Earth enclosing device for urban and rural planning

Abstract

The invention discloses a circling device for urban and rural planning, and relates to the technical field of urban and rural circling, and the technical scheme is characterized in that the circling device comprises a walking base and further comprises an insertion rod fixing mechanism, the insertion rod fixing mechanism is arranged on the walking base, and the insertion rod fixing mechanism is used for insertion rod fixing operation; the data acquisition module is used for acquiring multi-source heterogeneous data of an urban and rural planning enclosure area, and performing fusion processing on the multi-source heterogeneous data to construct a digital twin model fixed by an enclosure insertion rod; the data extraction module is used for extracting key elements in the insertion rod fixing operation based on a digital twinborn model, determining an association influence coefficient set among the key elements and determining a normal change range value of the association influence coefficient among the key elements; the device has the advantages that stability and adaptability of insertion rod fixing are effectively improved, the problem of operation failure caused by improper element adaptation is reduced, waste of construction resources is reduced, and normalization and reliability of urban and rural planning enclosure operation are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of urban and rural land enclosure technology, and more specifically, to a land enclosure device for urban and rural planning. Background Technology

[0002] In the process of urban and rural planning and construction, land demarcation devices, as core tools for defining land use scope and regulating land use order, are widely used in scenarios such as land acquisition, project construction, and ecological protection zone demarcation. Their performance directly affects the accuracy of planning implementation and the standardization of land management. With the accelerated advancement of new urbanization, the intensity of urban and rural land development continues to increase.

[0003] However, traditional land-enclosing devices rely heavily on manual experience for deployment, lacking scientific data support. Construction workers often choose the type of poles, deployment density, and fixing method based on subjective judgment, which cannot take into account the influence of site parameters such as soil moisture and terrain slope, nor can it match the functional requirements of different planning scenarios. For example, ecological protection areas need to balance permeability and warning, while construction sites require high-intensity protection. Blind deployment can easily lead to waste of resources or insufficient protection. In addition, traditional devices do not have status monitoring capabilities. Problems such as pole tilting and fence damage need to be discovered by manual inspection, resulting in a strong response lag. Especially in remote planning areas, this can easily lead to land boundary disputes.

[0004] In summary, the shortcomings of existing land-capturing devices in terms of terrain adaptability, structural stability, operational scientificity, and intelligent management have become prominent issues restricting the quality of urban and rural planning implementation. Developing new land-capturing devices that can adapt to complex site conditions, balance stability and environmental protection, and integrate data-driven management has become an urgent need in the field of urban and rural planning. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a land enclosure device for urban and rural planning.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A land-capturing device for urban and rural planning includes a walking base and further includes: A pole fixing mechanism is mounted on the walking base and is used for pole fixing operations. Data acquisition module: Collects multi-source heterogeneous data of urban and rural planning land-grabbing areas, and performs fusion processing on the multi-source heterogeneous data to construct a digital twin model of land-grabbing poles; Data extraction module: Based on the digital twin model, the key elements in the pole fixing operation are extracted, and the set of correlation influence coefficients between the key elements and the normal range of variation of the correlation influence coefficients between the key elements are determined. Data association module: After determining the degree of association between the current pole fixing operation and similar historical operations on key elements based on the association influence coefficient set, the current association influence degree set is obtained. After processing the current association influence degree set, it is compared with the preset normal influence range value to obtain the comparison result. Data comparison module: Combines the in-depth analysis signals in the comparison results with the dynamic evolution of the operation elements in the digital twin model to determine the changing trends of the key elements of the current pole fixing operation and generate the first judgment data set; The implementation status of the current pole fixing operation is analyzed based on the first judgment data set, and the first stability judgment value is obtained; Data statistics module: Statistics on the distribution of construction resources associated with pole fixing operations. Based on the distribution of construction resources, site environment data, and the first stability judgment value, a comprehensive judgment and analysis is performed on the current pole fixing operation to obtain the pole fixing control result.

[0007] Preferably, the multi-source heterogeneous data includes basic information on the land parcels, soil survey data, pole insertion parameter information, and historical pole insertion and fixing operation data.

[0008] Preferably, determining the set of correlation influence coefficients between key elements and determining the normal range of variation of the correlation influence coefficients between key elements specifically includes the following steps: The key elements include pole type, plot topography, soil bearing capacity, and pole density; Determine the interrelationships among the key elements and establish a set of correlation influence coefficients among the key elements; Obtain the correlation coefficients between key elements in historical qualified operations and historical failed operations respectively, and construct the historical qualified correlation coefficient set and the historical failed correlation coefficient set; Based on the historical qualified correlation influence coefficient set and the historical failed correlation influence coefficient set, determine the normal variation range of the correlation influence coefficients between each key element.

[0009] Preferably, the current set of associated influence levels is processed and compared with a preset normal influence range value to obtain a comparison result, specifically including the following steps: If there are data in the current cluster of associated impacts that are greater than the preset normal impact range, the key operational elements corresponding to the data in the current cluster of associated impacts that are greater than the preset normal impact range will be identified as stability warning signals for the comparison results. If all data in the current set of associated impacts are less than the preset normal impact range value, the difference set is obtained by calculating the difference between each data in the current set of associated impacts and the preset normal impact range value. A preset difference threshold is set. If there are data in the difference set that are greater than or equal to the difference threshold, the key elements of the current pole fixing operation will continue to be analyzed in depth and the deep analysis signal of the comparison result will be output.

[0010] Preferably, the first judgment data set is generated by combining the deep analysis signal with the dynamic evolution law of the operation elements in the digital twin model to judge the changing trend of the key elements of the current pole fixing operation, which specifically includes the following steps: Based on the deep analysis signal, the dynamic evolution characteristics of the key elements of the current pole fixing operation are extracted, and a dynamic evolution model of the key elements is established by combining the dynamic evolution law of the operation elements in the digital twin model. After using a dynamic evolution model to determine the changing trends of key elements in the current pole fixing operation within a preset time period, a first set of judgment data is generated. The first set of judgment data includes the pole type adaptation trend, terrain feature adaptation change trend, soil bearing capacity response trend, and pole deployment density optimization trend.

[0011] Preferably, the process of analyzing the current implementation status of the pole fixing operation based on the first judgment data set and obtaining a first stability judgment value specifically includes the following steps: After judging the degree of influence of environmental interference factors on key elements of the operation based on the first judgment data set and digital twin model, the environmental impact degree set is obtained; Based on the environmental impact degree set and the first judgment data set, determine the second judgment data set that determines the factors affecting the current pole fixing operation; Based on the second set of judgment data, the implementation status of the current pole fixing operation is determined, and the first stability judgment value of the current pole fixing operation is determined according to the implementation status of the current pole fixing operation.

[0012] Preferably, the distribution of construction resources includes the number of construction workers, the availability of pole-planting equipment, and the density of material storage points.

[0013] Preferably, the site environmental data includes plot slope, soil moisture, and surrounding meteorological conditions.

[0014] Preferably, the current pole fixing operation is comprehensively analyzed based on the distribution of construction resources, site environmental data, and the first stability judgment value to obtain the pole fixing control result, specifically including the following steps: The second stability assessment value for the current pole fixing operation is determined based on the distribution of construction resources and site environment data. Based on the first stability judgment value and the second stability judgment value, a comprehensive judgment and analysis result of the current pole fixing operation is determined, and control instructions for pole type selection, layout density adjustment and fixing process optimization are generated.

[0015] Preferably, the insertion rod fixing mechanism includes: The loading box is fixedly connected to the walking base. A storage box is fixedly connected to the top of the loading box. A drive motor is fixedly connected to the bottom of the loading box. The output of the drive motor passes through the bottom of the loading box and is fixedly connected to a rotating rod. A winding wheel is sleeved on the rotating rod, and a protective cloth is wound around the winding wheel. Insert rods, at least two of which are installed on the fencing fabric; A guide box is rotatably connected to the outer side wall of the loading box. A fixed box is provided on the top of the guide box, and an electric telescopic rod is fixedly connected to the bottom of the fixed box. A servo motor is fixedly connected to the top of the electric telescopic rod, and a drive rod is fixedly connected to the output shaft of the servo motor. The drive rod cooperates with the insertion rod. A limit component is provided at the bottom of the guide box, and the limit component cooperates with the insertion rod.

[0016] Compared with existing technologies, this invention has the following beneficial effects: By collecting basic information of land parcels and soil survey data from multiple sources to construct a digital twin model, it can accurately extract key elements such as pole types and terrain features and analyze their related impacts. This allows for the early identification of stability risks exceeding the normal range during pole fixing operations. Through in-depth signal analysis, it can capture subtle anomalies and predict the changing trends of key elements. Furthermore, by integrating construction resource distribution and site environment data with dual stability prediction values ​​to generate control commands, it can specifically optimize pole selection, deployment density, and fixing processes. This effectively improves the stability and adaptability of pole fixing, reduces operational failures caused by improper element adaptation, reduces construction resource waste, and ensures the standardization and reliability of urban and rural planning land grabbing operations. Attached Figure Description

[0017] Figure 1 A bottom view of a land enclosure device for urban and rural planning is provided for this invention; Figure 2 This invention provides a schematic diagram of the internal structure of a land enclosure device for urban and rural planning. Figure 3 for Figure 2 A magnified schematic diagram of the partial structure at point A in the middle; Figure 4 This invention provides a schematic diagram of the connection structure between the drive motor and the winding wheel in a land enclosure device for urban and rural planning. Figure 5 This invention provides a partial sectional view of the insert rod and connecting cylinder in a land enclosure device for urban and rural planning; In the diagram: 1. Walking base; 2. Loading box; 3. Storage box; 4. Drive motor; 5. Rotating rod; 6. Rewinding wheel; 7. Enclosure cloth; 8. Insert rod; 9. Guide box; 10. Fixing box; 11. Electric telescopic rod; 12. Servo motor; 13. Drive rod; 14; 15. Connecting cylinder; 16. Connecting ring; 17. Arc-shaped through hole; 18. Bearing plate; 19. Stop bar; 20. Return spring; 21. Baffle. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0021] Reference Figures 1-5 As shown.

[0022] Example 1 further illustrates a land enclosure device for urban and rural planning proposed in this invention.

[0023] A land-capturing device for urban and rural planning includes a walking base 1, and further includes: A pole fixing mechanism is installed on the walking base 1 and is used for pole fixing operations. Data acquisition module: Collects multi-source heterogeneous data of urban and rural planning land-grabbing areas, and performs fusion processing on the multi-source heterogeneous data to construct a digital twin model of land-grabbing poles; Data extraction module: Based on the digital twin model, the key elements in the pole fixing operation are extracted, and the set of correlation influence coefficients between the key elements and the normal range of variation of the correlation influence coefficients between the key elements are determined. Data association module: After determining the degree of association between the current pole fixing operation and similar historical operations on key elements based on the association influence coefficient set, the current association influence degree set is obtained. After processing the current association influence degree set, it is compared with the preset normal influence range value to obtain the comparison result. Data comparison module: Combines the in-depth analysis signals in the comparison results with the dynamic evolution of the operation elements in the digital twin model to determine the changing trends of the key elements of the current pole fixing operation and generate the first judgment data set; The implementation status of the current pole fixing operation is analyzed based on the first judgment data set, and the first stability judgment value is obtained; Data statistics module: Statistics on the distribution of construction resources associated with pole fixing operations. Based on the distribution of construction resources, site environment data, and the first stability judgment value, a comprehensive judgment and analysis is performed on the current pole fixing operation to obtain the pole fixing control result.

[0024] The device achieves flexible movement via a walking base 1, which is equipped with drive wheels and a motor for rotating the wheels. This facilitates the movement of the walking base 1 and allows for adjustment of the working position within the planned area according to land acquisition needs. The pole fixing mechanism on the walking base 1 performs pole fixing operations, precisely inserting and securing the land acquisition poles 8 into the ground. Before and during pole fixing operations, the data acquisition module comprehensively collects multi-source heterogeneous data from the urban and rural planning land acquisition area, including site topographic data such as slope, altitude, and soil type. Data on underground pipeline distribution, such as the burial depth and direction of water pipes and cables, meteorological data such as real-time wind speed and soil moisture, and the coordinates of land enclosure boundaries and the spacing requirements of poles in the planning drawings; due to the different formats and sources of different types of data, the data acquisition module will integrate and process them, and through data cleaning to remove outliers, data standardization to unify the format, and data association to complete missing information, a digital twin model of land enclosure pole fixing that highly matches the actual land enclosure scenario is finally constructed, which transforms the site environment and operation requirements of the physical world into digital information, providing a visualized and computable virtual carrier for subsequent analysis. Key factors include soil compaction, material and length of the insertion rod 8, preset insertion depth of the insertion rod 8, safe distance between underground pipelines and the insertion rod 8, and the impact of meteorological conditions on the stability of the insertion rod 8, which directly determine the quality and safety of the insertion rod 8 fixation. After extracting the key factors, the data extraction module calculates the set of correlation coefficients between each key factor through training with historical operation data and field test verification. For example, the coefficient between soil compaction and the required insertion force of the insertion rod 8, and the coefficient between wind speed and the risk of tilting of the insertion rod 8. At the same time, it determines the normal variation range of each correlation coefficient, which represents the interval in which the interaction between the factors is in a safe and reasonable state. The data association module, based on the established set of correlation influence coefficients, compares the key element parameters of the current pole-planting operation with those of similar historical operations, such as pole-planting operations under the same soil type and similar weather conditions. It calculates the degree of correlation influence between the current and historical operations on each key element, thus forming a current correlation influence degree set. The data extraction module then standardizes this set and compares it one by one with preset normal influence range values. If the correlation influence degree of all elements is within the normal range, the current operation is considered stable. If the correlation influence degree of one or more elements exceeds the range, a deep analysis signal is generated, indicating the need for further risk assessment. The data comparison module then performs in-depth processing on the deep analysis signals in the comparison results, and combines them with the dynamic evolution of the operational elements in the digital twin model. For example, as the insertion depth of the insertion pole 8 increases, the soil's gripping force on the insertion pole 8 changes, and the stability of the insertion pole 8 decreases when the wind speed increases. It analyzes the changing trends of the key elements of the current insertion pole fixing operation, and generates a first judgment data set containing the possible states, risk points, and development speed of each element in the future. The data comparison module further evaluates the implementation status of the current operation, and judges whether there are problems such as insufficient insertion depth of the insertion pole 8, increased risk of tilting, or contact with underground pipelines. Finally, it quantifies the results into a first stability judgment value. The higher the value, the more stable the current operation status and the lower the risk. The data statistics module analyzes the distribution of construction resources associated with the pole fixing operation, including the number of poles 8 in reserve, the operating status of the pole fixing mechanism, and the location of the workers. Combining site environmental data such as real-time soil moisture changes, the location of temporary obstacles, and the first stability judgment value, a comprehensive judgment is made through multi-dimensional weighted analysis: if construction resources are sufficient, the site environment is normal, and the first stability judgment value is high, a pole fixing control result is generated to maintain the current operating parameters; if a sudden increase in soil moisture in a certain area causes a decrease in the stability judgment value, and there are spare poles nearby, a control result is generated to adjust the depth of pole 8 to a deeper value and call the spare poles; if an underground pipeline risk is found along the path of pole 8, a control result is generated to suspend the operation and replan the position of pole 8, and this is converted into specific instructions transmitted to the pole fixing mechanism and the traveling base 1 to guide the device to accurately execute the pole fixing operation, ensuring the safety and accuracy of urban and rural planning land acquisition work.

[0025] Multi-source heterogeneous data includes basic information on land parcels, soil survey data, pole insertion parameter information, and historical pole insertion and fixing operation data.

[0026] The basic information of the land parcel includes the boundary coordinates, area, terrain slope, distribution of surrounding buildings, and underground pipeline routes. For example, the boundary coordinates of the planned land parcel are from 116 degrees east longitude and 39 degrees north latitude to 116.02 degrees east longitude and 39.01 degrees north latitude. The terrain is higher in the west and lower in the east with a slope of 5 degrees. There is a water pipe 50 centimeters underground. Soil survey data directly affects the fixing effect of the poles. It covers the soil type of different areas of the land parcel, such as clay, sand, loam, soil density, moisture content, and foundation bearing capacity. For example, the eastern part of the land parcel is sandy soil with a density of 1.6 grams per cubic centimeter and a moisture content of 15%, while the western part is clay with a density of 1.8 grams per cubic centimeter and a moisture content of 20%. The pole parameter information includes the pole material, such as steel or fiberglass, length, such as 1.5 meters or 2 meters, diameter, such as 3 centimeters or 5 centimeters, and preset insertion depth, such as 80 centimeters or 100 centimeters. Historical data on pole insertion operations provides empirical references, including records of pole insertion operations in similar soils and terrains, such as the operational parameters and final stability assessment results of using 1.8-meter steel poles with an insertion depth of 90 centimeters in an adjacent clay plot two years ago. The data acquisition module fuses heterogeneous data to construct a digital twin model. Due to the different formats and dimensions of the four data types—land parcel basic information primarily consisting of geographic coordinates and graphic data, soil survey data representing physicochemical parameters, pole insertion parameters representing engineering and technical data, and historical operation data representing time-series records—data cleaning is required first. This involves removing outliers in soil moisture content, correcting deviations in underground pipeline coordinates, and supplementing missing pole inclination data from historical operations. Then, data standardization unifies the conversion of parameters in different units, such as converting soil density (grams per cubic centimeter) and foundation bearing capacity (kilopascals) into a unified index. Data association establishes connections between the various data sets. For example, the western clay area of ​​the land parcel is linked to the corresponding density data, historical clay land parcel operation data, and preset 2-meter steel pole parameters, forming a digital twin model that fully corresponds to the actual land parcel. This model presents a complete panoramic view of the land parcel environment, pole insertion parameters, and historical experience in virtual space. The core factors affecting the fixation of the insertion poles were extracted from the fused data: soil compaction, moisture content, pole length, preset insertion depth, distance to underground pipelines, and the stability coefficient of the poles in historical operations. Through historical data fitting and field tests, the correlation coefficient set between each factor was calculated. For example, for every 0.1 g / cm³ increase in soil compaction, the required insertion force increases by 20%; for every 0.2 m increase in pole length, the risk of collision with underground pipelines increases by 15%. The normal range of variation for each coefficient was also determined. For instance, the normal range for the correlation coefficient between soil moisture content and pole gripping force is 0.8 to 1.2; exceeding this range indicates that excessively high or low soil moisture affects the fixation effect. Based on the set of correlation influence coefficients, the current operation parameters for the sandy soil area in the eastern part of the plot—1.5-meter steel rod, preset insertion depth of 80 cm, and soil moisture content of 15%—are compared with the operation data of historical sandy soil plots using the same rod, insertion depth of 75 cm, and moisture content of 14%. The correlation influence degree set between the two in terms of soil conditions and rod parameters is calculated. This set is compared with the normal influence range value. If all coefficients are within the range, the operation status is judged to be normal. If the current moisture content is slightly high, causing the gripping force coefficient to drop to 0.7, which exceeds the normal range, a depth analysis signal is generated. Referring to the evolution of increased moisture content, decreased soil cohesion, and decreased stability of the stake in the digital twin model, the potential tilting risk of stake 8 was analyzed, and a first judgment data set was generated. This set includes predictions that the tilt of stake 8 may increase from 0 to 3 degrees and the stability coefficient may decrease from 1.0 to 0.8 within the next 10 minutes. Therefore, the first stability judgment value is set to 65 points out of 100, indicating a medium risk. The distribution of construction resources was statistically analyzed: there were 50 1.5-meter steel poles and 30 2-meter steel poles on site. The pole fixing mechanism was operating normally, and the workers were on standby in the middle of the plot. Based on real-time site environmental data, such as the moisture content of the sandy soil in the eastern area still slowly increasing, and the underground pipeline being 1 meter away from the preset pole position, and considering the first stability judgment value of 65 points, sufficient resources, and pipeline safety, a control command was generated to adjust the pole insertion depth to 90 centimeters and maintain the current pole type. This command was then transmitted to the pole fixing mechanism to guide its precise operation, ensuring the stable fixing of pole 8 and avoiding risks.

[0027] Determining the set of correlation influence coefficients among key elements and determining the normal range of variation for these coefficients involves the following steps: Key factors include pole type, site topography, soil bearing capacity, and pole density; Determine the interrelationships among the key elements and establish a set of correlation influence coefficients among the key elements; Obtain the correlation coefficients between key elements in historical qualified operations and historical failed operations respectively, and construct the historical qualified correlation coefficient set and the historical failed correlation coefficient set; Based on the historical set of correlation coefficients for qualified and unqualified elements and the historical set of correlation coefficients for unqualified elements, determine the normal range of variation of the correlation coefficients between key elements.

[0028] The interrelationships among four key elements were determined, and a set of correlation coefficients was established. These four key elements are: pole type, site topography, soil bearing capacity, and pole placement density. Pole type determines the strength and stiffness of the pole itself; for example, steel poles are more resistant to bending than fiberglass poles. Site topography includes slope and flatness; steep slopes cause the poles to bear greater lateral forces. Soil bearing capacity includes soil density and foundation bearing capacity, directly affecting the soil's grip on the poles. Pole placement density refers to the number of poles per unit area; higher density results in stronger overall stability. When analyzing the interrelationships, the effects of each element on other elements are examined: for example, between the type of stake and soil bearing capacity, steel stakes require slightly lower soil density in soft soil than fiberglass stakes, showing a negative correlation; between the topographic features of the plot and the stake density, steep slopes require higher density to enhance overall stability, showing a positive correlation; the correlation coefficient between steel stakes and sand density (1.5 g / cm³) is set at 0.9, while that between fiberglass stakes and the same type of sand is set at 0.7, thus reflecting the difference in the impact of stake type on soil adaptability. The correlation coefficients of historical qualified operations and historical failed operations were obtained separately to construct two types of coefficient sets. Historical qualified operations refer to operation records in which the poles remained stable for a long period after fixing without tilting or falling off. The pole type, terrain, soil, and density parameters at that time were extracted from the records and substituted into the established correlation model to calculate the correlation coefficients between each element, which were then summarized to form the historical qualified operation correlation coefficient set. For example, from 500 qualified operation records of clay gentle slope plots, the coefficients of steel poles and clay bearing characteristics were mostly between 0.8 and 1.0, and the coefficients of pole placement density and slope were mostly between 0.7 and 0.9. Historical failed operations refer to operation records in which the poles tilted, collapsed, or failed to fix. Relevant parameters were extracted to calculate coefficients and construct the historical failed operation correlation coefficient set. For example, the failed operation records of steep slope plots show that the coefficient of fiberglass poles and low-density soil was only 0.5, and the coefficient of pole placement density and slope was 0.4, both significantly lower than the qualified operation coefficient level.

[0029] By combining two sets of historical coefficients, the normal range of variation for the correlation coefficients between various elements is determined. Through comparative analysis of historical qualified and failed coefficient sets, the boundary intervals between the two types of operational coefficients are identified: the minimum value in the qualified coefficient set is the safety threshold, and the maximum value in the failed coefficient set is the risk threshold. The range between these two is the normal range of variation. For example, the comparison reveals that the correlation coefficient between the type of stake and soil bearing capacity is lowest at 0.7 in the qualified set and highest at 0.6 in the failed set. Therefore, the normal range of variation for this pair of elements is determined to be an upper limit of 0.7 to 1.2, combined with the optimal operational state setting; the topographic features of the plot include slope. For angles within 5 degrees and the density coefficient of pole placement, the minimum value is 0.6 in the qualified set and the maximum value is 0.5 in the failed set. The normal range of variation is set to 0.6 to 1.0. For cases with multiple sets of overlapping data, a weighted calculation is performed based on the complexity of the working environment to ensure that the range covers the coefficients of most qualified operations while effectively excluding the coefficient ranges of failed operations. The final normal range of variation is determined as the standard for judging whether the interaction between elements in the current operation is safe and reasonable. If the current coefficient is within the range, it indicates that the element matching status is good. If it exceeds the range, it indicates that there is a risk in the operation and the parameters need to be adjusted in time.

[0030] The current set of associated influence levels is processed and compared with the preset normal influence range value to obtain the comparison result. The specific steps include: If there are data in the current cluster of associated impacts that are greater than the preset normal impact range, the key operational elements corresponding to the data in the current cluster of associated impacts that are greater than the preset normal impact range will be identified as stability warning signals for the comparison results. If all data in the current set of associated impacts are less than the preset normal impact range, the difference set is obtained by calculating the difference between each data in the current set of associated impacts and the preset normal impact range. A preset difference threshold is set. If there are data in the difference set that are greater than or equal to the difference threshold, the key elements of the current pole fixing operation will continue to be analyzed in depth and the deep analysis signal of the comparison result will be output.

[0031] The current set of correlation influence degrees is preprocessed. This set includes correlation influence coefficients for multiple pairs of key elements, such as pole type and plot topography, soil bearing capacity and pole placement density. Since these coefficients may have slight deviations due to differences in data collection accuracy or calculation dimensions, standardization conversion is performed during preprocessing to unify all coefficients with the preset normal influence range values ​​to ensure the fairness of the comparison. For example, the preset normal influence range for pole type and soil bearing capacity is 0.7 to 1.2, while the coefficient for this pair of elements in the current set of correlation influence degrees is 1.32. The values ​​are kept unchanged after preprocessing to ensure direct comparison with the range values. If the current concentration of related influences contains data exceeding the preset normal influence range, the upper limit of the preset normal influence range is determined based on historical qualified operation data to determine the safety threshold. A coefficient greater than the upper limit indicates that the interaction between the corresponding key elements has exceeded the safe adaptation range, which may cause operational stability issues. At this time, the system will directly lock the key operational elements corresponding to the out-of-range data and mark them as stability warning signals of the comparison results. For example, if the current concentration of related influences is such that the correlation coefficient between the pole placement density and the 8-degree slope of the plot is 1.3, and the upper limit of the normal influence range for this pair of elements is 1.2, the coefficient exceeding the upper limit indicates that the current pole placement density and the steep slope terrain are overmatched. Due to the excessive density, the poles are subjected to mutual force interference. The system will then output a stability warning signal, clearly pointing to the immediate risk of the key elements of pole placement density and plot terrain characteristics. If all data in the current set of associated impacts are less than the preset normal impact range, it is necessary to further determine whether the difference between the coefficient and the range value reaches the risk warning standard. The difference between each data in the current set of associated impacts and the preset normal impact range value is determined to form a difference set. The difference is calculated based on the lower limit of the normal impact range value. The difference between the lower limit and the current coefficient is obtained to obtain the difference for each pair of elements. The larger the difference, the further the adaptability between elements deviates from the safe state. For example, the preset lower limit of the normal impact range value for soil bearing capacity and pole type is 0.7. The current coefficient of this pair of elements is 0.5, and the difference is 0.2. The current coefficient of pole placement density and soil bearing capacity is 0.6. The normal lower limit is 0.7, and the difference is 0.1. Thus, they together constitute the difference set. A preset difference threshold is introduced, which is a risk threshold set based on historical failure operation data. When the difference reaches or exceeds this threshold, it indicates that the element's adaptability is close to the edge of failure, requiring in-depth investigation of potential hazards. Each value in the difference set is compared with the difference threshold one by one. If there is data greater than or equal to the difference threshold, a deep analysis process is triggered to make a more detailed judgment on the corresponding key elements and output a deep analysis signal. For example, if the preset difference threshold is 0.15, and the difference between the stake type and the soil bearing capacity in the above difference set is 0.2, which is greater than the threshold... This indicates that the current pole type and soil bearing capacity are approaching the critical risk level, possibly due to insufficient pole strength making it difficult to adapt to the soil bearing capacity. This results in the output of a deep analysis signal, suggesting that the dynamic evolution trend of this pair of elements needs to be further analyzed in conjunction with a digital twin model. If all data in the difference set are less than the difference threshold, such as the two values ​​in the above difference set being 0.08 and 0.05, both less than 0.15, it means that although the current element adaptability has not reached the optimal level, it is still within the safe range. No special signal needs to be output, and the operation can continue with the current parameters.

[0032] By comparing and judging in a hierarchical and multi-condition manner, different levels of operational risks can be accurately identified, and stability warning signals or in-depth analysis signals can be output respectively. This avoids overreaction to minor deviations and can capture potential hidden dangers in a timely manner, providing a reliable basis for subsequent operational status assessment and control decisions.

[0033] By combining deep analysis signals with the dynamic evolution patterns of operational elements in the digital twin model, the changing trends of key elements in the current pole-fixing operation are determined to generate the first set of judgment data. This specifically includes the following steps: Based on the deep analysis signal, the dynamic evolution characteristics of the key elements of the current pole fixing operation are extracted, and a dynamic evolution model of the key elements is established by combining the dynamic evolution law of the operation elements in the digital twin model. After using a dynamic evolution model to determine the changing trends of key elements in the current pole fixing operation within a preset time period, a first set of judgment data is generated. The first set of judgment data includes the trend of pole type adaptation, the trend of terrain feature adaptation change, the trend of soil bearing capacity response, and the trend of pole layout density optimization.

[0034] By deeply analyzing the signals, key element pairs with mismatches are identified. For example, if the soil bearing capacity and the pole type mismatch coefficient are lower than the lower limit of the normal range and the difference exceeds the threshold due to the increase in soil moisture content, these elements are marked as key analysis objects. Then, real-time dynamic data of the element pair are retrieved from the digital twin model to extract evolution characteristics. Taking soil bearing capacity and steel poles as an example, the changes in soil density from 1.7 g / cm³ to 1.5 g / cm³ and pole gripping force from 80 kN to 60 kN when soil moisture content increases by 2% per hour are extracted. At the same time, the influence of small fluctuations in terrain slope from 5 degrees to 6 degrees on the lateral force of the pole is recorded. The model is improved by combining the dynamic evolution patterns of operational elements accumulated in the digital twin model. Based on historical operational data and real-time monitoring data, it is summarized that, for example, the soil bearing capacity changes with moisture content by 0.05 for every 1% increase in moisture content; and the adaptation of pole placement density to terrain slope increases by 5% for every 1 degree increase in slope. The extracted current evolutionary characteristics are integrated with general laws to establish a dynamic evolution model of key elements for the current operational scenario. This model can accurately simulate the interaction and change logic between elements in the current environment. For example, for clay slopes, the model can clearly show the chain evolution logic of increasing soil moisture content, decreasing bearing capacity, weakened pole type adaptability, and the need to adjust placement density for compensation. The dynamic evolution model is used to predict the changing trends within a preset time period and generate a first judgment data set. The preset time period is usually set according to the operation progress and risk response cycle, such as the next 2 hours or 4 hours. Based on the current data, the dynamic evolution model infers four core trends within this time period: the pole type adaptation trend focuses on the changes in the adaptation status of the pole with the soil and terrain. If the current adaptation coefficient of the fiberglass pole in the soil with rising moisture content is 0.6, the model predicts that when the moisture content rises to 25% in 2 hours, the adaptation coefficient will drop to 0.4, and the trend is continuous weakening; the terrain feature adaptation change trend focuses on the impact of small changes in terrain. For example, if the slope is predicted to increase to 7 degrees due to rainfall erosion, the adaptation coefficient of the pole with the terrain will drop from 0.7 to 0.5, and the trend is gradual deterioration. The soil bearing capacity response trend focuses on the feedback changes of the soil to the poles. For example, if the current soil gripping force on the poles is 60 kN, the model, combined with the water content evolution law, predicts that the gripping force will further decrease to 45 kN after 2 hours, and the decay rate will accelerate over time, with a trend of continuous decline. The pole placement density optimization trend proposes optimization directions based on the first three trends. If the current density is 3 poles per square meter, the model, combined with the topography and soil deterioration trend, predicts that the density needs to be increased to 5 poles per square meter to maintain adaptability, with a trend of gradual densification. The four types of trend data are organized into a first judgment data set, which not only includes the trend direction, but also marks the rate of change and critical nodes. For example, the pole type adaptation trend will indicate that the adaptation coefficient will fall below the failure critical value of 0.3 after 1.5 hours. The soil bearing capacity response trend will indicate that emergency adjustment needs to be initiated when the gripping force drops to 50 kN. The first judgment data set allows operators to grasp the evolution trend of key elements in advance, providing accurate and forward-looking data support for subsequent assessment of implementation status and formulation of control strategies.

[0035] The analysis of the current pole-fixing operation based on the first set of judgment data yields the first stability judgment value, specifically including the following steps: After judging the degree of influence of environmental interference factors on key elements of the operation based on the first judgment data set and digital twin model, the environmental impact degree set is obtained; Based on the environmental impact degree set and the first judgment data set, determine the second judgment data set that determines the factors affecting the current pole fixing operation; Based on the second set of judgment data, the implementation status of the current pole fixing operation is determined, and the first stability judgment value of the current pole fixing operation is determined according to the implementation status of the current pole fixing operation.

[0036] Environmental disturbance factors refer to sudden or continuous external factors during operation, such as changes in soil moisture caused by real-time rainfall, lateral impact force of gusts on the poles, and local terrain deformation caused by temporary construction machinery. The digital twin model stores the correlation patterns between various environmental factors and key operational elements. For example, when the rainfall intensity is 5 mm per hour, the clay moisture content increases by 3% per hour, and the soil bearing capacity coefficient decreases by 0.08; when the gust wind speed is 8 m / s, the lateral force on the poles increases by 20%, and the deployment density adaptation coefficient decreases by 0.05. Combined with the trend base in the first judgment data set... For example, if the predicted trend of soil bearing capacity response is a continuous decline, the current environmental data in the digital twin model is retrieved, such as rainfall intensity of 4 mm per hour and gust wind speed of 6 meters per second in the next 2 hours. The superimposed impact of these factors on the trends of the four key elements is calculated: rainfall will accelerate the rate of decline of soil bearing capacity, and gusts will exacerbate the adaptation deviation between the pole type and the terrain. The impact of each type of environmental factor on each key element is quantified into specific values ​​to form a set of environmental impact degrees. For example, the impact degree of rainfall on soil bearing capacity is 0.2, and the impact degree of gusts on pole type adaptation is 0.15. Based on the environmental impact degree set and the first judgment data set, a second judgment data set is generated to assess the impact on the operation. The first judgment data set represents the natural evolution trend of key elements, while the environmental impact degree set shows the cumulative effect of external factors. Combining the two accurately identifies the core aspects and extent of the impact on the operation. For example, in the first judgment data set, the soil bearing capacity response trend is that the gripping force decreases from 60 kN to 45 kN within 2 hours. The environmental impact degree set shows that rainfall has an impact degree of 0.2, indicating that rainfall will cause an additional decrease of 6 kN in gripping force within 2 hours. The gripping force will drop to 39 kN; the optimization trend of pole placement density in the first judgment data set is to increase from 3 poles per square meter to 5 poles, with a gust impact level of 0.1, indicating that an additional density of 5.5 poles per square meter is needed to offset wind interference; the superimposed affected data are then categorized and organized by element to form the second judgment data set, which includes: the soil bearing capacity gripping force is decreasing rapidly due to the superposition of rainfall and natural evolution; the lateral stability risk of poles is escalating due to the superposition of gusts and deteriorating terrain adaptation; and the pole placement density needs to be further increased to adapt to the dual impacts. The implementation status of the operation is determined based on the second judgment data set and transformed into the first stability judgment value. The implementation status is a qualitative description of the overall state of the operation at present and for a period of time in the future. It is mainly divided into three categories: stable and controllable, critical warning, and prominent risk. By analyzing the degree of impact of each element in the second judgment data set: if all elements are still within the safety threshold after being affected, for example, although the gripping force of the pole decreases, it is still higher than the safety bottom line of 30 kN. After adjusting the deployment density, it can adapt to the environmental impact, and the implementation status is judged as stable and controllable; if some elements are close to the safety threshold, such as the gripping force is about to drop to 30 kN, and the parameters need to be adjusted immediately to maintain stability, it is judged as critical warning; if multiple elements have exceeded the safety threshold, such as the gripping force has dropped to 25 kN and the pole shows a significant tilting trend, it is judged as prominent risk; for example, the second judgment data set of the operation shows that the soil gripping force after superimposed impact is 38 kN, the safety bottom line is 30 kN, the pole type adaptation coefficient drops to 0.65, the safety lower limit is 0.6, and after adjusting the deployment density, it can rise to the adaptation requirements, and the implementation status is judged as critical warning. Based on the level corresponding to the implementation status and combined with the quantitative data of the impact on each element, the first stability judgment value is determined. A stable and controllable status corresponds to 80 to 100 points, a critical warning status corresponds to 60 to 79 points, and a prominent risk corresponds to below 60 points. At the same time, fine-tuning is made based on the specific impact values: if in a critical warning status, each element still has a certain margin from the safety threshold, such as a gripping force of 38 kN far exceeding the bottom line, the score is 75 points; if it is close to the bottom line, such as a gripping force of 32 kN, the score is 65 points. For the above critical warning status operation, because the gripping force is sufficient but the adaptation coefficient is close to the lower limit, the first stability judgment value is finally determined to be 70 points. This value represents the stability level of the current operation and provides a quantitative basis for subsequent comprehensive decision-making.

[0037] The distribution of construction resources includes the number of construction workers, the availability of pole-planting equipment, and the density of material storage points.

[0038] The distribution of three types of construction resources was statistically analyzed to construct a resource status profile. The statistics on the number of construction personnel included not only the total number of people on site but also the proportion of different trades, such as the number of technicians responsible for pole insertion, site survey technicians, and emergency support personnel. For example, at a site surveying operation, there were 20 construction workers, including 12 pole insertion technicians, 5 technicians, and 3 support personnel. The pole insertion equipment configuration covered the equipment type, quantity, and operating status, including the number of pole fixing mechanisms driven by the walking base 1, the equipment's insertion force parameters, failure rate, and adjustability. The scope of the equipment includes 8 pole-inserting machines on site, of which 6 are hydraulically driven with an insertion force of 80 kN and are in good working order, 2 are mechanically driven with an insertion force of 60 kN and 1 is under maintenance. All equipment can cover the east and west sides of the site. The density of material storage points reflects the supply guarantee capacity of pole materials. The number, location and inventory of various types of poles of the storage points are statistically analyzed. For example, 3 storage points are set up around the site, with an average of 1 point every 50 meters, storing a total of 500 steel poles and 300 fiberglass poles. The inventory can meet 1.5 times the current operation needs. The resource distribution data, site environment data, and first stability assessment value are integrated and analyzed in a multi-dimensional manner to determine the suitability of resources for operational needs. Site environment data provides constraints on resource scheduling, such as the low-lying area in the north of the site being prone to water accumulation after rain, which restricts the passage of heavy equipment; the steep slope in the south requires additional skilled workers to ensure safety. The first stability assessment value clarifies the current risk level of the operation. For example, if the first stability assessment value of the operation is 65 points, it is in a critical warning state, and stability needs to be improved by adjusting the depth of the poles and increasing the density of their deployment. During the fusion analysis, the first step is to determine whether resources can support the risk mitigation needs: If the first stability assessment value indicates that the insertion depth needs to be increased from 80 cm to 100 cm, it is necessary to confirm whether the insertion force of the insertion equipment meets the standard. The insertion force of the 6 hydraulic devices on site is sufficient, and the 12 technicians can be divided into groups to take turns operating, meeting the depth adjustment requirements; If the assessment value indicates that the deployment density in a local area needs to be increased from 3 to 5 per square meter within 2 hours, it is necessary to check the supply capacity of the material reserve point. The nearby reserve point is only 30 meters away from the target area, and the steel insertion pole inventory is sufficient and can be quickly replenished. Then, the resource allocation plan is adjusted in combination with the site environment constraints: In low-lying areas where heavy equipment cannot be used, lightweight mechanical drive equipment is deployed to remove the equipment to be repaired, and 3 technicians are assigned to focus on the work in this area; In steep slope areas, an additional technician is added to provide on-site guidance to ensure the accuracy of the insertion angle. Based on the adaptability assessment results, targeted control results for pole fixing are generated. If all three types of resources can fully adapt to operational needs and environmental constraints, and the first stability assessment value has room for improvement, the control results will focus on optimizing operational parameters and efficient resource scheduling. For example, instructing 12 technicians to work in pairs with hydraulic equipment to insert 100cm deep steel poles at a density of 5 poles per square meter in the steep southern slope area. The reserve point is replenished with materials every hour, and technicians conduct zoned inspections to ensure support. If there are resource deficiencies that affect risk mitigation, the control results will focus on resource replenishment and operational adjustments. For example, if the first stability assessment value is 55 points, indicating a significant risk, pole insertion needs to be densified immediately, but on-site inspection is pending. If equipment repairs lead to a shortage of available equipment, the control system will prioritize repairing the faulty equipment and temporarily allocate two hydraulic machines from nearby construction sites. During the operation, construction in low-lying areas will be suspended, and resources will be concentrated on dealing with high-risk steep slope areas first. Full-scale progress will resume once the equipment arrives. If resources are severely insufficient and cannot be replenished in the short term, the control system will trigger an operation suspension and plan reprogramming. For example, if the reserve pole inventory can only meet 0.8 times the current demand and the stability assessment value continues to decline, the system will instruct the operation to be suspended, coordinate with material suppliers for expedited delivery, and technicians will replan the pole spacing and type based on the resource gap to ensure that subsequent operations meet both safety standards and are adapted to the current resource situation. By deeply linking resource supply capacity with operational risk status and environmental constraints, the resulting control measures can ensure the stability of pole fixing operations and achieve efficient utilization of construction resources, avoiding operational delays and safety hazards caused by resource waste or insufficient supply.

[0039] Site environmental data includes plot slope, soil moisture, and surrounding meteorological conditions.

[0040] Collect and analyze three types of site environmental data to clarify the natural environmental constraints of the operation. The slope data of the plot needs to be refined to specific values ​​and trends in different areas. For example, the overall plot is higher in the west and lower in the east, with a slope of 8 degrees in the west, 5 degrees in the middle, and 2 degrees in the east. There are also some steep slopes of up to 12 degrees on the western edge. The slope difference directly determines the force direction of the poles and the difficulty of their placement. Soil moisture data needs to cover the real-time moisture content and rate of change in each area of ​​the plot. For example, the moisture content of the clay area in the east is 22%, which is moderate. The moisture content of the sandy soil area in the west has risen to 28% due to previous rainfall and is still slowly increasing at a rate of 1% per hour. The level of moisture directly relates to the soil bearing capacity and the gripping force of the poles. The surrounding meteorological conditions focus on short-term foreseeable weather changes. For example, there will be light wind turning into moderate rain in the next 3 hours, with the wind speed increasing from 3 meters per second to 6 meters per second and the rainfall being about 15 millimeters. Meteorological changes will further exacerbate the dynamic fluctuations of the site environment.

[0041] The site environmental data is correlated and adapted with the distribution of construction resources and the first stability judgment value. The distribution of construction resources serves as the supply side, matching the demand-side constraints brought by the environmental data. The number of construction personnel needs to be adapted to the slope differences. In the western steep slope areas of 8 degrees or more, two people need to work together to control the angle when inserting poles, while in the eastern gentle slope areas, a single person can work. The pole inserting equipment needs to be adapted to the soil moisture. The high-moisture sandy soil in the west has weak bearing capacity, so hydraulic drive equipment with greater insertion force is required to avoid pole tilting. The distribution density of material storage points needs to be adapted to weather changes. Considering the upcoming rainfall, it is necessary to ensure that each area is no more than 40 meters away from the storage point to facilitate the rapid replenishment of rainproof materials or replacement of pole types suitable for wet soil.

[0042] The first stability assessment value is used to measure the operational risk level under the current environmental and resource conditions. For example, if the first stability assessment value of a certain operation is 60 points, it is in a critical warning state. Combined with site environmental data, the root cause of the risk can be located: the high humidity sandy soil in the west leads to a decrease in soil bearing capacity, and the upcoming rainfall will further reduce the gripping force of the poles; the current deployment density in the steep slope area is insufficient, and it is easily affected by lateral forces after the wind speed increases. At this time, it is necessary to determine whether the existing resources can mitigate the risks caused by the environment: there are 12 construction workers on site, and 6 teams of two people can be deployed to be responsible for the steep slope area; 5 of the 8 pieces of equipment are hydraulically driven, and can be prioritized for deployment in the western sandy soil area; the 3 reserve points are all 35 meters apart, and there are long-handled steel poles suitable for wet soil in stock, so the resource supply can meet the demand.

[0043] Based on the adaptation analysis results, targeted control results for pole fixing are generated. If environmental constraints can be resolved through resource optimization scheduling, and the stability judgment value can be improved, the control results clearly define an environmentally adaptable resource allocation + parameter adjustment scheme: The instructions are to deploy 5 hydraulic devices to the western sandy area, with 6 teams of two people responsible for pole planting in this area, increasing the pole planting depth from 80 cm to 100 cm to enhance gripping force; in the eastern gentle slope area, mechanically driven equipment is used, with single-person operation maintaining a depth of 80 cm; steel poles are transported to various areas for temporary storage at the reserve point, and pole planting in steep slope areas is completed before rainfall. During rainfall, operations are suspended and technicians are dispatched to inspect the pole planting status.

[0044] If the site environment presents extreme constraints and resources are insufficient, the control outcome will focus on risk avoidance and resource replenishment. For example, in the western steep slope area with a gradient of 12 degrees, there are only 3 skilled workers among the existing construction personnel who are proficient in steep slope operations, and the upcoming moderate rain will cause the soil moisture to exceed 30%, lowering the first stability assessment value to 55 points. In this case, the control outcome will instruct to prioritize suspending operations in the western steep slope area, temporarily transferring 2 skilled workers for steep slope operations from nearby construction sites; allocating the longest steel poles in the inventory (2 meters) to this area; and, once the skilled workers arrive and the humidity decreases slightly during the rain intervals, using hydraulic equipment to install high-density poles (5 poles per square meter) at a depth of 120 centimeters; at the same time, requiring the reserve points to increase rainproof coverage to prevent the poles from getting damp and affecting their strength.

[0045] If environmental constraints exceed the resource carrying capacity limit, such as a sudden increase in rainfall to 20 millimeters per hour, a rapid rise in soil moisture to 35%, a significant decrease in the carrying capacity of all areas, and a first stability assessment score below 50 points, the control results will trigger an "emergency stop + environmental monitoring" instruction, requiring construction personnel to evacuate to a safe area and equipment to be moved to higher ground. After the rainfall ends and the soil moisture drops below 25%, the environmental data and resource status will be reassessed before a new work plan is formulated.

[0046] By deeply integrating dynamic environmental data with resource supply and safety status through this analytical logic, the control results can not only accurately adapt to the natural conditions of the site, but also maximize the effectiveness of construction resources, while improving operational stability and safety baseline.

[0047] Based on the distribution of construction resources, site environmental data, and the first stability judgment value, a comprehensive judgment and analysis of the current pole fixing operation is conducted to obtain the pole fixing control result, which specifically includes the following steps: The second stability assessment value for the current pole fixing operation is determined based on the distribution of construction resources and site environment data. Based on the first stability judgment value and the second stability judgment value, a comprehensive judgment and analysis result of the current pole fixing operation is determined, and control instructions for pole type selection, layout density adjustment and fixing process optimization are generated.

[0048] The second stability judgment value of the current operation is determined by combining the distribution of construction resources with site environmental data. The second stability judgment value focuses on whether the resource supply can adapt to environmental constraints and quantifies the ability of construction resources to support the stability of the operation under the current site conditions. The number of construction personnel, equipment and material reserves in the distribution of construction resources have a clear compatibility with the slope of the plot, soil moisture and surrounding meteorological conditions in the site environmental data. This compatibility is compared and scored one by one, and finally summarized into a second stability judgment value from 0 to 100. For example, in the land acquisition operation, site environmental data showed that the western part of the site had an 8-degree steep slope, soil moisture of 28%, and moderate rain expected in the next 2 hours. Construction resources included 12 workers (6 skilled workers for steep slope operations), 5 hydraulic pole-planting machines with an insertion force of 80 kN, and 3 reserve points 35 meters from the western area, with 400 2-meter steel poles available. When comparing suitability: the steep slope area required two-person collaborative work, and the 6 skilled workers could form 3 groups, matching the 3 western work zones, scoring 20 points; the hydraulic equipment's insertion force was suitable for the pole-planting depth requirements in the wet soil environment, and the number of fault-free devices was sufficient, scoring 25 points; the reserve points were at a suitable distance, and the steel poles were suitable for both steep slopes and wet soil, with sufficient inventory, scoring 20 points; regarding weather, pole planting in the core western area could be completed before the moderate rain, and resource allocation could mitigate the impact of rainfall, scoring 15 points. The overall second stability assessment score was 80 points, indicating good resource and environmental suitability, supporting stable operation. If the eastern part of the site has a gentle 5-degree slope but the soil moisture content reaches 32%, the construction resources are limited to only 3 mechanical pole-planting machines with a planting force of 60 kN, 2 skilled workers, and 1 reserve point 60 meters away from the eastern area. In the suitability comparison: insufficient planting force of the mechanical equipment, which may lead to insufficient pole-planting depth in high-moisture soil, deducts 20 points; the number of skilled workers can only cover one zone, failing to meet the operational needs of the entire eastern area, deducts 15 points; the reserve point is too far away, resulting in low material replenishment efficiency during rainfall, deducts 10 points. The overall second stability assessment score is 55 points, indicating poor resource and environmental suitability and a risk of insufficient support. Based on the first and second stability judgment values, a comprehensive analysis result is determined, generating three types of control instructions. The first stability judgment value reflects the stability trend of key operational elements, such as the compatibility of the pole with the soil and the impact of terrain on stability. The second stability judgment value reflects the resource's ability to support the environment. Both have equal weight, and the comprehensive judgment score is obtained by averaging them. Instructions are then generated by combining this score with specific data differences. When the overall assessment score is 80 or above, it indicates that the operation itself has a good stability trend and sufficient resource support, and the control instructions focus on optimizing details. For example, the first stability assessment score is 85, the second stability assessment score is 80, and the overall average score is 82.5. The site is a gentle slope with a soil moisture content of 20%. There are sufficient steel poles and hydraulic equipment available. The instructions can be set to select steel poles, maintain a deployment density of 3 poles per square meter, insert them to a depth of 100 cm in one go, and then compact them immediately. At the same time, the equipment can be scheduled to work in rotation by area to improve efficiency. When the overall assessment score falls within the moderate range of 60 to 79 points, it is necessary to adjust parameters to balance stability and resource adaptability. For example, a first stability assessment score of 65 points indicates a decrease in bearing capacity due to wet soil; a second stability assessment score of 70 points allows for resource adjustment but lacks redundancy, resulting in an overall score of 67.5 points. The western steep slope of the site has a humidity of 28%. Resources include some fiberglass poles, sufficient steel poles, and four hydraulic devices. The pole type was clarified, and steel poles were chosen to replace the originally planned fiberglass poles to enhance adaptability. The deployment density was adjusted from three poles per square meter to four poles to improve stability. Insertion was carried out in two stages: first to 80 cm, then allowed to stand for 5 minutes to allow for soil adhesion, before being inserted to 100 cm. Simultaneously, three hydraulic devices were prioritized for the western area, with one remaining as a backup. When the overall assessment score is 59 points or below, priority should be given to addressing resource shortages or mitigating environmental risks, with instructions primarily based on emergency adjustments and resource replenishment. For example, a first stability assessment score of 50 points indicates a significant stability risk due to a steep slope and high humidity; a second stability assessment score of 55 points indicates insufficient skilled workers and equipment; and the overall average score is 52.5 points. The northern part of the site has a 10-degree slope and 30% humidity, with only 2 steep slope technicians and 2 hydraulic equipment available. The order is to suspend all operations in the northern area and prioritize pole installation in the core boundary area; the longest steel poles (2.5 meters) should be selected, with a density of 5 poles per square meter; the fixing process should involve equipment insertion combined with manual assistance for straightening; simultaneously, 3 steep slope technicians and 2 hydraulic equipment should be urgently deployed, and operations in the remaining areas should only proceed once resources are available. During this period, technicians should monitor the status of the installed poles every 10 minutes. Through this dual stability quantification fusion and scenario-based instruction generation logic, the final pole fixing control result can not only accurately resolve the stability risks of the operation itself, but also fully adapt to existing construction resources and site environmental conditions, achieving a balance between safety and efficiency.

[0049] The pole fixing mechanism includes a loading box 2, which is fixedly connected to the walking base 1. A storage box 3 is fixedly connected to the top of the loading box 2, and a drive motor 4 is fixedly connected to the bottom of the loading box 2. The output of the drive motor 4 passes through the bottom of the loading box 2 and is fixedly connected to a rotating rod 5. A winding wheel 6 is sleeved on the rotating rod 5, and a retaining cloth 7 is wound on the winding wheel 6. Insertion rod 8, there are at least two insertion rods 8, and the insertion rods 8 are set on the enclosure cloth 7; The guide box 9 is rotatably connected to the outer wall of the loading box 2. A fixed box 10 is provided on the top of the guide box 9. An electric telescopic rod 11 is fixedly connected to the bottom of the fixed box 10. A servo motor 12 is fixedly connected to the top of the electric telescopic rod 11. A drive rod 13 is fixedly connected to the output shaft of the servo motor 12. The drive rod 13 cooperates with the insertion rod 8. A limit component is provided at the bottom of the guide box 9. The limit component cooperates with the insertion rod 8.

[0050] The lower end of the insertion rod 8 is provided with an external thread, and the upper end face of the insertion rod 8 is provided with a groove. Both sides of the groove are open. The lower end of the drive rod 13 is provided with a protrusion that can be inserted into the groove. When the output shaft of the servo motor 12 drives the drive rod 13 to rotate, the insertion rod 8 is driven to rotate through the cooperation of the protrusion and the groove. The insertion rod 8 is externally adjustable with a connecting cylinder 15. The upper end of the insertion rod 8 is rotatably connected with a connecting ring 16. The connecting ring 16 is slidably connected to the inner side wall of the connecting cylinder 15, and the connecting cylinder 15 is provided with an arc-shaped through hole. 17. The connecting cylinder 15 is sleeved on the enclosure cloth 7 through the arc-shaped through hole 17, and the lower end of the connecting cylinder 15 is provided with an internal thread that matches the external thread. When the insertion rod 8 rotates, its lower end is screwed out downward along the internal thread, so that the lower end of the insertion rod 8 is inserted into the soil. As the insertion rod 8 moves down continuously, the electric telescopic rod 11 drives the servo motor 12 to move down synchronously, so that the servo motor 12 moves down with the insertion rod 8, so that the servo motor 12 can continue to drive the insertion rod 8 to rotate, so that the insertion rod 8 can continuously screw into the soil through the external thread on its outer wall.

[0051] The limiting assembly includes symmetrically arranged support plates 18. The upper surface of the support plates 18 contacts the lower end face of the connecting cylinder 15, and the tip of the lower end of the insertion rod 8 is located between the two support plates 18. One end of the support plate 18 passes through the side wall of the guide box 9 and is fixedly connected to a stop rod 19. A return spring 20 is fixedly connected to the side wall of the stop rod 19, and one end of the return spring 20 is fixedly connected to the outer side wall of the guide box 9. A baffle 21 is fixedly connected to the end of the support plate 18 away from the loading box 2. The baffle 21 contacts the outer side wall of the connecting cylinder 15. An L-shaped through hole is opened on the side wall of the guide box 9 for the support plates 18 and the baffle 21 to pass through. The bottom and sides of the guide box 9 are also connected to the baffle 21. Both are open, with the lower end of the connecting cylinder 15 inclined. When the insert rod 8 moves downward, the tip of the insert rod 8 pushes the two bearing plates 18 to both sides. When the inclined surface of the connecting cylinder 15 contacts the side wall of the bearing plate 18, the bearing plate 18 is pushed to both sides as the position of the connecting cylinder 15 moves downward, and the two baffles 21 move to both sides. When the ends of the bearing plate 18 and the baffles 21 are flush with the inner side wall of the guide box 9, the walking base 1 can drive the loading box 2 and the guide box 9 to move forward. During the forward movement of the walking base 1, the drive motor 4 is started, which drives the rotating rod 5 and the winding wheel 6 to rotate, thereby realizing the unfolding of the enclosure cloth 7 and realizing the land enclosure operation.

[0052] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A land parceling device for urban and rural planning, comprising a walking base (1), characterized in that, Also comprising: The plug rod fixing mechanism is arranged on the walking base (1), and is used for plug rod fixing operation; The data acquisition module acquires the multi-source heterogeneous data of the urban and rural planning circle area, fuses the multi-source heterogeneous data, and constructs a digital twin model of the circle land insertion rod fixing; The data extraction module extracts key elements in the plug rod fixing operation based on the digital twin model, determines a correlation influence coefficient set between the key elements, and determines a normal variation range value of the correlation influence coefficient between the key elements; The data correlation module determines the correlation influence degree of the current plug rod fixing operation and the historical similar operation on the key elements according to the correlation influence coefficient set, obtains a current correlation influence degree set, compares the current correlation influence degree set after processing with a preset normal influence range value to obtain a comparison result; The data comparison module combines the deep analysis signal in the comparison result with the dynamic evolution law of the operation elements in the digital twin model to judge the change trend of the key elements of the current plug rod fixing operation, and generates a first judgment data set; According to the first judgment data set, the implementation situation of the current plug rod fixing operation is analyzed, and a first stability judgment value is obtained; The data statistics module: statistics the construction resource distribution corresponding to the plug rod fixing operation, and comprehensively judges and analyzes the current plug rod fixing operation according to the construction resource distribution, the site environment data and the first stability judgment value to obtain the plug rod fixing control result.

2. The land staking device for urban and rural planning of claim 1, wherein The multi-source heterogeneous data includes circle land plot basic information, soil survey data, plug rod parameter information and historical plug rod fixing operation data.

3. The land staking device for urban and rural planning of claim 2, wherein The correlation influence coefficient set between the key elements is determined, and the normal variation range value of the correlation influence coefficient between the key elements is determined, which specifically includes the following steps: The key elements include plug rod type, landform characteristics, soil bearing characteristics and plug rod arrangement density; The mutual influence relationship between the key elements is determined, and the correlation influence coefficient set between the key elements is established; The correlation influence coefficients between the key elements in the historical qualified operation and the historical failure operation are respectively acquired, and a historical qualified correlation influence coefficient set and a historical failure correlation influence coefficient set are constructed; According to the historical qualified correlation influence coefficient set and the historical failure correlation influence coefficient set, the normal variation range value of the correlation influence coefficient between the key elements is determined.

4. The land staking device for urban and rural planning of claim 3, wherein The current correlation influence degree set is processed and compared with the preset normal influence range value to obtain a comparison result, which specifically includes the following steps: If there is data greater than the preset normal influence range value in the current correlation influence degree set, the operation key elements corresponding to the data greater than the preset normal influence range value in the current correlation influence degree set are determined as the stability warning signal of the comparison result; If all the data in the current correlation influence degree set is less than the preset normal influence range value, the difference between the data in the current correlation influence degree set and the preset normal influence range value is calculated to obtain a difference set; The preset difference threshold is used to continue the deep analysis of the key elements of the current stake fixing operation and output a deep analysis signal of the comparison result if there is data greater than or equal to the difference threshold in the difference set.

5. The land staking device for urban and rural planning of claim 4, wherein, The deep analysis signal is combined with the dynamic evolution law of the operation elements in the digital twin model to determine the change trend of the key elements of the current stake fixing operation to generate a first judgment data set, which specifically includes the following steps: According to the deep analysis signal, the dynamic evolution characteristics of the key elements of the current stake fixing operation are extracted, and a dynamic evolution model of the key elements is established by combining the dynamic evolution law of the operation elements in the digital twin model; After determining the change trend of the key elements of the current stake fixing operation in a preset time period by using the dynamic evolution model, the first judgment data set is generated; The first judgment data set includes stake type adaptation trend, terrain feature adaptation change trend, soil bearing characteristic response trend, and stake arrangement density optimization trend.

6. The land staking device for urban and rural planning of claim 5, wherein, According to the first judgment data set, the implementation situation of the current stake fixing operation is analyzed to obtain a first stability judgment value, which specifically includes the following steps: Based on the first judgment data set and the digital twin model, the influence degree of environmental interference factors on the key elements of the operation is determined to obtain an environmental influence degree set; According to the environmental influence degree set and the first judgment data set, a second judgment data set of the current stake fixing operation affected by factors is determined; Based on the second judgment data set, the implementation situation of the current stake fixing operation is determined, and the first stability judgment value of the current stake fixing operation is determined according to the implementation situation of the current stake fixing operation.

7. The land staking device for urban and rural planning of claim 6, wherein, The construction resource distribution includes the number of construction personnel, the arrangement of stake equipment, and the distribution density of material storage points.

8. The land staking device for urban and rural planning of claim 7, wherein, The site environment data includes land slope, soil moisture, and surrounding weather conditions.

9. The land staking device for urban and rural planning of claim 8, wherein, According to the construction resource distribution, the site environment data, and the first stability judgment value, the current stake fixing operation is comprehensively judged and analyzed to obtain a stake fixing control result, which specifically includes the following steps: According to the construction resource distribution and the site environment data, a second stability judgment value of the current stake fixing operation is determined. According to the first stability judgment value and the second stability judgment value, a comprehensive judgment analysis result of the current stake fixing operation is determined, and a control instruction for stake type selection, arrangement density adjustment, and fixing process optimization is generated.

10. The land staking device for urban and rural planning of claim 9, wherein, The stake fixing mechanism includes: The loading box (2) is fixedly connected to the walking base (1), the top of the loading box (2) is fixedly connected with the storage box (3), the bottom of the loading box (2) is fixedly connected with the driving motor (4), the output of the driving motor (4) is connected through the bottom of the loading box (2) and fixedly connected with the rotating rod (5), the rotating rod (5) is sleeved with the winding wheel (6), and the winding wheel (6) is wound with the surrounding cloth (7); The stake (8) is at least two, and the stake (8) is arranged on the surrounding cloth (7). A guide box (9) is rotationally connected to the outer side wall of the loading box (2), a fixed box (10) is arranged at the top of the guide box (9), an electric telescopic rod (11) is fixedly connected to the bottom of the fixed box (10), a servo motor (12) is fixedly connected to the top of the electric telescopic rod (11), a driving rod (13) is fixedly connected to the output shaft of the servo motor (12), the driving rod (13) is matched with the inserting rod (8), and a limiting assembly is arranged at the bottom of the guide box (9) and matched with the inserting rod (8).

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