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

Abstract

The present invention provides a park greening inspection and maintenance method based on NFC and GIS park data association, which relates to the field of data processing technology. The method comprises: configuring a preset NFC tag on a greening object, and binding the identification information therein with the greening object location information registered in a GIS system; reading the NFC tag by a terminal device to obtain the identification information of the current greening object, and calling the corresponding geographic positioning information and historical maintenance data in the GIS system according to the identification information; collecting the on-site status information of the current greening object, including plant morphological parameters and surrounding environmental status information, and comparing and analyzing it with historical maintenance data; generating specific maintenance task content in combination with the maintenance standard of the corresponding greening object, and uploading it to a management platform in real time through the terminal device to form a maintenance task record. The present invention improves the autonomy and accuracy of park greening inspection and maintenance.

Description

Technical Field

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

[0002] Existing technologies primarily rely on manual inspections, with staff using paper forms or portable devices to record individual trees, lawns, flowers, and other green features within the park. Some systems incorporate GIS (Geographic Information System) technology to digitally model green areas and implement spatial management of maintenance tasks through map annotation. However, these systems typically lack real-time interaction with on-site equipment, requiring inspectors to manually match equipment with geographic information. This can lead to positioning errors and inconsistent information, limiting the real-time and accuracy of the data.

[0003] In specific application scenarios, such as when conducting spring greening inspections in a large municipal park, it is difficult for staff on-site to quickly confirm plant types and their maintenance records due to the large coverage area of ​​the greening area and the wide variety of vegetation. The current method requires comparison by reviewing paper records or the GIS interface of the terminal system. It is very easy for manual identification errors to cause a certain type of flower to not be fertilized or pruned on time, thereby affecting the quality of the landscape. For example, a park management office once missed the maintenance records of roses. The reason was that the inspection personnel failed to accurately locate the green plant block in the GIS system and mistakenly classified it as an adjacent lawn. This reflects that the current GIS system lacks close integration with on-site identification methods and cannot achieve efficient and accurate information exchange. Summary of the Invention

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

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] A park greening inspection and maintenance method based on NFC and GIS park data association, the method comprising:

[0007] A preset NFC tag is configured on the greening object. The NFC tag includes the identification information of the corresponding greening object, and the identification information is bound to the greening object location information registered in the GIS system to generate a greening identification data table;

[0008] The terminal device reads the NFC tag to obtain the identification information of the current greening object, and calls the corresponding geographic positioning information and historical maintenance data in the GIS system based on the identification information to form inspection positioning data;

[0009] Based on the geographic positioning 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 conditions, and compared with historical maintenance data to generate preliminary diagnostic data;

[0010] Based on the preliminary diagnostic data and combined 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.

[0011] Preferably, the identification information is bound to the greening object location information registered in the GIS coefficient to generate a greening identification data table, including:

[0012] By calling the preset greening object geographic location database in the GIS system, the node position of the target greening object in the geographic layer is determined, and the spatial coordinate information of the node is extracted;

[0013] Establish a corresponding relationship between identification information and spatial coordinate information, and set information fields respectively, including object name, identification code, spatial code, calibration time and layer to which it belongs, to generate a binding result;

[0014] According to the binding result, a greening identification data table is generated, and the greening identification data table is used for rapid matching of identification information and geographic location during subsequent inspections.

[0015] Preferably, based on the geographic positioning 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 conditions, and compared with the historical maintenance data to generate preliminary diagnostic data, including:

[0016] The terminal device retrieves the geographic positioning information in the inspection positioning data, locates the specific location of the greening object on site, and collects the current status data of the plant at the specific location, including color change, height value, leaf density, and soil moisture;

[0017] For the current status data and similar status items recorded in the historical maintenance data of the greening object, multi-cycle data is extracted to construct standard parameters for field correspondence comparison, and the degree of change in each status item is identified, and then the difference threshold range for the change of each status item is set;

[0018] According to the difference threshold range, the state item parameters with abnormal fluctuations are marked to form a difference feature set;

[0019] The diagnostic level is divided according to the degree of deviation of each state item in the differential feature set and the number of accumulated abnormal items, and preliminary diagnostic data is generated based on the diagnostic level and the differential feature set.

[0020] Preferably, based on the preliminary diagnostic 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:

[0021] Based on the preliminary diagnostic data, the system matches the target greening object with the corresponding processing rules in the preset maintenance standard library, and extracts the maintenance measure template corresponding to the diagnostic item, including pruning method, fertilization frequency, and watering cycle;

[0022] Decompose the maintenance measure template into several subtasks to form specific maintenance task content, and attach current identification information and geographic positioning information as task attributes. The subtasks are assigned a task level based on the diagnostic level determined in the preliminary diagnostic data. The task level is used to indicate the urgency or complexity of each subtask.

[0023] 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.

[0024] Preferably, a correspondence is established between the identification information and the spatial coordinate information, and information fields are set separately, including object name, identification code, spatial code, calibration time and layer to which it belongs, to generate a binding result, including:

[0025] Performing format normalization on the identification information, and performing coding standardization verification and duplication verification respectively through a preset coding rule library and a bound identification code to generate a first validity verification result;

[0026] Perform error range detection on the extracted spatial coordinate information to determine whether there is a risk of duplicate binding at the spatial position and generate a second validity verification result;

[0027] When both the first validity verification result and the second validity verification result are passed, the identification information and the spatial coordinate information are constructed into a bidirectional mapping relationship, and a binding result with the identification code as the primary key is established.

[0028] Preferably, for the current status data and similar status items recorded in the historical maintenance data of the greening object, multi-cycle data is extracted, standard parameters for field correspondence comparison are constructed, and the degree of change in each status item is identified, and then a difference threshold range is set for the change of each status item, including:

[0029] According to the status items included in the current status data, the historical records of the corresponding status items in the historical maintenance data over multiple time periods are called to construct a sequence of status values ​​arranged in chronological order, which is used as the basic data set for constructing standard parameters;

[0030] Eliminate outliers from historical data in the basic dataset, including identifying mutation points, excluding extreme values ​​outside the reasonable range, and eliminating abnormal data with large deviations from adjacent time points, to form a valid sample set for standard parameter calculation;

[0031] 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. The time weight of each sliding interval is set according to the preset time decay function based on the time interval between the current collection time and the historical data.

[0032] The local mean of each sliding interval is weighted and summarized with the corresponding time weight, and the results are normalized to obtain the reference mean of the standard parameters corresponding to each state item;

[0033] 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.

[0034] Preferably, the maintenance measure template is decomposed into tasks to generate several subtasks, forming specific maintenance task content, including:

[0035] According to the maintenance measure template, extract the processing content to be executed and divide it into several subtasks, each of which corresponds to an independent operation step;

[0036] For each subtask, the estimated time of the subtask is revised based on the execution time data and task level in the historical maintenance task records, and the revised estimated time is used as the duration parameter for task scheduling;

[0037] For each subtask's work content, the system combines the plant type identified by the identification information and the regional attributes corresponding to the geographic positioning information to screen available maintenance equipment options from the preset equipment information library and generate a list of recommended equipment.

[0038] The operation steps, estimated time and recommended equipment list of all subtasks are uniformly packaged into specific maintenance task content.

[0039] Preferably, the extracted spatial coordinate information is subjected to an error range detection to determine whether there is a risk of duplicate binding at the spatial position, and a second validity verification result is generated, including:

[0040] 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 in the buffer is extracted;

[0041] Compare the distance between the current coordinates and the coordinates of other objects in the buffer. If the minimum distance is less than the binding distance threshold set by the system, it is marked as a risk of spatial overlap.

[0042] In the case of spatial overlap risk, the risk level is determined by combining the greening object type corresponding to the identification information with the object distribution density corresponding to the location in the geographic layer, and a manual review prompt is triggered to generate a verification result marked as a high-risk binding.

[0043] Preferably, the value of each field in the current status data is compared with the reference mean of the corresponding standard parameter, the numerical difference of each status item is calculated, and the difference threshold range of each status item is set in combination with the plant type of the greening object and the parameter category of the status item, including:

[0044] According to the current value of each state item and the reference mean of the standard parameter, the value difference is calculated to construct the state difference data set;

[0045] For each field item in the state difference dataset, according to its corresponding plant type attribute, the preset plant species-parameter threshold correspondence table is retrieved to obtain the initial value of its acceptable fluctuation range;

[0046] According to the parameter category to which the field item belongs, the initial value fluctuation range is adjusted and corrected to form a difference threshold range under a specific plant type and a specific parameter category, and the parameter category includes morphological parameters and environmental parameters.

[0047] Preferably, for each subtask, the estimated time of the subtask is revised based on the execution time data and task level in the historical maintenance task records, and the revised estimated time is used as the duration parameter of the task scheduling, including:

[0048] Extract the execution samples corresponding to each subtask in the historical maintenance task records and construct a task execution time sample set, which includes the actual time consumption data of the approximate operation steps under different execution conditions;

[0049] Generate a basic estimated duration for the subtask based on the average execution time recorded in the task execution time sample set, and make a weighted adjustment to the basic estimated duration based on the execution complexity coefficient represented by the task level;

[0050] Based on the geographic location information of the area to which the subtask belongs, determine whether there are any work interference factors in the area. If so, set the time correction factor;

[0051] 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.

[0052] The above solution of the present invention includes at least the following beneficial effects:

[0053] By configuring preset NFC tags on green objects and binding the identification information contained in the tags with the location information of the green objects registered in the GIS system, a one-to-one correspondence between physical vegetation and spatial digital models is achieved. Inspection personnel can use terminal devices to quickly identify the current object and call up its geographic positioning and historical maintenance data, significantly improving the accuracy of inspection positioning and overcoming the problems of manual information matching prone to errors and inaccurate positioning in traditional methods.

[0054] During the inspection process, inspectors read NFC tags through terminal devices. Without having to check paper records or search for geographical nodes one by one in the GIS interface, they can immediately obtain the specific location and historical maintenance information of the greening object on site, effectively improving the real-time nature of data retrieval. In complex scenarios such as large-scale inspections in spring, the system can automatically associate object identification codes with map information to help staff quickly confirm plant types and maintenance records, reducing the risk of maintenance omissions due to human misjudgment. For example, for rose flowers, the pruning frequency and fertilization cycle can be directly retrieved by scanning the tag, avoiding tasks that are missed due to being misclassified as adjacent lawns.

[0055] Furthermore, by collecting status information about current greening objects through terminal devices and comparing it with historical maintenance data, the system can promptly detect changes in the status of greening objects and generate preliminary diagnostic data. This shifts maintenance work from "passive response" to "active early warning," promoting intelligent and precise maintenance strategies. This mechanism provides data-driven decision-making support for greening management, improving the efficiency and management level of the entire system in urban park greening maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flowchart of a park greening inspection and maintenance method based on NFC and GIS park data association provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying 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. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0058] like Figure 1 As shown, an embodiment of the present invention proposes a park greening inspection and maintenance method based on NFC and GIS park data association, the method comprising:

[0059] A preset NFC tag is configured on the greening object. The NFC tag includes the identification information of the corresponding greening object, and the identification information is bound to the greening object location information registered in the GIS system to generate a greening identification data table;

[0060] The terminal device reads the NFC tag to obtain the identification information of the current greening object, and calls the corresponding geographic positioning information and historical maintenance data in the GIS system based on the identification information to form inspection positioning data;

[0061] Based on the geographic positioning 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 conditions, and compared with historical maintenance data to generate preliminary diagnostic data;

[0062] Based on the preliminary diagnostic data and combined 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.

[0063] In an embodiment of the present invention, in response to the maintenance and management needs of park green areas, an NFC tag with a unique identification mark is pre-configured on each green object. This tag can be used by a terminal device with NFC reading function to directly collect information contactlessly, so that the green object has an identification that can be accurately tracked in the digital space. While writing this identification information into the NFC tag, data binding is performed through the location information of the green 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 green identification data table finally generated is used in subsequent inspection operations as a basic data source for quickly matching geographic information with object identity.

[0064] During inspections, staff use handheld devices to read NFC tags attached to landscaping objects. The devices instantly retrieve identification information and, through an interface with the GIS system, retrieve the corresponding geolocation data, accurately determining the object's spatial coordinates and distribution within the layer database. This identification information is also used to query historical maintenance data associated with the object, including past pruning records, fertilization records, and soil testing data, to generate inspection location data. This location data is not only real-time but also contains historical contextual information about the object's maintenance activities.

[0065] Based on the geographic coordinates and unique object identities contained in the positioning data, on-site inspectors further collect information on the current state of the greening object. This information covers typical ecological parameters such as plant color changes, stem height, leaf density, and ambient humidity. This information is digitally recorded through terminal input or image acquisition. This current state information is automatically compared and analyzed with historical data to generate preliminary diagnostic data. This diagnostic data initially reveals whether the object's current state deviates from the normal maintenance range and serves as the basis for subsequent task generation.

[0066] Subsequently, based on the preliminary diagnosis results and the standard maintenance specifications for the landscaping object, the system intelligently matches corresponding maintenance response measures and generates specific maintenance tasks. Tasks include action items, execution frequency, target time window, and other details. These tasks are then uploaded to the cloud management platform in real time via the terminal to complete task registration. The resulting maintenance task records serve as crucial data support for subsequent scheduling and execution, achieving a closed-loop management system from identification, positioning, diagnosis, to task generation.

[0067] In a preferred embodiment of the present invention, the identification information is bound to the greening object location information registered in the GIS coefficient to generate a greening identification data table, including:

[0068] By calling the preset greening object geographic location database in the GIS system, the node position of the target greening object in the geographic layer is determined, and the spatial coordinate information of the node is extracted;

[0069] Establish a corresponding relationship between identification information and spatial coordinate information, and set information fields respectively, including object name, identification code, spatial code, calibration time and layer to which it belongs, to generate a binding result;

[0070] According to the binding result, a greening identification data table is generated, and the greening identification data table is used for rapid matching of identification information and geographic location during subsequent inspections.

[0071] In this embodiment of the present invention, to achieve precise integration of digital identification of greening objects with spatial data, a pre-set geographic location database is first retrieved from the GIS system. This database records the spatial node information of each greening object. Based on the input target identification information, the system automatically locates the node position in the geographic layer and extracts the node's spatial coordinate data, such as longitude and latitude or the X and Y values ​​in the local plane coordinate system.

[0072] After extracting the spatial coordinates, a one-to-one correspondence is established between the aforementioned identification information and the corresponding coordinates. Information fields are then expanded to include multiple dimensions, including object name, unique identification code, spatial code, binding time, and layer of origin. These fields are structured as structured data, facilitating rapid retrieval and update operations within the database. This binding process ensures that each greening object's uniqueness in the actual physical space accurately corresponds to its internal identity within the system, effectively avoiding maintenance confusion caused by unclear object identification.

[0073] Based on the binding relationship results, a complete greening sign data table is generated. This data table, serving as the core data structure for information retrieval and positioning matching during subsequent inspections, is integrated into the terminal device's software platform. This allows inspectors to instantly access relevant coordinates and layer information via NFC reading on-site, significantly improving inspection efficiency and data consistency. This process strengthens the maintenance information system's digital control over on-site targets, laying the data foundation for high-frequency inspections and dynamic positioning.

[0074] The preset greening object geographic location database specifically includes:

[0075] The pre-defined greening object geolocation database is a system used to store all geographic information related to park greening. This database contains the location coordinates of all greening objects, environmental descriptions, and maintenance records associated with each greening object. This data is stored in the GIS system and can be queried based on specified criteria. Using the pre-defined greening object geolocation database, users can quickly locate any greening object and interact and analyze its geolocation information with other related data (such as historical maintenance data).

[0076] For example, when a park management department conducts greening inspections, it can use the database to accurately locate the specific location of a tree or flower in the park and retrieve historical maintenance records related to that location in order to develop targeted maintenance measures for the greening object.

[0077] Among them, according to the binding results, a greening identification data table is generated, specifically including:

[0078] By binding the identification information of green objects with their corresponding spatial coordinates, a green identification data table is generated. This data table stores the identification information of all green objects and their spatial coordinates in the GIS system. Each green object has a unique identification code, which is bound to its corresponding spatial coordinates (such as longitude, latitude, or other coordinate system data). Through this binding, each record in the data table accurately points to the physical location of the corresponding green object in the park.

[0079] The specific methods of the binding process include:

[0080] The spatial coordinate data of greening objects is retrieved from the GIS system, and pre-set rules are used to map each greening object's identification information to its spatial coordinates. For example, greening object A might correspond to a specific coordinate, and its corresponding identification information might be "Tree 001." This record is stored in a greening identification data table. The table fields may include information such as object name, identification code, spatial code (latitude and longitude coordinates), calibration time, and associated layer. The generated greening identification data table provides critical geolocation information for subsequent inspections, helping inspectors quickly match greening objects.

[0081] In a preferred embodiment of the present invention, based on the geographic positioning information of the inspection positioning data, the on-site status information of the current greening object, including plant morphological parameters and surrounding environmental conditions, is collected and compared with the historical maintenance data to generate preliminary diagnostic data, including:

[0082] The terminal device retrieves the geographic positioning information in the inspection positioning data, locates the specific location of the greening object on site, and collects the current status data of the plant at the specific location, including color change, height value, leaf density, and soil moisture;

[0083] For the current status data and similar status items recorded in the historical maintenance data of the greening object, multi-cycle data is extracted to construct standard parameters for field correspondence comparison, and the degree of change in each status item is identified, and then the difference threshold range for the change of each status item is set;

[0084] According to the difference threshold range, the state item parameters with abnormal fluctuations are marked to form a difference feature set;

[0085] The diagnostic level is divided according to the degree of deviation of each state item in the differential feature set and the number of accumulated abnormal items, and preliminary diagnostic data is generated based on the diagnostic level and the differential feature set.

[0086] In this embodiment of the present invention, based on the geographic location information contained in the inspection positioning data captured by the terminal device, inspectors can first accurately locate the actual spatial location of the target greening object and then collect a series of current plant status data at that location. This data includes, but is not limited to, the extent of plant color changes, actual stem or branch height measurements, leaf density per unit area, and surface soil moisture levels. Data collection can be performed through various methods, including manual observation input, image analysis, and portable sensors, to ensure the multi-dimensional authenticity of the status data.

[0087] The collected current status data is compared with similar status items recorded in the historical maintenance data of the target greening object. To improve the accuracy of the comparison, the recorded data of the status item across multiple maintenance cycles is extracted from the historical database to form a sequence of status values ​​with a time dimension. When processing this historical data, obviously abnormal point values, including sudden changes and outliers, are first identified and eliminated to construct more accurate standard parameters. The valid data set is divided using the sliding window method, and a time decay function is set for each interval to generate time weights to ensure that the recent data is more referenceable.

[0088] Subsequently, the local means within each sliding interval are weighted and aggregated according to the time weight, and normalized to form a reference mean for the standard parameter. This reference mean serves as the baseline for comparison, and the difference between the corresponding fields and the current state data is calculated. During the comparison process, not only is the numerical deviation of each parameter output, but a difference threshold is also set based on the plant type attributes and the parameter category (such as morphological or environmental). This set standard clearly determines whether there is a substantial deviation.

[0089] After the comparison is complete, we further determine whether any status items exceed the difference threshold and mark them as abnormal items, thus constructing a differential feature set. The diagnostic level is determined based on the number of abnormal items in the set and the degree of deviation. For example, items with many and large deviation parameters are assigned a high-level diagnostic level. The resulting preliminary diagnostic data clearly records the main problem characteristics and severity of the object, forming the basis for decision-making on the next task.

[0090] The terminal device retrieves the geographic positioning information in the inspection positioning data, locates the specific location of the on-site greening object, and collects the current status data of the plant at the specific location. The current status data includes color changes, height values, leaf density, soil moisture, and specifically includes:

[0091] By using a terminal device (such as a smartphone or handheld device) to retrieve geolocation information from inspection location data, the specific location of on-site greening objects can be accurately determined. When inspectors arrive at the site, they use the terminal device to obtain the greening object's location data (such as GPS coordinates) to determine its exact location. After locating the location, the device can collect data on the greening object's current status using built-in sensors or other measurement tools (such as cameras and photometers).

[0092] The current status data includes:

[0093] Color change: By collecting the color changes of the plant, you can judge its growth condition or whether it has diseases and insect pests.

[0094] Height: Measure the height of a plant to assess its growth progress or health.

[0095] Leaf density: Determine the health of the plant by collecting the number or distribution of leaves.

[0096] Soil moisture: Soil moisture sensors monitor the moisture level of the soil around plant roots in real time to infer irrigation needs.

[0097] For example, if inspectors find that the leaves of a greening object have turned yellow, its height is measured to be small, and the soil moisture is lower than normal, then the plant may have growth problems and require further maintenance.

[0098] Among them, according to the difference threshold range, the state item parameters with abnormal fluctuations are marked to form a difference feature set, which specifically includes:

[0099] Based on the aforementioned status data and the difference threshold settings, the current value of each status item can be compared against the normal range (standard parameters). If a status item changes beyond the preset difference threshold, the system will mark it as abnormal. This difference threshold is dynamically adjusted based on plant growth standards and historical fluctuation data. By evaluating the fluctuation range of various status items (such as color change and leaf density), abnormal fluctuations can be automatically identified and a set of difference features can be generated.

[0100] Specifically, if a plant's height, color, or leaf density fluctuates significantly beyond a set threshold, the system adds that status item to the differential feature set. These flagged status items are then used for further diagnosis to determine whether maintenance measures are necessary.

[0101] For example, suppose the leaf density of a particular flower has remained within a normal range for the past three months, but during this inspection, its leaf density drops dramatically, exceeding the set difference threshold. The system will then mark this status item as abnormal and add it to the difference feature set.

[0102] Among them, the diagnostic level is divided according to the deviation degree of each state item in the difference feature set and the number of accumulated abnormal items. Based on the diagnostic level and the difference feature set, preliminary diagnostic data is generated, including:

[0103] Based on 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:

[0104] Deviation degree of status item: For example, if the height of a plant changes by more than 10% from the normal range, the deviation degree of the status item is large, which may mean that the plant has a significant health problem.

[0105] Cumulative number of abnormal items: If multiple status items show abnormal fluctuations, this may indicate a more serious maintenance problem for the plant. Therefore, the cumulative number of abnormal items will also affect the classification of the diagnostic level.

[0106] For example, suppose the color change and leaf density of a plant exceed the set thresholds, and these two abnormal items have appeared in the past few inspections. The system will classify these status items as serious abnormalities and assign a high-level diagnosis to the plant, indicating that special maintenance measures are needed immediately.

[0107] 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.

[0108] In a preferred embodiment of the present invention, based on the preliminary diagnostic 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 via the terminal device to form a maintenance task record, including:

[0109] Based on the preliminary diagnostic data, the system matches the target greening object with the corresponding processing rules in the preset maintenance standard library, and extracts the maintenance measure template corresponding to the diagnostic item, including pruning method, fertilization frequency, and watering cycle;

[0110] Decompose the maintenance measure template into several subtasks to form specific maintenance task content, and attach current identification information and geographic positioning information as task attributes. The subtasks are assigned a task level based on the diagnostic level determined in the preliminary diagnostic data. The task level is used to indicate the urgency or complexity of each subtask.

[0111] 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.

[0112] In this embodiment of the present invention, the automatic generation and real-time reporting of specific maintenance tasks are achieved by combining preliminary diagnostic data with the maintenance standards corresponding to the greening objects. First, the preliminary diagnostic data generated during the inspection phase is read. This data already contains a set of differential features derived through comparative analysis, as well as diagnostic level information. By matching the diagnostic item to the category, the processing rules corresponding to that category in the preset maintenance standard library are automatically called to select the appropriate maintenance measure template. This template covers pruning methods (such as light pruning, thinning, and shaping), fertilization frequency (such as weekly and monthly), and watering cycles (such as every other day and weekly).

[0113] After obtaining the maintenance measures template, the template content is further refined into several subtasks based on the number of diagnostic items and the complexity of the processing rules. For example, if a diagnostic result indicates a decrease in leaf density, it may be broken down into three operation items: "cutting off dead leaves", "increasing nitrogen fertilizer application", and "enhancing 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.

[0114] After subtasks are generated, all tasks are packaged and uploaded to the management platform in real time via a terminal device. This upload includes not only a list of tasks but also the preliminary diagnostic data used to generate them, along with their source (such as time, data collector, and equipment number), facilitating backend management verification and task allocation. This process enables a rapid, closed-loop response from diagnosis to maintenance tasks, improving information transparency and response efficiency.

[0115] Among them, based on the preliminary diagnostic data, the corresponding processing rules of the target greening object in the preset maintenance standard library are matched, and the maintenance measure templates corresponding to the diagnostic items are extracted, including pruning methods, fertilization frequency, and watering cycles. Specifically, they include:

[0116] Preliminary diagnostic data is a comprehensive assessment of the status of a landscaping object, including plant morphology, environmental conditions, and comparative analysis with historical maintenance data. When generating maintenance tasks based on this preliminary diagnostic data, the specific diagnostic information for the landscaping object is first matched against a pre-set maintenance standard library. This maintenance standard library contains specific processing rules for different plant types and maintenance requirements. These rules describe in detail how to perform maintenance tasks such as pruning, fertilizing, and watering based on the plant's current condition.

[0117] The matching process involves extracting corresponding maintenance action templates from a standard library based on parameters of the plant's current state (such as color, leaf density, and height). For example, if a plant's leaf density is low and soil moisture is insufficient, the system will extract relevant treatment methods based on the rules in the maintenance standard library, such as increasing fertilization frequency and adjusting watering cycles. If the plant requires more pruning, the corresponding pruning method will be extracted and the pruning time and frequency will be adjusted based on the plant's growth status.

[0118] For example, for a type of rose, if the preliminary diagnostic data indicates that its leaves are relatively dry and the soil moisture is low, the system will extract a template for rose maintenance measures from the standard library, indicating that appropriate pruning should be carried out, the frequency of fertilization should be increased, and the watering cycle should be adjusted to promote its growth and recovery.

[0119] In a preferred embodiment of the present invention, a correspondence is established between identification information and spatial coordinate information, and information fields are set separately, including object name, identification code, spatial code, calibration time and layer to which it belongs, to generate a binding result, including:

[0120] Performing format normalization on the identification information, and performing coding standardization verification and duplication verification respectively through a preset coding rule library and a bound identification code to generate a first validity verification result;

[0121] Perform error range detection on the extracted spatial coordinate information to determine whether there is a risk of duplicate binding at the spatial position and generate a second validity verification result;

[0122] When both the first validity verification result and the second validity verification result are passed, the identification information and the spatial coordinate information are constructed into a bidirectional mapping relationship, and a binding result with the identification code as the primary key is established.

[0123] In this embodiment of the present invention, a dual validation mechanism is introduced during the binding process of identification information and spatial coordinate information to ensure data quality and spatial logical consistency. Before establishing the binding relationship, the identification information undergoes format normalization, including standardization of numbering rules (e.g., adoption of a unified prefix and digit limits) and character validation. Subsequently, a repetitive search of the identification information within the bound records is performed to ensure uniqueness, generating a first validation result.

[0124] At the same time, the spatial coordinate information extracted from the identification information is checked for errors. Based on preset error tolerance rules (such as a maximum overlap radius), the coordinates are checked to determine whether they might overlap with other objects in the existing binding data. This detection method involves establishing a buffer zone centered on the target coordinates and analyzing whether there are other bound object coordinates within it. If the minimum distance falls below a binding threshold, a binding conflict is considered, generating a second validity verification result.

[0125] The binding relationship is established only when both verification results pass. This binding result uses a data structure with the identification code as the primary key, enabling a bidirectional mapping between spatial coordinates and object identity. This not only ensures that each NFC tag corresponds to a unique geographic entity but also lays the foundation for subsequent spatial positioning and object management. This mechanism effectively improves overall binding accuracy and spatial data consistency.

[0126] The identification information is formatted and processed, and a coding standard check and a repeatability check are performed using a preset coding rule library and a bound identification code to generate a first validity verification result, specifically including:

[0127] Identification information for greening objects (such as plant numbers and identifiers) must be standardized to ensure that all identification information conforms to a unified standard. Using a pre-set coding rule library, the system automatically checks whether the format of identification information meets the requirements. For example, the identification code may include letters, numbers, and other symbols. The system ensures that the arrangement and combination of these symbols conform to pre-defined coding rules, such as length limits and symbol types. If the identification information does not conform to the standard, the system will prompt you to re-enter the information that meets the standard.

[0128] In addition to format standardization, the system also performs duplication checks on each greening object's identification information to ensure that there are no duplicate codes or identifiers in the system. This is to prevent the same greening object from being mistakenly entered multiple times and to ensure that each identification information is unique. If duplicate identification information is found, the system will prompt the user to manually verify or adjust it.

[0129] For example, when a new green object identification is entered, the system checks whether it conforms to the pre-set format (such as length and character type) and compares it with all existing identification information in the system to ensure there are no duplicate numbers. This validation ensures the accuracy and uniqueness of the data in the system.

[0130] When both the first validity verification result and the second validity verification result are passed, a bidirectional mapping relationship is established between the identification information and the spatial coordinate information, and a binding result with the identification code as the primary key is established, specifically including:

[0131] After passing format standardization and repeatability checks, the system establishes a bidirectional mapping between the identification information of a greening object and its spatial coordinate information. This means that each greening object not only has unique identification information, but also maintains a one-to-one correspondence with the spatial coordinates corresponding to that information (such as longitude and latitude or position in another coordinate system). This bidirectional mapping ensures that after obtaining a greening object through identification information, the system can also accurately locate its spatial location information. Furthermore, using the spatial coordinate information, the system can also locate the greening object's identification information.

[0132] For example, in a city park, each tree has a unique identification code (such as "Tree 001"), and its location is marked by spatial coordinates. Through a bidirectional mapping relationship, when a user queries the identification code, the system can locate the tree's specific location; and when the spatial coordinates are queried, the system can return the corresponding tree identification information.

[0133] This binding ensures the uniqueness of each record by using the identification code as the primary key. This mechanism not only enhances data reliability but also greatly improves the efficiency of data query and operation.

[0134] For example, when conducting greening inspections, workers can scan the identification information of greening objects through the device and instantly obtain the object's geographic location. Conversely, they can also query the greening objects at that location based on the location. This two-way binding relationship greatly improves inspection efficiency and reduces the occurrence of errors.

[0135] In a preferred embodiment of the present invention, multi-period data is extracted for the current status data and similar status items recorded in the historical maintenance data of the greening object, and standard parameters for field correspondence comparison are constructed. The degree of change in each status item is identified, and then a difference threshold range is set for the change of each status item, including:

[0136] According to the status items included in the current status data, the historical records of the corresponding status items in the historical maintenance data over multiple time periods are called to construct a sequence of status values ​​arranged in chronological order, which is used as the basic data set for constructing standard parameters;

[0137] Eliminate outliers from historical data in the basic dataset, including identifying mutation points, excluding extreme values ​​outside the reasonable range, and eliminating abnormal data with large deviations from adjacent time points, to form a valid sample set for standard parameter calculation;

[0138] 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. The time weight of each sliding interval is set according to the preset time decay function based on the time interval between the current collection time and the historical data.

[0139] The local mean of each sliding interval is weighted and summarized with the corresponding time weight, and the results are normalized to obtain the reference mean of the standard parameters corresponding to each state item;

[0140] 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.

[0141] In this embodiment of the present invention, the collected current status data must be carefully compared with historical standard parameters to accurately identify potential health fluctuations in greening objects. To this end, historical records of the same fields covered by the current status data over multiple time periods are first extracted from the historical maintenance database. These historical values ​​are organized into an ordered time series, forming the basic dataset for constructing standard parameters.

[0142] Next, to ensure the validity of the comparison data, the raw historical data is processed using an anomaly detection algorithm to remove outliers and sudden changes that may be caused by measurement errors or extreme weather. This results in a valid sample set with higher statistical reliability. The sample set is divided into sliding time windows, and the local mean is calculated within each window to characterize local trends. Furthermore, a time decay function is applied to each window based on the time interval between the current collection time and the historical data, giving a higher weight to historical data closer to the current time.

[0143] All local means are weighted, aggregated, and standardized, ultimately yielding reference mean values ​​for each parameter. These reference values ​​serve as a benchmark for comparison with current data. For each field, the difference between the current value and the reference mean is calculated. Based on the plant type and parameter category (morphological or environmental) corresponding to that field, a reasonable threshold range for this difference is determined and set. This threshold range represents the acceptable range of fluctuation for a particular plant type in a particular parameter category; any value exceeding this range is considered abnormal.

[0144] 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.

[0145] 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 addition, the time interval between the current collection time and the historical data is combined, and the time weight of each sliding interval is set according to the preset time decay function. Specifically, it includes:

[0146] Based on historical maintenance data and current status data, an appropriate time window is first selected to partition the valid sample set into sliding intervals. Each time window contains multiple data points, arranged in chronological order, representing the status changes of a particular greening object in the historical maintenance data. The sliding interval partitioning process is typically performed by selecting a fixed time span, such as the data points of the past 7 days or the past 30 days as an interval.

[0147] After defining a time window, the system processes the data within each sliding interval and calculates the local mean within that interval. The local mean is the average of the data points within a sliding interval, reflecting the state change trend of the greening object within that interval. For example, if a time window represents the state changes over the past seven days, the system will calculate the seven-day average value for relevant state items (such as leaf density and soil moisture) within this window.

[0148] To account for the time interval between the current collection time and historical data, the system weights the local mean of each sliding interval according to a preset time decay function. This function adjusts the weight of each sliding interval based on the time interval between data collection, with data points closer to the current collection time receiving a greater weight, and vice versa. This is because data closer to the current time is more important for predicting and analyzing the current state. Therefore, the time decay function typically uses a mathematical model, such as an exponential decay function, in which newer data points are given a greater weight, while more distant historical data points are given a gradually decreasing weight.

[0149] For example, if the current data collection date is May 30, 2025, historical data from the past seven days will be given a higher weight, while older data (such as data from 15 days ago) will be given a lower weight. This approach ensures more accurate condition assessments, allowing timely implementation of appropriate maintenance measures.

[0150] 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, including:

[0151] After calculating the local mean and time weight for each sliding interval, the system performs a weighted aggregation of these local means and the corresponding time weight. This weighted aggregation process multiplies the local mean for each sliding interval by its corresponding time weight and then sums all the weighted results. This weighted aggregation ensures that the time decay function assigns a greater influence to state changes in more recent time periods, resulting in a more accurate reference value.

[0152] For example, suppose a time window contains data from the past three days. The state mean for each day is calculated, and then a weight is assigned to each day based on the time decay function (for example, the weight for day 1 is 0.2, the weight for day 2 is 0.3, and the weight for day 3 is 0.5). The system multiplies the state mean at each time point by the corresponding weight, and then sums the results to obtain a weighted result. This is then normalized to ensure comparability of data across different time periods.

[0153] The goal of normalization is to convert all weighted results into a standardized reference mean, ensuring that their values ​​are within a certain range and independent of dimension. Common methods of normalization include dividing all results by the maximum value or scaling them by the standard deviation to ensure that the data has a uniform standard.

[0154] Through this method, the standard parameter reference mean of each status item obtained by the system can accurately reflect the status changes of greening objects in different time periods and eliminate data deviations caused by time factors.

[0155] In a preferred embodiment of the present invention, the maintenance measure template is decomposed into several subtasks to form specific maintenance task contents, including:

[0156] According to the maintenance measure template, extract the processing content to be executed and divide it into several subtasks, each of which corresponds to an independent operation step;

[0157] For each subtask, the estimated time of the subtask is revised based on the execution time data and task level in the historical maintenance task records, and the revised estimated time is used as the duration parameter for task scheduling;

[0158] For each subtask's work content, the system combines the plant type identified by the identification information and the regional attributes corresponding to the geographic positioning information to screen available maintenance equipment options from the preset equipment information library and generate a list of recommended equipment.

[0159] The operation steps, estimated time and recommended equipment list of all subtasks are uniformly packaged into specific maintenance task content.

[0160] In this embodiment of the present invention, after the maintenance action template is decomposed into multiple subtasks, the time schedule for each subtask needs to be refined. To achieve this, execution samples with similar steps to the current subtask are first retrieved from historical maintenance task records to form a set of task execution time samples. This set contains time data for completing similar tasks at different times and under different circumstances, forming the basis for estimating the time of future tasks.

[0161] Within the sample set, the average duration is statistically extracted to generate a base estimate for the subtask. Next, based on the task level information generated in the previous step, the execution complexity represented by the task level is converted into a weighting coefficient. Combining the base estimate with the weighting coefficient completes the initial time adjustment for the task.

[0162] To further improve the accuracy of scheduling, the system also analyzes whether the subtask's geographic area is subject to operational interference factors, such as park congestion, slope of the work area, or seasonal variations in water supply. If such factors are detected, a time correction factor is applied based on the interference level to produce the final corrected time.

[0163] Ultimately, the revised estimated time is incorporated into the subtask's scheduling parameters, providing an accurate reference for the platform's subsequent maintenance scheduling. This mechanism improves task scheduling flexibility and resource allocation efficiency, and is particularly suitable for complex scenarios involving simultaneous inspection and maintenance of multiple targets.

[0164] For each subtask, the system combines the plant type identified by the identification information and the regional attributes corresponding to the geographic location information to select available maintenance equipment options from the preset equipment information library and generate a list of recommended equipment, including:

[0165] Maintenance tasks are divided into multiple subtasks, each involving different types of operations, such as pruning, fertilizing, and watering. When generating subtasks, the system combines the identification information of each subtask's corresponding landscaping object (such as plant type) and the plant's geographic location (i.e., regional attributes) to select appropriate maintenance equipment from a pre-set equipment database.

[0166] First, the system searches for relevant maintenance equipment based on the specific subtask requirements (e.g., pruning, watering, etc.). For example, for pruning, the system might select tools like shears and electric trimmers, while for fertilizing, it might select equipment like fertilizer carts and sprayers. Equipment selection also takes into account plant type and regional characteristics; for example, some equipment might be suitable for smaller plants, while others are better suited for larger lawns or trees.

[0167] Based on the suitability of maintenance equipment, the system generates a recommended equipment list, listing all possible equipment options for the task. For example, for pruning, manual shears and electric pruners might be recommended, while for watering, an automatic watering system or a handheld sprayer might be recommended. This allows workers to select the most suitable equipment based on the list, improving work efficiency and reducing equipment selection errors.

[0168] Furthermore, equipment recommendations may be adjusted based on geographic location. Certain equipment may be more suitable for specific areas. For example, some lawn care equipment is suitable for open areas, while some tree trimming equipment is suitable for densely wooded areas. The system dynamically adjusts equipment recommendations based on regional attributes to ensure smooth operation.

[0169] The preset equipment information library refers to the database in the system that contains information about different maintenance equipment. Example:

[0170] The system has a pre-set equipment database containing all equipment types and functions suitable for landscaping maintenance. For example, information on pruning tools, fertilizer spreaders, watering systems, and other equipment includes model numbers, applicable plant types, operating parameters, and usage conditions. Depending on the maintenance task, the system automatically selects the appropriate equipment from this database. For example, if the task is to prune roses, the system might recommend "pruning tool model X" and provide operating instructions for the tool.

[0171] In a preferred embodiment of the present invention, an error range detection is performed on the extracted spatial coordinate information to determine whether there is a risk of duplicate binding at the spatial position, and a second validity verification result is generated, including:

[0172] 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 in the buffer is extracted;

[0173] Compare the distance between the current coordinates and the coordinates of other objects in the buffer. If the minimum distance is less than the set binding distance threshold, it is marked as a risk of spatial overlap.

[0174] In the case of spatial overlap risk, the risk level is determined by combining the greening object type corresponding to the identification information with the object distribution density corresponding to the location in the geographic layer, and a manual review prompt is triggered to generate a verification result marked as a high-risk binding.

[0175] To avoid spatial coordinate conflicts during the NFC and GIS binding process, this embodiment of the present invention introduces an error range detection and risk assessment mechanism before establishing the binding relationship between identification information and spatial coordinates. Specifically, after obtaining the spatial coordinates corresponding to the identification information, a buffer zone centered on that coordinate point is automatically constructed. The radius of the buffer zone is automatically set based on the configured binding distance threshold, ensuring that detection accuracy is adapted to the actual management scale.

[0176] The spatial coordinate information of all bound objects within the buffer is further extracted to construct a set of neighboring object coordinates. The distance between the current coordinate point and each neighboring coordinate point is calculated to determine whether the minimum distance is less than the binding distance threshold. If so, the process is flagged as "risk of spatial overlap" and the binding operation is not performed immediately.

[0177] In scenarios where overlapping risk exists, a composite assessment model based on plant type and spatial distribution density is introduced. Identification information includes a field for the greening object type (e.g., tree, shrub, grassland, etc.), and spatial density is calculated based on the distribution density of that type within the layer to which the current location belongs (e.g., the number of objects per unit area). If multiple binding points of the same type are identified within a high-density area, the risk level is further increased. Based on the assessment results, a "high-risk binding" indicator is generated, and the identification record is pushed to the management platform for manual review.

[0178] Through this embodiment, a high-reliability binding mechanism is implemented to avoid coordinate conflicts and rebinding problems in a multi-object, multi-point spatial environment, effectively improving the consistency of spatial data and the positioning accuracy of field inspections, and avoiding identification confusion or data anomalies due to coordinate overlap.

[0179] The risk level refers to the pre-set risk level classification of spatial overlap risk or operational interference risk. Example:

[0180] The system uses preset risk levels to assess spatial overlap. For example, if the spatial coordinate distance between two greenery objects is less than a set threshold, the system will mark it as "low risk." If the density of greenery objects in the overlapping area is high, it will be 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 trigger different review processes to ensure the smooth progress of maintenance tasks.

[0181] In the case of spatial overlap risk, the risk level is determined by combining the greening object type corresponding to the identification information with the object distribution density corresponding to the location in the geographic layer. This triggers a manual review prompt and generates a verification result marked as a high-risk binding. Specifically, the following are included:

[0182] When the system detects that the spatial coordinates of two or more green objects overlap or are very close together, it first assesses the risk of this overlap. This assessment is performed by combining the green object type identified in the identification information with the object distribution density at that location in the geographic layer. Specifically, different types of green objects (such as trees, lawns, and flowers) have different distribution characteristics in the geographic layer. For example, trees tend to require a larger space, while lawns or flowers may be more densely distributed.

[0183] To determine the risk level of spatial overlap, the system first queries the geographic distribution density of the location based on the different types of green objects. For example, if an area is typically planted with trees but the spatial density of the area is too high, or if the green objects are lawns but highly concentrated, spatial overlap may occur. The system will determine whether there is an overlap risk by calculating the distribution density of the area. If there is a high density of overlap in the area, the system will determine a risk level based on the distribution density, the degree of overlap, and the type of green objects.

[0184] If the risk level is high, the system will trigger a manual review prompt, requiring staff to check and confirm whether there are any issues with the landscaping objects in the overlapping area. This manual review prompt can be displayed through the system's user interface and marked as "high-risk binding." For example, certain areas may appear densely populated with lawns and flowers on the layer. The system will prompt a manual review to confirm any overlap errors, thus avoiding errors in subsequent maintenance tasks.

[0185] This risk level setting ensures that during the greening object information binding process, the spatial overlapping areas can be accurately judged and the possible incorrect binding or omission of maintenance tasks can be corrected in a timely manner, thereby improving the accuracy of the system.

[0186] In a preferred embodiment of the present invention, the value of each field in the current status data is compared with the reference mean of the corresponding standard parameter, the numerical difference of each status item is calculated, and the difference threshold range of each status item is set in combination with the plant type of the greening object and the parameter category of the status item, including:

[0187] According to the current value of each state item and the reference mean of the standard parameter, the value difference is calculated to construct the state difference data set;

[0188] For each field item in the state difference dataset, according to its corresponding plant type attribute, the preset plant species-parameter threshold correspondence table is retrieved to obtain the initial value of its acceptable fluctuation range;

[0189] According to the parameter category to which the field item belongs, the initial value fluctuation range is adjusted and corrected to form a difference threshold range under a specific plant type and a specific parameter category, and the parameter category includes morphological parameters and environmental parameters.

[0190] In this embodiment of the present invention, after quantitatively analyzing the status data collected on-site, threshold correction is applied to the differences, taking into account plant type and parameter category, to accommodate the status assessment needs of various greening objects. First, the numerical difference between the current value of each field item and its standard parameter reference mean is calculated. This generates a status difference dataset, which contains the deviations of multiple field items, reflecting the degree of status fluctuation.

[0191] Each field item in the state difference dataset is further classified according to the plant type attribute to which it is bound. For example, if a field item belongs to "leaf density" and the plant type is "evergreen tree," a table of plant species-parameter threshold values ​​is retrieved to obtain an initial acceptable fluctuation range for that parameter item for that plant type. This fluctuation range is typically generated based on historical data statistics and is highly representative and adaptable to engineering applications.

[0192] Considering that the parameter categories of different field items may affect their sensitivity and judgment tolerance, a parameter category correction mechanism is introduced to dynamically adjust the original initial values. Specifically, if the field item belongs to a "morphological parameter" (such as color, density, and length), the morphological threshold adjustment factor is used for correction; if it belongs to an "environmental parameter" (such as humidity and light), the environmental correction coefficient is used for fine-tuning. After the correction is completed, the final difference threshold range is generated, which serves as the key basis for subsequent diagnostic level determination and task generation.

[0193] The above method takes into account the individual differences of plants and the sensitivity of parameter attributes, realizes flexible adaptation of difference judgment, and has wide applicability in complex green space environments where multiple plant types are mixed, which helps to significantly improve the accuracy and practicality of diagnostic judgment.

[0194] Among them, according to the current value of each state item and the reference mean of the standard parameter, the value difference is calculated to construct the state difference data set, which specifically includes:

[0195] The system compares the currently collected state data with the reference mean of the standard parameters. Each state item (e.g., plant color change, leaf density, soil moisture, etc.) has a current value and a corresponding reference mean of the standard parameter, which is obtained through historical maintenance data and a standardization process.

[0196] To calculate the difference between the current state item and the reference mean of the standard parameter, the system extracts the value of each state item from the current state data and compares it with the corresponding standard parameter. By calculating the difference between the current state item and the standard reference value, a "numeric difference" is obtained. This difference can be positive or negative, depending on whether the current state deviates from the standard parameter.

[0197] For example, if the standard reference value for plant height is 50 cm, and the current status data shows 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" that records the difference values ​​of all status items and provides a basis for subsequent diagnosis and maintenance tasks.

[0198] This process helps to quickly identify which greening objects have deviated from the expected standards and propose corresponding maintenance plans accordingly.

[0199] Among them, the "Plant Species-Parameter Threshold Correspondence Table" and "According to the parameter category to which the field item belongs, the initial value fluctuation range is adjusted and corrected to form the difference threshold range under specific plant types and specific parameter categories, specifically including:

[0200] The plant species-parameter threshold correspondence table is a table used to match plant species with their associated maintenance parameters. Each plant type (e.g., roses, banyan trees, etc.) has specific growth characteristics, and each plant can tolerate different parameter fluctuation ranges under different maintenance conditions. Therefore, to ensure the accuracy and specificity of maintenance tasks, the present invention introduces a plant species-parameter threshold correspondence table to clearly define the standard maintenance parameter ranges for different plant species at different growth stages or seasons.

[0201] For example, the soil moisture 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 for a specific plant species under specific environmental conditions.

[0202] In addition, the system also makes adjustments and corrections based on the parameter category to which the field item belongs. For example, plant leaf density and soil moisture 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 adjusts its initial fluctuation range based on the type of status item (morphological or environmental). In this way, the system can automatically adjust the fluctuation range of each parameter based on the specific plant type and parameter category to meet actual maintenance needs.

[0203] 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 environmental factors vary greatly and have a more indirect impact on plants.

[0204] Through this mechanism, the system can provide customized maintenance parameters for each plant, thus ensuring the accuracy and practicality of maintenance work.

[0205] In a preferred embodiment of the present invention, for each subtask, the estimated time of the subtask is revised based on the execution time data and task level in the historical maintenance task records, and the revised estimated time is used as the duration parameter of the task scheduling, including:

[0206] Extract the execution samples corresponding to each subtask in the historical maintenance task records and construct a task execution time sample set, which includes the actual time consumption data of the approximate operation steps under different execution conditions;

[0207] Generate a basic estimated duration for the subtask based on the average execution time recorded in the task execution time sample set, and make a weighted adjustment to the basic estimated duration based on the execution complexity coefficient represented by the task level;

[0208] Based on the geographic location information of the area to which the subtask belongs, determine whether there are any work interference factors in the area. If so, set the time correction factor;

[0209] 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.

[0210] In this embodiment of the present invention, a scheduling optimization method based on historical data and real-world scenarios is used to refine the estimated time of each subtask, improving overall task scheduling efficiency. First, execution samples similar to the current subtask's operations are extracted from historical maintenance task records to construct a set of task execution time samples. This set includes time-consuming data for similar tasks under various execution conditions, such as time period, climate, and staffing.

[0211] Using the data from the sample set, a weighted average or median method is used to determine the base estimated duration for the current subtask. To further enhance the discriminability of scheduling parameters, each level is mapped to an execution complexity coefficient based on the previously determined task level information. This complexity coefficient represents the expected number of operational steps, equipment usage intensity, or human coordination requirements during task execution. The base estimated duration is multiplied by the complexity coefficient to form the first-stage revised duration.

[0212] Next, consider any potential interference factors that the subtask may face at its specific execution location, such as proximity to water bodies, elevation differences, or frequent traffic. Using the geolocation information associated with the subtask and a pre-set regional attribute model, these interference factors are analyzed and evaluated. If any influencing factors exist, a time correction factor is set based on their severity, such as multiplying the delay by a factor greater than 1.

[0213] Finally, the complexity-corrected duration is multiplied by the time correction factor to obtain the final corrected estimated time, which is then written into the subtask's scheduling parameter field. This parameter is used in the scheduling engine to prioritize tasks, match equipment, and generate personnel scheduling strategies.

[0214] Through the above embodiments, dynamic optimization of task time evaluation is achieved, which not only improves the rationality of task execution arrangement, but also effectively avoids task concentration or arrangement conflicts, and helps to maximize inspection and maintenance efficiency under conditions of limited resources.

[0215] The basic estimated duration of the subtask is generated based on the average execution time recorded in the task execution time sample set, and the basic estimated duration is weighted and adjusted according to the execution complexity coefficient represented by the task level, including:

[0216] To ensure reasonable and scientific task scheduling, the system first calculates a baseline estimated duration for each subtask based on sample execution data from historical maintenance task records. This process is achieved by collecting samples of historical tasks similar to the current subtask and calculating the average execution time of these samples. The execution sample records include actual execution time data under different conditions, which helps provide a reference for the current task.

[0217] For example, if a maintenance task involves pruning roses, and the execution time records for multiple rose pruning tasks in the past were 20 minutes, 25 minutes, and 30 minutes, the system will calculate the average execution time of these records to obtain a base estimate of 25 minutes. This base estimate represents the standard time to complete the subtask under similar conditions.

[0218] Then, the basic estimated duration is weightedly adjusted according to the execution complexity coefficient represented by the task grade. The setting of task grades is usually based on the complexity of the tasks. For example, a pruning task may be more complex than a fertilizing task, so its task grade coefficient may be higher. The system assigns a "complexity coefficient" to each task 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, resulting in a revised estimated duration of 30 minutes. In this way, the system can reasonably adjust the estimated execution time of the task according to its actual complexity.

[0219] This method helps to arrange the execution of tasks more accurately, avoiding insufficient time due to overly complex tasks or wasting time resources due to overly simple tasks.

[0220] Based on the geographic location information of the area to which the subtask belongs, it is determined whether there are any work interference factors in the area. If so, a time correction factor is set, specifically including:

[0221] During the actual parkland inspection and maintenance process, the geographical environment of the work area can impact task execution. For example, certain areas may have high traffic volume, construction sites, or weather changes, all of which can affect task execution efficiency. To ensure the accuracy and effectiveness of task scheduling, the system of the present invention introduces a task interference factor detection mechanism based on geolocation information.

[0222] Specifically, the system first analyzes the geolocation information for each subtask. This information includes the specific geographic coordinates of the subtask and its surroundings. For example, if a subtask is located near a park's main entrance or exit, where traffic is heavy, or if there's construction going on nearby, the system will identify these potential disruptions.

[0223] Once the system detects disruptions to a task, it applies a "time correction factor" based on these factors. For example, if heavy traffic is slowing down a task, the system might set a correction factor of, say, 1.3, indicating a 30% increase in the task's execution time. If the task is located in a construction zone, the correction factor might be set to 1.5, indicating a 50% increase in the estimated time due to the disruption.

[0224] This approach allows the system to adjust estimated times in real time, ensuring more accurate scheduling of tasks during actual operations and avoiding delays or inefficiencies caused by uncontrollable factors. This approach enhances the system's adaptability to environmental changes and improves the overall management efficiency and execution of maintenance tasks.

[0225] An embodiment of the present invention further provides a park greening inspection and maintenance system based on NFC and GIS park data association, the system comprising:

[0226] 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 greening object location information registered in the GIS system to generate a greening identification data table;

[0227] The terminal device reading module is used to read the NFC tag through the terminal device to obtain the identification information of the current greening object, and call the corresponding geographic positioning information and historical maintenance data in the GIS system based on the identification information to form inspection positioning data;

[0228] The on-site status acquisition module is used to collect on-site status information of the current greening object, including plant morphological parameters and surrounding environmental conditions, based on the geographic positioning information of the inspection positioning data, and compare and analyze it with historical maintenance data to generate preliminary diagnostic data;

[0229] The maintenance task generation module is used to generate specific maintenance task content based on preliminary diagnostic data and the maintenance standards of the corresponding greening objects, and upload it to the management platform in real time through the terminal device to form a maintenance task record.

[0230] It should be noted that this system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0231] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the above-described method. All implementations in the above-described method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0232] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the above-described method. All implementations in the above-described method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0233] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A park greening inspection and maintenance method based on NFC and GIS park data association, characterized in that: The method comprises: A preset NFC tag is configured on the greening object. The NFC tag includes the identification information of the corresponding greening object, and the identification information is bound to the greening object location information registered in the GIS system to generate a greening identification data table; The terminal device reads the NFC tag to obtain the identification information of the current greening object, and calls the corresponding geographic positioning information and historical maintenance data in the GIS system based on the identification information to form inspection positioning data; The terminal device retrieves the geographic positioning information in the inspection positioning data, locates the specific location of the greening object on site, and collects the current status data of the plant at the specific location, including color change, height value, leaf density, and soil moisture; For the current status data and similar status items recorded in the historical maintenance data of the greening object, multi-cycle data is extracted to construct standard parameters for field correspondence comparison, and the degree of change in each status item is identified, and then the difference threshold range for the change of each status item is set; According to the difference threshold range, the state item parameters with abnormal fluctuations are marked to form a difference feature set; According to the degree of deviation of each state item in the differential feature set and the number of accumulated abnormal items, the diagnostic level is divided, and preliminary diagnostic data is generated based on the diagnostic level and the differential feature set; Based on the preliminary diagnostic data, the system matches the target greening object with the corresponding processing rules in the preset maintenance standard library, and extracts the maintenance measure template corresponding to the diagnostic item, including pruning method, fertilization frequency, and watering cycle; Decompose the maintenance measure template into several subtasks to form specific maintenance task content, and attach current identification information and geographic positioning information as task attributes. The subtasks are assigned a task level based on the diagnostic level determined in the preliminary diagnostic data. The task level is used to indicate the urgency or complexity of each subtask. Upload the maintenance task content to the management platform in real time through the terminal device and generate a maintenance task record, which includes preliminary diagnostic data and its source information; The maintenance measure template is decomposed into several subtasks to form specific maintenance task contents, including: According to the maintenance measure template, extract the processing content to be executed and divide it into several subtasks, each of which corresponds to an independent operation step; For each subtask, the estimated time of the subtask is revised based on the execution time data and task level in the historical maintenance task records, and the revised estimated time is used as the duration parameter for task scheduling; For each subtask's work content, the system combines the plant type identified by the identification information and the regional attributes corresponding to the geographic positioning information to screen available maintenance equipment options from the preset equipment information library and generate a list of recommended equipment. The operation steps, estimated time and recommended equipment list of all subtasks are uniformly packaged into specific maintenance task content.

2. The park greening inspection and maintenance method based on NFC and GIS park data association according to claim 1 is characterized in that: Bind the identification information with the greening object location information registered in the GIS coefficient to generate a greening identification data table, including: By calling the preset greening object geographic location database in the GIS system, the node position of the target greening object in the geographic layer is determined, and the spatial coordinate information of the node is extracted; Establish a corresponding relationship between identification information and spatial coordinate information, and set information fields respectively, including object name, identification code, spatial code, calibration time and layer to which it belongs, to generate a binding result; According to the binding result, a greening identification data table is generated, and the greening identification data table is used for rapid matching of identification information and geographic location during subsequent inspections.

3. The park greening inspection and maintenance method based on NFC and GIS park data association according to claim 2 is characterized in that: Establish a correspondence between identification information and spatial coordinate information, and set information fields separately, including object name, identification code, spatial code, calibration time and layer to which it belongs, to generate binding results, including: Performing format normalization on the identification information, and performing coding standardization verification and duplication verification respectively through a preset coding rule library and a bound identification code to generate a first validity verification result; Perform error range detection on the extracted spatial coordinate information to determine whether there is a risk of duplicate binding at the spatial position and generate a second validity verification result; When both the first validity verification result and the second validity verification result are passed, the identification information and the spatial coordinate information are constructed into a bidirectional mapping relationship, and a binding result with the identification code as the primary key is established.

4. The park greening inspection and maintenance method based on NFC and GIS park data association according to claim 1 is characterized in that: For the current status data and similar status items recorded in the historical maintenance data of the greening object, multi-cycle data is extracted to construct standard parameters for field correspondence comparison, and the degree of change in each status item is identified. Then, the difference threshold range for the change of each status item is set, including: According to the status items included in the current status data, the historical records of the corresponding status items in the historical maintenance data over multiple time periods are called to construct a sequence of status values ​​arranged in chronological order, which is used as the basic data set for constructing standard parameters; Eliminate outliers from historical data in the basic dataset, including identifying mutation points, excluding extreme values ​​outside the reasonable range, and eliminating abnormal data with large deviations from adjacent time points, to form a valid sample set for standard parameter calculation; 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. The time weight of each sliding interval is set according to the preset time decay function based on the time interval between the current collection time and the historical data. The local mean of each sliding interval is weighted and summarized with the corresponding time weight, and the results are normalized to obtain the reference mean of the standard parameters corresponding to each state 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.

5. The park greening inspection and maintenance method based on NFC and GIS park data association according to claim 3 is characterized in that: Perform error range detection on the extracted spatial coordinate information to determine whether there is a risk of duplicate binding in the spatial position and generate a second validity verification result, 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 in the buffer is extracted; Compare the distance between the current coordinates and the coordinates of other objects in the buffer. If the minimum distance is less than the binding distance threshold set by the system, it is marked as a risk of spatial overlap. In the case of spatial overlap risk, the risk level is determined by combining the greening object type corresponding to the identification information with the object distribution density corresponding to the location in the geographic layer, and a manual review prompt is triggered to generate a verification result marked as a high-risk binding.

6. The park greening inspection and maintenance method based on NFC and GIS park data association according to claim 4 is characterized in that: Compare the values ​​of each field in the current status data with the reference mean of the corresponding standard parameters, calculate the numerical difference 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: According to the current value of each state item and the reference mean of the standard parameter, the value difference is calculated to construct the state difference data set; For each field item in the state difference dataset, according to its corresponding plant type attribute, the preset plant species-parameter threshold correspondence table is retrieved to obtain the initial value of its acceptable fluctuation range; According to the parameter category to which the field item belongs, the initial value fluctuation range is adjusted and corrected to form a difference threshold range under a specific plant type and a specific parameter category, and the parameter category includes morphological parameters and environmental parameters.

7. The park greening inspection and maintenance method based on NFC and GIS park data association according to claim 1 is characterized in that: For each subtask, the estimated time of the subtask is revised based on the execution time data and task level in the historical maintenance task records, and the revised estimated time is used as the duration parameter of the task scheduling, including: Extract the execution samples corresponding to each subtask in the historical maintenance task records and construct a set of task execution time samples; Generate a basic estimated duration for the subtask based on the average execution time recorded in the task execution time sample set, and make a weighted adjustment to the basic estimated duration based on the execution complexity coefficient represented by the task level; Based on the geographic location information of the area to which the subtask belongs, determine whether there are any work interference factors in the area. If so, set the 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.

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