Park greening inspection and maintenance method based on NFC and GIS park data association

Abstract

The invention provides a park greening polling maintenance method based on NFC and GIS park data association, and relates to the technical field of data processing, and the method comprises the steps: configuring a preset NFC label on a greening object, and binding the recognition information in the preset NFC label with the greening object position information registered in a GIS system; reading the NFC label through terminal equipment, obtaining identification information of the current greening object, and calling corresponding geographic positioning information and historical maintenance data in a GIS system according to the identification information; collecting field state information, including plant morphological parameters and surrounding environment condition information, of the current greening object, and comparing and analyzing the field state information with historical maintenance data; in combination with the maintenance standard of the corresponding greening object, generating specific maintenance task content, and uploading the specific maintenance task content to the management platform in real time through the terminal equipment to form a maintenance task record; the autonomy and accuracy of park greening inspection and maintenance are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a park greening inspection and maintenance method based on the association of NFC and GIS park data. Background Art

[0002] In the prior art, the greening maintenance of urban parks mainly relies on manual inspection methods. Staff members hold paper forms or portable terminal devices to record each greening object such as trees, lawns, and flowers in the park one by one. Some systems introduce GIS (Geographic Information System) technology to digitally model the greening area and achieve spatial management of maintenance tasks through map annotation. However, such systems usually do not achieve instant interaction with on-site devices. Inspectors need to manually match the devices with geographical information, which is prone to positioning errors or information inconsistencies, limiting the real-time and accuracy of data.

[0003] In specific application scenarios, for example, during the spring greening inspection in a large municipal park, due to the large coverage area of the greening area and the variety of vegetation, it is difficult for staff to quickly confirm the plant type and its maintenance records on-site. In the current method, it is necessary to check paper records or the GIS interface of the terminal system for comparison, which is extremely prone to manual recognition errors, resulting in a certain type of flower not being fertilized or pruned on schedule, thereby affecting the landscape quality. For example, there was once an omission in the rose maintenance record in a park management office because the inspector failed to accurately locate the green plant area in the GIS system and misclassified it as an adjacent lawn, reflecting that the current GIS system lacks a close combination with on-site identification means and cannot achieve efficient and accurate information interaction. Summary of the Invention

[0004] The purpose of the present invention is to provide a park greening inspection and maintenance method based on the association of NFC and GIS park data, aiming to solve the problems mentioned in the background art.

[0005] To solve the above technical problems, the technical solution of the present invention is as follows: A park greening inspection and maintenance method based on the association of NFC and GIS park data, the method includes: Configuring a preset NFC tag on the greening object, the NFC tag includes the identification information corresponding to the greening object, and binding the identification information with the location information of the greening object registered in the GIS system to generate a greening identification data table; Reading the NFC tag through a terminal device, obtaining the identification information of the current greening object, and calling the corresponding geographical positioning information and historical maintenance data in the GIS system according to the identification information to form inspection and positioning data; Collect the on-site status information of the current greening object according to the geolocation information of the inspection and positioning data, including plant morphological parameters and surrounding environmental condition information, and compare and analyze it with the historical maintenance data to generate preliminary diagnosis data; Generate specific maintenance task content according to the preliminary diagnosis data and the maintenance standards corresponding to the greening object, and upload it to the management platform in real time through the terminal device to form a maintenance task record.

[0006] Preferably, bind the identification information with the location information of the greening object registered in the GIS coefficient to generate a greening identification data table, including: By retrieving the preset geographical location database of the greening object in the GIS system, determine the node location of the target greening object in the geographical layer, and extract the spatial coordinate information of the node; Establish a corresponding relationship between the identification information and the spatial coordinate information, and set information fields respectively, including object name, identification code, spatial code, calibration time and the layer to which it belongs, to generate a binding result; Generate a greening identification data table according to the binding result, and the greening identification data table is used to quickly match the identification information with the geographical location during subsequent inspection processes.

[0007] Preferably, collect the on-site status information of the current greening object according to the geolocation information of the inspection and positioning data, including plant morphological parameters and surrounding environmental condition information, and compare and analyze it with the historical maintenance data to generate preliminary diagnosis data, including: Retrieve the geolocation information in the inspection and positioning data through the terminal device, locate the specific location of the on-site greening object, and collect the current status data of the plant at the specific location. The current status data includes color change, height value, leaf density, and soil humidity; Extract multi-period data from the current status data and the same type of status items recorded in the historical maintenance data of the greening object, construct standard parameters for field-by-field comparison, and identify the degree of change in each status item, and then set a difference threshold range for the change in each status item; Mark the status item parameters with abnormal fluctuations according to the difference threshold range to form a difference feature set; Divide the diagnosis level according to the deviation degree of each status item in the difference feature set and the cumulative number of abnormal items, and generate preliminary diagnosis data based on the diagnosis level and the difference feature set.

[0008] Preferably, generate specific maintenance task content according to the preliminary diagnosis data and the maintenance standards corresponding to the greening object, and upload it to the management platform in real time through the terminal device to form a maintenance task record, including: According to the preliminary diagnosis data, match the processing rules corresponding to the target greening object in the preset maintenance standard library, and extract the maintenance measure templates corresponding to the diagnosis items, including pruning methods, fertilization frequencies, and watering cycles. Decompose the maintenance measure templates into several subtasks to generate specific maintenance task content, and attach the current identification information and geographical location information as task accessory attributes. The subtasks determine their corresponding task levels according to the diagnosis levels determined in the preliminary diagnosis data, and the task levels are used to indicate the execution urgency or complexity of each subtask. Real-time package and upload the maintenance task content to the management platform through the terminal device, and generate a maintenance task record, which includes the preliminary diagnosis data and its source information.

[0009] Preferably, establish a corresponding relationship between the identification information and the spatial coordinate information, and set information fields respectively, including object name, identification code, spatial code, calibration time, and the layer to which it belongs, to generate a binding result, including: Perform format normalization processing on the identification information, and conduct coding specification inspection and repeatability inspection respectively through the preset coding rule library and the bound identification code to generate the first validity verification result. Detect the error range of the extracted spatial coordinate information, judge whether there is a risk of repeated binding of the spatial position, and generate the second validity verification result. When both the first validity verification result and the second validity verification result pass, construct a bi-directional mapping relationship between the identification information and the spatial coordinate information, and establish a binding result with the identification code as the primary key.

[0010] Preferably, extract multi-period data for the current status data and the same type of status items recorded in the historical maintenance data of this greening object, construct standard parameters for field corresponding comparison, and identify the degree of change in each status item. Furthermore, set a difference threshold range for the change of each status item, including: According to the status items included in the current status data, call the historical records of the corresponding status items in the historical maintenance data within multiple time periods, construct a sequence of status values arranged in chronological order, and use it as the basic data set for constructing standard parameters. Eliminate outliers from the historical data in the basic data set, including identifying mutation points, excluding extreme values outside the reasonable range, and eliminating abnormal data with a large deviation from adjacent time points, to form an effective sample set for standard parameter calculation. Divide the effective sample set into sliding intervals according to time windows, calculate the local mean values within each sliding interval respectively, and combine the time interval between the current acquisition time and the historical data, and set the time weights of each sliding interval according to the preset time decay function. The local means of each sliding interval are weighted and aggregated with the corresponding time weights, and the result is normalized to obtain the standard parameter reference mean corresponding to each status item; Compare the field values in the current status data with the corresponding standard parameter reference means, calculate the numerical differences of each status item, and set the difference threshold range for each status item in combination with the plant type of the greening object and the parameter category of the status item.

[0011] Preferably, decompose the maintenance measure template into several subtasks to form specific maintenance task content, including: Extract the processing content to be executed according to the maintenance measure template and divide it into several subtasks, with each subtask corresponding to an independent operation step; For each subtask, correct the estimated time of the subtask according to the execution time data and task level in the historical maintenance task record, and use the corrected estimated time as the duration parameter for task scheduling; For the operation content of each subtask, combine the plant type identified by the identification information and the regional attributes corresponding to the geographical location information, screen the available maintenance equipment options from the preset equipment information library, and generate a recommended equipment list; Unify and package the operation steps, estimated time, and recommended equipment list of all subtasks into specific maintenance task content.

[0012] Preferably, perform an error range detection on the extracted spatial coordinate information, determine whether there is a risk of duplicate binding of spatial positions, and generate a second validity verification result, including: Establish a buffer range centered on the coordinate point according to the spatial coordinates corresponding to the identification information, and extract the spatial coordinate information of all bound objects within the buffer; Compare the current coordinate with the coordinates of other objects within the buffer. If the minimum distance is less than the system-set binding spacing threshold, mark it as having a spatial overlap risk; In the case of a spatial overlap risk, combine the type of greening object corresponding to the identification information and the object distribution density corresponding to this location in the geographical layer to determine the risk level, and trigger an artificial review prompt to generate a verification result marked as a high-risk binding.

[0013] Preferably, compare the field values in the current status data with the corresponding standard parameter reference means, calculate the numerical differences of each status item, and set the difference threshold range for each status item in combination with the plant type of the greening object and the parameter category of the status item, including: Calculate the numerical difference based on the current value and the standard parameter reference mean of each status item, and construct a status difference data set; For each field item in the status difference dataset, retrieve the preset plant species - parameter threshold correspondence table according to its corresponding plant type attribute to obtain the initial value of its acceptable fluctuation range; According to the parameter category to which the field item belongs, adjust and correct the initial value fluctuation range to form a difference threshold range for a specific plant type and a specific parameter category, where the parameter category includes morphological parameters and environmental parameters.

[0014] Preferably, for each subtask, correct the estimated time of the subtask according to the execution time data and task level in the historical maintenance task record, and use the corrected estimated time as the duration parameter for task scheduling, including: Extract the execution samples corresponding to each subtask in the historical maintenance task record to construct a task execution time sample set, where the task execution time sample set includes the actual time consumption data of approximate operation steps under different execution conditions; Generate the basic estimated duration of the subtask according to the average execution time recorded in the task execution time sample set, and perform weighted adjustment on the basic estimated duration according to the execution complexity coefficient represented by the task level; Based on the geographical location information of the area where the subtask is located, determine whether there is an operation interference factor in the area. If so, set a time correction factor; Multiply the weighted adjustment time by the time correction factor to obtain the corrected estimated time, and write the estimated time into the subtask scheduling parameters.

[0015] The above - mentioned solution of the present invention has at least the following beneficial effects: By configuring a preset NFC tag on the greening object and binding the identification information contained in the tag with the location information of the greening object registered in the GIS system, one - to - one correspondence between the physical vegetation and the spatial digital model is achieved, enabling the inspection personnel to quickly identify the current object with the help of the terminal device and call its geographical location and historical maintenance data, significantly improving the accuracy of inspection positioning and overcoming the problems of easy error in manual information matching and inaccurate positioning in traditional methods.

[0016] During the inspection process, the inspection personnel can read the NFC tag through the terminal device and obtain the specific location and historical maintenance information of the greening object immediately on - site without referring to paper records or searching for geographical nodes one by one in the GIS interface, effectively improving the real - time performance of data call. In complex scenarios such as large - scale inspections in spring, the system can automatically associate the object identification code with the map information to help the staff quickly confirm the plant type and maintenance records, reducing the risk of maintenance omission caused by human judgment errors. For example, for rose - like flowers, the pruning frequency and fertilization cycle can be directly retrieved through tag scanning, avoiding task omission caused by being misclassified as adjacent lawns.

[0017] In addition, by collecting the status information of the current greening object through the terminal device and comparing and analyzing it with the historical maintenance data, the system can timely detect the status changes of the greening object and generate preliminary diagnosis data, transforming the maintenance work from "passive response" to "active early warning", and promoting the intelligence and precision of the maintenance strategy. This mechanism provides data-driven decision support for greening management, improving the efficiency and management level of the entire system in the greening maintenance of urban parks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a park greening inspection and maintenance method based on the association of NFC and GIS park data provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0020] As Figure 1 shown, an embodiment of the present invention proposes a park greening inspection and maintenance method based on the association of NFC and GIS park data, and the method includes: Configuring a preset NFC tag on the greening object, where the NFC tag includes the identification information corresponding to the greening object, and binding the identification information with the location information of the greening object registered in the GIS system to generate a greening identification data table; Reading the NFC tag through the terminal device to obtain the identification information of the current greening object, and calling the corresponding geographical location information and historical maintenance data in the GIS system according to the identification information to form inspection and positioning data; Collecting the on-site status information of the current greening object according to the geographical location information of the inspection and positioning data, including plant morphological parameters and surrounding environmental condition information, and comparing and analyzing it with the historical maintenance data to generate preliminary diagnosis data; According to the preliminary diagnosis data, combining with the maintenance standards of the corresponding greening object, generating specific maintenance task content, and uploading it to the management platform in real time through the terminal device to form a maintenance task record.

[0021] In the embodiments of the present invention, for the maintenance management requirements of park greening areas, NFC tags with unique identification identifiers are pre-configured on each greening object. This tag can be directly used by a terminal device with NFC reading function for non-contact information collection, enabling the greening object to have an accurately traceable identifier in the digital space. When writing the identification information into the NFC tag, data binding is performed through the location information of the greening object registered in the GIS system to establish a one-to-one mapping relationship between the object in the physical space and the digital system. The finally generated greening identification data table is used as the basic data source for quickly matching geographical information and object identity in subsequent inspection operations.

[0022] During the inspection process, the staff reads the NFC tag on the surface of the greening object through a handheld terminal device. The terminal can immediately obtain the identification information and call the geographical location data corresponding to the identification information through the interface established with the GIS system, so as to accurately obtain the spatial coordinates and distribution area of the object in the layer database. At the same time, the identification information is also used to query the historical maintenance data associated with the object, including previous pruning records, fertilization records, soil detection data, etc., and summarize them to form inspection and positioning data. This positioning data not only has real-time performance but also contains the historical context information of the object's maintenance behavior.

[0023] Based on the geographical coordinates and the unique identity of the object stated in the positioning data, the on-site inspection personnel further collect the actual status information of the current greening object. The status information covers typical ecological parameters such as the color change of the plant, the height of the stem, the leaf density, and the humidity in the surrounding environment, and is digitally recorded by means of terminal input or image acquisition. The current status information is automatically compared and analyzed with the historical data to form preliminary diagnosis data. This diagnosis data preliminarily reveals whether the current status of the object deviates from the normal maintenance range and is the basis for generating subsequent tasks.

[0024] Subsequently, according to the preliminary diagnosis result and combined with the standard maintenance specifications of the greening object, the corresponding maintenance response measures are intelligently matched, and the specific maintenance task content is generated accordingly. The task content includes operation items, execution frequencies, target time windows, etc., and is uploaded to the cloud management platform in real time through the terminal to complete task registration. The resulting maintenance task record becomes an important data support for subsequent scheduling and execution, realizing a closed-loop management from identification, positioning, diagnosis to task generation.

[0025] In a preferred embodiment of the present invention, the identification information is bound to the location information of the greening object registered in the GIS system to generate a greening identification data table, including: By retrieving the preset geographical location database of the greening object in the GIS system, determining the node position of the target greening object in the geographical layer, and extracting the spatial coordinate information of the node; Establish a correspondence between the recognition information and the spatial coordinate information, and set information fields respectively, including the object name, recognition code, spatial code, calibration time, and the layer to which it belongs, to generate a binding result; Generate a greening identification data table according to the binding result, and the greening identification data table is used to quickly match the recognition information and the geographical location during subsequent inspection processes.

[0026] In the embodiment of the present invention, to achieve the digital recognition of greening objects and the precise binding of spatial data, first, a preset geographical location database is retrieved in the GIS system. This database records the spatial node information of each greening object. The system automatically locates the node position of the input target recognition information in the geographical layer and extracts the coordinate data of this node in the spatial dimension, such as longitude and latitude or the X and Y values in the local plane coordinate system.

[0027] After the spatial coordinate extraction is completed, establish a one-to-one correspondence between the above recognition information and the corresponding coordinates, and expand the setting of information fields to cover multiple information dimensions such as object name, unique recognition code, spatial code, binding time, and belonging layer. These fields are constructed as structured data to facilitate rapid retrieval and update operations in the database. This binding process ensures the uniqueness of each greening object in the actual physical space and the precise correspondence of its internal identity in the system, effectively avoiding the maintenance chaos problem caused by unclear object identification.

[0028] Based on the construction result of the binding relationship, further generate a complete greening identification data table. This data table, as the core data structure for information invocation and positioning matching during subsequent inspection processes, is integrated into the software platform of the terminal device, supporting the inspection personnel to obtain relevant coordinate and layer information by reading through NFC on-site, greatly improving the inspection efficiency and data consistency. This process strengthens the digital control ability of the maintenance information system for on-site targets and lays a data foundation for high-frequency inspections and dynamic positioning.

[0029] Among them, the preset geographical location database of greening objects specifically includes: The preset geographical location database of greening objects is a system for storing all geographical information data related to park greening. This database contains the position coordinate data of all greening objects, environmental description information, and maintenance records related to each greening object. These data are stored in the GIS system and can be queried by specifying conditions. Through the preset geographical location database of greening objects, users can quickly locate any greening object and interact and analyze based on the geographical location information of this object and other relevant data (such as historical maintenance data, etc.).

[0030] For example, when the park management department conducts greening inspections, it can accurately locate the specific location of a certain tree or flower in the park through this database and retrieve the historical maintenance records related to this location, so as to formulate targeted maintenance measures for this greening object.

[0031] Among them, according to the binding result, a greening identification data table is generated, specifically including: By establishing a binding relationship between the identification information of the greening object and the corresponding spatial coordinate information, a greening identification data table is generated. This data table is used to store the identification information of all greening objects and their spatial coordinates in the GIS system. Each greening object will have a unique identification code, which is bound to the corresponding spatial coordinates (such as longitude, latitude or other coordinate system data). Through such binding, each record in the data table can accurately point to the physical location of the corresponding greening object in the park.

[0032] The specific methods of the binding process include: Retrieve the spatial coordinate data of the greening object in the GIS system, and establish a mapping relationship between the identification information of each greening object and its spatial coordinates through preset rules. For example, greening object A may correspond to a specific coordinate, and its corresponding identification information may be "Tree 001". This record will be stored in the greening identification data table, and the table fields may include information such as object name, identification code, spatial code (longitude and latitude coordinates), calibration time, and the layer to which it belongs. The generated greening identification data table provides key geolocation information for subsequent inspection tasks, helping inspection personnel quickly match greening objects.

[0033] In a preferred embodiment of the present invention, according to the geolocation information of the inspection positioning data, the on-site status information of the current greening object is collected, including plant morphological parameters and surrounding environmental condition information, and compared and analyzed with the historical maintenance data to generate preliminary diagnosis data, including: Retrieve the geolocation information in the inspection positioning data through the terminal device, locate the specific location of the on-site greening object, and collect the current status data of the plant at this specific location. The current status data includes color change, height value, leaf density, and soil humidity; Extract multi-period data for the current status data and the same type of status items recorded in the historical maintenance data of this greening object, construct standard parameters for field-by-field comparison, and identify the degree of change in each status item, and then set a difference threshold range for the change in each status item; Mark the status item parameters with abnormal fluctuations according to the difference threshold range to form a set of difference characteristics; Based on the deviation degree of each status item in the difference feature set and the cumulative number of abnormal items, the diagnosis level is divided, and based on the diagnosis level and the difference feature set, preliminary diagnosis data is generated.

[0034] In the embodiment of the present invention, based on the geographical location information in the inspection and patrol positioning data obtained by the terminal device, the inspection and patrol personnel can first accurately locate the actual spatial position of the target greening object, and collect a series of current plant state data at this position. The collection content includes but is not limited to: the change range of plant color, the actual height measurement value of the stem or branch, the density of leaves per unit area, and the humidity level of the surface soil. Data collection can be carried out through various methods such as manual observation input, image analysis, and portable sensors to ensure the multi-dimensional authenticity of the state data.

[0035] The currently collected state data will be compared with the same type of state items recorded in the historical maintenance data of the target greening object. To improve the comparison accuracy, the recorded data of this state item in multiple maintenance cycles is extracted from the historical database to form a state value sequence with a time dimension. When processing these historical data, first, obvious abnormal point values are identified and removed, including mutation values, outliers, etc., so as to construct more accurate standard parameters. The effective data set is divided by the sliding window method, and a time decay function is set for each interval to generate time weights to ensure that recent data is more referenceable.

[0036] Subsequently, the local means in each sliding interval are weighted and summed according to the time weights, and after normalization, a standard parameter reference mean is formed. This reference mean is used as the benchmark value for comparison, and the difference calculation corresponding to the fields is performed with the current state data. During the comparison process, not only the numerical deviation of each parameter is output, but also the difference threshold is set in combination with the type attribute of the plant and the category to which the parameter belongs (such as morphological or environmental). This setting standard clarifies whether there is a substantial deviation.

[0037] After the comparison, it is further determined whether there are status items that exceed the difference threshold, and they are marked as abnormal items to construct a difference feature set. According to the number of abnormal items in the set and their respective deviation degrees, the diagnosis level is set. For example, those with more deviation parameters and larger amplitudes are designated as high-level diagnosis items. The finally generated preliminary diagnosis data clearly records the current main problem characteristics of the object and its severity, and becomes the decision-making basis for the next task formulation.

[0038] Among them, by calling the geographical positioning information in the inspection and patrol positioning data through the terminal device, the specific position of the on-site greening object is located, and the current plant state data is collected at this specific position. The current state data includes color change, height value, leaf density, and soil humidity, specifically including: By using a terminal device (such as a smartphone or a handheld terminal) to retrieve the geolocation information in the patrol location data, the specific location of the on-site greening object can be accurately determined. When the patrol personnel arrive at the scene, the location data (such as GPS coordinate information) of the greening object is obtained through the terminal device, and then its exact location is determined. After locating this position, the device can collect the current status data of the greening object through built-in sensors or other measurement tools (such as cameras, photometers, etc.).

[0039] The current status data described above includes: Color change: By collecting the color change of the plant, its growth condition or the presence of pests and diseases can be judged.

[0040] Height value: By measuring the height of the plant, its growth progress or health condition can be evaluated.

[0041] Leaf density: By collecting the number or distribution of leaves, the health condition of the plant can be judged.

[0042] Soil humidity: Through a soil humidity sensor, the humidity level of the soil around the plant roots is monitored in real time, and then the irrigation requirement is inferred.

[0043] For example, if the patrol personnel find that the leaves of a certain greening object turn yellow, and at the same time its measured height is small, and the soil humidity is lower than the normal value, then this plant may have growth problems and requires further maintenance.

[0044] Among them, according to the difference threshold range, the parameter of the status item with abnormal fluctuation is marked to form a difference feature set, which specifically includes: According to the foregoing status data and the setting of the difference threshold, the current value of each status item can be compared with the normal range (standard parameter). If the change of a certain status item exceeds the preset difference threshold range, the system will mark this status item as abnormal. This difference threshold is dynamically adjusted based on the growth standard of the plant and historical fluctuation data. By evaluating the fluctuation range of each status item (such as color change, leaf density, etc.), abnormal fluctuations can be automatically identified and a difference feature set can be formed.

[0045] Specifically, if parameters such as the height value, color change or leaf density of the plant show significant fluctuations and exceed the set threshold, the system will add this status item to the difference feature set. These marked status items will be further used for diagnosis to determine whether maintenance measures need to be taken.

[0046] For example, assume that the leaf density of a certain flower has been maintained within a normal range in the past three months, but during this patrol, it is found that its leaf density has dropped sharply and exceeds the set difference threshold. At this time, the system will mark this status item as abnormal and add it to the difference feature set.

[0047] Among them, the diagnosis level is divided according to the deviation degree of each state item in the difference feature set and the number of accumulated abnormal items, and preliminary diagnosis data is generated based on the diagnosis level and the difference feature set, including: According to each abnormal status item in the difference feature set, the diagnostic level is determined by evaluating the degree of deviation of each status item and the cumulative number of abnormal items. The diagnostic level is used to reflect the current health status of the greening object, and is usually divided according to the following factors: Degree of deviation of status item: For example, if the height of a plant varies by more than 10% from the normal range, the degree of deviation of that status item is large and may mean that the plant has a significant health problem.

[0048] Cumulative number of abnormal items: If multiple status items show abnormal fluctuations, this may indicate that the plant has more serious maintenance problems. Therefore, the cumulative number of abnormal items will also affect the classification of diagnostic levels.

[0049] For example, suppose the color change and leaf density of a plant exceed the set thresholds, and these two abnormal items have been present in the past few inspections. The system will classify these status items as severe abnormalities and assign a high-level diagnosis to the plant, indicating that special maintenance measures are needed immediately.

[0050] Based on these diagnostic levels and differential feature sets, preliminary diagnostic data is generated to provide support for subsequent maintenance tasks, ensuring that maintenance measures are targeted and timely.

[0051] In a preferred embodiment of the present invention, based on the preliminary diagnosis data and in combination with the maintenance standards of the corresponding greening objects, specific maintenance task content is generated and uploaded to the management platform in real time through the terminal device to form a maintenance task record, including: According to the preliminary diagnosis data, the corresponding processing rules of the target greening object in the preset maintenance standard library are matched, and the maintenance measure template corresponding to the diagnosis item is extracted, including pruning method, fertilization frequency, and watering cycle; Decomposing the maintenance measure template into tasks, generating several subtasks, forming specific maintenance task contents, and attaching current identification information and geographic positioning information as task attributes. The subtasks are determined by the diagnostic levels determined in the preliminary diagnostic data to determine their corresponding task levels. The task levels are used to indicate the execution urgency or complexity of each subtask. The maintenance task content is packaged and uploaded to the management platform in real time through the terminal device, and a maintenance task record is generated, which includes preliminary diagnostic data and its source information.

[0052] In an embodiment of the present invention, the preliminary diagnostic data is combined with the maintenance standards corresponding to the greening objects to achieve automatic generation and real-time reporting of specific maintenance tasks. First, the preliminary diagnostic data generated during the inspection phase is read, which already contains a set of differential features and diagnostic level information obtained through comparison and analysis. By matching the category to which the diagnostic item belongs, the processing rules corresponding to the category in the preset maintenance standard library are automatically called to select a suitable maintenance measure template. The template content covers pruning methods (such as light pruning, thinning, shaping), fertilization frequency (such as weekly, monthly), watering cycles (such as every other day, weekly), etc.

[0053] After obtaining the maintenance measures template, the template content is further refined into several subtasks according to the number of diagnostic items and the complexity of the processing rules. For example, if a diagnostic result indicates that the leaf density is reduced, it may be broken down into three operation items: "cut off dead leaves", "increase nitrogen fertilizer application", and "enhance irrigation", each of which is defined as a subtask record. Each subtask is attached with the identification information and geographic positioning information of the current greening object to ensure that the task can be quickly located to the target greening object during the actual execution stage. At the same time, in order to reflect the urgency of the task or the complexity of the processing, the diagnostic level is mapped to the corresponding task level, and each subtask is given an appropriate scheduling weight.

[0054] After the subtasks are generated, all task contents are packaged and uploaded to the management platform in real time through the terminal device. The uploaded content not only includes the operation task list, but also the preliminary diagnostic data of these tasks and their sources (such as time, collector, equipment number, etc.), which is convenient for back-end management personnel to verify and assign tasks. The above process realizes a rapid closed-loop response from diagnosis to maintenance tasks, improving information transparency and response efficiency.

[0055] Among them, according to the preliminary diagnosis data, the corresponding processing rules of the target greening object in the preset maintenance standard library are matched, and the maintenance measure template corresponding to the diagnosis item is extracted, including pruning method, fertilization frequency, and watering cycle, including: The preliminary diagnostic data is a comprehensive assessment of the state of the greening object, including plant morphology, environmental conditions, and comparative analysis with historical maintenance data. When generating maintenance tasks based on the preliminary diagnostic data, the specific diagnostic information of the greening object is first matched with the preset maintenance standard library. The maintenance standard library stores specific processing rules for different plant types and maintenance needs. These rules describe in detail how to perform maintenance tasks such as pruning, fertilizing, and watering according to the current state of the plant.

[0056] The matching process includes extracting the corresponding maintenance measure templates from the standard library based on the parameters of the current state of the plant (such as color, leaf density, height, etc.). For example, if the leaf density of the plant is low and the soil moisture is insufficient, the system will extract the relevant treatment methods according to the rules in the maintenance standard library, such as increasing the fertilization frequency and adjusting the watering cycle. If the pruning requirement of the plant is high, the corresponding pruning method will be extracted, and the pruning time and frequency will be adjusted according to the growth condition of the plant.

[0057] For example, for a rose flower, if the preliminary diagnosis data shows that its leaves are relatively dry and the soil moisture is low, the system will extract the maintenance measure template for the rose flower from the standard library, indicating that appropriate pruning should be carried out, the fertilization frequency should be increased, and the watering cycle should be adjusted to promote its growth and recovery.

[0058] In a preferred embodiment of the present invention, a correspondence relationship is established between the identification information and the spatial coordinate information, and information fields are respectively set, including the object name, identification code, spatial code, calibration time, and the layer to which it belongs, and a binding result is generated, including: The identification information is subjected to format normalization processing, and through a preset coding rule library and the already bound identification code, coding specification verification and repeatability verification are respectively carried out to generate a first validity verification result; The extracted spatial coordinate information is subjected to an error range detection to determine whether there is a risk of duplicate binding of the spatial position, and a second validity verification result is generated; When both the first validity verification result and the second validity verification result pass, the identification information and the spatial coordinate information are constructed into a two-way mapping relationship, and a binding result with the identification code as the primary key is established.

[0059] In the embodiment of the present invention, for the binding process of the identification information and the spatial coordinate information, in order to ensure the data quality and spatial logic consistency, a double validity verification mechanism is introduced. Before establishing the binding relationship, first, the identification information is subjected to format normalization processing, and the processing content includes standardizing the numbering rules (such as using a unified prefix and digit limit), character legality verification, etc. Subsequently, the identification information is retrieved for repeatability in the already bound records to ensure uniqueness, thereby forming a first validity verification result.

[0060] At the same time, error detection is carried out on the spatial coordinate information extracted from the identification information. According to the preset error tolerance rules (such as the maximum overlap radius), it is judged whether the coordinate may spatially coincide with other objects in the existing bound data. The detection method includes establishing a buffer zone centered on the target coordinate and analyzing whether there are other bound object coordinates in it. If the minimum distance is lower than the binding threshold, it is regarded as having a binding conflict, thereby generating a second validity verification result.

[0061] The operation of establishing the binding relationship is only executed when both verification results are in a passed state. The binding result adopts a data structure with the identification code as the primary key to achieve the two-way mapping of spatial coordinates and object identities. This not only ensures that each NFC tag corresponds to a unique geographical entity but also lays a foundation for subsequent spatial positioning and object management. This mechanism effectively improves the overall binding accuracy and the consistency of spatial data.

[0062] Among them, the identification information is processed for format standardization, and the encoding standardization check and repeatability check are respectively carried out through a preset encoding rule library and the bound identification codes to generate the first validity verification result, specifically including: The identification information of greening objects (such as plant numbers, identifiers, etc.) needs to be standardized to ensure that all identification information conforms to a unified standard. Through the preset encoding rule library, the system can automatically check whether the format of the identification information meets the requirements. For example, the identification code may include letters, numbers, and other symbols, and the system will ensure that the arrangement and combination of these symbols conform to the preset encoding rules, such as length limits, symbol types, etc. If the identification information does not conform to the specification, the system will give a prompt and require re-entering the information that conforms to the specification.

[0063] On the basis of format standardization, the system will also conduct a repeatability check, that is, check for duplicates of the identification information of each greening object to ensure that there are no duplicate codes or identifiers in the system. This is to avoid the same greening object being wrongly entered multiple times and ensure that each identification information is unique. If duplicate identification information is found, the system will prompt the user to conduct manual verification or adjustment.

[0064] For example, when a new greening object identification information is input, the system will check whether the information conforms to the preset format (such as length, character type, etc.) and compare it with all the identification information stored in the system to ensure that there are no duplicate numbers. Through this validity verification, the accuracy and uniqueness of the data in the system can be ensured.

[0065] Among them, when both the first validity verification result and the second validity verification result pass, the identification information and the spatial coordinate information are constructed into a two-way mapping relationship, and a binding result with the identification code as the primary key is established, specifically including: After passing through format normalization and duplicate checking, the system will establish a two-way mapping relationship between the identification information of the greening objects and their spatial coordinate information. This means that each greening object not only has a unique identification information, but also maintains a one-to-one correspondence with the spatial coordinates (such as longitude and latitude or positions in other coordinate systems) corresponding to this information. The establishment of the two-way mapping relationship can ensure that after obtaining the greening object through the identification information, the system can also accurately find the spatial location information of the greening object, and through the spatial coordinate information, the system can also locate the identification information of the greening object.

[0066] For example, in a certain urban park, each tree has a unique identification code (such as "Tree 001"), and at the same time, the position of the tree is marked by spatial coordinates. Through the two-way mapping relationship, when the user queries the identification code, the system can locate the specific position of the tree; when the spatial coordinates are queried, the system can return the tree identification information corresponding to this position.

[0067] This binding result uses the identification code as the primary key to ensure the uniqueness of each record. This mechanism not only enhances the reliability of the data, but also greatly improves the efficiency of data query and operation.

[0068] For example, during the greening inspection, the staff can scan the identification information of the greening object through the device and immediately obtain the geographical location of the object. Conversely, they can also query the greening objects existing at this position according to the location. This two-way binding relationship greatly improves the inspection efficiency and reduces the occurrence of errors.

[0069] In a preferred embodiment of the present invention, for the current state data and the same type of state items recorded in the historical maintenance data of the greening object, multi-period data is extracted, standard parameters for field corresponding comparison are constructed, and the degree of change in each state item is identified. Furthermore, a difference threshold range is set for the change in each state item, including: According to the state items included in the current state data, call the historical records of the corresponding state items in the historical maintenance data within multiple time periods, construct a sequence of state values arranged in chronological order, and use it as the basic data set for constructing the standard parameters; Eliminate the outliers in the historical data in the basic data set, including identifying mutation points, excluding extreme values beyond the reasonable range, and eliminating abnormal data with a large deviation from adjacent time points, to form an effective sample set for standard parameter calculation; Divide the effective sample set into sliding intervals according to time windows, calculate the local means within each sliding interval respectively, and combine the time interval between the current acquisition time and the historical data, and set the time weights of each sliding interval according to the preset time decay function; The local mean of each sliding interval is weighted and summarized with the corresponding time weight, and the result is normalized to obtain the reference mean of the standard parameters corresponding to each state item; Compare the value of each field in the current status data with the reference mean of the corresponding standard parameter, calculate the numerical difference of each status item, and set the difference threshold range of each status item in combination with the plant type of the greening object and the parameter category of the status item.

[0070] In the embodiment of the present invention, the collected current status data needs to be carefully compared with the historical standard parameters in order to accurately identify the health fluctuation trend that may occur in the greening object. To this end, firstly, according to the field items covered by the current status data, the historical records of the same field in multiple time periods are extracted from the historical maintenance database. These historical values are organized into an ordered time series to form a basic data set for constructing standard parameters.

[0071] Next, to ensure the validity of the comparison data, the original historical data is processed by an anomaly detection algorithm to remove outliers and mutation values that may be caused by measurement errors or extreme weather. The valid sample set obtained in this way has higher statistical reliability. The sample set is divided in a sliding time window manner, and the local mean is calculated in each window to characterize the local trend. At the same time, according to the time interval between the current collection time and the historical data, the time decay function is applied to set the time weight of each window, so that the closer the historical data is to the current time, the higher the proportion.

[0072] All local means are weighted, aggregated and standardized under the action of weights, and finally the reference mean of the standard parameters of each state item is obtained. These reference values serve as the benchmark for the current state data comparison. For each field item, the difference between its current value and the reference mean is calculated, and combined with the plant type and parameter category (morphological or environmental) corresponding to the field, a reasonable difference threshold range is queried and set. This threshold range reflects the acceptable normal fluctuation range of a certain type of plant in a certain type of parameter item, and it is considered abnormal if it exceeds the range.

[0073] This difference detection mechanism ensures that the personalized indicators of different plant objects can be dynamically adapted, avoiding the risk of misjudgment caused by the use of unified standards and improving the accuracy of inspection and diagnosis.

[0074] The valid sample set is divided into sliding intervals according to the time window, and the local mean in each sliding interval is calculated respectively. In combination with the time interval between the current acquisition time and the historical data, the time weight of each sliding interval is set according to the preset time decay function, which specifically includes: Based on historical maintenance data and current status data, an appropriate time window is first selected to divide the effective sample set into sliding intervals. Each time window contains multiple data points, which are arranged in chronological order and represent the state changes of a certain greening object in the historical maintenance data. The process of sliding interval division is usually carried out by selecting a fixed time span, such as the data points of the past 7 days or the data points of the past 30 days as an interval.

[0075] After defining the time window, the system processes the data within each sliding interval and calculates the local mean within that interval. The local mean refers to the average value of the data points within a certain sliding interval, which reflects the state change trend of the greening object within that interval. For example, if a time window represents the state changes in the past 7 days, then within this window, the system calculates the average value within 7 days for relevant state items (such as leaf density, soil moisture, etc.).

[0076] To combine the time interval between the current collection time and the historical data, the system weights the local mean of each sliding interval according to a preset time decay function. The role of the time decay function is to adjust the weight of each sliding interval according to the time interval of data collection. The data points closer to the current collection time have a greater weight, and vice versa. This is because the data closer to the current time is more important for predicting and analyzing the current state. Therefore, the time decay function usually uses a mathematical model, such as an exponential decay function, where the weights of the newer data points are larger and the weights of the older historical data points gradually decrease.

[0077] For example, if the current collection time is May 30, 2025, the historical data of the past 7 days will have a higher weight, while the earlier data (such as the data 15 days ago) will have a lower weight. This method can ensure a more accurate state assessment for timely taking corresponding maintenance measures.

[0078] Among them, the local means of each sliding interval are weighted and aggregated with the corresponding time weights, and the result is normalized to obtain the standard parameter reference mean corresponding to each state item, specifically including: After calculating the local mean and time weight of each sliding interval, the system weights and aggregates these local means with the corresponding time weights. The process of weighted aggregation is to multiply the local mean of each sliding interval by its corresponding time weight and then sum all the weighted results. This weighted aggregation process can ensure that the time decay function gives a higher influence to the state changes in the recent time period, thus obtaining a more accurate reference value.

[0079] For example, assume that a time window contains data for the past 3 days. Calculate the state mean for each day respectively, and then assign a weight to each day according to a time decay function (for example, the weight for the first day is 0.2, the weight for the second day is 0.3, and the weight for the third day is 0.5). The system multiplies the state mean at each time point by the corresponding weight and then sums them up to obtain a weighted result, and then performs normalization processing to ensure the comparability of data in different time periods.

[0080] The purpose of normalization processing is to convert all weighted results into a standardized reference mean, ensure that its value is within a certain range, and is not affected by the dimension. Common methods of normalization include dividing all results by the maximum value or scaling through the standard deviation to ensure that the data has a unified standard.

[0081] Through this method, the standard parameter reference mean of each state item obtained by the system can accurately reflect the state changes of the greening object in different time periods and eliminate the data deviation caused by time factors.

[0082] In a preferred embodiment of the present invention, the maintenance measure template is decomposed into tasks to generate several subtasks, forming specific maintenance task contents, including: According to the maintenance measure template, extract the processing content to be executed and divide it into several subtasks, and each subtask corresponds to an independent operation step; For each subtask, according to the execution time data and task level in the historical maintenance task record, correct the estimated time of the subtask, and use the corrected estimated time as the duration parameter for task scheduling; For the operation content of each subtask, combine the plant type identified by the identification information and the regional attributes corresponding to the geographical location information, screen available maintenance equipment options from the preset equipment information library, and generate a recommended equipment list; Unify and package the operation steps, estimated time, and recommended equipment list of all subtasks into specific maintenance task contents.

[0083] In the embodiment of the present invention, after the maintenance measure template is decomposed into multiple subtasks, it is necessary to further refine the time scheduling of each subtask. To achieve this goal, first retrieve execution samples similar to the operation steps of the current subtask from the historical maintenance task record to form a task execution time sample set. This set contains the time-consuming data for completing similar tasks in different times and environments, which forms the basis for estimating the time of future tasks.

[0084] In the sample set, the average time consumption is extracted according to statistical methods to generate the basic estimated duration of the subtask. Next, according to the task level information generated in the previous steps, the execution complexity represented by the task level is converted into a weighting coefficient. The basic estimated time is combined with the weighting coefficient to complete the preliminary time adjustment of the task.

[0085] To further improve the actual accuracy of scheduling, it is also necessary to analyze whether there are job interference factors in the geographical area corresponding to the subtask, such as the congestion level of the park, the slope of the operation area, or the seasonal water supply difference. If such factors are detected, a time correction factor is set according to the interference level to form the final corrected time.

[0086] Finally, the corrected estimated time is written into the scheduling parameters of the subtask, providing an accurate reference basis for the subsequent maintenance scheduling of the platform. This mechanism improves the flexibility of task scheduling and the efficiency of resource allocation, especially suitable for complex scenarios of multi-object simultaneous inspection and maintenance.

[0087] Among them, for the operation content of each subtask, combined with the plant type identified by the identification information and the regional attributes corresponding to the geographical location information, available maintenance equipment options are screened from the preset equipment information library, and a recommended equipment list is generated, specifically including: The maintenance task is divided into multiple subtasks, and the execution content of these subtasks involves different types of operation operations, such as pruning, fertilizing, watering, etc. In the process of generating subtasks, the system needs to combine the identification information (such as plant type) of the greening object corresponding to each subtask and the geographical location (i.e., regional attributes) where the plant is located, and screen out the maintenance equipment suitable for performing the task from the preset equipment information library.

[0088] First, the system will search for relevant maintenance equipment according to the operation requirements of each subtask (such as pruning, watering, etc.). For example, for the pruning task, the system may select tools such as scissors and electric pruning machines, while for the fertilizing task, the system may select equipment such as fertilizer trucks and sprayers. The selection of equipment also takes into account the type of plant and regional characteristics. For example, some equipment may be suitable for smaller plants, while others are suitable for large areas of lawns or trees.

[0089] The system will generate a recommended equipment list according to the adaptability of the maintenance equipment, listing all the equipment options that can be used for the task. For example, the pruning task may recommend manual scissors and electric pruning machines, while the watering task may recommend automatic watering systems or handheld sprayers. In this way, the staff can select the most suitable equipment according to the equipment list, thereby improving work efficiency and reducing mistakes in equipment selection.

[0090] In addition, the device recommendations may also be adjusted according to the attributes of the geographical location. Certain devices may be more suitable within specific regions. For example, some lawn maintenance devices are suitable for open areas, while some tree pruning devices are suitable for dense forest areas. The system dynamically adjusts the device recommendations based on the regional attributes to ensure the smooth progress of the operation.

[0091] Among them, the preset device information library refers to the database in the system that contains information about different maintenance devices. Example: The system presets a device information library that contains all types of devices suitable for greening maintenance and their functions. For example, device information such as pruning tools, fertilizer spreaders, watering systems, etc., including their models, applicable plant types, operation parameters, usage conditions, etc. According to different maintenance tasks, the system will automatically screen out the devices suitable for the current task from this library. For example, if the task is to prune roses, the system may recommend using "pruning tool of model X" and provide the operation instructions for this tool.

[0092] In a preferred embodiment of the present invention, error range detection is performed on the extracted spatial coordinate information to determine whether there is a risk of duplicate binding of the spatial position, and a second validity verification result is generated, including: According to the spatial coordinates corresponding to the identification information, a buffer range centered on the coordinate point is established, and the spatial coordinate information of all bound objects within the buffer is extracted; The distance between the current coordinate and the coordinates of other objects within the buffer is compared. If the minimum distance is less than the set binding spacing threshold, it is marked as having a risk of spatial overlap; In the case of having a risk of spatial overlap, combined with the type of greening object corresponding to the identification information and the object distribution density corresponding to this position in the geographical layer, the risk level is determined, and an artificial review prompt is triggered to generate a verification result marked as a high-risk binding.

[0093] In the embodiment of the present invention, to avoid spatial coordinate conflicts during the binding process of NFC and GIS, a set of error range detection and risk assessment mechanisms are introduced before establishing the binding relationship between the identification information and the spatial coordinates. Specifically, after obtaining the spatial coordinates corresponding to the identification information, a buffer centered on this coordinate point is automatically constructed, and the radius of the buffer is automatically set according to the set binding spacing threshold to ensure that the detection accuracy adapts to the actual management scale.

[0094] Further, the spatial coordinate information of all bound objects within this buffer is extracted to construct a set of adjacent object coordinates. By calculating the distance between the current coordinate point and each adjacent coordinate point, it is determined whether the minimum distance is less than the binding spacing threshold. If the judgment result is yes, this is marked as "having a risk of spatial overlap", and the binding operation is not immediately executed.

[0095] In scenarios with a risk of overlap, a composite evaluation model based on plant type and spatial distribution density is introduced: Identify that the identification information contains a greening object type field (such as trees, shrubs, grasslands, etc.), and calculate the spatial density based on the distribution density of this type in the layer where the current location belongs (such as the number of objects per unit area). If multiple binding points of the same type are identified in a high-density area, the risk level is further increased. A prompt flag of "high-risk binding" is generated according to the evaluation result, and the identification record is pushed to the management platform for manual review.

[0096] Through this embodiment, a highly reliable binding mechanism that avoids coordinate conflicts and rebinding problems in a multi-object and multi-point spatial environment is achieved, effectively improving the consistency of spatial data and the positioning accuracy of on-site inspections, and avoiding identification confusion or data anomalies caused by coordinate overlap.

[0097] Among them, the risk level refers to the pre-set risk level classification for spatial overlap risks or operation interference. Example: The system presets different risk levels to evaluate spatial overlap situations. For example, when the spatial coordinates of two greening objects are less than the set threshold, the system will mark it as "low risk"; if the density of greening objects in the overlapping area is high, it is marked as "medium risk"; if the overlapping area is a core maintenance area with many facilities, the system will mark it as "high risk". Different risk levels will trigger different review processes to ensure the smooth progress of maintenance tasks.

[0098] Among them, in the case of a risk of spatial overlap, combining the type of greening object corresponding to the identification information with the object distribution density corresponding to this location in the geographical layer, determining the risk level, and triggering a manual review prompt, generating a verification result marked as high-risk binding, specifically including: When the system detects that the spatial coordinates of two or more greening objects overlap or are very close, it will first evaluate the risk of this overlap situation. This evaluation is carried out by combining the type of greening object identified in the identification information with the object distribution density at this location in the geographical layer. Specifically, the distribution characteristics of different types of greening objects (such as trees, lawns, flowers, etc.) in the geographical layer are different. For example, trees usually require a larger space, while lawns or flowers may have a denser distribution.

[0099] To determine the risk level of spatial overlap, the system first queries the geographical distribution density of a location based on the different types of greening objects. For example, if a region is usually planted with trees, but the spatial density in this region is too high, or the type of greening object is lawn but highly concentrated, it may lead to spatial overlap. The system will judge whether there is an overlap risk by calculating the distribution density of this region. If there is a high-density overlap in this region, the system will determine a risk level according to the level of the distribution density, the degree of overlap, and the type of greening object.

[0100] If the risk level is high, the system will trigger a prompt for manual review, requiring the staff to check and confirm whether there are problems with the greening objects in the overlapping area. The manual review prompt can be displayed through the operation interface of the system and marked as "high-risk binding". For example, in some regions, there may be a dense distribution of lawns and flowers on the layer, and the system will prompt for manual inspection to determine whether there is indeed an overlap error to avoid errors in subsequent maintenance tasks.

[0101] This setting of the risk level ensures that during the process of binding greening object information, the spatial overlapping area can be accurately judged, and possible incorrect bindings or omissions of maintenance tasks can be corrected in a timely manner, thereby improving the accuracy of the system.

[0102] In a preferred embodiment of the present invention, each field value in the current status data is compared with the corresponding standard parameter reference mean value, the numerical difference of each status item is calculated, and in combination with the plant type of the greening object and the parameter category of the status item, the difference threshold range of each status item is set, including: Calculate the numerical difference based on the current value and the standard parameter reference mean value of each status item, and construct a status difference data set; For each field item in the status difference data set, according to its corresponding plant type attribute, retrieve the preset plant species - parameter threshold correspondence table to obtain the initial value of its acceptable fluctuation range; According to the parameter category to which the field item belongs, adjust and correct the initial value fluctuation range to form a difference threshold range for a specific plant type and a specific parameter category, and the parameter category includes morphological parameters and environmental parameters.

[0103] In the embodiment of the present invention, after performing quantitative difference analysis on the status data collected on-site, it is necessary to correct the difference threshold in combination with the plant type and the parameter category to adapt to the status evaluation requirements of various greening objects. First, based on the current value of each field item and its standard parameter reference mean value, calculate the numerical difference, and then generate a status difference data set. This data set contains the deviation amounts of multiple field items, reflecting the degree of its status fluctuation.

[0104] Further classify each field item in the status difference dataset according to the plant type attribute bound to the field. For example, if a field item belongs to "leaf density" and the plant type is "evergreen tree", the plant species - parameter threshold correspondence table will be retrieved to obtain the initial value of the acceptable fluctuation range of this type of plant for this parameter item. This fluctuation range is usually generated based on historical data statistics and has good representativeness and engineering adaptability.

[0105] Considering that the parameter categories to which different field items belong may affect their sensitivity and judgment tolerance, a parameter category correction mechanism is introduced to dynamically adjust the original initial value. Specifically, if a field item belongs to "morphological parameters" (such as color, density, length), it is corrected using the morphological threshold adjustment factor; if it belongs to "environmental parameters" (such as humidity, light), the environmental correction coefficient is called for fine-tuning. After the correction is completed, the final difference threshold range is generated as the key basis for subsequent diagnosis level determination and task generation.

[0106] The above method takes into account the individual differences of plants and the sensitivity of parameter attributes, realizes the flexible adaptation of difference judgment, has wide applicability in the complex green space environment with mixed growth of multiple plant types, and helps to significantly improve the accuracy and practicality of diagnosis and judgment.

[0107] Among them, according to the current value of each status item and the standard parameter reference mean value, the numerical difference is calculated to construct a status difference dataset, specifically including: The system will compare the currently collected status data with the standard parameter reference mean value. Each status item (for example, the color change of the plant, leaf density, soil humidity, etc.) will have a current value and a corresponding standard parameter reference mean value, and the latter is obtained through historical maintenance data and the standardization process.

[0108] To calculate the difference between the current status item and the standard parameter reference mean value, the system extracts the value of each status item from the current status data and compares it with the corresponding standard parameter. By calculating the difference between the current status item and the standard reference value, a "numerical difference" is obtained. This difference can be positive or negative, depending on whether the current status deviates from the standard parameter.

[0109] For example, if the standard reference value of the plant height is 50 cm and the current status data shows that the plant height is 45 cm, the numerical difference is -5 cm. By calculating the difference for each status item, the system generates a "status difference dataset", which records the difference values of all status items and provides a basis for subsequent diagnosis and maintenance tasks.

[0110] This process helps to quickly identify which greening objects' status deviates from the expected standard and can specifically propose corresponding maintenance plans.

[0111] Among them, the "Plant Species - Parameter Threshold Correspondence Table" and "adjust and correct the initial value fluctuation range according to the parameter category to which the field item belongs to form a difference threshold range for a specific plant type and a specific parameter category, specifically including: The plant species - parameter threshold correspondence table is a table used to match plant species with their related maintenance parameters. Each plant type (for example, rose, banyan tree, etc.) has its specific growth characteristics, and each plant can accept different ranges of parameter fluctuations under different maintenance states. Therefore, in order to ensure the accuracy and pertinence of the maintenance tasks, the plant species - parameter threshold correspondence table is introduced in the present invention to clarify the standard maintenance parameter ranges of different plant species in different growth stages or seasons.

[0112] For example, the soil humidity range required by roses in spring is different from that of banyan trees. By looking up the plant species - parameter threshold correspondence table, the system can accurately obtain the threshold range of a specific plant species under specific environmental conditions.

[0113] In addition, the system will also adjust and correct according to the parameter category to which the field item belongs. For example, the two parameters of the leaf density and soil humidity of plants belong to different parameter categories. The former belongs to the morphological parameter category, and the latter belongs to the environmental parameter category. When calculating the fluctuation range of each status item, the system will adjust its initial fluctuation range according to the type (morphological or environmental) of this status item. In this way, the system can automatically correct the fluctuation range of each parameter according to the specific plant type and parameter category to make it meet the actual maintenance requirements.

[0114] For example, for morphological parameters, the system may set a smaller fluctuation range because they have a more direct impact on plant growth; while for environmental parameters, the system may set a looser fluctuation range because the changes in environmental factors are larger and have a more indirect impact on plants.

[0115] Through this mechanism, the system can provide customized maintenance parameters for each plant, thereby ensuring the accuracy and practicality of the maintenance work.

[0116] In a preferred embodiment of the present invention, for each subtask, according to the execution time data and task level in the historical maintenance task record, correct the estimated time of this subtask, and use the corrected estimated time as the duration parameter for task scheduling, including: Extract the execution samples corresponding to each subtask in the historical maintenance task record to construct a task execution time sample set, and the task execution time sample set includes the actual time consumption data of approximate operation steps under different execution conditions; Generate the basic estimated duration of the subtask according to the average execution time recorded in the task execution time sample set, and perform weighted adjustment on this basic estimated duration according to the execution complexity coefficient represented by the task level; Based on the geolocation information of the area where the subtask belongs, determine whether there are job interference factors in this area. If there are, set the time correction factor; Multiply the weighted adjustment time by the time correction factor to obtain the corrected estimated time, and write this estimated time into the subtask scheduling parameters.

[0117] In the embodiment of the present invention, for the subtasks obtained after decomposing the maintenance task, a scheduling optimization method based on historical data and actual scenarios is adopted to refine the estimated time consumption of each subtask to improve the overall task scheduling efficiency. First, extract the execution samples similar to the operation content of the current subtask from the historical maintenance task records to construct a task execution time sample set. This set contains the time-consuming data of similar tasks under various execution conditions such as different time periods, climates, and personnel configurations.

[0118] Through the data in the sample set, use the weighted average or median method to obtain the basic estimated duration of the current subtask. To further enhance the discrimination of the scheduling parameters, according to the task level information determined above, map each level to an execution complexity coefficient. This complexity coefficient represents the expected number of operation steps, equipment usage intensity, or personnel collaboration requirements during the task implementation process. Multiply the basic estimated duration by the complexity coefficient to form the first-stage corrected duration.

[0119] After that, it is also necessary to consider the job interference factors that the subtask may face at the specific execution location, such as whether the location is close to a water area, whether there is a terrain height difference, whether it is an area with frequent traffic, etc. Use the geolocation information bound to the subtask and the preset regional attribute model to analyze and evaluate the interference factors. If there are influencing factors, set the time correction factor according to the degree of influence, such as multiplying by a delay value greater than 1.

[0120] Finally, multiply the complexity corrected duration by the time correction factor to obtain the final corrected estimated time, and write it into the scheduling parameter field of the subtask. This parameter will be used in the scheduling engine for task priority sorting, equipment matching, and personnel scheduling strategy generation.

[0121] Through the above embodiments, the dynamic optimization of task time assessment is realized, which not only improves the rationality of task execution arrangements, but also effectively avoids task concentration or layout conflicts, and helps to maximize the inspection and maintenance efficiency under limited resources.

[0122] Among them, based on the average execution time recorded in the task execution time sample set, the basic estimated duration of the subtask is generated, and the basic estimated duration is weighted and adjusted according to the execution complexity coefficient represented by the task level. Specifically, it includes: To ensure the rationality and scientificity of task scheduling, the system first calculates the basic estimated duration of each subtask based on the execution sample data in the historical maintenance task records. This process is achieved by collecting historical task samples similar to the current subtask and calculating the average execution time of these samples. The records of the execution samples include the actual time-consuming data under different conditions, which helps to provide a reference for the current task.

[0123] For example, if a maintenance task involves pruning rose flowers, and the execution time records of multiple past rose pruning tasks are 20 minutes, 25 minutes, and 30 minutes respectively, the system will calculate the average execution time of these records, and the basic estimated duration is 25 minutes. This basic duration represents the standard time to complete the subtask under similar conditions.

[0124] Then, the basic estimated duration is weighted and adjusted according to the execution complexity coefficient represented by the task level. The task level is usually set according to the complexity of the task. For example, the pruning task may be more complex than the fertilization task, so its task level coefficient may be higher. The system multiplies each task by a "complexity coefficient" and multiplies it by the basic estimated duration to obtain the adjusted estimated time. For example, if the complexity coefficient of the pruning task is 1.2, then the basic estimated duration of 25 minutes will be multiplied by 1.2, and the corrected estimated duration is 30 minutes. In this way, the system can reasonably adjust the estimated execution time according to the actual complexity of the task.

[0125] This method helps to arrange the execution of tasks more accurately, avoiding insufficient time caused by overly complex tasks or waste of time resources caused by overly simple tasks.

[0126] Among them, based on the geolocation information of the area where the subtask is located, it is judged whether there is an operation interference factor in this area. If there is, a time correction factor is set. Specifically, it includes: In the actual process of park greening inspection and maintenance, the geographical environment of the operation area will affect the task execution. For example, there may be factors such as high traffic flow, construction areas, or weather changes in some areas, which may all affect the task execution efficiency. To ensure the accuracy and effectiveness of task scheduling, the system of the present invention introduces an operation interference factor detection mechanism based on geolocation information.

[0127] Specifically, the system will first analyze the geolocation information of the areas involved in each subtask. This information includes the specific geographical coordinates where the subtask is located and relevant data on its surrounding environment. For example, if the execution area of a certain subtask is near the main entrance of a park and there is a large traffic flow in this area, or other construction work is underway near this area, the system will identify these potential operation interference factors.

[0128] Once the operation interference factors are detected, the system will set a "time correction factor" based on these factors. For example, if a large traffic flow causes a slower execution speed of the operation, the system can set a correction factor for this subtask, such as 1.3, indicating that the execution time of the task needs to be increased by 30%. If the task is located in a construction area, the correction factor may be set to 1.5, indicating that due to operation interference, the expected time of the task will increase by 50%.

[0129] In this way, the system can adjust the expected time in real time, ensure that tasks can be arranged more accurately in actual operations, and avoid task delays or inefficiencies caused by uncontrollable factors. Such a method enhances the system's adaptability to environmental changes and improves the management efficiency and execution effect of the overall maintenance tasks.

[0130] An embodiment of the present invention also provides a park greening inspection and maintenance system based on the association of NFC and GIS park data. The system includes: The NFC tag configuration module is used to configure a preset NFC tag on the greening object. The NFC tag contains the identification information of the corresponding greening object, and binds the identification information with the location information of the greening object registered in the GIS system to generate a greening identification data table; The terminal device reading module is used to read the NFC tag through the terminal device, obtain the identification information of the current greening object, and call the corresponding geolocation information and historical maintenance data in the GIS system according to the identification information to form inspection and positioning data; The on-site status collection module is used to collect the on-site status information of the current greening object according to the geolocation information of the inspection and positioning data, including plant morphological parameters and surrounding environment condition information, and compare and analyze it with the historical maintenance data to generate preliminary diagnosis data; The maintenance task generation module is used to generate specific maintenance task content according to the preliminary diagnosis data, combined with the maintenance standards of the corresponding greening object, and upload it to the management platform in real time through the terminal device to form a maintenance task record.

[0131] It should be noted that this system corresponds to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0132] An embodiment of the present invention further provides a computing device, including: a processor and a memory storing a computer program. When the computer program is run by the processor, the above-described method is executed. All implementation manners in the above method embodiments are applicable to this embodiment and can also achieve the same technical effects.

[0133] An embodiment of the present invention further provides a computer-readable storage medium storing instructions. When the instructions are run on a computer, the computer is caused to execute the above-described method. All implementation manners in the above method embodiments are applicable to this embodiment and can also achieve the same technical effects.

[0134] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A park greening inspection and maintenance method based on the association of NFC and GIS park data, characterized in that, The method includes: Configuring a preset NFC tag on the greening object, where the NFC tag includes the identification information corresponding to the greening object, and binding the identification information with the location information of the greening object registered in the GIS system to generate a greening identification data table; Reading the NFC tag through a terminal device to obtain the identification information of the current greening object, and calling the corresponding geographic location information and historical maintenance data in the GIS system according to the identification information to form inspection and positioning data; Collecting the on-site status information of the current greening object according to the geographic location information of the inspection and positioning data, including plant morphological parameters and surrounding environmental condition information, and comparing and analyzing it with the historical maintenance data to generate preliminary diagnosis data; Generating specific maintenance task content according to the preliminary diagnosis data, combined with the maintenance standards of the corresponding greening object, and uploading it to the management platform in real time through the terminal device to form a maintenance task record.

2. The park greening inspection and maintenance method based on the association of NFC and GIS park data according to claim 1, characterized in that, Binding the identification information with the location information of the greening object registered in the GIS coefficient to generate a greening identification data table, including: Determining the node position of the target greening object in the geographic layer by retrieving the preset geographic location database of the greening object in the GIS system, and extracting the spatial coordinate information of the node; Establishing a corresponding relationship between the identification information and the spatial coordinate information, and respectively setting information fields, including object name, identification code, spatial code, calibration time, and the layer to which it belongs, to generate a binding result; Generating a greening identification data table according to the binding result, and the greening identification data table is used to quickly match the identification information with the geographic location during subsequent inspection processes.

3. A park greening inspection and maintenance method based on the association of NFC and GIS park data according to claim 1, characterized in that, Collecting the on-site status information of the current greening object according to the geographic location information of the inspection and positioning data, including plant morphological parameters and surrounding environmental condition information, and comparing and analyzing it with the historical maintenance data to generate preliminary diagnosis data, including: Retrieving the geographic location information in the inspection and positioning data through the terminal device to locate the specific location of the on-site greening object, and collecting the current status data of the plant at the specific location, where the current status data includes color change, height value, leaf density, and soil humidity; Extracting multi-period data for the current status data and the same type of status items recorded in the historical maintenance data of the greening object, constructing standard parameters for field-by-field comparison, and identifying the degree of change in each status item, and then setting a difference threshold range for the change in each status item; Marking the parameter of the status item with abnormal fluctuations according to the difference threshold range to form a difference feature set; Dividing the diagnosis level according to the deviation degree of each status item in the difference feature set and the cumulative number of abnormal items, and generating preliminary diagnosis data based on the diagnosis level and the difference feature set.

4. A park greening inspection and maintenance method based on the association of NFC and GIS park data according to claim 3, characterized in that, Generating specific maintenance task content according to the preliminary diagnosis data, combined with the maintenance standards of the corresponding greening object, and uploading it to the management platform in real time through the terminal device to form a maintenance task record, including: Matching the corresponding processing rules of the target greening object in the preset maintenance standard library according to the preliminary diagnosis data, and extracting the maintenance measure templates corresponding to the diagnosis items, including pruning method, fertilization frequency, and irrigation cycle; Decompose the maintenance measure template into several subtasks to form specific maintenance task content, and attach the current identification information and geolocation information as task affiliated attributes. The subtasks determine their corresponding task levels according to the diagnosis levels determined in the preliminary diagnosis data, and the task levels are used to indicate the execution urgency or complexity of each subtask; Real-time package and upload the maintenance task content to the management platform through the terminal device, and generate a maintenance task record, which includes the preliminary diagnosis data and its source information.

5. The park greening inspection and maintenance method based on the association of NFC and GIS park data according to claim 2, characterized in that, Establish a corresponding relationship between the identification information and the spatial coordinate information, and set information fields respectively, including the object name, identification code, spatial code, calibration time and the affiliated layer, and generate a binding result, including: Perform format normalization processing on the identification information, and conduct coding specification inspection and repeatability inspection respectively through the preset coding rule library and the bound identification code to generate the first validity verification result; Detect the error range of the extracted spatial coordinate information, and judge whether there is a risk of repeated binding of the spatial position to generate the second validity verification result; When both the first validity verification result and the second validity verification result pass, construct a two-way mapping relationship between the identification information and the spatial coordinate information, and establish a binding result with the identification code as the primary key.

6. The park greening inspection and maintenance method based on the association of NFC and GIS park data according to claim 3, characterized in that, Extract multi-period data from the current status data and the same type of status items recorded in the historical maintenance data of this greening object, construct standard parameters for field corresponding comparison, and identify the change degree in each status item, and then set a difference threshold range for the change of each status item, including: According to the status items included in the current status data, call the historical records of the corresponding status items in the historical maintenance data in multiple time periods, construct a sequence of status values arranged in chronological order, and use it as the basic data set for constructing standard parameters; Eliminate outliers from the historical data in the basic data set, including identifying mutation points, excluding extreme values beyond the reasonable range, and eliminating abnormal data with a large deviation from adjacent time points to form an effective sample set for standard parameter calculation; Divide the effective sample set into sliding intervals according to the time window, calculate the local mean in each sliding interval respectively, and combine the time interval between the current collection time and the historical data, and set the time weight of each sliding interval according to the preset time decay function; Perform weighted aggregation on the local mean of each sliding interval and the corresponding time weight, and normalize the result to obtain the standard parameter reference mean corresponding to each status item. Compare the field values in the current status data with the corresponding standard parameter reference mean, calculate the numerical difference of each status item, and set the difference threshold range of each status item in combination with the plant type of the greening object and the parameter category of the status item.

7. A park greening inspection and maintenance method based on the association of NFC and GIS park data according to claim 4, characterized in that, Decompose the maintenance measure template into several subtasks to form specific maintenance task content, including: Extract the processing content to be executed according to the maintenance measure template, and divide it into several subtasks, and each subtask corresponds to an independent operation step; For each subtask, based on the execution time data and task level in the historical maintenance task record, correct the estimated time of the subtask, and use the corrected estimated time as the duration parameter for task scheduling; For the operation content of each subtask, combine the plant type identified by the recognition information and the regional attributes corresponding to the geographic location information, screen the available maintenance equipment options from the preset equipment information library, and generate a recommended equipment list; Unify and encapsulate the operation steps, estimated time, and recommended equipment list of all subtasks into the specific maintenance task content.

8. A park greening inspection and maintenance method based on the association of NFC and GIS park data according to claim 5, characterized in that, Perform an error range detection on the extracted spatial coordinate information, determine whether there is a risk of duplicate binding of spatial positions, and generate a second validity verification result, including: Based on the spatial coordinates corresponding to the recognition information, establish a buffer range centered on the coordinate point, and extract the spatial coordinate information of all bound objects within the buffer; Compare the distance between the current coordinate and the coordinates of other objects within the buffer. If the minimum distance is less than the set binding spacing threshold of the system, mark it as having a risk of spatial overlap; In the case of a risk of spatial overlap, combine the type of greening object corresponding to the recognition information and the object distribution density corresponding to this location in the geographic layer to determine the risk level, trigger an artificial review prompt, and generate a verification result marked as a high-risk binding.

9. A park greening inspection and maintenance method based on the association of NFC and GIS park data according to claim 6, characterized in that, Compare the field values in the current status data with the corresponding standard parameter reference means, calculate the numerical differences of each status item, and combine the plant type of the greening object and the parameter category of the status item to set the difference threshold range of each status item, including: Calculate the numerical differences based on the current value and the standard parameter reference mean of each status item, and construct a status difference data set; For each field item in the status difference data set, retrieve the preset plant species - parameter threshold correspondence table according to its corresponding plant type attribute to obtain the initial value of its acceptable fluctuation range; Adjust and correct the initial value fluctuation range according to the parameter category to which the field item belongs to form a difference threshold range for a specific plant type and a specific parameter category, where the parameter category includes morphological parameters and environmental parameters.

10. A park greening inspection and maintenance method based on the association of NFC and GIS park data according to claim 7, characterized in that For each subtask, based on the execution time data and task level in the historical maintenance task record, correct the estimated time of the subtask, and use the corrected estimated time as the duration parameter for task scheduling, including: Extract the execution samples corresponding to each subtask in the historical maintenance task record to construct a task execution time sample set; Generate the basic estimated duration of the subtask based on the average execution time recorded in the task execution time sample set, and perform weighted adjustment on the basic estimated duration according to the execution complexity coefficient represented by the task level; Based on the geographic location information of the area where the subtask is located, determine whether there are operation interference factors in this area. If so, set a time correction factor; Multiply the weighted adjustment time by the time correction factor to obtain the corrected estimated time, and write the estimated time into the subtask scheduling parameters.

Images