Intelligent data processing method for public facility design

Abstract

The invention relates to the technical field of collaborative design, in particular to an intelligent data processing method for public facility design, and the method comprises the steps: determining to carry out hierarchical loading based on a comprehensive evaluation value or carry out trigger loading based on a data abundance value and behavior feedback abnormality according to the category of a design end; determining whether a loading strategy of the target design end at each loading moment of the current loading design period is adjusted from synchronous loading to asynchronous loading or not according to the increment reference value and the spatial loading difference degree; in the asynchronous loading, determining a loading priority coefficient corresponding to each reference design end according to the abnormal evaluation value, and determining whether to perform in-end loading optimization for the reference design end according to the iteration fluctuation degree and the abnormal sub-region proportion; and in the in-end loading optimization, executing an optimization strategy of sub-region grouping loading, and determining whether to adjust the optimization strategy into hierarchical compression loading or not according to a space time sequence coupling abnormal coefficient. According to the invention, the design data loading stability can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of collaborative design, in particular to an intelligent data processing method for public facility design. BACKGROUND

[0002] With the advancement of smart city construction, public facility design presents multi-specialty and cross-departmental collaboration characteristics. The number of collaborative designers increases, and the amount of design data surges. The design data needs to be synchronized in real time between each collaborative design end to ensure the consistency of design content and the efficiency of collaborative design. However, during the synchronization and loading process of design data, due to factors such as too many collaborative people and data transmission link fluctuations, network congestion, loading delay, system lag, and even crashes may easily occur, which not only seriously affects the operation experience of designers and the progress of collaborative design, but also may cause design parameters to be lost and design content to be deviated due to loading abnormalities, thereby affecting the quality and safety of public facility design. Therefore, how to optimize the transmission and loading process of design data to improve the loading stability of design data is a technical problem to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN109063265A discloses a cross-domain and off-site collaborative design method and device in a mass data environment. The method includes: performing lightweight processing on a three-dimensional model to obtain a lightweight model; constructing a cross-domain and off-site collaborative design network environment; completing three-dimensional design model loading in the cross-domain and off-site collaborative design environment; and switching the loaded lightweight model to the three-dimensional design model to carry out collaborative design. However, the above-mentioned scheme has the following problems: it is unable to develop differentiated loading strategies according to actual application scenarios, lacks fine and dynamic optimization of the transmission and loading process, and is difficult to ensure the loading stability requirement in the mass data collaborative design scenario. SUMMARY

[0004] Therefore, the present application provides an intelligent data processing method for public facility design to overcome the problems in the prior art that it is unable to develop differentiated loading strategies according to actual application scenarios, lacks fine and dynamic optimization of the transmission and loading process, and is difficult to ensure the loading stability requirement in the mass data collaborative design scenario.

[0005] To achieve the above-mentioned purpose, the present application provides an intelligent data processing method for public facility design, comprising: determining the design end category according to the design dependency and the loading abnormality coefficient, and determining hierarchical loading based on the comprehensive evaluation value or trigger loading based on the data abundance value and the behavior feedback abnormality degree according to the design end category; determining whether to adjust the loading strategy of the target design end at each loading time in the current loading design period from synchronous loading to asynchronous loading according to the incremental reference value and the spatial loading heterogeneity; In the asynchronous loading, the loading priority coefficient corresponding to each reference design end is determined according to the abnormal evaluation value, and whether to perform the end loading optimization for the reference design end is determined according to the iteration fluctuation degree and the abnormal sub-region proportion; wherein the abnormal sub-region is determined based on the sub-neighborhood density index and the sub-space stagger degree. In the end loading optimization, the optimization strategy of the sub-region grouping loading is executed, and whether to adjust the optimization strategy to the hierarchical compression loading is determined according to the space timing coupling abnormal coefficient. In the hierarchical compression loading, whether to perform the full hierarchical compression of the delayed sub-region or the partial hierarchical compression of all sub-regions is determined according to the influence level radiation coefficient and the interference superposition degree.

[0006] Further, for a type of design end with a design dependency greater than or equal to a preset design dependency or a loading abnormality coefficient greater than or equal to a preset loading abnormality coefficient, hierarchical loading is performed based on a comprehensive evaluation value. In the hierarchical loading, the loading period of each type of design end is determined based on the comprehensive evaluation value, and loading is performed at the end time of each loading period corresponding to each type of design end. The duration of the loading period corresponding to a single type of design end is negatively correlated with the comprehensive evaluation value corresponding to the type of design end.

[0007] Further, for a type of design end with a design dependency less than a preset design dependency and a loading abnormality coefficient less than a preset loading abnormality coefficient, triggered loading is performed based on a data abundance value and a behavior feedback abnormality degree. In the triggered loading, whether to perform loading is determined according to the data abundance value and the behavior feedback abnormality degree, and loading is performed for the type of design end at the time when the data abundance value is greater than a preset data abundance value or the behavior feedback abnormality degree is greater than a preset behavior feedback abnormality degree.

[0008] Further, for a loading time when an incremental reference value is greater than or equal to a preset incremental reference value or a spatial loading heterogeneity is greater than or equal to a preset spatial loading heterogeneity, the loading strategy of the target design end is adjusted from synchronous loading to asynchronous loading.

[0009] Further, the loading priority coefficient corresponding to each reference design end is determined according to the abnormal evaluation value. The loading priority coefficient of a single reference design end is positively correlated with the abnormal evaluation value corresponding to the reference design end.

[0010] Further, for a reference design end with an iteration fluctuation degree greater than or equal to a preset iteration fluctuation degree or an abnormal sub-region proportion greater than or equal to a preset abnormal sub-region proportion, end loading optimization is performed.

[0011] Further, when the optimization strategy of the sub-region grouping loading is executed, the associated regions are determined based on the conflict coefficient and the abnormal accumulation threshold, and the loading priority coefficient of each associated region is determined based on the design fitting degree; The abnormal accumulation threshold and the loading density are in a positive correlation.

[0012] Further, if the spatial timing coupling abnormality coefficient is greater than or equal to a preset spatial timing coupling abnormality coefficient, the optimization strategy is adjusted to hierarchical compression loading.

[0013] Further, for the reference design end with an influence level radiation coefficient greater than or equal to a preset influence level radiation coefficient or an interference superposition degree less than a preset interference superposition degree, all sub-regions are partially compressed at a level.

[0014] Further, for the reference design end with an influence level radiation coefficient less than a preset influence level radiation coefficient and an interference superposition degree greater than or equal to a preset interference superposition degree, all sub-regions are fully compressed at a level.

[0015] Compared with the prior art, the beneficial effects of the present application are that, in the technical scheme of the present application, the design dependence and the loading abnormality coefficient effectively reflect the associated dependence and the loading stability between the design ends, and then the hierarchical loading based on the comprehensive evaluation value or the triggered loading based on the data abundance value and the behavior feedback abnormality degree is adaptively selected according to the design end category, which is beneficial to avoid loading conflicts and reduce invalid loading consumption, realizes on-demand scheduling and precise control of design end loading, and improves the overall design data transmission stability, thereby avoiding data loading abnormalities in the design process of public facilities and optimizing the scheduling efficiency of design ends.

[0016] Further, in the present application, when the incremental reference value is greater than or equal to a preset incremental reference value or the spatial loading heterogeneity is greater than or equal to a preset spatial loading heterogeneity, it indicates that the incremental data size at the current loading time is large, the concurrent quantity of the reference design end to be loaded is high, the aggregation degree of the sub-region to be loaded is high, the overlapping interference of the sub-region corresponding to different reference design ends is obvious, and the spatial loading confusion degree exceeds the reasonable range, and synchronous loading is prone to cause data transmission congestion, loading delay, data loss and other problems, and adjusting the loading strategy of the target design end from synchronous loading to asynchronous loading is beneficial to disperse the loading pressure and avoid concurrent loading conflicts, flexibly regulate the transmission and loading time sequence of the incremental data of each reference design end, reduce the loading abnormality probability, and further improve the fluency and stability of the target design end loading.

[0017] Further, in the present application, the difference fluctuation degree of the data to be loaded in the reference design end, the spatial abnormal distribution proportion of the sub-regions to be loaded, and the data stability and spatial interference risk in the single-end loading process are effectively reflected by the iterative fluctuation degree and the abnormal sub-region proportion, and then whether to perform end loading optimization for the reference design end is adaptively selected according to the iterative fluctuation degree and the abnormal sub-region proportion, which is beneficial to accurately positioning the reference design end with high abnormal risk, solving the loading risks caused by too large end data difference and abnormal sub-region aggregation, reducing the interference of end loading abnormality on the overall loading process, providing support for the orderly advancement of asynchronous loading, and further optimizing the stability of the overall loading system.

[0018] Further, in the present application, when the spatial timing coupling abnormality coefficient is greater than or equal to the preset spatial timing coupling abnormality coefficient, the time urgency and the spatial loading abnormality degree of the current loading time are large, which easily leads to loading congestion, data synchronization deviation, and aggravated loading delay, and the optimization strategy is adjusted to hierarchical compression loading to accurately split the loading task of the spatial timing coupling abnormality, so as to reduce the loading conflict and data transmission loss, and further improve the stability of the loading process.

[0019] Further, in the present application, the core influence range of the design layer and the sub-region loading delay interference degree are effectively reflected by the influence level radiation coefficient and the interference superposition degree, and then whether to perform hierarchical compression of all levels of the delay sub-region or hierarchical compression of part of the levels of all sub-regions is adaptively selected according to the influence level radiation coefficient and the interference superposition degree, which is beneficial to realizing accurate adaptation of hierarchical compression, avoiding invalid compression operation and data loss caused by excessive compression, reducing the data transmission pressure of the abnormal region and relieving the loading delay interference, and minimizing the data transmission amount on the premise of ensuring the loading integrity and accuracy of the core design layer. BRIEF DESCRIPTION OF DRAWINGS

[0020] Fig. 1 It is a schematic diagram of the intelligent data processing method for public facility design of the present application; Fig. 2 It is a flowchart of the present application for determining hierarchical loading based on a comprehensive evaluation value or trigger loading based on a data abundance value and behavior feedback abnormality degree according to the design end category; Fig. 3 It is a flowchart of the present application for determining whether to adjust the optimization strategy to hierarchical compression loading according to the spatial timing coupling abnormality coefficient; Fig. 4 It is a flowchart of the present application for determining whether to perform hierarchical compression of all levels of the delay sub-region or hierarchical compression of part of the levels of all sub-regions according to the influence level radiation coefficient and the interference superposition degree. DETAILED DESCRIPTION

[0021] In order to make the objects and advantages of the present application more clear, the present application is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0022] The preferred embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0023] Please refer to Figs. 1 to 4 As shown in the drawings, the present application provides an intelligent data processing method for public facility design, comprising: According to the design dependency and the loading abnormality coefficient, the design end category is determined, and based on the comprehensive evaluation value, the hierarchical loading is determined according to the design end category, or based on the data abundance value and the behavior feedback abnormality, the trigger loading is determined; According to the incremental reference value and the space loading abnormality, it is determined whether to adjust the loading strategy of the target design end at each loading time in the current loading design cycle from synchronous loading to asynchronous loading; In the asynchronous loading, the loading priority coefficient corresponding to each reference design end is determined according to the abnormality evaluation value, and it is determined whether to perform end loading optimization for the reference design end according to the iteration fluctuation degree and the abnormal sub-region proportion; wherein the abnormal sub-region is determined based on the sub-neighborhood density index and the sub-space interlacing degree; In the end loading optimization, the optimization strategy of sub-region grouping loading is executed, and it is determined whether to adjust the optimization strategy to hierarchical compression loading according to the space time coupling abnormality coefficient; In the hierarchical compression loading, according to the influence level radiation coefficient and the interference superposition degree, the whole level compression of the slow sub-region or the partial level compression of the whole sub-region is determined.

[0024] The application scenario of the present application is a public facility collaborative design scene with multiple terminals participating, specifically aiming at the application environment of multiple design end parallel design and real-time data interaction in large public facility collaborative design process, and is intended to adaptively optimize the distributed transmission, real-time synchronization and dynamic loading process of design data in the collaborative design process, to improve the data transmission fluency and loading stability during multiple design end parallel loading, and to ensure the continuous and reliable operation of the complex public facility collaborative design process.

[0025] In the present application, there are several design ends, a single design end is a terminal device participating in public facility collaborative design, having the functions of design data receiving, loading and transmission, and can independently complete the local design of public facilities, and can realize collaborative linkage with other design ends, and is the basic execution unit of design data loading and processing in the collaborative design scene.

[0026] The application is provided with a loading setting period, the length of a single loading design period is 1h, and the user can determine the design end category at the end of each loading design period; the loading design period adjacent to the current loading design period and earlier than the current loading design period is recorded as a reference period; Specifically, for a type of design end with a design dependency greater than or equal to a preset design dependency or a loading abnormality coefficient greater than or equal to a preset loading abnormality coefficient, hierarchical loading is performed based on the comprehensive evaluation value; In hierarchical loading, the loading period of each type of design end is determined based on the comprehensive evaluation value, and loading is performed at the end of each loading period corresponding to each type of design end; The length of the loading period corresponding to a single type of design end is negatively correlated with the comprehensive evaluation value corresponding to the type of design end.

[0027] Specifically, the design dependency corresponding to a single design end = the number of dependent design ends corresponding to the design end / the total amount of design ends; For any design end, the design end is recorded as a target design end, and other design ends except the target design end are recorded as reference design ends, and the dependent design end corresponding to the target design end is a reference design end that can start design only by directly calling the design parameters or design results output by the target design end in the reference period; The loading time corresponding to a single design end is the initial time when the incremental data of the design end starts to be transmitted, and the incremental data corresponding to a single loading time is the layer content of each layer performing operation between the loading time and the left adjacent loading time corresponding to the loading time. For a single layer, the layer content corresponding to the layer is the design data of the layer containing only the sub-region performing operation for the layer, and the design data of the layer includes vector data, attribute data and topology index structure; the left adjacent loading time corresponding to a single loading time is the loading time earlier than and adjacent to the loading time, if there is no loading time earlier than the loading time, the initial time of the reference period is recorded as the left adjacent loading time; The confirmation manner of the loading abnormality coefficient is that a target design end is detected at each loading time of a reference period and is recorded as a reference loading time, and the loading abnormality coefficient corresponding to the target design end is the average value of the sub-loading abnormality degrees corresponding to each reference loading time, the sub-loading abnormality degree corresponding to a single reference loading time is the average value of the loading duration of the target design end loaded to each reference design end at the reference loading time / the preset loading duration average value*the first weight coefficient+the standard deviation of the loading duration of the target design end loaded to each reference design end at the reference loading time / the preset loading duration standard deviation*the second weight coefficient, the first weight coefficient and the second weight coefficient are both 0.5; and the loading duration of the target design end loaded to a single reference design end at a single reference loading time is the duration of the incremental data of the target design end being transmitted and loaded to the reference design end at the reference loading time; It should be noted that if there is no reference period, the design dependency and the loading abnormality coefficient are both recorded as 0. The values of the preset loading duration average value and the preset loading duration standard deviation can be determined by a user according to an actual application scenario. The loading duration average value and the loading duration standard deviation effectively reflect the time consumption and stability of the design data loading. The greater the demand of the user for the loading response speed and the transmission stability is, the smaller the values of the preset loading duration average value and the preset loading duration standard deviation are. In the embodiment, the preset loading duration average value is 3 s, and the preset loading duration standard deviation is 0.5 s. The values of the preset design dependency and the preset loading abnormality coefficient can be determined by the user according to the loading stability requirement. The design dependency and the loading abnormality coefficient effectively reflect the associated and dependent closeness between the design ends and the abnormality frequency in the design data loading process. Therefore, when the design dependency or the loading abnormality coefficient is large, it indicates that the design end has a high degree of association with other design ends and has a large number of abnormal conditions in the loading process. The hierarchical loading can control the loading period according to the actual abnormality degree of the design end, avoid loading conflicts, and improve the overall loading efficiency and stability. When the design dependency and the loading abnormality coefficient are small, it indicates that the design end has strong independence and a relatively stable loading process, and does not need high-frequency hierarchical loading. Triggering loading based on the data abundance value and the behavior feedback abnormality degree can achieve on-demand loading, reduce invalid loading consumption, and the greater the demand of the user for the scheduling efficiency of the design end, the loading stability and the rationality of resource utilization is, the smaller the values of the preset design dependency and the preset loading abnormality coefficient are. In the embodiment, the preset design dependency is 0.5, and the preset loading abnormality coefficient is 0.3.

[0028] The comprehensive evaluation value=design dependency / preset design dependency*dependency weight coefficient+loading abnormality coefficient / preset loading abnormality coefficient*abnormality weight coefficient, the dependency weight coefficient and the abnormality weight coefficient are both 0.5. The duration of the loading period corresponding to the single first-type design end is [1 - (the comprehensive evaluation value corresponding to the first-type design end - the preset comprehensive evaluation value) / the preset comprehensive evaluation value] x the period duration threshold, and the period duration threshold is 5 min; The value of the preset comprehensive evaluation value can be determined by a user according to an actual application scenario. The comprehensive evaluation value effectively reflects the overall abnormality degree and scheduling priority of the first-type design end. The greater the demand of the user for rapid scheduling and priority loading of a high abnormality design end, the smaller the value of the preset comprehensive evaluation value. In this embodiment, the preset comprehensive evaluation value is 1.2.

[0029] Specifically, for the second-type design end with a design dependency less than a preset design dependency and a loading abnormality coefficient less than a preset loading abnormality coefficient, triggering loading is performed based on the data abundance value and the behavior feedback abnormality degree. In the triggering loading, it is determined whether to perform loading according to the data abundance value and the behavior feedback abnormality degree, and loading is performed for the second-type design end at the moment when the data abundance value is greater than a preset data abundance value or the behavior feedback abnormality degree is greater than a preset behavior feedback abnormality degree.

[0030] Specifically, for a single moment, the moment is denoted as a target moment, and a loading moment closest to the target moment and earlier than the target moment is denoted as a target loading moment. The public facility design is evenly divided into a plurality of sub-regions, and each sub-region is a square with an area of 0.5 m x 0.5 m. The data abundance value corresponding to the target moment is (the data amount of the target moment - the data amount of the target loading moment) / the data amount of the target loading moment. The data amount of a single moment is the memory amount occupied by a single loading end public facility design, and the unit is MB. The behavior feedback abnormality degree corresponding to the target moment is the ratio of the operation rollback frequency of the reference interval to the rollback feedback reference value. Each loading moment before the current loading design period and with a sub-loading abnormality degree greater than or equal to a preset sub-loading abnormality degree is denoted as an analysis moment, and each analysis moment corresponds to a left adjacent loading moment. The analysis interval corresponding to a single analysis moment is [the left adjacent loading moment corresponding to the analysis moment, the analysis moment], and the reference interval is [the target loading moment, the target moment]. The rollback feedback reference value is an average value of operation rollback frequency of an analysis interval corresponding to each to-be-analyzed moment; the operation rollback frequency corresponding to a single interval is (the number of undo operations in the interval + the number of delete operations in the interval) / the total number of operations in the interval; the interval is an analysis interval or a reference interval; the number of undo operations is the total number of times of executing an “undo” instruction, the number of delete operations is the total number of times of executing a “delete” instruction, and the total number of operations is the total number of all design-related operations executed, including but not limited to undo operations, delete operations, drawing operations, parameter modification operations, and save operations; It can be understood that a large rollback feedback reference value indicates that the current user operation rollback behavior is abnormal, which indirectly reflects that the second type of design terminal is prone to data deviation if data loading is not timely.

[0031] The preset data abundance value and the preset behavior feedback abnormality value can be determined by the user according to the actual application scenario. The data abundance value and the behavior feedback abnormality value effectively reflect the design data update range of the second type of design terminal and the abnormality degree of the user operation behavior. The greater the user's demand for the on-demand loading accuracy, system resource saving, and operation experience smoothness of the second type of design terminal, the smaller the preset data abundance value and the preset behavior feedback abnormality value. In this embodiment, the preset data abundance value is 0.35, and the preset behavior feedback abnormality value is 0.7.

[0032] The preset sub-loading abnormality value can be determined by the user according to the actual application scenario. The sub-loading abnormality value effectively reflects the abnormality degree and loading stability of the design terminal data transmission at a single loading moment. The greater the user's demand for loading process stability, transmission real-time performance, and abnormal fault tolerance, the smaller the preset sub-loading abnormality value. In this embodiment, the preset sub-loading abnormality value is 0.35.

[0033] Specifically, for the loading moment at which the incremental reference value is greater than or equal to the preset incremental reference value or the spatial loading abnormality degree is greater than or equal to the preset spatial loading abnormality degree, the loading strategy of the target design terminal is adjusted from synchronous loading to asynchronous loading.

[0034] For the loading moment at which the incremental reference value is less than the preset incremental reference value and the spatial loading abnormality degree is less than the preset spatial loading abnormality degree, the loading strategy of the target design terminal does not need to be adjusted from synchronous loading to asynchronous loading.

[0035] The target design terminal is a design terminal that receives incremental data of other design terminals, and the reference design terminal is a design terminal other than the target design terminal; The loading moments of the current loading design period include the end moments of the loading periods of each first type of design terminal, and moments at which the data abundance value of the second type of design terminal is greater than the preset data abundance value or the behavior feedback abnormality value is greater than the preset behavior feedback abnormality value. The incremental reference value of a single loading time = the average value of the data abundance values corresponding to each reference design end at the loading time / the preset data abundance value + the number of reference design ends that need to be loaded at the loading time / the total number of design ends; The spatial loading heterogeneity = the neighborhood density index / the preset neighborhood density index x the density weight coefficient + the spatial interlacing degree / the preset spatial interlacing degree x the interlacing weight coefficient, and the density weight coefficient and the interlacing weight coefficient are both 0.5; The confirmation manner of the neighborhood density index is that, for a single loading time, the loading time is recorded as a target loading time, each sub-region in the incremental data corresponding to each reference design end loaded at the target loading time is recorded as a to-be-loaded sub-region, and the neighborhood density index is the average value of the sub-neighborhood density indexes corresponding to each to-be-loaded sub-region. The sub-neighborhood density index corresponding to a single to-be-loaded sub-region is the number of to-be-loaded sub-regions that have an intersection with the loading influence region corresponding to the to-be-loaded sub-region in the loading influence region corresponding to the to-be-loaded sub-region. The loading influence region corresponding to a single to-be-loaded sub-region is a circle with the center point of the to-be-loaded sub-region as the center and a preset influence distance as the radius. The loading time before the sub-loading abnormality degree of the current loading design period is less than the preset sub-loading abnormality degree is recorded as a normal loading time, the preset influence distance is the product of the average value of the interval average values of each normal loading time corresponding to the target design end and the proportion coefficient, the proportion coefficient is 0.5, the interval average value corresponding to a single normal loading time is the average value of the interval reference values corresponding to each to-be-loaded sub-region of the normal loading time, and the interval reference value corresponding to a single to-be-loaded sub-region of a single normal loading time is the average value of the center point connection distances of the to-be-loaded sub-region and other to-be-loaded sub-regions except the to-be-loaded sub-region. The center point connection distance of any two to-be-loaded sub-regions is the straight line distance between the center points of the two to-be-loaded sub-regions. The spatial interlacing degree is the average value of the sub-spatial interlacing degrees corresponding to each to-be-loaded sub-region, and the sub-spatial interlacing degree corresponding to a single to-be-loaded sub-region is the ratio of the number of different reference design ends corresponding to the to-be-loaded sub-region to the total number of reference design ends. The values of the preset neighborhood density index and the preset spatial interlacing degree can be determined by the user according to the loading stability requirements of the public facility design. The neighborhood density index and the spatial interlacing degree effectively reflect the aggregation degree of the to-be-loaded sub-region, the overlapping interference degree of the sub-regions corresponding to different reference design ends, and the confusion degree of the spatial loading. The greater the user's demand for improving the loading stability, the smaller the values of the preset neighborhood density index and the preset spatial interlacing degree. In the embodiment, the preset neighborhood density index is 0.6, and the preset spatial interlacing degree is 0.4. The preset increment reference value and the preset spatial loading abnormality degree are determined by the user according to an actual application scene. The increment data size at the current loading time, the concurrent quantity of the to-be-loaded design end and the abnormal interference degree of the spatial loading are effectively reflected through the increment reference value and the spatial loading abnormality degree. The greater the user's demand for loading fluency and loading stability is, the smaller the preset increment reference value and the preset spatial loading abnormality degree are. In the embodiment, the preset increment reference value is 1.2, and the preset spatial loading abnormality degree is 0.4. The synchronous loading comprises: at a single loading time, increment data of all reference design ends corresponding to the loading time is synchronously transmitted to the target design end, and after the transmission to the target design end is completed, loading is performed; Specifically, the loading priority coefficient of each reference design end is determined according to the abnormality evaluation value. The loading priority coefficient of a single reference design end is in a positive correlation with the abnormality evaluation value corresponding to the reference design end.

[0036] Specifically, the abnormality evaluation value corresponding to a single reference design end = iteration fluctuation degree / preset iteration fluctuation degree + abnormal sub-region proportion / preset abnormal sub-region proportion. The loading priority coefficient of a single reference design end = the abnormality evaluation value corresponding to the reference design end * 1.0. According to the order of the loading priority coefficient from large to small, the increment data of each reference design end is loaded into the target design end in sequence. After the increment data of a single reference design end is transmitted to the target design end and the loading is completed, the transmission and loading operation of the increment data of the next reference design end are performed.

[0037] Specifically, the in-end loading optimization is performed on the reference design end whose iteration fluctuation degree is greater than or equal to the preset iteration fluctuation degree or whose abnormal sub-region proportion is greater than or equal to the preset abnormal sub-region proportion.

[0038] Specifically, the in-end loading optimization is not needed for the reference design end whose iteration fluctuation degree is less than the preset iteration fluctuation degree and whose abnormal sub-region proportion is less than the preset abnormal sub-region proportion.

[0039] At a single loading time, the iteration fluctuation degree corresponding to a single reference design end is the standard deviation of the design difference degrees corresponding to each to-be-loaded sub-region of the reference design end at the loading time. The design difference degree corresponding to the single to-be-loaded sub-region of the single reference design end at a single loading moment is 1 minus the number of same layers / max (a1, a2); wherein the number of same layers is the number of layers involved in the to-be-loaded sub-region in the reference design end and the layers involved in all sub-regions in the target design end, the number of layers involved in the to-be-loaded sub-region in the reference design end is a1, and the total number of layers involved in all sub-regions in the target design end is a2; the layers involved in the single to-be-loaded sub-region are the layers on which operations are performed for the to-be-loaded sub-region between the loading moment and the left adjacent loading moment corresponding to the loading moment; The abnormal sub-region proportion corresponding to the single reference design end at a single loading moment is the ratio of the number of abnormal sub-regions in the to-be-loaded sub-region corresponding to the reference design end in the target design end to the total number of to-be-loaded sub-regions corresponding to the reference design end in the target design end. The to-be-loaded sub-region with a sub-neighborhood density index greater than or equal to a preset sub-neighborhood density index or a sub-space interleaving degree greater than or equal to a preset sub-space interleaving degree is recorded as an abnormal sub-region. The values of the preset sub-neighborhood density index and the preset sub-space interleaving degree can be determined by the user according to the actual application scenario. The sub-neighborhood density index and the sub-space interleaving degree effectively reflect the spatial aggregation degree of the to-be-loaded sub-region and the interleaving interference degree of the multi-end design data. The greater the user's demand for loading stability, the smaller the values of the preset sub-neighborhood density index and the preset sub-space interleaving degree. In the embodiment, the preset sub-neighborhood density index is 0.6, and the preset sub-space interleaving degree is 0.4.

[0040] The values of the preset iteration fluctuation degree and the preset abnormal sub-region proportion can be determined by the user according to the loading stability requirement. The iteration fluctuation degree and the abnormal sub-region proportion effectively reflect the internal data difference degree of the design end and the distribution proportion of the spatial loading abnormal region. The greater the user's demand for loading abnormal fault tolerance control, the smaller the values of the preset iteration fluctuation degree and the preset abnormal sub-region proportion. In the embodiment, the preset iteration fluctuation degree is 0.45, and the preset abnormal sub-region proportion is 0.5.

[0041] Specifically, when the optimization strategy of sub-region grouping loading is executed, the associated regions are determined based on the conflict coefficient and the abnormal accumulation threshold, and the loading priority coefficient of each associated region is determined based on the design fitting degree. The abnormal accumulation threshold and the loading tightness have a positive correlation.

[0042] Specifically, when the optimization strategy of sub-region grouping loading is performed for a single reference design end, the associated regions are determined based on the conflict coefficient and the abnormal accumulation threshold value, the correlation analysis is performed for each to-be-loaded sub-region corresponding to the reference design end, when the correlation analysis is performed for a single to-be-loaded sub-region, the to-be-loaded sub-region is recorded as a target to-be-loaded sub-region, each to-be-loaded sub-region of the reference design end, except the target to-be-loaded sub-region, which is not recorded in the associated region, is recorded as a reference to-be-loaded sub-region, the abnormal reference value of the target to-be-loaded sub-region is taken as an initial accumulation value, the abnormal reference values of the corresponding reference to-be-loaded sub-regions are sequentially accumulated in the order of the conflict coefficient from small to large between each reference to-be-loaded sub-region and the target to-be-loaded sub-region, until the accumulation result exceeds the abnormal accumulation threshold value, all reference to-be-loaded sub-regions participating in the accumulation before the accumulation is terminated and the target to-be-loaded sub-region are collectively recorded as an associated region, and the correlation analysis is continuously performed for the to-be-loaded sub-regions not recorded in the associated region, until each to-be-loaded sub-region is recorded in the corresponding associated region, and then the correlation analysis is stopped. If the loading density is greater than or equal to the preset loading density, the abnormal accumulation threshold value is set as: abnormal accumulation threshold value = the sum of the abnormal reference values of each to-be-loaded sub-region × the first proportion coefficient. If the loading density is less than the preset loading density, the abnormal accumulation threshold value is set as: abnormal accumulation threshold value = the sum of the abnormal reference values of each to-be-loaded sub-region × the second proportion coefficient. In the embodiment, the first proportion coefficient is preferably in the range of [0.4, 0.5]; the second proportion coefficient is preferably in the range of [0.3, 0.35]; and in the embodiment, 0.42 and 0.33 are taken respectively.

[0043] The loading density = 1 - the time length between the first loading time and the second loading time / the time length threshold value, the time length threshold value is 30 s, the loading time at which the optimization strategy of sub-region grouping loading is performed is recorded as the first loading time, and the loading time earlier than the first loading time and adjacent to the first loading time is recorded as the second loading time. The value of the preset loading density can be determined by the user according to the actual application scenario, the loading density effectively reflects the time interval compactness between the current loading time and the previous loading time, the greater the user's demand for loading stability, the smaller the value of the preset loading density, and in the embodiment, the preset loading density is 0.7. The conflict coefficient corresponding to any two to-be-loaded sub-regions = abnormal accumulation value / preset abnormal accumulation value × accumulation weight coefficient + (1 - overlap interference coefficient / preset overlap interference coefficient) × overlap weight coefficient, the accumulation weight coefficient and the overlap weight coefficient are both 0.5. The abnormality accumulation value corresponding to any two to-be-loaded sub-regions is the sum of the abnormality reference values corresponding to the two to-be-loaded sub-regions, and the abnormality reference value corresponding to a single to-be-loaded sub-region is the sub-neighborhood density index / preset sub-neighborhood density index + sub-space interleaving degree / preset sub-space interleaving degree; The overlap interference coefficient corresponding to any two to-be-loaded sub-regions is the number of the same layers in all the layers involved by the two sub-regions / the total number of the layers involved by the two sub-regions of the reference design end; The preset abnormality accumulation value and the preset overlap interference coefficient can be determined by the user according to the actual application scenario. The abnormality accumulation value and the overlap interference coefficient effectively reflect the abnormality superposition degree and the layer overlap interference intensity between the to-be-loaded sub-regions. The greater the user's demand for the sub-region loading stability and the data transmission reliability is, the smaller the preset abnormality accumulation value is, and the greater the preset overlap interference coefficient is. In the embodiment, the preset abnormality accumulation value is 1.3, and the preset overlap interference coefficient is 0.5. The design fitting degree corresponding to a single associated region is the area of the overlap between the smallest rectangle capable of containing each to-be-loaded sub-region in the associated region and the smallest rectangle capable of containing all the sub-regions of the target design end / the area of the smallest rectangle capable of containing all the sub-regions of the target design end; The loading priority coefficient of a single associated region is the design fitting degree corresponding to the associated region x 1.0. For a single reference design end that needs to be optimized for end-to-end loading, the design content of the layers of each associated region is transmitted to the target design end in the order of the loading priority coefficients of the associated regions from large to small, and the loading operation is performed. After the design content of the layers of a single associated region is loaded, the next associated region is loaded.

[0044] Specifically, if the space-time coupling abnormality coefficient is greater than or equal to the preset space-time coupling abnormality coefficient, the optimization strategy is adjusted to hierarchical compression loading.

[0045] Specifically, if the space-time coupling abnormality coefficient is less than the preset space-time coupling abnormality coefficient, the optimization strategy does not need to be adjusted to hierarchical compression loading.

[0046] At a single loading time, the space-time coupling abnormality coefficient of a single reference design end is the loading density corresponding to the loading time / preset loading density x third weight coefficient + the sum of the abnormality reference values corresponding to each to-be-loaded sub-region of the reference design end / abnormality accumulation threshold x fourth weight coefficient, and the third weight coefficient and the fourth weight coefficient are both 0.5. The preset space-time coupling abnormality coefficient value can be determined by the user according to an actual application scenario. The space-time coupling abnormality coefficient effectively reflects the time urgency and the space loading abnormality degree at the current loading moment. The greater the user's demand for loading process fluency, the smaller the preset space-time coupling abnormality coefficient value. In this embodiment, the preset space-time coupling abnormality coefficient is 1.2.

[0047] Specifically, for the reference design end with an influence level radiation coefficient greater than or equal to a preset influence level radiation coefficient or an interference superposition degree less than a preset interference superposition degree, all sub-regions are partially compressed at a level.

[0048] Specifically, the confirmation manner of the influence level radiation coefficient is that, for a single loading moment, layers with an operation focus degree greater than a preset focus operation degree in layers involved by each to-be-loaded sub-region of the target design end are recorded as influence layers, if the total amount of the influence layers is 0, the influence level radiation coefficient is directly recorded as 0, for a single reference design end, to-be-loaded sub-regions with an influence layer proportion greater than a preset influence layer proportion in the reference design end are recorded as influence sub-regions, if the total amount of the influence layers is greater than 0, the influence level radiation coefficient = the sum of abnormal reference values of each influence sub-region / the sum of abnormal reference values of each to-be-loaded sub-region in the reference design end. The focus analysis interval corresponding to the loading moment is [the start time of the reference period, the loading moment], and the operation focus degree of a single layer = the time length of operation on the layer in the focus analysis interval / the time length from the start time of the reference period to the loading moment. The preset focus operation degree value can be determined by the user according to an actual application scenario. The focus operation degree effectively reflects the operation concentration degree of the designer on each design layer. The greater the user's demand for accurate identification of core design layers and priority loading of key areas, the smaller the preset focus operation degree value. In this embodiment, the preset focus operation degree is 0.5.

[0049] The influence layer proportion of a single to-be-loaded sub-region = the number of influence layers in the to-be-loaded sub-region / the total amount of influence layers. The preset influence layer proportion value can be determined by the user according to an actual application scenario. The influence layer proportion effectively reflects the association degree between the to-be-loaded sub-region and the user's core operation layer. The greater the user's demand for accurate identification of key areas and efficient use of loading resources, the smaller the preset influence layer proportion value. In this embodiment, the preset influence layer proportion is 0.4.

[0050] The preset influence level radiation coefficient and the preset interference superposition degree are determined by the user according to the actual application scene, and the influence level radiation coefficient and the interference superposition degree effectively reflect the core influence range of the design layer and the interference degree of the delayed sub-region. When the influence level radiation coefficient is large or the interference superposition degree is small, it indicates that the core design area has a large influence range and weak spatial interference, and the partial level compression of all sub-regions can further reduce the data transmission amount on the premise of ensuring the loading effect of the core layer; when the influence level radiation coefficient and the interference superposition degree are small, it indicates that the proportion of the non-core area is high, and the delayed sub-region has a significant impact on the overall loading, and the full level compression of the delayed sub-region can reduce the transmission pressure of the abnormal area and avoid the spread of loading conflicts. The larger the user's demand for loading stability is, the smaller the preset influence level radiation coefficient and the preset interference superposition degree are. In the embodiment, the preset influence level radiation coefficient is 0.5, and the preset interference superposition degree is 0.4.

[0051] For a single loading time, the loading time is recorded as an interference analysis loading time, and the corresponding to-be-loaded sub-region of each reference design end corresponding to the loading time earlier than the interference analysis loading time and adjacent to the interference analysis loading time. The to-be-loaded sub-region of any reference design end that is not completed at the interference analysis loading time is recorded as a delayed sub-region. For a single loading time, the interference superposition degree corresponding to a single reference loading end = the number of delayed sub-regions of the reference design end in the to-be-loaded sub-region corresponding to the to-be-loaded time / the total amount of the delayed sub-region corresponding to the loading time. When the partial level compression of all sub-regions of a single reference design end is performed, the layers involved in the reference design end are selected for compression processing in the order of decreasing operation focus degree, and the number of selected layers is the smallest integer greater than or equal to w; w=(spatial timing coupling abnormality coefficient-preset spatial timing coupling abnormality coefficient) / preset spatial timing coupling abnormality coefficient x the number of layers involved in each to-be-loaded sub-region of the reference design end; the compressed layers and uncompressed layers of all to-be-loaded sub-regions of the reference design end are transmitted to the target design end at the same time, and the loading is completed after decompression. When the compression processing is performed, the vector data, attribute data and topology index structure of the layer are compressed at the byte level by using the LZ77 lossless compression algorithm. The specific compression operation process is a common technical means for those skilled in the art, and will not be described in detail.

[0052] Specifically, for the reference design end with an influence level radiation coefficient less than the preset influence level radiation coefficient and an interference superposition degree greater than or equal to the preset interference superposition degree, the full level compression of the delayed sub-region is performed.

[0053] When performing full-level compression of a slow sub-region for a single reference design, all layers involved in each slow sub-region within all sub-regions to be loaded corresponding to that reference design are compressed. Layers involved in sub-regions to be loaded other than slow sub-regions do not need to be compressed. The compressed layers involved in slow sub-regions and the uncompressed layers involved in the remaining sub-regions to be loaded are simultaneously transmitted to the target design, and loading is completed after decompression.

[0054] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An intelligent data processing method for public facility design, characterized in that, include: The design end category is determined based on the design dependency and loading anomaly coefficient, and then the layered loading is determined based on the comprehensive evaluation value, or the triggered loading is determined based on the data abundance value and behavioral feedback anomaly. Based on the incremental reference value and spatial loading heterogeneity, determine whether to adjust the loading strategy of the target design at each loading moment in the current loading design cycle from synchronous loading to asynchronous loading; During asynchronous loading, the loading priority coefficient corresponding to each reference design end is determined based on the anomaly evaluation value, and whether to perform in-end loading optimization for the reference design end is determined based on the iteration fluctuation and the proportion of abnormal sub-regions; wherein, the abnormal sub-regions are determined based on the sub-neighborhood density index and subspace crossover degree; In the in-terminal loading optimization, the sub-region group loading optimization strategy is implemented, and the spatial-temporal coupling anomaly coefficient is used to determine whether to adjust the optimization strategy to hierarchical compression loading. During the hierarchical compression loading process, the decision to perform slow sub-region compression at all levels or partial sub-region compression is determined based on the radiation coefficient and interference superposition degree of the influencing level.

2. The intelligent data processing method for public facility design according to claim 1, characterized in that, For a class of designs whose design dependency is greater than or equal to the preset design dependency or whose loading anomaly coefficient is greater than or equal to the preset loading anomaly coefficient, layered loading is performed based on the comprehensive evaluation value. In the layered loading process, the loading cycle of each type of design end is determined based on the comprehensive evaluation value, and loading is performed at the end of each loading cycle corresponding to each type of design end. The duration of the loading cycle for a single design type is negatively correlated with the overall evaluation value of that design type.

3. The intelligent data processing method for public facility design according to claim 2, characterized in that, For the second type of design end, where the design dependency is less than the preset design dependency and the loading anomaly coefficient is less than the preset loading anomaly coefficient, loading is triggered based on data abundance value and behavioral feedback anomaly degree. During the loading process, the loading is determined based on the data abundance value and the abnormality of the behavioral feedback. Loading is performed on the second-type design end when the data abundance value is greater than the preset data abundance value or the abnormality of the behavioral feedback is greater than the preset abnormality of the behavioral feedback.

4. The intelligent data processing method for public facility design according to claim 1, characterized in that, For loading moments where the incremental reference value is greater than or equal to the preset incremental reference value or the spatial loading heterogeneity is greater than or equal to the preset spatial loading heterogeneity, the loading strategy of the target design end is adjusted from synchronous loading to asynchronous loading.

5. The intelligent data processing method for public facility design according to claim 4, characterized in that, The loading priority coefficient for each reference design end is determined based on the abnormal evaluation value. The loading priority coefficient of a single reference design end is positively correlated with the anomaly evaluation value corresponding to that reference design end.

6. The intelligent data processing method for public facility design according to claim 5, characterized in that, For reference designs where the iteration volatility is greater than or equal to the preset iteration volatility or the proportion of abnormal sub-regions is greater than or equal to the preset proportion of abnormal sub-regions, in-terminal loading optimization is performed.

7. The intelligent data processing method for public facility design according to claim 1, characterized in that, When implementing the sub-region grouping loading optimization strategy, the associated regions are determined based on the conflict coefficient and the abnormal accumulation threshold, and the loading priority coefficient of each associated region is determined based on the design fit. The abnormal accumulation threshold is positively correlated with the loading density.

8. The intelligent data processing method for public facility design according to claim 1, characterized in that, If the spatial-temporal coupling anomaly coefficient is greater than or equal to the preset spatial-temporal coupling anomaly coefficient, the optimization strategy will be adjusted to hierarchical compression loading.

9. The intelligent data processing method for public facility design according to claim 8, characterized in that, For reference design ends where the radiation coefficient of the influence level is greater than or equal to the preset radiation coefficient of the influence level or the interference superposition degree is less than the preset interference superposition degree, partial level compression of all sub-regions is performed.

10. The intelligent data processing method for public facility design according to claim 9, characterized in that, For reference design ends where the radiation coefficient of the affected level is less than the preset radiation coefficient of the affected level and the interference superposition degree is greater than or equal to the preset interference superposition degree, all levels of the slow sub-region are compressed.

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