficult to distinguish between upstream driving elements and core response elements that collectively undertake multiple functions, and also makes it difficult to explain why a particular update element should be prioritized.

[0004] For a relationship judgment problem involving n demand elements, if each expert is required to complete an n×n direct influence matrix item by item, n(n-1) relationship judgments need to be completed. When the number of demand elements is large, the workload increases significantly, easily leading to missing items, mechanical scoring, or decreased consistency. While a sparse data collection method that only records the K most significant relationships for each element can reduce the amount of data collected, unselected relationships may indicate no direct influence, a weak influence, or indirect influence through other elements. Directly treating all unselected relationships as having no influence would result in the loss of relationship information.

[0005] Furthermore, existing demand prioritization methods typically compress all relationships into a single weight, failing to preserve the system centrality and causal driving force of each element. For the renewal of rooftop spaces in high-rise buildings, elements such as take-off and landing flow lines, take-off and landing platforms, and freight distribution may have higher overall response priorities, while elements such as structural load-bearing capacity, communication sensing, modular layout, and environmental disturbance control, although not necessarily having the highest apparent weight, are prerequisites for implementing other renewal content. If only a single ranking is output, it is easy to miss these low-ranking but highly driving renewal demands.

[0006] Meanwhile, the focus of top-space updates differs for different low-altitude economic tasks. Production activities emphasize R&D testing, operation and maintenance support, and flexible expansion; air traffic emphasizes takeoff and landing, boarding, transfers, and safe demarcation; low-altitude logistics emphasizes temporary storage, sorting, warehousing, refueling, and scheduling. Existing methods often rely on manual interpretation to generate demand frameworks for different scenarios after obtaining the overall ranking. The output process is difficult to reproduce and is not conducive to forming structured update demand data that can be read by computers.

[0007] Therefore, there is a need for a method to generate update requirements for the rooftop space of high-rise buildings that can reduce the amount of influence relationship collection while compensating for the indirect influence in sparse relationships, calculate priority response weights and pre-driving weights respectively, and map the two types of weights to production, transportation and storage scenarios. Summary of the Invention

[0008] The purpose of this invention is to solve the problems in existing methods, such as the heavy burden of filling in the complete influence relationship matrix, the loss of indirect influence information due to the direct setting of sparse relationships to zero, the difficulty in distinguishing core response elements from pre-driving elements by a single weight, and the difficulty in reproducing multi-scenario demand frameworks due to reliance on manual induction. The invention provides a method for generating update requirements for the roof space of high-rise buildings based on sparse relationship compensation and causal dual-channel mapping.

[0009] The present invention provides a method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping, which is implemented according to the following steps:

[0010] S1. Obtain multi-source data on the update requirements of the rooftop space of high-rise buildings, perform structured processing on the multi-source data to form n requirement elements, and set element code, element name, element category and scene association label for each requirement element;

[0011] S2. Based on the demand elements, obtain m records of the influence relationships of the demand elements. For each record, retain the K affected elements with the highest influence intensity or those determined by the recorder to be the most critical, along with their influence intensity. Thus, expand the k-th record of the influence relationships of the demand elements into an n×n sparse direct influence matrix. ;

[0012]

[0013] In the formula, This is the sparse direct influence matrix corresponding to the kth record of the influence relationship of demand factors. The influence strength of the k-th recorder on demand element i directly affecting demand element j; k=1,2,…,m; i,j=1,2,…,n; K is the number of key affected elements retained for each source of influence, K is a positive integer and less than n;

[0014] S3, Set the record weight ωk For m sparse direct influence matrices After weighted aggregation, the direct influence matrix A of the group is obtained;

[0015]

[0016] S4. Calculate the standardization coefficient s of the group direct influence matrix A, and standardize the group direct influence matrix A into a direct influence matrix X. Calculate the total influence matrix T based on the direct influence matrix X to simultaneously characterize the direct influence between demand elements and the multi-order indirect influence transmitted through other demand elements, thereby compensating for the indirect influence of weak direct relationships that were not selected in the sparse matrix.

[0017]

[0018]

[0019]

[0020] In the formula, I is an n-order identity matrix; This indicates the intensity of the combined influence of demand factor i on demand factor j;

[0021] S5. Calculate the influence degree D of each demand element based on the overall influence matrix T. i Influence R i Centrality C i and the degree of cause Q i ;

[0022]

[0023]

[0024] Then based on the centrality C i Forming priority response weight p i According to the degree of cause Q i The positive part forms the pre-driving weight q i This forms a priority output through a causal dual-channel approach;

[0025]

[0026]

[0027] Among them, C r and Q r Let the centrality and causality of the r-th demand element be represented respectively.

[0028] S6. Establish a requirement element-scene association matrix based on the scene association tags in step S1. , where bic This indicates the strength of the association between demand element i and scenario c;

[0029] S7, prioritize response weight p i and the pre-driving weight q i Map each scenario separately and calculate the response weight h within each scenario. ic and scene-driven weights v ic This allows us to identify the core response elements and pre-driving elements for each scenario.

[0030]

[0031]

[0032] In the formula, h ic v represents the response weight of demand element i within scenario c. ic Let r be the driving weight of demand element i in scenario c, and r be the summation index within the scenario. rc The strength of the association between demand element r and scenario c;

[0033] S8. Based on the core response elements and pre-driving elements of the corresponding scenario determined in step S7, a structured update requirement framework is generated. The task and update direction are designed through the structured update requirement framework, thereby completing the method for generating update requirements of the roof space of high-rise buildings based on sparse relationship compensation and causal dual-channel mapping.

[0034] The method for generating update requirements for rooftop space in high-rise buildings based on sparse relation compensation and causal dual-channel mapping, as proposed in this invention, has the following beneficial effects:

[0035] First, the present invention adopts Top-K sparse relation acquisition and n×n matrix expansion method, which reduces the number of element relations that need to be judged for each record from n(n-1) to nK, and can significantly reduce the amount of relation data to be collected when K is much smaller than n.

[0036] Second, this invention calculates both direct and multi-level indirect influences simultaneously through the total influence matrix, enabling relationships that are not included in the Top-K records but can be transmitted through other elements to obtain a non-zero comprehensive influence value, thus avoiding the simple equation of not being selected as having no influence at all.

[0037] Third, the present invention sets up two independent output channels: priority response weight and pre-drive weight. It can identify the core response content of the system, such as take-off and landing platform, boarding flow, and freight organization, as well as the pre-drive conditions, such as structural bearing capacity, communication sensing, modular layout and environmental control.

[0038] Fourth, this invention completes the automatic mapping of production, transportation and storage scenarios through the demand element-scenario association matrix, so that the overall causal calculation results can be converted into a scenario-based update demand framework, reducing the result differences caused by relying on manual secondary induction.

[0039] Fifth, this invention does not rely on machine learning model training, and does not require training samples, labeled data, or model parameter tuning; it can be repeatedly calculated after the given demand elements and influencing relationships are recorded, which facilitates the implementation of the patented solution and the verification of results.

[0040] Sixth, the data output by this invention simultaneously includes element codes, weights, causal attributes, scenarios, and renewal / renovation directions, which can be further called by computer programs, databases, or building information models, thereby improving the data structuring level of the analysis of the renewal needs of the rooftop space of high-rise buildings. Detailed Implementation

[0041] Specific Implementation Method 1: This implementation method, based on sparse relation compensation and causal dual-channel mapping, for generating the renewal requirements of the roof space of high-rise buildings, is carried out according to the following steps:

[0042] S1. Obtain multi-source data on the update requirements of the rooftop space of high-rise buildings, perform structured processing on the multi-source data to form n requirement elements, and set element code, element name, element category and scene association label for each requirement element;

[0043] S2. Based on the demand elements, obtain m records of the influence relationships of the demand elements. For each record, retain the K affected elements with the highest influence intensity or those determined by the recorder to be the most critical, along with their influence intensity. Thus, expand the k-th record of the influence relationships of the demand elements into an n×n sparse direct influence matrix. ;

[0044]

[0045] In the formula, This is the sparse direct influence matrix corresponding to the kth record of the influence relationship of demand factors. The influence strength of the k-th recorder on demand element i directly affecting demand element j; k=1,2,…,m; i,j=1,2,…,n; K is the number of key affected elements retained for each source of influence, K is a positive integer and less than n;

[0046] S3, Set the record weight ω k For m sparse direct influence matrices After weighted aggregation, the direct influence matrix A of the group is obtained;

[0047]

[0048] S4. Calculate the standardization coefficients s of the group direct influence matrix A, and standardize the group direct influence matrix A into a direct influence matrix X. Calculate the total influence matrix T based on the direct influence matrix X to simultaneously characterize the direct influence between demand elements and the multi-order indirect influence transmitted through other demand elements, thereby compensating for the indirect influence of weak direct relationships that were not selected in the sparse matrix.

[0049]

[0050]

[0051]

[0052] In the formula, I is an n-order identity matrix; This indicates the intensity of the combined influence of demand factor i on demand factor j;

[0053] S5. Calculate the influence degree D of each demand element based on the overall influence matrix T. i Influence R i Centrality C i and the degree of cause Q i ;

[0054]

[0055]

[0056] Then, based on the centrality C i Forming priority response weight p i According to the degree of cause Q i The positive part forms the pre-driving weight q i This forms a priority output through a causal dual-channel approach;

[0057]

[0058]

[0059] Among them, C r and Q r Let p represent the centrality and causality of the r-th demand element, respectively; i Used to characterize the overall response priority of demand element i in all interaction relationships; q i Used to characterize the degree to which demand element i acts as an upstream prerequisite of the system;

[0060] S6. Establish a requirement element-scene association matrix based on the scene association tags in step S1. , where b ic This indicates the strength of the association between demand element i and scenario c;

[0061] S7, prioritize response weight p i and the pre-driving weight q i Map each scenario separately and calculate the response weight h within each scenario. ic and scene-driven weights v ic This allows us to identify the core response elements and pre-driving elements for each scenario.

[0062]

[0063]

[0064] In the formula, h ic v represents the response weight of demand element i within scenario c. ic Let r be the driving weight of demand element i within scenario c, and r be the summation index within the scenario. rc The correlation strength between demand element r and scenario c; for scenario c, according to the response weight h within the scenario. ic Select the first L from largest to smallest H The demand element is used as the core response element; in the causal degree Q i Among the requirement elements that are >0 and associated with scenario c, according to the driving weight v within the scenario. ic Select the first L from largest to smallest V Item demand elements serve as preceding driving factors. L H and L V All are positive integers and are not greater than the number of required elements that meet the selection criteria in the corresponding scenario; when the number of required elements that meet the criteria is less than the preset number, all required elements that meet the criteria are selected.

[0065] S8. Based on the core response elements and pre-driving elements of the corresponding scenario determined in step S7, a structured update requirement framework is generated, and tasks and update and renovation directions are generated, thereby completing the method for generating update requirements of the roof space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping.

[0066] This implementation method uses the elements of high-rise building rooftop space renewal needs as calculation nodes. Each node is identified by a unique element code and includes at least a name, description, category, data source, applicable building type, and scenario-related tags. Element categories may include structural safety and resilience protection, spatial flexibility and composite organization, core function support and facility support, service facilities and circulation integration, system connection and intelligent support, and environmental adaptation and sustainable renewal.

[0067] The multi-source data may include academic literature, policies and regulations, technical standards, industry white papers, typical case texts, survey records of existing building rooftop spaces, and expert verification records. Synonymous expressions are merged, overlapping expressions are distinguished, and expressions that correspond to updates in space, facilities, structure, circulation, or operational support are converted into standardized requirement elements.

[0068] In this implementation, scenario association tags are used to establish an association matrix B. Production space scenarios correspond to tasks such as R&D, testing, operation and maintenance, training, demonstration, and operational control; transportation space scenarios correspond to tasks such as takeoff and landing, boarding, waiting, air-to-ground transfer, and evacuation; storage space scenarios correspond to tasks such as goods receiving, temporary storage, sorting, warehousing, refueling, and redistribution. A demand element can be associated with two or more scenarios simultaneously.

[0069] In this implementation method, step S2 records only the K most critical affected factors for each demand element. This can be achieved through a questionnaire interface, spreadsheet, or database form. Each record includes the source code of the influence, the target code, and the strength of the influence. The strength of the influence can be expressed using a 0-5 scale or other discrete scales. When using a 1-5 scale, unselected relationships are recorded as 0, and 1 to 5 represent weak, relatively weak, moderate, relatively strong, and strong direct influence, respectively.

[0070] In this embodiment, the total influence matrix T can recover multi-order relationships that are not recorded in the sparse direct influence matrix.

[0071] Impact D i A relatively large value indicates that element i has a strong overall impact on the system's output; the degree of influence R i A larger value indicates that element i primarily receives the support of other elements; centrality C i Reflects the overall degree of correlation of element i in the demand network; causality Q i A value greater than 0 is considered a causal factor, Q. i If the value is less than 0, it is considered a result element.

[0072] This implementation does not force causality and centrality to be compressed into a single index, but instead generates priority response weights p separately. i and the pre-driving weight q i Therefore, a factor can be listed as a core response factor due to its high centrality, or as a precursor driving factor due to its high positive causation degree; the same factor can also have both roles simultaneously.

[0073] The system selects the required elements for each scenario according to preset rules. An example rule is: by h... ic Selecting the first L in descending order H The item serves as the core response element; in q i Elements with a value greater than 0 are categorized by v. ic Selecting the first L in descending orderV The first item serves as the preceding driving element; the remaining elements related to the scene serve as general supporting elements. Alternatively, an h-value can be used. ic or v ic The selection can be made by means of a preset threshold or a preset weight.

[0074] The update and modification directions in the output records are obtained by mapping the requirement element codes to a preset direction dictionary. For example, E14 corresponds to boarding routes, takeoff and landing operation flow lines, and passenger and freight separation; E19 corresponds to takeoff and landing platforms and safety setbacks; E01 corresponds to structural load-bearing verification and safety redundancy; and E12 corresponds to communication, data, and sensing facilities. This mapping is used to convert the calculation results into update requirement entries that can be read by subsequent design tasks.

[0075] This implementation method is applicable to the analysis of the renewal needs of production space, traffic space and storage space on the roof of existing high-rise public buildings.

[0076] Specific Implementation Method Two: This implementation method differs from Specific Implementation Method One in that the scene association tags mentioned in step S1 include production space scene, traffic space scene, and storage space scene.

[0077] Specific Implementation Method 3: This implementation method differs from Specific Implementation Method 1 or 2 in that K in step S2 is an integer in the range of 4 to 6.

[0078] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One to Three in that step S4 involves a... ij =0 but t ij element pairs >0, t ij This represents the indirect impact formed through the transmission of other demand elements, and therefore the relationship will not be directly judged as having no impact simply because it is not included in the Top-K records.

[0079] Specific Implementation Method Five: This implementation method differs from Specific Implementation Methods One to Four in that in step S5, when all Q... i When all q values ​​are not greater than 0, all q values ​​will be used. i Set to 0.

[0080] Specific Implementation Method Six: This implementation method differs from Specific Implementation Methods One to Five in that, in step S5, when the degree of cause Q... i When the degree of causation Q is greater than 0, the overall output impact of demand element i is greater than the overall absorption impact, and it is defined as a causal element; when the degree of causation Q is greater than 0, it is defined as a causal element. i When Q is less than 0, it is defined as a result element; when Q... i When the value is 0, it is defined as a neutral element.

[0081] Specific Implementation Method Seven: This implementation method differs from Specific Implementation Methods One through Six in that step S6 b... icb represents the strength of the association between demand element i and scenario c. ic Take a value between 0 and 1, where 0 indicates no correlation and 1 indicates a strong correlation.

[0082] This implementation method b ic It is also possible to not use a hierarchy, where 0 indicates no association and 1 indicates a strong association.

[0083] Specific Implementation Method Eight: This implementation method differs from Specific Implementation Methods One through Seven in that, in step S7, for scenario c, the response weight h within the scenario is... ic Select the first L from largest to smallest H The demand element is used as the core response element; in the causal degree Q i Among the requirement elements that are >0 and associated with scenario c, according to the driving weight v within the scenario. ic Select the first L from largest to smallest V Demand factors serve as leading drivers.

[0084] This implementation method L H and L V All values ​​are positive integers and are no greater than the number of required elements that meet the selection criteria in the corresponding scenario. When the number of required elements that meet the criteria is less than the preset number, all required elements that meet the criteria are selected. Alternatively, core response elements and pre-driving elements can be selected by using a weight that is not less than a preset threshold or by using a cumulative weight that reaches a preset ratio.

[0085] Specific Implementation Method Nine: This implementation method differs from Specific Implementation Methods One to Eight in that the structured update requirement framework in step S8 is output in the form of tabular records, JSON records, database records, or building information model attribute tables.

[0086] The structured update requirement framework described in this embodiment can be implemented using spreadsheet software, numerical calculation programs, database programs, or secondary development programs for building information models that have matrix operation and data writing functions.

[0087] Specific Implementation Method 10: This implementation method differs from Specific Implementation Methods 1 to 9 in that in step S8, the preceding driving elements are written into the preceding verification list task, and the core response elements are written into the priority construction list task.

[0088] In this implementation method, the preliminary verification task is used to verify the structural load-bearing capacity, communication sensing, energy supply, environmental control, system integration, or modularization conditions required for the implementation of core response elements; the priority construction task is used to determine the spatial carriers, functional interfaces, facility systems, and organizational flows that should be prioritized for development or improvement. The preliminary verification task, in principle, completes its technical assessment before the corresponding priority construction task, and determines the implementation conditions, priority, and collaborative relationships of the priority construction task based on the verification results; when relevant disciplines can simultaneously carry out design, equipment configuration, or construction coordination, the preliminary verification task and the priority construction task can also be advanced concurrently.

[0089] Example: This example demonstrates a method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping, implemented according to the following steps:

[0090] S1. Obtain multi-source data on the needs for updating the roof space of high-rise buildings. The multi-source data covers academic literature, policies, regulations and technical standards, industry white papers and typical cases. The multi-source data is structured, terms are merged and experts are checked to form 21 standardized demand elements, as shown in Table 1. Each demand element is assigned an element code, element name, element category and scenario association label.

[0091] Table 1. Factors affecting the renewal needs of rooftop spaces in high-rise buildings.

[0092]

[0093] S2. In this embodiment, 117 questionnaires were distributed to experts in relevant fields, and 20 were returned. After completeness verification, 17 valid records of demand element influence relationships (m) were obtained. Each influence relationship record identified the five most critical K influencing elements and assigned a direct influence strength using a 1-5 scale. Direct influence relationships not included in the Top-K set, as well as the direct influence of each demand element on itself, were recorded as 0. Unselected direct influence relationships refer to element pairs not included in the corresponding Top-K set of the influence source, and do not presuppose the absence of indirect influence transmitted through other elements. Traditionally, completing a 21×21 matrix for each questionnaire requires judging 21×20=420 relationships, while the Top-K method in this embodiment only requires judging 21×5=105 relationships, reducing the number of relationships entered by 75%.

[0094] Therefore, the 17 records of demand factor influence relationships (m) are expanded into a 21×21 sparse direct influence matrix. ;

[0095]

[0096] In the formula, This is the sparse direct influence matrix corresponding to the kth record of the influence relationship of demand factors. Let K be the influence strength of the k-th recorder on demand element i directly affecting demand element j; k=1,2,…,m; i,j=1,2,…,n; n is the number of demand elements, m is the number of effective influence relationship records, K is the number of key influenced elements retained for each influence source, and K is a positive integer less than n;

[0097] S3. Set record weights For m sparse direct influence matrices After weighted aggregation, the direct influence matrix A of the group is obtained;

[0098]

[0099] Table 2 shows the 20 relationships with the highest direct influence in the matrix.

[0100] Table 2. Key Relationships in the Group Direct Influence Matrix

[0101]

[0102]

[0103] S4. Calculate the standardization coefficient s of the group direct influence matrix A. In this embodiment, s = 31.235294. Then, standardize the group direct influence matrix A into a direct influence matrix X. Calculate the total influence matrix T based on the direct influence matrix X to simultaneously characterize the direct influence between demand elements and the multi-order indirect influence transmitted through other demand elements, thereby compensating for the indirect influence of weak direct relationships that were not selected in the sparse matrix.

[0104]

[0105]

[0106]

[0107] In the formula, I is an n-order identity matrix; This represents the combined influence strength of demand factor i on demand factor j; taking the influence of E01 on E07 as an example, in the group direct influence matrix, a 1,7 =0, but t in the total influence matrix 1,7 =0.001410, indicating that structural load-bearing capacity and safety resilience can have multiple indirect effects on low-altitude industrial R&D and supporting space through other demand factors; taking the influence of E02 on E12 as an example, a 2,12 =0 and t 2,12 =0.004446. This shows that relationships that did not make it into the Top-K were not necessarily interpreted as having no effect.

[0108] S5. Calculate the influence degree D of each demand element based on the overall influence matrix T. i Influence R i Centrality C i and the degree of cause Q i ;

[0109]

[0110]

[0111] Then, based on the centrality C i Forming priority response weight p i According to the degree of cause Q i The positive part forms the pre-driving weight q i This forms a priority output with a causal dual-channel approach; when Q... i When Q is greater than 0, the overall output impact of demand element i is greater than its overall absorption impact, and it is defined as a causal element; when Q i When Q is less than 0, it is defined as a result element; when Q... i When the value is 0, it is defined as a neutral element;

[0112]

[0113]

[0114] Among them, C r and Q r Let p represent the centrality and causality of the r-th demand element, respectively; i Used to characterize the overall response priority of demand element i in all interaction relationships; q i Used to characterize the degree to which demand element i acts as an upstream prerequisite of the system;

[0115] Influence D in this embodiment i Influence R i Centrality C i , reason Q i The settlement results of the overall response priority are shown in Table 3. The priority response weight is obtained by centrality normalization, and the elements with causal degree greater than 0 are further entered into the calculation of the front-end driving weight.

[0116] Table 3. Calculation Results of the Causal-Driven Path of Demand Factors

[0117]

[0118] Influence degree D in Table 3 i Influence R i Centrality C i , reason Qi Priority response weight p i Both causal attributes and influence degree D characterize the role of demand elements in the top-space update demand network from different perspectives. i D represents the total combined impact of demand factor i on other demand factors. i The larger the value, the stronger the ability of this demand element to influence other elements in the system; it can be used to identify demand elements that have a strong driving effect on subsequent updates. The degree of influence R... i R represents the total amount of influence that demand factor i receives from the combined effects of other demand factors. i The larger the value, the more easily the demand element is constrained by structure, facilities, environment, flow lines, or operating conditions. It can be used to identify demand elements that require a concentrated response to multiple preconditions.

[0119] Centrality C i Used to reflect the overall degree of correlation of demand element i within the entire demand relationship network. C i The larger the value, the stronger the integrated connection between this requirement element and other requirement elements, and the more likely it should be prioritized in the design task during top-space updates. Priority Response Weight p i From centrality C i The normalized result is used to convert the overall correlation of different demand elements into comparable priority values, and serves as the input for subsequent calculation of the core response elements of each scenario and their response weights within the scenario.

[0120] Reason Q i For influence D i With influence R i The difference is used to determine whether the demand element primarily affects the output end or the receiving end in the demand relationship network. When Q... i When Q > 0, the overall output impact of demand element i is greater than its overall acceptance impact, and it is determined to be a causal element, indicating that this demand element usually constitutes a prerequisite that needs to be met before or simultaneously with the implementation of other update content; when Q i When Q < 0, it is judged as a result element, indicating that this demand element mainly undertakes the role of other elements, usually manifested as the space, facilities, interface or circulation that the top space renewal needs to finalize or focus on improving; when Q i When the value is 0, it is considered a neutral element.

[0121] Q for causality i The positive part is normalized to obtain the pre-driving weight q. i Pre-driving weight q i This is used to compare the relative importance of each causal factor as a prerequisite for implementation, and serves as input for subsequent calculations of the prerequisite driving factors for each scenario and their driving weights within the scenario. Therefore, the influence degree Di And the degree of influence R i Centrality C is used to describe the direction and magnitude of influence between elements. i and priority response weight p i Used to identify elements that should be updated first, reason degree Q i and the preceding driving weight q i Causal attributes are used to identify the conditions that should be met before implementing an update, while causal attributes are used to distinguish the functional roles of demand elements in the update process.

[0122] As shown in Table 3, E14, the takeoff and landing and boarding flow organization, has the highest priority response weight of 0.0922, but its causality degree is -0.9988, indicating it is a result element that centrally receives the effects of other conditions. E07, the low-altitude industry R&D and supporting space, has a priority response weight of only 0.0216, but its causality degree is 0.3131, the highest among all elements, indicating it is a leading factor. When using a single sorting method, E07 is easily considered a low-priority content; however, with the causal dual-channel output of this invention, E14 and E07 enter the core response channel and the leading factor channel, respectively.

[0123] S6. Establish a requirement element-scene association matrix based on the scene association tags in step S1. In this embodiment, three types of scenarios are preset: production space, transportation space, and storage space. The number of scenarios c is 3, and the scenario numbers are 1, 2, and 3 respectively; b ic This indicates the strength of the association between demand element i and scenario c;

[0124] This embodiment further establishes an association matrix B for three scenarios: production space, transportation space, and storage space. The composite production space is associated with E02, E03, E04, E05, E06, E07, and E12; the multi-level transportation space is associated with E01, E10, E12, E13, E14, E19, and E21; and the dynamic storage space is associated with E05, E09, E10, E11, E12, E16, and E17. The above association elements are defined by matrix B. ic Select 1 for the first option and 0 for the rest.

[0125] S7, prioritize response weight p i and the pre-driving weight q i Map each scenario separately and calculate the response weight h within each scenario. ic and scene-driven weights v ic And select the first 4 items according to the response weight (L H =4), select the first 3 items according to the driving weight (L V =3), thereby determining the core response elements and pre-driving elements for each scenario;

[0126]

[0127]

[0128] In the formula, h ic v represents the response weight of demand element i within scenario c. ic Let r be the driving weight of demand element i in scenario c, and r be the summation index within the scenario. rc The strength of the association between demand element r and scenario c;

[0129] S8. Based on the core response elements and pre-driving elements of the corresponding scenario determined in step S7, a structured update requirement framework is generated. The structural bearing capacity, communication sensing, energy supply, environmental control or modular conditions corresponding to the pre-driving elements are written into the pre-verification task. The spatial carrier, functional interface and organizational flow corresponding to the core response elements are written into the priority construction task. In principle, the pre-verification task is completed first in terms of technical judgment. The priority construction task is implemented after the corresponding pre-conditions are confirmed. If the conditions for simultaneous design or construction are available, the two can also be promoted simultaneously, thereby guiding the actual high-rise building roof space update.

[0130] This embodiment yields the scenario-based update requirement framework shown in Table 4.

[0131] Table 4 Output of the Three-Scenario Update Requirement Framework

[0132]

[0133] The core response elements and their weights, the preceding driving elements and their weights, and the direction of updates and renovations in Table 4 together constitute the scenario-based top-level space update requirement framework. Among them, the core response elements are used to answer the elements that need to be prioritized or improved in the corresponding scenario, and their response weights within the scenario are h. ic This indicates the relative response priority of the element in the corresponding scenario; the preceding driving element is used to answer what conditions need to be met before or simultaneously with the formation of the above core response content, and its driving weight v within the scenario. ic It indicates the relative importance of the element as an implementation condition; the renovation and transformation direction is used to answer what kind of architectural space, facility system, construction measure or organizational task the selected element will be implemented into, and is generated by the selected demand element and the preset element-renovation direction mapping relationship.

[0134] The three elements form a progressive relationship: update goal – implementation conditions – design tasks. Core response elements determine the target objects for the top space update; prerequisite driving elements determine the conditions that need to be prioritized for verification or synchronous configuration to achieve the goal; and the update and renovation direction transforms the core response elements and prerequisite driving elements into specific tasks that can be adopted for architectural update design. The weights within a scenario determine the relative priority of implementation for various tasks within the same scenario, and are not directly equivalent to the absolute time sequence of engineering construction. Core response elements with higher weights should be included in the space update plan more frequently, and prerequisite driving elements with higher weights should complete condition verification, system configuration, or technical coordination more frequently. Precursor driving elements and the core response elements they support can be promoted synchronously based on professional collaboration and engineering implementation conditions.

[0135] According to Table 4, the composite production space should prioritize flexible spatial form and function, intensive and composite layout, time-sharing composite use, and modular layout, with low-altitude industry R&D and supporting space, communication data and sensing systems, and modular layout as prerequisite driving conditions; the three-dimensional transportation space should prioritize take-off and landing flow lines, take-off and landing platforms, structural safety, and air-to-ground connection, with environmental interference control, communication sensing, and structural safety as prerequisite implementation conditions; the dynamic storage space should prioritize freight organization, parking and storage, intensive layout, and intelligent scheduling, with communication sensing, multi-energy supply, and intelligent scheduling as prerequisite driving conditions.

[0136] Finally, the system generates a structured record for each selected element. For example, record E14 in the three-dimensional transportation space includes: element code E14, overall priority response weight 0.0922, causality degree -0.9988, scene-specific response weight 0.2440, demand role as the core response element, and the direction of update and renovation is the organization of take-off and landing operation flow and boarding flow; record E21 includes: element code E21, overall priority response weight 0.0289, causality degree 0.3044, scene-specific driving weight 0.3443, demand role as the preceding driving element, and the direction of update and renovation is sound insulation and vibration reduction and environmental interference control.

[0137] The above embodiments demonstrate that the method of the present invention, while reducing the amount of relationship data collected, can recover the indirect effects in sparse records through the overall influence matrix, and simultaneously retain the core content and implementation preconditions through priority response channels and pre-driving channels, thereby generating a top-level spatial update demand framework for production, transportation, and storage through determined matrix operations. The final result of this method consists of the overall causal calculation result and the scenario-based update demand framework. The overall causal calculation result is determined by the influence degree D. i Influence R i Centrality C i , reason Q i Priority response weight p i Pre-driving weight qi And causal attributes, determine the influence direction, overall importance and functional role of each demand element in the entire top-level space update system; the scenario-based update demand framework further determines the response weight h within the scenario. ic and scene-driven weights v ic We need to identify the core response content that should be prioritized in different scenarios and the prerequisites that should be met first, and then convert them into specific update and transformation targets.

[0138] In the actual process of renovating the rooftop space of high-rise buildings, a preliminary verification list is first formed according to the pre-driving factors and their weights. Conditions such as structural load-bearing capacity, safety resilience, environmental interference, communication sensing, energy supply, and system integration are verified sequentially. After meeting the corresponding pre-conditions, a priority construction list is formed according to the core response factors and their weights to determine the update sequence of space, facilities, interfaces, and circulation. Finally, a design task list is generated based on the renovation direction and used to guide scheme design, professional collaboration, equipment configuration, and renovation implementation. Thus, this method transforms sparse influence relationship data into a high-rise building rooftop space renovation demand framework with clear objectives, implementation conditions, and renovation tasks.

Claims

1. A method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping, characterized in that... The method for generating renewal requirements for the roof space of high-rise buildings is implemented according to the following steps: S1. Obtain multi-source data on the update requirements of the rooftop space of high-rise buildings, perform structured processing on the multi-source data to form n requirement elements, and set element code, element name, element category and scene association label for each requirement element; S2. Based on the demand elements, obtain m records of the influence relationships of the demand elements. For each record, retain the K affected elements with the highest influence intensity or those determined by the recorder to be the most critical, along with their influence intensity. Thus, expand the k-th record of the influence relationships of the demand elements into an n×n sparse direct influence matrix. ; In the formula, This is the sparse direct influence matrix corresponding to the kth record of the influence relationship of demand factors. The influence strength of the k-th recorder on demand element i directly affecting demand element j; k=1,2,…,m; i,j=1,2,…,n; K is the number of key affected elements retained for each source of influence, K is a positive integer and less than n; S3, Set the record weight ω k For m sparse direct influence matrices After weighted aggregation, the direct influence matrix A of the group is obtained; S4. Calculate the standardization coefficients s of the group direct influence matrix A, and standardize the group direct influence matrix A into a direct influence matrix X. Calculate the total influence matrix T based on the direct influence matrix X to simultaneously characterize the direct influence between demand elements and the multi-order indirect influence transmitted through other demand elements, thereby compensating for the indirect influence of weak direct relationships that were not selected in the sparse matrix. In the formula, I is an n-order identity matrix; This indicates the intensity of the combined influence of demand factor i on demand factor j; S5. Calculate the influence degree D of each demand element based on the overall influence matrix T. i Influence R i Centrality C i and the degree of cause Q i ; Then based on the centrality C i Forming priority response weight p i According to the degree of cause Q i The positive part forms the pre-driving weight q i This forms a priority output through a causal dual-channel approach; Among them, C r and Q r Let the centrality and causality of the r-th demand element be represented respectively. S6. Establish a requirement element-scene association matrix based on the scene association tags in step S1. , where b ic This indicates the strength of the association between demand element i and scenario c; S7, prioritize response weight p i and the pre-driving weight q i Map each scenario separately and calculate the response weight h within each scenario. ic and scene-driven weights v ic This allows us to identify the core response elements and pre-driving elements for each scenario. In the formula, h ic v represents the response weight of demand element i within scenario c. ic Let r be the driving weight of demand element i within scenario c, and r be the summation index within the scenario. rc The strength of the association between demand element r and scenario c; S8. Based on the core response elements and pre-driving elements of the corresponding scenario determined in step S7, a structured update requirement framework is generated. The task and update direction are designed through the structured update requirement framework, thereby completing the method for generating update requirements of the roof space of high-rise buildings based on sparse relationship compensation and causal dual-channel mapping.

2. The method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping according to claim 1, characterized in that... The scene association tags mentioned in step S1 include production space scene, transportation space scene and storage space scene.

3. The method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping according to claim 1, characterized in that... In step S2, K takes an integer from 4 to 6.

4. The method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping according to claim 1, characterized in that... In step S4, for a ij =0 but t ij element pairs >0, t ij This represents the indirect impact formed through the transmission of other demand elements, and therefore the relationship will not be directly judged as having no impact simply because it is not included in the Top-K records.

5. The method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping according to claim 1, characterized in that... In step S5, when all Q i When all q values ​​are not greater than 0, all q values ​​will be used. i Set to 0.

6. The method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping according to claim 1, characterized in that... In step S5, when the degree of cause Q i When the degree of causation Q is greater than 0, the overall output impact of demand element i is greater than the overall absorption impact, and it is defined as a causal element; when the degree of causation Q is greater than 0, it is defined as a causal element. i When Q is less than 0, it is defined as a result element; when Q... i When the value is 0, it is defined as a neutral element.

7. The method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping according to claim 1, characterized in that... In step S6, b ic b represents the strength of the association between demand element i and scenario c. ic Take a value between 0 and 1, where 0 indicates no correlation and 1 indicates a strong correlation.

8. The method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping according to claim 1, characterized in that... In step S7, for scenario c, according to the response weight h within the scenario... ic Select the first L from largest to smallest H These demand elements are used as core response elements; In causality Q i Among the requirement elements that are >0 and associated with scenario c, according to the driving weight v within the scenario. ic Select the first L from largest to smallest V Demand factors serve as leading drivers.

9. The method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping according to claim 1, characterized in that... In step S8, the structured update requirement framework is output in the form of tabular records, JSON records, database records, or building information model attribute tables.

10. The method for generating update requirements for the rooftop space of high-rise buildings based on sparse relation compensation and causal dual-channel mapping according to claim 1, characterized in that... In step S8, the preceding driving elements are written into the preceding verification list task, and the core response elements are written into the priority construction list task.

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